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Das sind relativ einfache Beispiele für Simulationen, aber auch komplexere Zusammenhänge lassen sich modellieren, zum Beispiel das Weltklima oder die Wirtschaftsentwicklung. Lassen sich Simulationen auch im öffentlichen Sektor gewinnbringend nutzen als digitales Werkzeug zur Analyse und Prognose? Das hat ein Team des Kompetenzzentrums öffentlicher IT erforscht und zwar in einem Selbstversuch. Es wird im öfid Podcast restart digital heute also richtig praktisch und hands on. Ich bin Franziska Weiser und mit mir im Studio in Berlin Tiegel sitzen meine beiden Co Hosts für heute. Das sind Jens Tiemann, wissenschaftlicher Mitarbeiter im Kompetenzzentrum öffentlicher IT, kurz öFIT, und dort beschäftigt schwerpunktmäßig mit Internet und Vernetzung sowie mit neuen Technologien. Hallo Jens. Hallo. Und ebenfalls dabei mal wieder Doktor Mike Weber, Trendforscher und stellvertretender Leiter des Kompetenzzentrums Öffentlicher IT, kurz öfid. Hallo Mike. Hallo Franziska. Es gibt ja vom Urknall über Brückenstatik bis zum Arzneimitteltest wirklich viele Bereiche, wo Simulationen schon genutzt werden. Wie ist das denn in der Verwaltung? Wir haben im Rahmen unserer Arbeit versucht, einen kleinen Überblick dadrüber zu bekommen, wo überall schon Simulationen benutzt werden und sind nicht so wirklich fündig geworden. Es gibt 'n paar wissenschaftliche Paper zu speziellen Fragestellungen. Es gibt auch grade im Umweltbereich, wenn ich mich recht entsinne, so ein paar kleine Ansätze dazu. Aber insgesamt muss man sagen, obwohl der Bedarf ja enorm wäre, grade für diese Fragestellung, ganz gezielt für die öffentliche Verwaltung, ist das Feld relativ frei. Und Du sagst jetzt, der Bedarf ist groß. Also wo könnten diese Erkenntnisse denn genutzt werden? Also wer profitiert davon? Also grade wenn man in der aktuellen Diskussion, wie Staat und Verwaltung neu aufgestellt werden sollen, ist ja Wirkungsorientierung ein ganz, ganz wichtiger Aspekt. Also zu wissen, was passiert eigentlich mit den Gesetzen und mit den Dingen, die die Verwaltung dann anstellt. Und auch mit den Geldern ne, wo setzen wir die gut ein zum Beispiel. Genau, genau. Simulationen sind eine Möglichkeit grundsätzlich zu ergründen, wie bestimmte Maßnahmen wirken. Was ist denn mit diesem Bereich Smart Cities? Also das ist ja 'n Thema, das haben wir hier im Podcast schon öfter besprochen, da gibt's sogenannte digitale Zwillinge, also das sind virtuelle Kopien von 'nem Stadtviertel. Ist das dann schon eine Simulation oder eine Vorstufe dazu? 'N digitaler Zwilling ist ja eine Abbildung der Realität und hat dann die Aufgabe, dass der auf dem Zwilling arbeiten kann auf dem digitalen Zwilling und zum Beispiel Simulationen durchführen kann, dann eine Aussage zu kriegen, wie sich gewisse Maßnahmen in der Smart City auswirken werden, in der Realität auswirken würden. Und ja, ums jetzt mal konkret zu machen, also mal angenommen, ich hätte 'n digitalen Zwilling von 'nem Stadtviertel und dann könnte ich sowas ausprobieren wie, was passiert denn, wenn ich da eine Straßenbahn hinlege oder solche Sachen Genau, genau, solche Sachen. Genau, solche Sachen. Also zum Beispiel, dass 'n großer Bereich in dem Simulation eine Rolle spielen, also zum Beispiel Verkehr oder Mobilität allgemein, dass man sagt, welche Wege das hat, welche Vorteile es hat. Ist ja auch immer wieder in der Presse, in der Planung gehört man ja, jetzt hier in Berlin zum Beispiel, Mexiko Platz, ob das 'n neuer Umsteigepunkt sein soll und dann muss man natürlich gucken, wie viel Leute werden davon profitieren? Bekommen da Leute vorbei und dann wird man sowas simulieren und schauen, ob sich das lohnt. Mhm. Also das heißt, in dem Bereich könnte man sagen, öffnet sich die Verwaltung gerade für 'n Thema, was mit Simulationen zu tun hat? Ist es vielleicht son son Einfallstor, zu sagen, so guck mal, da nutzt ihr's, da funktioniert's, macht doch mal in anderen Bereichen auch? Ja, einerseits das, aber ich denke bei den Fachleuten, da werden ja Simulationen schon immer benutzt. Eigentlich hört man immer wieder diese Modellierung, das ist doch so einfach oder mit Computertechnik heute mit immer besseren grafischen Oberflächen und so, müsste das doch eigentlich immer einfacher werden, dass jedermann sozusagen modellieren kann. Und da haben wir geguckt, geht das eigentlich? Und unsere Idee war, kann man nicht sozusagen stärker direkt die Fachpersonen, die jetzt eine fachliche Ahnung haben, aber nicht zu viel Ahnung vom Programmieren, vom Modellieren, vom Simulieren, kann man diese Person nicht in die Lage versetzen, Modellierungen zu nutzen. Also Fachpersonal heißt, in der Wissenschaft, da ist es ja sehr verbreitet, ne? Und Ja, genau. Genau. Genau. Und dann aber jetzt Fachpersonal in Behörden. Genau, jetzt meine ich dann eben Fachpersonal in Behörden. Das schließt son bisschen vielleicht auch an die Idee von den Datenlaboren an, nicht? Dass man immer mehr, das ist ja auch in der Presse, dass man so sagt, okay, wir ein Ministerium oder eine Behörde braucht eben Daten, möchte eben Datenlabore aufbauen, stellt Daten der Öffentlichkeit zur Verfügung, arbeitet verstärkt mit Daten und da ist natürlich dieses direkter Anschluss zu sagen, wir haben die Daten oder wir wollen jetzt Fragestellungen mithilfe dieser Daten beantworten. Ja, weil die Daten natürlich die Grundlage für vieles sind, aber eben auch für Simulationen. Genau. Ja, mhm. Mhm. Ich hab ja versprochen in der Anmoderation, dass wir heute wirklich praktisch werden. Jens, ihr habt für die Veröffentlichung Wirkungen verstehen und nutzen, agentenbasierte Simulationen für den öffentlichen Sektor, ja selbst eine Simulation erstellt. Was habt ihr denn da fürn Beispiel ausgewählt und warum auch? Wir haben uns diese verschiedenen Anwendungsfälle angeguckt oder was es da schon gibt und sind eigentlich zu dieser Idee gekommen, dass man so was wie Akzeptanz oder Verbreitung von Angeboten der Verwaltung, dass man sowas eigentlich ganz gut Mhm. Modellieren können müsste und wir sind dann auf die EID gekommen, sprich 'n elektronischer Personalausweis und haben dort eben unsere Modelle schrittweise aufgebaut, zu gucken, wie verbreitet er sich nach welchen Mechanismen und mit welchen Mechanismen könnte ich das fördern. Ja, lustigerweise, da haben wir glaube ich sogar schon mal eine Folge dazu gemacht, Mike, ne? Elektronischer Personalausweis und warum der sich nicht so richtig durchsetzt. Da haben sich ja schon viele kluge Menschen Gedanken dazu gemacht. Was meinst Du, kann jetzt eine Simulation so dazu beitragen? Mhm. Also erst mal, wir wollten ganz bewusst halt von diesen klassischen Feldern, Umwelt, Verkehr und so was runter und dann zu so was, was man als Kernverwaltung bezeichnen kann oder Kerndienstleistungen der Verwaltung in der Hoffnung, dass es sich einfach simulieren lässt als Innovationsdiffusionsprozess. Der entscheidende Vorteil bei der Simulation ist, dass man sich wirklich über jeden einzelnen Schritt Gedanken machen muss. Also sonst, man kann natürlich, wie wir Sie auch gemacht haben in in dieser Podcastfolge, man kann so einzelne Punkte ganz gut identifizieren, woran es denn gescheitert sein könnte, dass diese Diffusion nicht so funktioniert hat, wie man sich das erhofft und erträumt hatte. Bei 'ner Simulation muss man ganz unten anfangen. Was ist die Grundmotivation? Jetzt sind wir wieder bei den Agenten, also wenn man wenn man Die Agenten individuellen Themen bleibt. Ne? Also Bürger und Bürgerinnen. Genau, also Du Ja. Unterstützend klingt so nach eben 0 0 7, aber Agenten sind Bürger, ne? Das ist einfach der Simulations Slang, was Ja, vielleicht müsste man das kurz mal erläutern. Ja. Son agentenbasierte Simulation, das kann man sich wie Spielfiguren aufm Spielfeld vorstellen, dass man so sagt, okay, man hat diese Umwelt, also das Spielfeld und man hat dort Agenten da drauf. Das sind also Einheiten in der Simulation, die eben interagieren miteinander, die Akteure sozusagen. Das sind aber nicht nur die Bürgerinnen und Bürger. Bei uns sind es zum Beispiel auch Organisationen wie zum Beispiel die Verwaltung oder Firmen. Also auch Organisationen werden als Agenten betrachtet und die interagieren untereinander oder aber mit ihrer Umwelt. Okay und das ist ein Ansatz, den man wählen kann, wenn man eine Simulation macht Genau, das ist ja 1, der wir eben ausgewählt haben. Genau und das ist ein sehr einfacher Ansatz, also sehr einfach verständlich. 2 Akteure treffen sich. Was passiert dann? Mhm, okay. Das heißt, ihr habt im ersten Schritt geguckt, wer sind überhaupt die Agenten? Genau. Also In unserem Fall sind das erst mal die Bürger, die diese EID haben oder noch nicht haben, eine Motivation haben, diese EID zu nutzen. Es sind die Verwaltungen, bei denen ich das einsetzen kann oder noch nicht. Und wir haben das dann schrittweise aufgebaut und haben gesagt, okay, was wäre dann, wenn wir jetzt zusätzlich noch Firmen haben, Unternehmen, wo man auch die Serien mit dem Ausweis identifizieren kann. Weil man dann einfach sozusagen, das war unsere Annahme, die wir reingesteckt haben, dass wir gesagt haben, na ja, je öfter ich das nutze, desto mehr und das einfach funktioniert, also eine EID zu nutzen, war unsere Annahme, desto überzeugter bin ich ja, mir die zu holen oder wenn ich schon habe, zu verlängern oder wenn ich davon gehört hab, dass andere damit gut zurechtkommen, dass ich mir auch so eine EID hole. Ein Aspekt dabei sind ja die langen Zeiträume, also Weil ich den Ausweis ja nicht Genau. Dauernd verlängere, sondern nur alle 10 Jahre. Alle 10 Jahre, genau Und darüber kommen natürlich dann sehr lange Zeiträume in so eine Betrachtung. Mhm. Ich sag ich mal, aus der Verkaufspsychologie oder so gibt es ja vielleicht Modelle, die sagen, was ist eine positive Erfahrung? Wie wirkt sich die aus? Wie wirkt sich eine negative Erfahrung aus? Wie viel Leuten erzähle ich von 1 positiven oder negativen Erfahrung? Nicht und solche Sachen, dort kann man dann natürlich auch eine wissenschaftliche Grundlage suchen und finden. Uns war wichtig, ein Schritt eigentlich vorher eben, kann ich Fachpersonen, die jetzt eben nicht täglich mit Simulationen arbeiten 'N großer Anteil an der Simulation ist ja die Modellierung. Also die Wirklichkeit vereinfacht zu beschreiben. Modellierung bedeutet eben auch, dass ich mir selber Gedanken mache, wie sind Zusammenhänge. Ich kann das auch nachvollziehbar machen, weil andere meine Modellierung, die ich aufschreibe, nicht, dass ich sage, dieser oder jener Faktor, den schätze ich als sehr stark ein. Also ist 'n großer Einfluss, das schreibe ich in meine Modellierung rein und jemand anders kann dann sagen, nee, nee, das ist aber viel geringer. Das heißt, die Simulation und eben da im Speziellen die Modellierung ist ja auch der Weg, so eine so eine Diskussion zu Versachlichen, Sachen nachvollziehbar zu machen. Mhm. Zu lernen und weiterzuentwickeln und so weiter. Und auf diese Mechanismen, das wollten wir eigentlich gucken, wie gut geht das heutzutage schon? Sind aber neben nicht tief in die fachlichen. Genau, weil's ja wirklich nur drum ging, das einfach mal auszuprobieren. Genau. Und ja, mhm. Aber spannender Punkt trotzdem, dass man natürlich bei dem Simulieren an sich auch Erkenntnisgewinne hat, ne. Genau. Also dass man eben zum Beispiel merkt, ach Mensch, ich müsste jetzt über die Agenten das und das wissen, da gibt's aber gar keine Zahlen dazu, also muss ich die irgendwo besorgen oder so was. Genau. Solche Sachen können ja da auch rauskommen. Ja und Simulation ist ja auch zum Beispiel ein ganz wichtiger Punkt und auch mit dieser Sprache, die wir benutzt haben, Net Logo, die frei verfügbar ist, es ist ja auch 'n sehr starker Erkenntnisgewinn. Also zum Beispiel Mikes Lieblingsbeispiel ist eine Visualisierung, wie ein Stau auf 'ner Straße entsteht. Und das ist da 'n fertiges Simulationsmodul, was man sich also ausm Internet runterladen kann, wo dann kleine Autos überm Bildschirm fahren und wo man dann je nach Verkehrsdichte sehen kann, wie eben Staus aus dem Nichts entstehen. Also da gibt's Dinge, die kann ich nicht schnappen eigentlich. Genau, das ist das. Ne, als jemand, der genau. Ja. Und in diesem Stauseimulationsbeispiel, da ist es also eher sozusagen der Erkenntnisgewinn, dass man sich das mal praktisch angucken kann, dass man son Beispiel hat, wie man Simulationen aufbauen kann und nicht. Oder aber wenn man eben selber sagt, selber sich ran setzt, dann wird's natürlich etwas schwieriger, dass man die richtigen Parameter auswählt. Ja, ja. Aber mit der Transparenz ist auch spannend, weil wir ja immer wenn wir über KI zum Beispiel diskutieren, diese Blackbox Geschichte haben und das ist ja bei Simulation, kann man ja dann sehr genau nachvollziehen irgendwie. Genau, vor allen Dingen als also diesem also ich find das sehr wichtig. Es ist ja allgemeine Forderung, dass gesagt wird, ja viel mehr Menschen müssten programmieren lernen zum Beispiel. Aber eigentlich ist es, wenn man's 'n bisschen genauer beschreibt, ist es eigentlich die Modellierung und das ist die Verbindung, dass man eine Sprache benutzt, eine formale Sprache benutzt, die sowohl Menschen als auch Computer verstehen können. Und in unserem Fall kann dann zum Beispiel ein Verwaltungsexperte, eine Verwaltungsexpertin könnte also in dieser Fachsprache eine Simulation formulieren, die dann so transparent ist, dass jemand anders sie lesen kann, dass sie aber auch vom Computer ausgeführt werden. Ja. Da kommen wir gleich noch mal dazu, wie ihr das Ganze dann programmiert habt, da ja auch in 2 Varianten, ne? Einmal mit Genau. Und einmal ohne. Genau. Mike, Du wolltest noch was ergänzen, glaube ich. Ja, ich würd ganz gerne noch mal wieder zurück zu den Daten. Also es stimmt, es ist keine Simulation, die wir da gemacht haben, wo wir empirische Daten genommen haben. Das gibt's ja durchaus auch. Wettervorhersage ist Paradebeispiel, wo ein ganzer Strauß von verschiedenen Daten erfasst wird und dann in diese Simulation reingeschoben wird. Das war genau nicht unser Ansatz, sondern wir haben uns natürlich 'n paar Daten angeguckt, also so Durchdringung von EAD Funktion früher und heute und so was. Aber wir haben dann sind dann den Weg gegangen, die Simulation so flexibel zu machen, dass man diese Daten quasi für die eigenen Simulationsdurchgänge selbst einstellen kann. Und das ist noch mal eine Besonderheit, die bei NetLogo halt auch ganz besonders leicht umzusetzen ist. Und dann geht es in der Tat da, also habt ihr ja schon gesagt, also dann erst mal festzustellen, welche Daten hab ich denn gar nicht? Welcher hätt ich denn gerne, diese Simulation noch mehr an die Realität dranzub bringen? Und das ist die große große Krux dabei und die große Chance, aus diesen Simulationen auch zu lernen. Ja. Man kann ja auch wirklich damit rumspielen, weil ihr das alles online stellt und man könnte mit diesen Dingen, die ihr da erarbeitet habt, weil Du grad gesagt hast, Mike irgendwie Zahlen rauf runterstellen und sowas. Das kann man wirklich machen, ne? Es ist jetzt eine frei verfügbares Spielmaterial sozusagen, was da entstanden ist. Genau, wir haben die Modelle alle online gestellt, also alle Zwischenschritte, die wir gemacht haben und auch den Weg dahin und jetzt kann sich jede und jeder, der genauer gucken will, was wir dafür Annahmen implizit und explizit drin haben, kann sich diesen Code angucken, kann sich auch erklären lassen durch entsprechende KI Tools oder durch entsprechende Expertinnen und Experten und kann dann genau sehen, welche Annahmen da drin stecken. Mhm. Und natürlich dann auch widersprechen an jedem Einkaufszentrum. Okay, zu wie viel Prozent tritt das jetzt ein oder nicht, ne? Ist es eine exakte Vorhersage der Zukunft? Wie genau kommt es daran? Was spielen denn da für Faktoren eine Rolle? Also ich glaub, da muss man noch 'n halben Schritt zurückgehen, Ja. Was für unterschiedliche Typen von Simulationen es gibt. Wie eben schon gesagt, das Wetterbeispiel oder auch solche Simulationen im Verkehrsbereich sind sehr nah daran, Vorhersagen treffen zu wollen. Mhm. Also Und auch zu können? Ja, beim Wetter funktioniert's ganz gut, oder? Ja. Wird vor allem immer besser und eher auch kleinräumig, was früher völlig illusogen ist. Regenradar. Ja. Nach meiner subjektiven Wahrnehmung. Wir haben da eher 'n anderen Simulationsansatz. Also eher, der in Richtung Verstehen geht. Also wir wollen verstehen, wenn wir genau im Sinne der Wirkungsorientierung, was passiert, wenn ich jetzt eine bestimmte politische Maßnahme oder eine Kommunikationsmaßnahme oder irgendetwas, wenn ich das jetzt in dieses modellierte System reinbringe? Was passiert dann? Steigt dann die oder beschleunigt sich die Diffusion oder geht sie zurück oder wie auch immer? Und da sind wir dann im Bereich, wo's gar nicht so sehr darum geht, vorherzusagen, sind es jetzt in Zukunft 80 Prozent, 90 Prozent Leute, die das nutzen. Mhm. Das interessante an der Vorhersage ist ja, dass wir als Menschen lineare Zusammenhänge supergut abschätzen können, nicht? Also wenn wir wissen, was weiß ich, wir machen Wasserhahn auf und das Waschbecken, das ist halbvoll. In 2 Minuten, dann wissen wir in 4 Minuten ist es ganz voll oder sowas. Das kriegen wir ja gut hin. Aber exponentielles Wachstum zum Beispiel kriegen wir als Menschen nicht hin, dieses berühmte Seerosenbeispiel auf dem Teich oder aber auch sowas wie Sättigung. Also wenn irgendwas natürlich gesättigt ist, wenn ich einfach keine Steigerung mehr drin habe. Oder wenn ich Kipppunkte drin habe, was Aber das kennt man ja vom Klima. Genau. Das kennt man jetzt vom Klima, ist das jetzt bekannt, aber solche Kipppunkte. Das bedeutet ja, dass ich nichtlineare Zusammenhänge habe und aber auch plötzlich ganze Systeme umkippen oder sich Sachen ganz fundamental ändern. Und das ist sehr schwer sozusagen mit seinem Alltagsverstand oder mit 'nem einfachen Draufgucken, jetzt ohne Computerhilfe zu sagen, was passiert da eigentlich? Wann tritt so ein Effekt ein? Was ist eine Auswirkung von exponentiellem Wachstum zum Beispiel? Ja und dafür sind Simulationen durch, ne? Ach so, genau. Es könnte halt sein, erst sind 20 Prozent mit dem Ja. Elektronischen Personalausweis unterwegs und dann auf einmal, wir haben eine gute Werbemaßnahme gemacht, auf einmal sind's 80 oder? Genau, das haben wir ja son bisschen gesehen. Ich glaube, in den letzten Jahren bei Kartenzahlen, beim kontaktlosen Zahlen mit mit mit Karte, was ja auch durch verschiedene Maßnahmen gefördert wurde und so weiter, wo aber dann auch irgendwo plötzlich eine Akzeptanz überall da war und das dann auch genutzt wird. Zum Beispiel schalte ich Werbung? Mache ich Kommunikationsmaßnahmen? Ne? Das war eine Sache, die ihr untersucht habt. Ich finde es ganz interessant, weil wir ja gerade gesagt haben, uns geht's gar nicht darum zu prognostizieren, sind's irgendwie 87 Prozent, sind's 89 Prozent, sondern wir wollen ja wissen, bringt's was, bringt's nichts, ne? Das ist eigentlich die Frage, die wir wissen wollen und da habt ihr untersucht, zum Beispiel Kommunikationsmaßnahmen, was was noch? So ein Akteur, also Bürger geht aufs Amt und dann ist die Frage, ist das Angebot überhaupt da? Dann hat er's positiv auf sone Stimmung eingezahlt sozusagen, auf sone Bereitschaft, die EID gut zu finden, sage ich mal, war es einfach und dadurch stieg dann dieser Wert von diesem Agenten. Ja. Und im Laufe der Zeit, wenn man keine Interaktionen hat, dann vergisst man das wieder, dann sinkt dieser Wert wieder. Das war unsere ganz einfache Modellannahme, zum Beispiel ein Zusammenhang, den wir drin hatten. Ja. Okay und also genau, ums nochmal zu sagen, es ging ja wirklich nur darum zu versuchen, kann man mit so 'ner Agentenbasierten Simulation überhaupt so was untersuchen. Gibt's trotzdem irgendwie Erkenntnisse, wo ihr sagt, ach Mensch, das hat uns überrascht, der hätten wir gar nicht gesagt gedacht, dass das so wirkt oder so. Hätten wir so gar nicht abschätzen können. Bei 1 Berechnung haben wir sehr häufig nachgeguckt, ob's wirklich richtig ist. Also wir haben ja Kommunikationsmaßnahmen untersucht und sind auch zu dem Ergebnis gekommen, dass es dass sie positiv wirken im Grunde. Positiv wirken auf die tatsächliche Nutzung und haben dann auch noch mal differenziert, sollte man das lieber aufm Block am Anfang kann schnell oder lieber über Zeitstrecken? Das war alles im Ergebnis relativ gleichwertig. Da gab's keine großen Unterschiede. Aber aber und jetzt kommt das große aber, die Einstellung zur eID ist dabei abgefallen. Also die die Leute standen der EID negativer gegenüber. Je mehr drüber geredet wurde sozusagen. Je mehr sie drüber Und mehr Kommunikationspartner es gibt und der der Hebel dahinter war einfach, dass wir ein Modell genommen haben, wo's sehr wenig Angebote gab. Mhm. Und deswegen waren die Leute einfach frustriert. Und je mehr sie durch die Kommunikationsmaßnahmen darauf hingewiesen worden sind, desto frustrierter waren sie. Ja. Also dann, wenn das Angebot nicht stimmt und man in Anführungszeichen nur kommuniziert, dann hat es tatsächlich mittelfristig negative Effekte. Ja, ja, macht auch Sinn, ne, wenn man sich so überlegt, die standen quasi in Startlöchern und dann konnten sie nicht loslegen, ja. Genau. Und das ist sehr auch eine, der neben den nicht linearen Zusammenhängen ist das ja eine der besonderen Reize an der Simulation, solche Sachen, die erstmals sehr kontraintuitiv sind, dabei rauszufinden. Also erst mal, wenn man so drauf guckt, wie ich mache Kommunikationsmaßnahmen, bewerbe das das Ganze und trotzdem geht die Einstellung runter, ist irgendwie komisch. Und dann, wenn man dahinter guckt, wird es dann ganz einfach. Ah, okay. Jetzt lass uns noch mal gerne über das technische Vorgehen reden. Ist grad schon der Name gefallen von 1 Programmiersprache, die da genutzt wird, ne, Simulationsumgebung? Genau, das ist sone Simulationsumgebung, die heißt NetLogo. Ja. Die ist frei verfügbar, ist gut dokumentiert. Wichtig war für uns, dass man natürlich Beispiele findet, Tutorials, da einzusteigen, loslegen zu können, dass es auch nichts kostet. War jetzt auch eine unserer Bedingungen, dass wir gesagt haben, alle Sachen, die wir machen, müssen frei verfügbar sein. Dann haben wir ja schon gesagt, hinterher stellen wir das ja auch wieder zur Verfügung. Also wir wollen dann schon so einsteigen in einen Zyklus, dass man die Tools einfach zur Verfügung hat und auch wenn man damit arbeitet, seine Ergebnisse weitergibt. Und wie hoch oder niedrigschwellig ist das, würdet ihr sagen? Also wenn ich jetzt sage, ich hab jetzt nicht Zeit, mich 2 Wochen fortzubilden, die IT Abteilung ist auch nicht hilfreich, ich bin total auf mich alleine gestellt und hab eher so Erfahrungen mit MS Office Paketen. Wie schnell finde ich deinen Zugang? Müsste Maike jetzt 'n bisschen antworten. Ich bin Hab vielleicht gemerkt. Ist natürlich für euch schwer, sich vorzustellen. Nein, das ist Also ich bin mehr so Ich bin Hab mehr Programmierkenntnisse und man muss sagen, dass es ganz wichtig ist, das erste Beispiel durchzuarbeiten. Also es gibt ja in den Tutorials oder so was gibt es immer so Einführung. Mhm. Und das ist unglaublich wichtig. Einmal zu verstehen, was ist der Ansatz von so 1 Programmiersprache? Wie blickt die auf die Welt sozusagen? Wie soll ich damit umgehen? Und dann, wenn man das mal so verstanden hat, also zum Beispiel in diesem Fall eben auch die Agenten. Ja. Nicht? Was ist das? Also ich hab die Plan da bitten Genau. Leute, ja. Da wegen sich Leute, genau. Wie möchte ich das aufsetzen? Das muss man einmal verstanden haben. Dann ist es eben eine spezielle Sprache, die jetzt besonders geeignet ist für diese für diese Art von Simulation. Man wird also ganz viel zum Beispiel Zufallsfunktion, das ist alles da, nicht? Dass man sozusagen zufällig irgendwelchen Agenten irgendwelche zufälligen Werte geben kann, dass die irgendwie handeln können. Da nimmt man oft den Zufall, dass die also durch die Gegend rennen sozusagen, jetzt im Bild und dann zufällig aneinanderstoßen, aufeinanderstoßen. Mhm. Und da nimmt man Zufallsfunktion und da gibt es dann halt sehr viele Programmunterstützung oder aber auch für die grafische Auswertung, dass man schöne Grafik machen kann. Da gibt es dann auch eben viele Elemente. Und das muss man einmal verstanden haben sozusagen, welche Elemente es gibt, was überhaupt der Ansatz ist und dann denke ich, ist das schon eine Ausbildungssprache, mit der man gut reinkommt. Diese Modelle, die schon bereits verfügbar sind, die sind immer so aufgebaut, dass man eine Oberfläche hat, mit der man die bedient. Dann dahinter eine Beschreibung, was dieses Modell erreichen will und auch mit son paar Beispielen, was man vielleicht mal einstellen sollte, 'n Gespür dafür zu kriegen, wie das Ding funktioniert. Und dahinter dann der Code. Also Okay, also in so 3 man kann so 3 Schichten abziehen und das nach und nach verstehen son bisschen. Genau und das ist natürlich 'n wunderbarer Einstieg. Zum Beispiel mein kleines Verkehrsbeispiel, mein Staubeispiel. Das ist ganz, ganz simpel und dann, wenn man sich da einmal durchgearbeitet hat, son bisschen durcharbeiten, muss man sich wohl dann auch sehr gut nachvollziehbar. Die Sprache ist 'n bisschen ruckelig, aber natürlich auch speziell für diese Simulationsanforderung Ja. Gebaut. Also man wenn man 'n bisschen drin ist, kann man sich fast lesen wie normalen Text. Okay und also jetzt für die Leute, die aber noch nicht so überzeugt sind und immer noch Hürden haben, habt ihr ja noch 'n anderen Einsatz probiert, nämlich das Ganze mit KI zu machen, ne? Genau, zu dem Zeitpunkt, wo wir die Arbeiten gemacht haben, war das natürlich eine neue Möglichkeit Programmieren mithilfe von generativer KI mit Chatbots wie ChatGPT und so weiter zu unterstützen. Auch hier haben wir wieder gesagt, wir wollen jetzt keine speziellen Programme benutzen, sondern wir wollen die kostenlosen öffentlichen Angebote nutzen, weil im Zweifel ist es ja so, dass dann spezialisierte Sachen irgendwie besser sind. Wir wollten ja mal gucken, was geht überhaupt? Auch möglichst geringe Hürden und haben das eben dann unser Simulationsproblem beschrieben und haben dann die KI sozusagen aufgefordert, uns eine Simulation dazu zu schreiben. Und diese Programmiersprache Ja. Über die wir gerade gesprochen haben, kennen die üblichen Chat Bots sozusagen. Also das heißt, die sprechen miteinander auf eine Art, ne? Also man hat wie son Dolmetscher. Kann man sich das so vorstellen vielleicht? Es ist so, die die Programmiersprache net Logo, die hat da 'n kleines Problem. Diese grafische Oberfläche, es ist eigentlich gemacht sozusagen, dass man auf der einen Seite seine Simulationsbeschreibung hat. Das ist 'n klassischer Programmiercode, aber diese grafische Oberfläche dazu, das sind also grafische Elemente wie Knöpfe oder Schieber, Werte einzustellen. In den Normalfall ordnet man die per Hand an sozusagen. Und da hat jetzt die KI leichte Schwierigkeiten, jetzt leichte Schwierigkeiten, jetzt im allgemeinen Fall dieses diese komplette Simulation zu machen. Was man aber machen kann, ist halt, man ordnet selber diese Schieber an und sagt, was man machen möchte und kann sich dann den Code schreiben lassen, die Simulation durchzuführen. Okay, also wenn man jetzt in diesem durchzuführen. Okay, also wenn man jetzt in diesem Ebenenmodell bleibt von Mike gerade, also die kann die unterste und zweitunterste Ebene kriegt die gut hin und dann das oberste, das was man sieht. Genau, da das das wird man sicherlich auch lösen können und da gibt's auch spezielle Ansätze, sowas zu lösen. Aber in diesem Fall, wenn man jetzt da so ganz naiv rangeht, dann muss man sich 'n Weg überlegen. Wenn das nicht funktioniert, dann kann man auch immer die KI fragen, wenn's dann gibt's eine Fehlermeldung und dann kann man die KI fragen, was ist denn mit diesem Fehler und dann wird die KI diesen Fehler korrigieren. Also aber es jetzt noch mal zusammenzufassen, also die KI macht schon noch mal ein Stück zugänglicher Auf jeden Fall, ja. Im Falle, ja. Also die KI ist für uns glaube ich, 'n ganz großer, also wirklich eine Möglichkeit, das dieses Gebiet zu öffnen. Man muss immer noch dieses Grundverständnis muss man haben, das ist ganz wichtig. Man muss verstehen auch die ersten Ansätze, die die KI zum Beispiel in diesem Netlogob Beispiel bringt. Da hab ich ja gesagt, es gibt eben das Spielfeld und es gibt die Spielfiguren. Und nun könnte man sagen zum Beispiel im Spielfeld sind schon die Behörden sind schon im Spielfeld oder sind fest sozusagen. Die sind keine Agenten. Genau, die sind keine Agenten, sondern die sind Teil des Spielfelds. Nicht und da müsste man muss man sich überlegen, was will ich denn später simulieren? Wo will ich hin? Welche Vorstellung hab ich? Und wenn die KI dann einen Weg vorschlägt, dann kann ich ganz zu Anfang schon sehen, ist es der Weg, den ich gehen möchte? Oder aber möchte ich noch mal fragen und krieg eine neue Antwort von der KI. Was aber toll ist an der Stelle ist, ich brauche natürlich nicht so wahnsinnig viel beschreiben. Ich muss nur die Grundzüge beschreiben und dann wird mir die KI ganz viel von der Routinearbeit abnehmen und wird die genaue Bezeichnung von den Funktionen, wie ich die Zufallsfunktion aufrufen muss und so weiter, wie eine Grafik gemacht werden muss. Das wird mir die KI machen. Mhm. Okay. Ich hatte in meinem Beispiel, wie ich das noch dazu führen darf, innere Einstellung der Bürgerinnen und Bürger zur EID. Und ich hab dann gesagt, okay, diese Einstellung kann sich ändern und die KI hatte von selber vorgeschlagen, dass die Farbe des Agenten auf dem Spielfeld von dieser Einstellung abhängig gemacht wird. Mhm. Dass man also schon gleich eine neue Funktion hat, über die ich ja noch gar nicht nachgedacht hab in meiner Simulation. In meiner Simulation wollte ich erst mal die Simulation laufen lassen und wollte mir hinterher Gedanken machen, wie ich das denn darstellen kann. Und die KI hat aber gleich dieses Farbschema auf son kleines Männchen übertragen. Also hat 'n Männchensymbol für Bürger ausgewählt. Und hat dann dem Männchen noch eine Farbe gegeben Also das ist ja zum Beispiel grün, wenn's voll heiß ist auf die Erde. Genau. Und rot, wenn's total Genau. Spannend ist. Mhm. Okay. Mike, kannst Du vielleicht noch mal so sagen, wie könnte denn zum Beispiel son prompt lauten? Also ich müsste sagen, mach mir eine agentenbasierte Simulation mit dem und dem Programm. Also was sage ich da? Was sind so die wichtigsten Inhalte, die ich da liefern muss, damit die KI eben in die richtige Richtung losläuft? Ich kann's jetzt ganz leicht machen. Ja. Das haben wir alles veröffentlicht, inklusive der PONTS, Okay. Wie man das am besten beschreibt. Aber ja, natürlich muss muss die KI wissen, in welchem Umfeld, also mit welchem Programm, NetLogo oder Python, mit welcher Bibliothek ich das machen will, an welcher Sprache ich das machen will. Und dann muss ich aber ganz normal alltagssprachlich beschreiben, was ich denn da gerne simuliert haben möchte. Dann die Aufforderung, mach mir 'n Code. Genau. Interessant wird das dann an der Stelle, wo man das nicht explizit formuliert, sondern wenn jetzt Agenten aufeinandertreffen, was dann passieren soll, hab ich natürlich ganz vorne was im Kopf, was dann passieren soll und das beschreibe ich. Aber die anderen Fälle, die es da auch noch gibt, die beschreibe ich jetzt vielleicht gar nicht. Aber wenn das technisch umgesetzt wird, dann muss ja eine Entscheidung getroffen werden, ne? Oder es wird eben bewusst keine Entscheidung getroffen, was aber aus technischer Sicht wieder eine Entscheidung ist. Und dann sehe ich ja plötzlich auch, was in meiner Beschreibung fehlt. Ob ich unpräzise bin, die KI etwas vereinfacht hat, was ich aber gar nicht vereinfacht haben möchte und dort muss ich dann präziser werden. Gut. Also alles ist veröffentlicht. Man kann direkt loslegen, Mike. Ein Aspekt, der noch ganz wichtig ist, die Syntax wird abgenommen von der KI und die KI kann die richtige Syntax, das ist Gold wert. Okay. Auch so Kleinigkeiten, aber genau da auf dem Punkt wollt ich noch mal eingehen. Das ist natürlich auch absolutes Gold wert, weil man weiß, also man kann dann auch anfangen, die KI zu hinterfragen, was sie da in den Beschreibungslücken, die geblieben sind, was sie da automatisch einsetzt. Macht das denn überhaupt Sinn, was sie da einsetzt? Und stellt sich dann die nächste Frage, wo sich das dann vielleicht noch mal irgendwie inhaltlich hinterfragen und ganz anders bauen? Also so gesehen ist das wie bei anderen bei Textfragen oder bei Brainstorming Prozessen ist der KI da auch 'n Schilderspartner, wenn man dann weiterkommen kann. Mhm. Okay. Wo jetzt zum Abschluss seht ihr denn heiße Anwendungsfelder, Low hanging fruits? Wer wer sollte jetzt sofort sich euer Beispiel aufrufen und damit loslegen? Ich glaube, diese Felder werden immer größer. Swan City hatten wir ja schon, da ist halt Mhm. Das der enorme Vorteil, dass die Daten oder sehr, sehr viele Daten halt einfach schon da sind. Das ist natürlich immer Gold wert, darauf Simulationen aufzusetzen. Aber in dem Bereich passiert ja auch schon 'n bisschen was. Vielleicht nicht ganz so low hanging, aber ich glaube, ganz, ganz wichtig, ganz zentral für die Modernisierung von Staat und Verwaltung ist halt wirklich diese Wirkungsorientierung und das wirklich eigentlich überall sich Gedanken zu machen, wie wirkt das, was ich tue? Weil da kann man dann anekdotisch sehr schön einige kontraintuitive Effekte mal beobachten, aber wenn man's dann in der Simulation hat mit diesem gesamten Reflexionsprozess, das ist dann noch mal sehr viel wert. Ja. Und so wie ich das verstehe, also es funktioniert auch auf allen Ebenen, ne. Also man könnte sagen auf Bundesebene zu sagen, erhöhe ich das Elterngeld, erhöhe ich's nicht, aber genauso wie führ ich in meiner Kleinstadt eine Mikrowährung lokal ein oder so was. Also man könnte, ne, so wie ich's versteh Die Methode ist komplett offen. Ja. Es ist son bisschen Fluch und Segen der Methode. Es ist komplett offen. Man kann letztlich jede Fragestellung damit simulieren. Mal angemessen und mal weniger angemessen. Manchmal tut's vielleicht auch eine Befragung, aber im Prinzip ist die Methode für fast alles geeignet. Ja. Okay. Gut, dann hoffen wir, dass ganz viele Lust bekommen haben, da einfach mal mit rumzuspielen mit diesen Reglern, die man da auch bedienen kann und sich das hoffentlich 'n bisschen mehr durchsetzt. Vielen Dank euch beiden auch fürs Ausprobieren. So jemand muss ja den Anfang machen. Wir danken dir. Ja, Dankeschön. Simulationen als digitales Werkzeug. Das war unser Thema heute bei restart digital. Meine Gesprächspartner heute waren Mike Weber und Jens Tiemann vom Kompetenzzentrum öffentlicher IT am Fraunhofer Institut Focus in Berlin. Wir verlinken euch die Publikation zu dem Thema und auch alle anderen Ressourcen, über die wir gesprochen haben, als Link in den Shownotes zu dem Podcast. Ihr findet sie auch online unter ö fit Punkt d e mit o e geschrieben. Diesen Podcast findet ihr kostenfrei auf Apple Podcasts, Spotify und vielen weiteren Plattformen. Wenn ihr unsere Themen spannend findet und keine Folge verpassen wollt, dann könnt ihr uns da auch abonnieren. Schön, dass ihr dabei wart. Ich bin Franziska Walser. 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"bald.", "speaker": 0, "speaker_confidence": 0.29553688 } ], "summaries": null, "paragraphs": { "transcript": "\nSpeaker 0: Simulationen sind nur Annäherungen an die Wirklichkeit, sie sind keine Prophezeiungen. Aber in vielen Fällen verlassen wir uns auf ihre Zuverlässigkeit. Wir nehmen zum Beispiel einen Schirm mit, wenn für den Nachmittag Regen angesagt ist, oder wir setzen uns ohne Sorge in ein Flugzeug, dessen Pilot oder Pilotin mithilfe 1 Flugsimulators ausgebildet wurde. Das sind relativ einfache Beispiele für Simulationen, aber auch komplexere Zusammenhänge lassen sich modellieren, zum Beispiel das Weltklima oder die Wirtschaftsentwicklung. Lassen sich Simulationen auch im öffentlichen Sektor gewinnbringend nutzen als digitales Werkzeug zur Analyse und Prognose?\n\nDas hat ein Team des Kompetenzzentrums öffentlicher IT erforscht und zwar in einem Selbstversuch. Es wird im öfid Podcast restart digital heute also richtig praktisch und hands on. Ich bin Franziska Weiser und mit mir im Studio in Berlin Tiegel sitzen meine beiden Co Hosts für heute. Das sind Jens Tiemann, wissenschaftlicher Mitarbeiter im Kompetenzzentrum öffentlicher IT, kurz öFIT, und dort beschäftigt schwerpunktmäßig mit Internet und Vernetzung sowie mit neuen Technologien. Hallo Jens.\n\nSpeaker 1: Hallo.\n\nSpeaker 0: Und ebenfalls dabei mal wieder Doktor Mike Weber, Trendforscher und stellvertretender Leiter des Kompetenzzentrums Öffentlicher IT, kurz öfid. Hallo Mike.\n\nSpeaker 2: Hallo Franziska.\n\nSpeaker 0: Es gibt ja vom Urknall über Brückenstatik bis zum Arzneimitteltest wirklich viele Bereiche, wo Simulationen schon genutzt werden. Wie ist das denn in der Verwaltung?\n\nSpeaker 2: Wir haben im Rahmen unserer Arbeit versucht, einen kleinen Überblick dadrüber zu bekommen, wo überall schon Simulationen benutzt werden und sind nicht so wirklich fündig geworden. Es gibt 'n paar wissenschaftliche Paper zu speziellen Fragestellungen. Es gibt auch grade im Umweltbereich, wenn ich mich recht entsinne, so ein paar kleine Ansätze dazu. Aber insgesamt muss man sagen, obwohl der Bedarf ja enorm wäre, grade für diese Fragestellung, ganz gezielt für die öffentliche Verwaltung, ist das Feld relativ frei.\n\nSpeaker 0: Und Du sagst jetzt, der Bedarf ist groß. Also wo könnten diese Erkenntnisse denn genutzt werden? Also wer profitiert davon?\n\nSpeaker 2: Also grade wenn man in der aktuellen Diskussion, wie Staat und Verwaltung neu aufgestellt werden sollen, ist ja Wirkungsorientierung ein ganz, ganz wichtiger Aspekt. Also zu wissen, was passiert eigentlich mit den Gesetzen und mit den Dingen, die die Verwaltung dann anstellt.\n\nSpeaker 0: Und auch mit den Geldern ne, wo setzen wir die gut ein zum Beispiel.\n\nSpeaker 2: Genau, genau. Simulationen sind eine Möglichkeit grundsätzlich zu ergründen, wie bestimmte Maßnahmen wirken.\n\nSpeaker 0: Was ist denn mit diesem Bereich Smart Cities? Also das ist ja 'n Thema, das haben wir hier im Podcast schon öfter besprochen, da gibt's sogenannte digitale Zwillinge, also das sind virtuelle Kopien von 'nem Stadtviertel. Ist das dann schon eine Simulation oder eine Vorstufe dazu?\n\nSpeaker 1: 'N digitaler Zwilling ist ja eine Abbildung der Realität und hat dann die Aufgabe, dass der auf dem Zwilling arbeiten kann auf dem digitalen Zwilling und zum Beispiel Simulationen durchführen kann, dann eine Aussage zu kriegen, wie sich gewisse Maßnahmen in der Smart City auswirken werden, in der Realität auswirken würden.\n\nSpeaker 0: Und ja, ums jetzt mal konkret zu machen, also mal angenommen, ich hätte 'n digitalen Zwilling von 'nem Stadtviertel und dann könnte ich sowas ausprobieren wie, was passiert denn, wenn ich da eine Straßenbahn hinlege oder solche Sachen Genau,\n\nSpeaker 1: genau, solche Sachen. Genau, solche Sachen. Also zum Beispiel, dass 'n großer Bereich in dem Simulation eine Rolle spielen, also zum Beispiel Verkehr oder Mobilität allgemein, dass man sagt, welche Wege das hat, welche Vorteile es hat. Ist ja auch immer wieder in der Presse, in der Planung gehört man ja, jetzt hier in Berlin zum Beispiel, Mexiko Platz, ob das 'n neuer Umsteigepunkt sein soll und dann muss man natürlich gucken, wie viel Leute werden davon profitieren? Bekommen da Leute vorbei und dann wird man sowas simulieren und schauen, ob sich das lohnt.\n\nSpeaker 0: Mhm. Also das heißt, in dem Bereich könnte man sagen, öffnet sich die Verwaltung gerade für 'n Thema, was mit Simulationen zu tun hat? Ist es vielleicht son son Einfallstor, zu sagen, so guck mal, da nutzt ihr's, da funktioniert's, macht doch mal in anderen Bereichen auch?\n\nSpeaker 1: Ja, einerseits das, aber ich denke bei den Fachleuten, da werden ja Simulationen schon immer benutzt. Eigentlich hört man immer wieder diese Modellierung, das ist doch so einfach oder mit Computertechnik heute mit immer besseren grafischen Oberflächen und so, müsste das doch eigentlich immer einfacher werden, dass jedermann sozusagen modellieren kann. Und da haben wir geguckt, geht das eigentlich? Und unsere Idee war, kann man nicht sozusagen stärker direkt die Fachpersonen, die jetzt eine fachliche Ahnung haben, aber nicht zu viel Ahnung vom Programmieren, vom Modellieren, vom Simulieren, kann man diese Person nicht in die Lage versetzen, Modellierungen zu nutzen.\n\nSpeaker 0: Also Fachpersonal heißt, in der Wissenschaft, da ist es ja sehr verbreitet, ne?\n\nSpeaker 1: Und Ja, genau. Genau.\n\nSpeaker 0: Genau. Und\n\nSpeaker 1: dann aber jetzt Fachpersonal in Behörden. Genau, jetzt meine ich dann eben Fachpersonal in Behörden. Das schließt son bisschen vielleicht auch an die Idee von den Datenlaboren an, nicht? Dass man immer mehr, das ist ja auch in der Presse, dass man so sagt, okay, wir ein Ministerium oder eine Behörde braucht eben Daten, möchte eben Datenlabore aufbauen, stellt Daten der Öffentlichkeit zur Verfügung, arbeitet verstärkt mit Daten und da ist natürlich dieses direkter Anschluss zu sagen, wir haben die Daten oder wir wollen jetzt Fragestellungen mithilfe dieser Daten beantworten.\n\nSpeaker 0: Ja, weil die Daten natürlich die Grundlage für vieles sind, aber eben auch für Simulationen.\n\nSpeaker 1: Genau.\n\nSpeaker 0: Ja, mhm. Mhm. Ich hab ja versprochen in der Anmoderation, dass wir heute wirklich praktisch werden. Jens, ihr habt für die Veröffentlichung Wirkungen verstehen und nutzen, agentenbasierte Simulationen für den öffentlichen Sektor, ja selbst eine Simulation erstellt. Was habt ihr denn da fürn Beispiel ausgewählt und warum auch?\n\nSpeaker 1: Wir haben uns diese verschiedenen Anwendungsfälle angeguckt oder was es da schon gibt und sind eigentlich zu dieser Idee gekommen, dass man so was wie Akzeptanz oder Verbreitung von Angeboten der Verwaltung, dass man sowas eigentlich ganz gut Mhm. Modellieren können müsste und wir sind dann auf die EID gekommen, sprich 'n elektronischer Personalausweis und haben dort eben unsere Modelle schrittweise aufgebaut, zu gucken, wie verbreitet er sich nach welchen Mechanismen und mit welchen Mechanismen könnte ich das fördern.\n\nSpeaker 0: Ja, lustigerweise, da haben wir glaube ich sogar schon mal eine Folge dazu gemacht, Mike, ne? Elektronischer Personalausweis und warum der sich nicht so richtig durchsetzt. Da haben sich ja schon viele kluge Menschen Gedanken dazu gemacht. Was meinst Du, kann jetzt eine Simulation so dazu beitragen? Mhm.\n\nSpeaker 2: Also erst mal, wir wollten ganz bewusst halt von diesen klassischen Feldern, Umwelt, Verkehr und so was runter und dann zu so was, was man als Kernverwaltung bezeichnen kann oder Kerndienstleistungen der Verwaltung in der Hoffnung, dass es sich einfach simulieren lässt als Innovationsdiffusionsprozess. Der entscheidende Vorteil bei der Simulation ist, dass man sich wirklich über jeden einzelnen Schritt Gedanken machen muss. Also sonst, man kann natürlich, wie wir Sie auch gemacht haben in in dieser Podcastfolge, man kann so einzelne Punkte ganz gut identifizieren, woran es denn gescheitert sein könnte, dass diese Diffusion nicht so funktioniert hat, wie man sich das erhofft und erträumt hatte. Bei 'ner Simulation muss man ganz unten anfangen. Was ist die Grundmotivation?\n\nJetzt sind wir wieder bei den Agenten, also wenn man wenn man Die Agenten individuellen Themen bleibt.\n\nSpeaker 0: Ne? Also Bürger und Bürgerinnen.\n\nSpeaker 2: Genau, also\n\nSpeaker 0: Du Ja. Unterstützend klingt so nach eben 0 0 7, aber Agenten sind Bürger, ne? Das ist einfach der Simulations Slang, was Ja,\n\nSpeaker 1: vielleicht müsste man das kurz mal erläutern. Ja. Son agentenbasierte Simulation, das kann man sich wie Spielfiguren aufm Spielfeld vorstellen, dass man so sagt, okay, man hat diese Umwelt, also das Spielfeld und man hat dort Agenten da drauf. Das sind also Einheiten in der Simulation, die eben interagieren miteinander, die Akteure sozusagen. Das sind aber nicht nur die Bürgerinnen und Bürger.\n\nBei uns sind es zum Beispiel auch Organisationen wie zum Beispiel die Verwaltung oder Firmen. Also auch Organisationen werden als Agenten betrachtet und die interagieren untereinander oder aber mit ihrer Umwelt.\n\nSpeaker 0: Okay und das ist ein Ansatz, den man wählen kann, wenn man eine Simulation macht Genau, das\n\nSpeaker 1: ist ja\n\nSpeaker 0: 1, der wir eben ausgewählt haben.\n\nSpeaker 1: Genau und das ist ein sehr einfacher Ansatz, also sehr einfach verständlich. 2 Akteure treffen sich. Was passiert dann?\n\nSpeaker 0: Mhm, okay. Das heißt, ihr habt im ersten Schritt geguckt, wer sind überhaupt die Agenten?\n\nSpeaker 1: Genau. Also In unserem Fall sind das erst mal die Bürger, die diese EID haben oder noch nicht haben, eine Motivation haben, diese EID zu nutzen. Es sind die Verwaltungen, bei denen ich das einsetzen kann oder noch nicht. Und wir haben das dann schrittweise aufgebaut und haben gesagt, okay, was wäre dann, wenn wir jetzt zusätzlich noch Firmen haben, Unternehmen, wo man auch die Serien mit\n\nSpeaker 0: dem Ausweis identifizieren kann.\n\nSpeaker 1: Weil man dann einfach sozusagen, das war unsere Annahme, die wir reingesteckt haben, dass wir gesagt haben, na ja, je öfter ich das nutze, desto mehr und das einfach funktioniert, also eine EID zu nutzen, war unsere Annahme, desto überzeugter bin ich ja, mir die zu holen oder wenn ich schon habe, zu verlängern oder wenn ich davon gehört hab, dass andere damit gut zurechtkommen, dass ich mir auch so eine EID hole. Ein Aspekt dabei sind ja die langen Zeiträume, also\n\nSpeaker 0: Weil ich den Ausweis ja nicht Genau. Dauernd verlängere, sondern nur alle 10 Jahre.\n\nSpeaker 1: Alle 10 Jahre, genau Und darüber kommen natürlich dann sehr lange Zeiträume in so eine Betrachtung. Mhm.\n\nSpeaker 2: Ich sag\n\nSpeaker 1: ich mal, aus der Verkaufspsychologie oder so gibt es ja vielleicht Modelle, die sagen, was ist eine positive Erfahrung? Wie wirkt sich die aus? Wie wirkt sich eine negative Erfahrung aus? Wie viel Leuten erzähle ich von 1 positiven oder negativen Erfahrung? Nicht und solche Sachen, dort kann man dann natürlich auch eine wissenschaftliche Grundlage suchen und finden.\n\nUns war wichtig, ein Schritt eigentlich vorher eben, kann ich Fachpersonen, die jetzt eben nicht täglich mit Simulationen arbeiten 'N großer Anteil an der Simulation ist ja die Modellierung. Also die Wirklichkeit vereinfacht zu beschreiben. Modellierung bedeutet eben auch, dass ich mir selber Gedanken mache, wie sind Zusammenhänge. Ich kann das auch nachvollziehbar machen, weil andere meine Modellierung, die ich aufschreibe, nicht, dass ich sage, dieser oder jener Faktor, den schätze ich als sehr stark ein. Also ist 'n großer Einfluss, das schreibe ich in meine Modellierung rein und jemand anders kann dann sagen, nee, nee, das ist aber viel geringer.\n\nDas heißt, die Simulation und eben da im Speziellen die Modellierung ist ja auch der Weg, so eine so eine Diskussion zu Versachlichen, Sachen nachvollziehbar zu machen. Mhm. Zu lernen und weiterzuentwickeln und so weiter. Und auf diese Mechanismen, das wollten wir eigentlich gucken, wie gut geht das heutzutage schon? Sind aber neben nicht tief in die fachlichen.\n\nSpeaker 0: Genau, weil's ja wirklich nur drum ging, das einfach mal auszuprobieren. Genau.\n\nSpeaker 2: Und\n\nSpeaker 0: ja, mhm. Aber spannender Punkt trotzdem, dass man natürlich bei dem Simulieren an sich auch Erkenntnisgewinne hat, ne. Genau. Also dass man eben zum Beispiel merkt, ach Mensch, ich müsste jetzt über die Agenten das und das wissen, da gibt's aber gar keine Zahlen dazu, also muss ich die irgendwo besorgen oder so was. Genau.\n\nSolche Sachen können ja da auch rauskommen.\n\nSpeaker 1: Ja und Simulation ist ja auch zum Beispiel ein ganz wichtiger Punkt und auch mit dieser Sprache, die wir benutzt haben, Net Logo, die frei verfügbar ist, es ist ja auch 'n sehr starker Erkenntnisgewinn. Also zum Beispiel Mikes Lieblingsbeispiel ist eine Visualisierung, wie ein Stau auf 'ner Straße entsteht. Und das ist da 'n fertiges Simulationsmodul, was man sich also ausm Internet runterladen kann, wo dann kleine Autos überm Bildschirm fahren und wo man dann je nach Verkehrsdichte sehen kann, wie eben Staus aus dem Nichts entstehen.\n\nSpeaker 0: Also da gibt's Dinge, die kann ich nicht schnappen eigentlich.\n\nSpeaker 1: Genau, das ist das.\n\nSpeaker 0: Ne, als jemand, der genau. Ja.\n\nSpeaker 1: Und in diesem Stauseimulationsbeispiel, da ist es also eher sozusagen der Erkenntnisgewinn, dass man sich das mal praktisch angucken kann, dass man son Beispiel hat, wie man Simulationen aufbauen kann und nicht. Oder aber wenn man eben selber sagt, selber sich ran setzt, dann wird's natürlich etwas schwieriger, dass man die richtigen Parameter auswählt.\n\nSpeaker 0: Ja, ja. Aber mit der Transparenz ist auch spannend, weil wir ja immer wenn wir über KI zum Beispiel diskutieren, diese Blackbox Geschichte haben und das ist ja bei Simulation, kann man ja dann sehr genau nachvollziehen irgendwie.\n\nSpeaker 1: Genau, vor allen Dingen als also diesem also ich find das sehr wichtig. Es ist ja allgemeine Forderung, dass gesagt wird, ja viel mehr Menschen müssten programmieren lernen zum Beispiel. Aber eigentlich ist es, wenn man's 'n bisschen genauer beschreibt, ist es eigentlich die Modellierung und das ist die Verbindung, dass man eine Sprache benutzt, eine formale Sprache benutzt, die sowohl Menschen als auch Computer verstehen können. Und in unserem Fall kann dann zum Beispiel ein Verwaltungsexperte, eine Verwaltungsexpertin könnte also in dieser Fachsprache eine Simulation formulieren, die dann so transparent ist, dass jemand anders sie lesen kann, dass sie aber auch vom Computer ausgeführt werden.\n\nSpeaker 0: Ja. Da kommen wir gleich noch mal dazu, wie ihr das Ganze dann programmiert habt, da ja auch in 2 Varianten, ne? Einmal mit Genau. Und einmal ohne. Genau.\n\nMike, Du wolltest noch was ergänzen, glaube ich.\n\nSpeaker 2: Ja, ich würd ganz gerne noch mal wieder zurück zu den Daten. Also es stimmt, es ist keine Simulation, die wir da gemacht haben, wo wir empirische Daten genommen haben. Das gibt's ja durchaus auch. Wettervorhersage ist Paradebeispiel, wo ein ganzer Strauß von verschiedenen Daten erfasst wird und dann in diese Simulation reingeschoben wird. Das war genau nicht unser Ansatz, sondern wir haben uns natürlich 'n paar Daten angeguckt, also so Durchdringung von EAD Funktion früher und heute und so was.\n\nAber wir haben dann sind dann den Weg gegangen, die Simulation so flexibel zu machen, dass man diese Daten quasi für die eigenen Simulationsdurchgänge selbst einstellen kann. Und das ist noch mal eine Besonderheit, die bei NetLogo halt auch ganz besonders leicht umzusetzen ist. Und dann geht es in der Tat da, also habt ihr ja schon gesagt, also dann erst mal festzustellen, welche Daten hab ich denn gar nicht? Welcher hätt ich denn gerne, diese Simulation noch mehr an die Realität dranzub bringen? Und das ist die große große Krux dabei und die große Chance, aus diesen Simulationen auch zu lernen.\n\nSpeaker 0: Ja. Man kann ja auch wirklich damit rumspielen, weil ihr das alles online stellt und man könnte mit diesen Dingen, die ihr da erarbeitet habt, weil Du grad gesagt hast, Mike irgendwie Zahlen rauf runterstellen und sowas. Das kann man wirklich machen, ne? Es ist jetzt eine frei verfügbares Spielmaterial sozusagen, was da entstanden ist.\n\nSpeaker 2: Genau, wir haben die Modelle alle online gestellt, also alle Zwischenschritte, die wir gemacht haben und auch den Weg dahin und jetzt kann sich jede und jeder, der genauer gucken will, was wir dafür Annahmen implizit und explizit drin haben, kann sich diesen Code angucken, kann sich auch erklären lassen durch entsprechende KI Tools oder durch entsprechende Expertinnen und Experten und kann dann genau sehen, welche Annahmen da drin stecken. Mhm. Und natürlich dann auch widersprechen an jedem Einkaufszentrum.\n\nSpeaker 0: Okay, zu wie viel Prozent tritt das jetzt ein oder nicht, ne? Ist es eine exakte Vorhersage der Zukunft? Wie genau kommt es daran? Was spielen denn da für Faktoren eine Rolle?\n\nSpeaker 2: Also ich glaub, da muss man noch 'n halben Schritt zurückgehen, Ja. Was für unterschiedliche Typen von Simulationen es gibt. Wie eben schon gesagt, das Wetterbeispiel oder auch solche Simulationen im Verkehrsbereich sind sehr nah daran, Vorhersagen treffen zu wollen. Mhm. Also\n\nSpeaker 0: Und auch zu können?\n\nSpeaker 2: Ja, beim Wetter funktioniert's ganz gut, oder?\n\nSpeaker 0: Ja. Wird\n\nSpeaker 2: vor allem immer besser und eher auch kleinräumig, was früher völlig illusogen\n\nSpeaker 0: ist. Regenradar.\n\nSpeaker 2: Ja. Nach meiner subjektiven Wahrnehmung. Wir haben da eher 'n anderen Simulationsansatz. Also eher, der in Richtung Verstehen geht. Also wir wollen verstehen, wenn wir genau im Sinne der Wirkungsorientierung, was passiert, wenn ich jetzt eine bestimmte politische Maßnahme oder eine Kommunikationsmaßnahme oder irgendetwas, wenn ich das jetzt in dieses modellierte System reinbringe?\n\nWas passiert dann? Steigt dann die oder beschleunigt sich die Diffusion oder geht sie zurück oder wie auch immer? Und da sind wir dann im Bereich, wo's gar nicht so sehr darum geht, vorherzusagen, sind es jetzt in Zukunft 80 Prozent, 90\n\nSpeaker 0: Prozent Leute, die das nutzen. Mhm.\n\nSpeaker 1: Das interessante an der Vorhersage ist ja, dass wir als Menschen lineare Zusammenhänge supergut abschätzen können, nicht? Also wenn wir wissen, was weiß ich, wir machen Wasserhahn auf und das Waschbecken, das ist halbvoll. In 2 Minuten, dann wissen wir in 4 Minuten ist es ganz voll oder sowas. Das kriegen wir ja gut hin. Aber exponentielles Wachstum zum Beispiel kriegen wir als Menschen nicht hin, dieses berühmte Seerosenbeispiel auf dem Teich oder aber auch sowas wie Sättigung.\n\nAlso wenn irgendwas natürlich gesättigt ist, wenn ich einfach keine Steigerung mehr drin habe. Oder wenn ich Kipppunkte drin\n\nSpeaker 2: habe, was Aber das kennt\n\nSpeaker 0: man ja vom Klima. Genau.\n\nSpeaker 1: Das kennt man jetzt vom Klima, ist das jetzt bekannt, aber solche Kipppunkte. Das bedeutet ja, dass ich nichtlineare Zusammenhänge habe und aber auch plötzlich ganze Systeme umkippen oder sich Sachen ganz fundamental ändern. Und das ist sehr schwer sozusagen mit seinem Alltagsverstand oder mit 'nem einfachen Draufgucken, jetzt ohne Computerhilfe zu sagen, was passiert da eigentlich? Wann tritt so ein Effekt ein? Was ist eine Auswirkung von exponentiellem Wachstum zum Beispiel?\n\nSpeaker 0: Ja und dafür sind Simulationen durch, ne?\n\nSpeaker 1: Ach so, genau.\n\nSpeaker 0: Es könnte halt sein, erst sind 20 Prozent mit dem Ja. Elektronischen Personalausweis unterwegs und dann auf einmal, wir haben eine gute Werbemaßnahme gemacht, auf einmal sind's 80 oder?\n\nSpeaker 1: Genau, das haben wir ja son bisschen gesehen. Ich glaube, in den letzten Jahren bei Kartenzahlen, beim kontaktlosen Zahlen mit mit mit Karte, was ja auch durch verschiedene Maßnahmen gefördert wurde und so weiter, wo aber dann auch irgendwo plötzlich eine Akzeptanz überall da war und das dann auch genutzt wird.\n\nSpeaker 0: Zum Beispiel schalte ich Werbung? Mache ich Kommunikationsmaßnahmen? Ne? Das war eine Sache, die ihr untersucht habt. Ich finde es ganz interessant, weil wir ja gerade gesagt haben, uns geht's gar nicht darum zu prognostizieren, sind's irgendwie 87 Prozent, sind's 89 Prozent, sondern wir wollen ja wissen, bringt's was, bringt's nichts, ne?\n\nDas ist eigentlich die Frage, die wir wissen wollen und da habt ihr untersucht, zum Beispiel Kommunikationsmaßnahmen, was was noch?\n\nSpeaker 1: So ein Akteur, also Bürger geht aufs Amt und dann ist die Frage, ist das Angebot überhaupt da? Dann hat er's positiv auf sone Stimmung eingezahlt sozusagen, auf sone Bereitschaft, die EID gut zu finden, sage ich mal, war es einfach und dadurch stieg dann dieser Wert von diesem Agenten. Ja. Und im Laufe der Zeit, wenn man keine Interaktionen hat, dann vergisst man das wieder, dann sinkt dieser Wert wieder. Das war unsere ganz einfache Modellannahme, zum Beispiel ein Zusammenhang, den wir drin hatten.\n\nSpeaker 0: Ja. Okay und also genau, ums nochmal zu sagen, es ging ja wirklich nur darum zu versuchen, kann man mit so 'ner Agentenbasierten Simulation überhaupt so was untersuchen. Gibt's trotzdem irgendwie Erkenntnisse, wo ihr sagt, ach Mensch, das hat uns überrascht, der hätten wir gar nicht gesagt gedacht, dass das so wirkt oder so. Hätten wir so gar nicht abschätzen können.\n\nSpeaker 2: Bei 1 Berechnung haben wir sehr häufig nachgeguckt, ob's wirklich richtig ist. Also wir haben ja Kommunikationsmaßnahmen untersucht und sind auch zu dem Ergebnis gekommen, dass es dass sie positiv wirken im Grunde. Positiv wirken auf die tatsächliche Nutzung und haben dann auch noch mal differenziert, sollte man das lieber aufm Block am Anfang kann schnell oder lieber über Zeitstrecken? Das war alles im Ergebnis relativ gleichwertig. Da gab's keine großen Unterschiede.\n\nAber aber und jetzt kommt das große aber, die Einstellung zur eID ist dabei abgefallen. Also die die Leute standen der EID negativer gegenüber.\n\nSpeaker 0: Je mehr drüber geredet wurde sozusagen. Je mehr sie drüber\n\nSpeaker 2: Und mehr Kommunikationspartner es gibt und der der Hebel dahinter war einfach, dass wir ein Modell genommen haben, wo's sehr wenig Angebote gab. Mhm. Und deswegen waren die Leute einfach frustriert. Und je mehr sie durch die Kommunikationsmaßnahmen darauf hingewiesen worden sind, desto frustrierter waren sie. Ja.\n\nAlso dann, wenn das Angebot nicht stimmt und man in Anführungszeichen nur kommuniziert, dann hat es tatsächlich mittelfristig negative Effekte.\n\nSpeaker 0: Ja, ja, macht auch Sinn, ne, wenn man sich so überlegt, die standen quasi in Startlöchern und dann konnten sie nicht loslegen, ja.\n\nSpeaker 2: Genau. Und das ist sehr auch eine, der neben den nicht linearen Zusammenhängen ist das ja eine der besonderen Reize an der Simulation, solche Sachen, die erstmals sehr kontraintuitiv sind, dabei rauszufinden. Also erst mal, wenn man so drauf guckt, wie ich mache Kommunikationsmaßnahmen, bewerbe das das Ganze und trotzdem geht die Einstellung runter, ist irgendwie komisch. Und dann, wenn man dahinter guckt, wird es dann ganz einfach.\n\nSpeaker 0: Ah, okay. Jetzt lass uns noch mal gerne über das technische Vorgehen reden. Ist grad schon der Name gefallen von 1 Programmiersprache, die da genutzt wird, ne, Simulationsumgebung?\n\nSpeaker 1: Genau, das ist sone Simulationsumgebung, die heißt NetLogo. Ja. Die ist frei verfügbar, ist gut dokumentiert. Wichtig war für uns, dass man natürlich Beispiele findet, Tutorials, da einzusteigen, loslegen zu können, dass es auch nichts kostet. War jetzt auch eine unserer Bedingungen, dass wir gesagt haben, alle Sachen, die wir machen, müssen frei verfügbar sein.\n\nDann haben wir ja schon gesagt, hinterher stellen wir das ja auch wieder zur Verfügung. Also wir wollen dann schon so einsteigen in einen Zyklus, dass man die Tools einfach zur Verfügung hat und auch wenn man damit arbeitet, seine Ergebnisse weitergibt.\n\nSpeaker 0: Und wie hoch oder niedrigschwellig ist das, würdet ihr sagen? Also wenn ich jetzt sage, ich hab jetzt nicht Zeit, mich 2 Wochen fortzubilden, die IT Abteilung ist auch nicht hilfreich, ich bin total auf mich alleine gestellt und hab eher so Erfahrungen mit MS Office Paketen. Wie schnell finde ich deinen Zugang?\n\nSpeaker 1: Müsste Maike jetzt 'n bisschen antworten. Ich bin Hab vielleicht gemerkt.\n\nSpeaker 0: Ist natürlich für euch schwer, sich vorzustellen.\n\nSpeaker 1: Nein, das ist Also ich bin mehr so Ich bin Hab mehr Programmierkenntnisse und man muss sagen, dass es ganz wichtig ist, das erste Beispiel durchzuarbeiten. Also es gibt ja in den Tutorials oder so was gibt es immer so Einführung. Mhm. Und das ist unglaublich wichtig. Einmal zu verstehen, was ist der Ansatz von so 1 Programmiersprache?\n\nWie blickt die auf die Welt sozusagen? Wie soll ich damit umgehen? Und dann, wenn man das mal so verstanden hat, also zum Beispiel in diesem Fall eben auch die Agenten. Ja. Nicht?\n\nWas ist das? Also ich hab die Plan\n\nSpeaker 0: da bitten Genau. Leute, ja.\n\nSpeaker 1: Da wegen sich Leute, genau. Wie möchte ich das aufsetzen? Das muss man einmal verstanden haben. Dann ist es eben eine spezielle Sprache, die jetzt besonders geeignet ist für diese für diese Art von Simulation. Man wird also ganz viel zum Beispiel Zufallsfunktion, das ist alles da, nicht?\n\nDass man sozusagen zufällig irgendwelchen Agenten irgendwelche zufälligen Werte geben kann, dass die irgendwie handeln können. Da nimmt man oft den Zufall, dass die also durch die Gegend rennen sozusagen, jetzt im Bild und dann zufällig aneinanderstoßen, aufeinanderstoßen. Mhm. Und da nimmt man Zufallsfunktion und da gibt es dann halt sehr viele Programmunterstützung oder aber auch für die grafische Auswertung, dass man schöne Grafik machen kann. Da gibt es dann auch eben viele Elemente.\n\nUnd das muss man einmal verstanden haben sozusagen, welche Elemente es gibt, was überhaupt der Ansatz ist und dann denke ich, ist das schon eine Ausbildungssprache, mit der man gut reinkommt.\n\nSpeaker 2: Diese Modelle, die schon bereits verfügbar sind, die sind immer so aufgebaut, dass man eine Oberfläche hat, mit der man die bedient. Dann dahinter eine Beschreibung, was dieses Modell erreichen will und auch mit son paar Beispielen, was man vielleicht mal einstellen sollte, 'n Gespür dafür zu kriegen, wie das Ding funktioniert. Und dahinter dann der Code. Also\n\nSpeaker 0: Okay, also in so 3 man kann so 3 Schichten abziehen und das nach und nach verstehen son bisschen.\n\nSpeaker 2: Genau und das ist natürlich 'n wunderbarer Einstieg. Zum Beispiel mein kleines Verkehrsbeispiel, mein Staubeispiel. Das ist ganz, ganz simpel und dann, wenn man sich da einmal durchgearbeitet hat, son bisschen durcharbeiten, muss man sich wohl dann auch sehr gut nachvollziehbar. Die Sprache ist 'n bisschen ruckelig, aber natürlich auch speziell für diese Simulationsanforderung Ja. Gebaut.\n\nAlso man wenn man 'n bisschen drin ist, kann man sich fast lesen wie normalen Text.\n\nSpeaker 0: Okay und also jetzt für die Leute, die aber noch nicht so überzeugt sind und immer noch Hürden haben, habt ihr ja noch 'n anderen Einsatz probiert, nämlich das Ganze mit KI zu machen,\n\nSpeaker 1: ne? Genau, zu dem Zeitpunkt, wo wir die Arbeiten gemacht haben, war das natürlich eine neue Möglichkeit Programmieren mithilfe von generativer KI mit Chatbots wie ChatGPT und so weiter zu unterstützen. Auch hier haben wir wieder gesagt, wir wollen jetzt keine speziellen Programme benutzen, sondern wir wollen die kostenlosen öffentlichen Angebote nutzen, weil im Zweifel ist es ja so, dass dann spezialisierte Sachen irgendwie besser sind. Wir wollten ja mal gucken, was geht überhaupt? Auch möglichst geringe Hürden und haben das eben dann unser Simulationsproblem beschrieben und haben dann die KI sozusagen aufgefordert, uns eine Simulation dazu zu schreiben.\n\nSpeaker 0: Und diese Programmiersprache Ja. Über die wir gerade gesprochen haben, kennen die üblichen Chat Bots sozusagen. Also das heißt, die sprechen miteinander auf eine Art, ne? Also man hat wie son Dolmetscher. Kann man sich das so vorstellen vielleicht?\n\nSpeaker 1: Es ist so, die die Programmiersprache net Logo, die hat da 'n kleines Problem. Diese grafische Oberfläche, es ist eigentlich gemacht sozusagen, dass man auf der einen Seite seine Simulationsbeschreibung hat. Das ist 'n klassischer Programmiercode, aber diese grafische Oberfläche dazu, das sind also grafische Elemente wie Knöpfe oder Schieber, Werte einzustellen. In den Normalfall ordnet man die per Hand an sozusagen. Und da hat jetzt die KI leichte Schwierigkeiten, jetzt leichte Schwierigkeiten, jetzt im allgemeinen Fall dieses diese komplette Simulation zu machen.\n\nWas man aber machen kann, ist halt, man ordnet selber diese Schieber an und sagt, was man machen möchte und kann sich dann den Code schreiben lassen, die Simulation durchzuführen. Okay, also wenn man jetzt in diesem durchzuführen.\n\nSpeaker 0: Okay, also wenn man jetzt in diesem Ebenenmodell bleibt von Mike gerade, also die kann die unterste und zweitunterste Ebene kriegt die gut hin und dann das oberste, das was man sieht.\n\nSpeaker 1: Genau, da das das wird man sicherlich auch lösen können und da gibt's auch spezielle Ansätze, sowas zu lösen. Aber in diesem Fall, wenn man jetzt da so ganz naiv rangeht, dann muss man sich 'n Weg überlegen. Wenn das nicht funktioniert, dann kann man auch immer die KI fragen, wenn's dann gibt's eine Fehlermeldung und dann kann man die KI fragen,\n\nSpeaker 2: was\n\nSpeaker 1: ist denn mit diesem Fehler und dann wird die KI diesen Fehler korrigieren.\n\nSpeaker 0: Also aber es jetzt noch mal zusammenzufassen, also die KI macht schon noch mal ein Stück zugänglicher Auf\n\nSpeaker 1: jeden Fall, ja.\n\nSpeaker 0: Im Falle, ja.\n\nSpeaker 1: Also die KI ist für uns glaube ich, 'n ganz großer, also wirklich eine Möglichkeit, das dieses Gebiet zu öffnen. Man muss immer noch dieses Grundverständnis muss man haben, das ist ganz wichtig. Man muss verstehen auch die ersten Ansätze, die die KI zum Beispiel in diesem Netlogob Beispiel bringt. Da hab ich ja gesagt, es gibt eben das Spielfeld und es gibt die Spielfiguren. Und nun könnte man sagen zum Beispiel im Spielfeld sind schon die Behörden sind schon im Spielfeld oder sind fest sozusagen.\n\nSpeaker 0: Die sind keine Agenten.\n\nSpeaker 1: Genau, die sind keine Agenten, sondern die sind Teil des Spielfelds. Nicht und da müsste man muss man sich überlegen, was will ich denn später simulieren? Wo will ich hin? Welche Vorstellung hab ich? Und wenn die KI dann einen Weg vorschlägt, dann kann ich ganz zu Anfang schon sehen, ist es der Weg, den ich gehen möchte?\n\nOder aber möchte ich noch mal fragen und krieg eine neue Antwort von der KI. Was aber toll ist an der Stelle ist, ich brauche natürlich nicht so wahnsinnig viel beschreiben. Ich muss nur die Grundzüge beschreiben und dann wird mir die KI ganz viel von der Routinearbeit abnehmen und wird die genaue Bezeichnung von den Funktionen, wie ich die Zufallsfunktion aufrufen muss und so weiter, wie eine Grafik gemacht werden muss. Das wird mir die KI machen.\n\nSpeaker 0: Mhm. Okay.\n\nSpeaker 1: Ich hatte in meinem Beispiel, wie ich das noch dazu führen darf, innere Einstellung der Bürgerinnen und Bürger zur EID. Und ich hab dann gesagt, okay, diese Einstellung kann sich ändern und die KI hatte von selber vorgeschlagen, dass die Farbe des Agenten auf dem Spielfeld von dieser Einstellung abhängig gemacht wird. Mhm. Dass man also schon gleich eine neue Funktion hat, über die ich ja noch gar nicht nachgedacht hab in meiner Simulation. In meiner Simulation wollte ich erst mal die Simulation laufen lassen und wollte mir hinterher Gedanken machen, wie ich das denn darstellen kann.\n\nUnd die KI hat aber gleich dieses Farbschema auf son kleines Männchen übertragen. Also hat 'n Männchensymbol für Bürger ausgewählt. Und hat dann dem Männchen noch eine Farbe gegeben Also das ist ja\n\nSpeaker 0: zum Beispiel grün, wenn's voll heiß ist auf die Erde. Genau. Und rot, wenn's total Genau. Spannend ist. Mhm.\n\nOkay. Mike, kannst Du vielleicht noch mal so sagen, wie könnte denn zum Beispiel son prompt lauten? Also ich müsste sagen, mach mir eine agentenbasierte Simulation mit dem und dem Programm. Also was sage ich da? Was sind so die wichtigsten Inhalte, die ich da liefern muss, damit die KI eben in die richtige Richtung losläuft?\n\nSpeaker 2: Ich kann's jetzt ganz leicht machen. Ja. Das haben wir alles veröffentlicht, inklusive der PONTS, Okay. Wie man das am besten beschreibt. Aber ja, natürlich muss muss die KI wissen, in welchem Umfeld, also mit welchem Programm, NetLogo oder Python, mit welcher Bibliothek ich das machen will, an welcher Sprache ich das machen will.\n\nUnd dann muss ich aber ganz normal alltagssprachlich beschreiben, was ich denn da gerne simuliert haben möchte. Dann die Aufforderung, mach mir 'n Code.\n\nSpeaker 1: Genau. Interessant wird das dann an der Stelle, wo man das nicht explizit formuliert, sondern wenn jetzt Agenten aufeinandertreffen, was dann passieren soll, hab ich natürlich ganz vorne was im Kopf, was dann passieren soll und das beschreibe ich. Aber die anderen Fälle, die es da auch noch gibt, die beschreibe ich jetzt vielleicht gar nicht. Aber wenn das technisch umgesetzt wird, dann muss ja eine Entscheidung getroffen werden, ne? Oder es wird eben bewusst keine Entscheidung getroffen, was aber aus technischer Sicht wieder eine Entscheidung ist.\n\nUnd dann sehe ich ja plötzlich auch, was in meiner Beschreibung fehlt. Ob ich unpräzise bin, die KI etwas vereinfacht hat, was ich aber gar nicht vereinfacht haben möchte und dort muss ich dann präziser werden.\n\nSpeaker 0: Gut. Also alles ist veröffentlicht. Man kann direkt loslegen, Mike.\n\nSpeaker 2: Ein Aspekt, der noch ganz wichtig ist, die Syntax wird abgenommen von der KI und die KI kann die richtige Syntax, das ist Gold wert. Okay. Auch so Kleinigkeiten, aber genau da auf dem Punkt wollt ich noch mal eingehen. Das ist natürlich auch absolutes Gold wert, weil man weiß, also man kann dann auch anfangen, die KI zu hinterfragen, was sie da in den Beschreibungslücken, die geblieben sind, was sie da automatisch einsetzt. Macht das denn überhaupt Sinn, was sie da einsetzt?\n\nUnd stellt sich dann die nächste Frage, wo sich das dann vielleicht noch mal irgendwie inhaltlich hinterfragen und ganz anders bauen? Also so gesehen ist das wie bei anderen bei Textfragen oder bei Brainstorming Prozessen ist der KI da auch 'n Schilderspartner, wenn man dann weiterkommen kann.\n\nSpeaker 0: Mhm. Okay. Wo jetzt zum Abschluss seht ihr denn heiße Anwendungsfelder, Low hanging fruits? Wer wer sollte jetzt sofort sich euer Beispiel aufrufen und damit loslegen?\n\nSpeaker 2: Ich glaube, diese Felder werden immer größer. Swan City hatten wir ja schon, da ist halt Mhm. Das der enorme Vorteil, dass die Daten oder sehr, sehr viele Daten halt einfach schon da sind. Das ist natürlich immer Gold wert, darauf Simulationen aufzusetzen. Aber in dem Bereich passiert ja auch schon 'n bisschen was.\n\nVielleicht nicht ganz so low hanging, aber ich glaube, ganz, ganz wichtig, ganz zentral für die Modernisierung von Staat und Verwaltung ist halt wirklich diese Wirkungsorientierung und das wirklich eigentlich überall sich Gedanken zu machen, wie wirkt das, was ich tue? Weil da kann man dann anekdotisch sehr schön einige kontraintuitive Effekte mal beobachten, aber wenn man's dann in der Simulation hat mit diesem gesamten Reflexionsprozess, das ist dann noch mal sehr viel wert.\n\nSpeaker 0: Ja. Und so wie ich das verstehe, also es funktioniert auch auf allen Ebenen, ne. Also man könnte sagen auf Bundesebene zu sagen, erhöhe ich das Elterngeld, erhöhe ich's nicht, aber genauso wie führ ich in meiner Kleinstadt eine Mikrowährung lokal ein oder so was. Also man könnte, ne, so wie ich's versteh\n\nSpeaker 2: Die Methode ist komplett offen. Ja. Es ist son bisschen Fluch und Segen der Methode. Es ist komplett offen. Man kann letztlich jede Fragestellung damit simulieren.\n\nMal angemessen und mal weniger angemessen. Manchmal tut's vielleicht auch eine Befragung, aber im Prinzip ist die Methode für fast alles geeignet.\n\nSpeaker 0: Ja. Okay. Gut, dann hoffen wir, dass ganz viele Lust bekommen haben, da einfach mal mit rumzuspielen mit diesen Reglern, die man da auch bedienen kann und sich das hoffentlich 'n bisschen mehr durchsetzt. Vielen Dank euch beiden auch fürs Ausprobieren. So jemand muss ja den Anfang machen.\n\nSpeaker 1: Wir danken dir. Ja, Dankeschön.\n\nSpeaker 0: Simulationen als digitales Werkzeug. Das war unser Thema heute bei restart digital. Meine Gesprächspartner heute waren Mike Weber und Jens Tiemann vom Kompetenzzentrum öffentlicher IT am Fraunhofer Institut Focus in Berlin. Wir verlinken euch die Publikation zu dem Thema und auch alle anderen Ressourcen, über die wir gesprochen haben, als Link in den Shownotes zu dem Podcast. Ihr findet sie auch online unter ö fit Punkt d e mit o e geschrieben.\n\nDiesen Podcast findet ihr kostenfrei auf Apple Podcasts, Spotify und vielen weiteren Plattformen. Wenn ihr unsere Themen spannend findet und keine Folge verpassen wollt, dann könnt ihr uns da auch abonnieren. Schön, dass ihr dabei wart. Ich bin Franziska Walser. Tschüs und bis bald.", "paragraphs": [ { "sentences": [ { "text": "Simulationen sind nur Annäherungen an die Wirklichkeit, sie sind keine Prophezeiungen.", "start": 1.4399999, "end": 5.8599997 }, { "text": "Aber in vielen Fällen verlassen wir uns auf ihre Zuverlässigkeit.", "start": 6.48, "end": 9.22 }, { "text": "Wir nehmen zum Beispiel einen Schirm mit, wenn für den Nachmittag Regen angesagt ist, oder wir setzen uns ohne Sorge in ein Flugzeug, dessen Pilot oder Pilotin mithilfe 1 Flugsimulators ausgebildet wurde.", "start": 9.679999, "end": 20.385 }, { "text": "Das sind relativ einfache Beispiele für Simulationen, aber auch komplexere Zusammenhänge lassen sich modellieren, zum Beispiel das Weltklima oder die Wirtschaftsentwicklung.", "start": 21.085, "end": 29.43 }, { "text": "Lassen sich Simulationen auch im öffentlichen Sektor gewinnbringend nutzen als digitales Werkzeug zur Analyse und Prognose?", "start": 30.53, "end": 36.63 } ], "start": 1.4399999, "end": 36.63, "num_words": 90.0, "speaker": 0 }, { "sentences": [ { "text": "Das hat ein Team des Kompetenzzentrums öffentlicher IT erforscht und zwar in einem Selbstversuch.", "start": 37.17, "end": 42.07 }, { "text": "Es wird im öfid Podcast restart digital heute also richtig praktisch und hands on.", "start": 42.835, "end": 48.295 }, { "text": "Ich bin Franziska Weiser und mit mir im Studio in Berlin Tiegel sitzen meine beiden Co Hosts für heute.", "start": 56.595, "end": 61.5 }, { "text": "Das sind Jens Tiemann, wissenschaftlicher Mitarbeiter im Kompetenzzentrum öffentlicher IT, kurz öFIT, und dort beschäftigt schwerpunktmäßig mit Internet und Vernetzung sowie mit neuen Technologien.", "start": 61.5, "end": 72.255 }, { "text": "Hallo Jens.", "start": 72.395, "end": 73.134995 } ], "start": 37.17, "end": 73.134995, "num_words": 73.0, "speaker": 0 }, { "sentences": [ { "text": "Hallo.", "start": 73.195, "end": 73.695 } ], "start": 73.195, "end": 73.695, "num_words": 1.0, "speaker": 1 }, { "sentences": [ { "text": "Und ebenfalls dabei mal wieder Doktor Mike Weber, Trendforscher und stellvertretender Leiter des Kompetenzzentrums Öffentlicher IT, kurz öfid.", "start": 74.55499, "end": 81.835 }, { "text": "Hallo Mike.", "start": 81.835, "end": 82.38 } ], "start": 74.55499, "end": 82.38, "num_words": 20.0, "speaker": 0 }, { "sentences": [ { "text": "Hallo Franziska.", "start": 82.619995, "end": 83.979996 } ], "start": 82.619995, "end": 83.979996, "num_words": 2.0, "speaker": 2 }, { "sentences": [ { "text": "Es gibt ja vom Urknall über Brückenstatik bis zum Arzneimitteltest wirklich viele Bereiche, wo Simulationen schon genutzt werden.", "start": 83.979996, "end": 90.32 }, { "text": "Wie ist das denn in der Verwaltung?", "start": 90.78, "end": 92.24 } ], "start": 83.979996, "end": 92.24, "num_words": 25.0, "speaker": 0 }, { "sentences": [ { "text": "Wir haben im Rahmen unserer Arbeit versucht, einen kleinen Überblick dadrüber zu bekommen, wo überall schon Simulationen benutzt werden und sind nicht so wirklich fündig geworden.", "start": 93.899994, "end": 101.564995 }, { "text": "Es gibt 'n paar wissenschaftliche Paper zu speziellen Fragestellungen.", "start": 101.564995, "end": 104.384995 }, { "text": "Es gibt auch grade im Umweltbereich, wenn ich mich recht entsinne, so ein paar kleine Ansätze dazu.", "start": 105.085, "end": 109.985 }, { "text": "Aber insgesamt muss man sagen, obwohl der Bedarf ja enorm wäre, grade für diese Fragestellung, ganz gezielt für die öffentliche Verwaltung, ist das Feld relativ frei.", "start": 110.3, "end": 122.16 } ], "start": 93.899994, "end": 122.16, "num_words": 78.0, "speaker": 2 }, { "sentences": [ { "text": "Und Du sagst jetzt, der Bedarf ist groß.", "start": 122.585, "end": 124.505 }, { "text": "Also wo könnten diese Erkenntnisse denn genutzt werden?", "start": 124.505, "end": 127.064995 }, { "text": "Also wer profitiert davon?", "start": 127.064995, "end": 128.605 } ], "start": 122.585, "end": 128.605, "num_words": 20.0, "speaker": 0 }, { "sentences": [ { "text": "Also grade wenn man in der aktuellen Diskussion, wie Staat und Verwaltung neu aufgestellt werden sollen, ist ja Wirkungsorientierung ein ganz, ganz wichtiger Aspekt.", "start": 130.025, "end": 138.63 }, { "text": "Also zu wissen, was passiert eigentlich mit den Gesetzen und mit den Dingen, die die Verwaltung dann anstellt.", "start": 139.09001, "end": 144.29001 } ], "start": 130.025, "end": 144.29001, "num_words": 42.0, "speaker": 2 }, { "sentences": [ { "text": "Und auch mit den Geldern ne, wo setzen wir die gut ein zum Beispiel.", "start": 144.29001, "end": 146.77 } ], "start": 144.29001, "end": 146.77, "num_words": 14.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, genau.", "start": 146.77, "end": 147.64499 }, { "text": "Simulationen sind eine Möglichkeit grundsätzlich zu ergründen, wie bestimmte Maßnahmen wirken.", "start": 147.64499, "end": 152.545 } ], "start": 146.77, "end": 152.545, "num_words": 13.0, "speaker": 2 }, { "sentences": [ { "text": "Was ist denn mit diesem Bereich Smart Cities?", "start": 153.48499, "end": 155.48499 }, { "text": "Also das ist ja 'n Thema, das haben wir hier im Podcast schon öfter besprochen, da gibt's sogenannte digitale Zwillinge, also das sind virtuelle Kopien von 'nem Stadtviertel.", "start": 155.48499, "end": 162.94499 }, { "text": "Ist das dann schon eine Simulation oder eine Vorstufe dazu?", "start": 163.46, "end": 166.36 } ], "start": 153.48499, "end": 166.36, "num_words": 46.0, "speaker": 0 }, { "sentences": [ { "text": "'N digitaler Zwilling ist ja eine Abbildung der Realität und hat dann die Aufgabe, dass der auf dem Zwilling arbeiten kann auf dem digitalen Zwilling und zum Beispiel Simulationen durchführen kann, dann eine Aussage zu kriegen, wie sich gewisse Maßnahmen in der Smart City auswirken werden, in der Realität auswirken würden.", "start": 166.66, "end": 184.495 } ], "start": 166.66, "end": 184.495, "num_words": 51.0, "speaker": 1 }, { "sentences": [ { "text": "Und ja, ums jetzt mal konkret zu machen, also mal angenommen, ich hätte 'n digitalen Zwilling von 'nem Stadtviertel und dann könnte ich sowas ausprobieren wie, was passiert denn, wenn ich da eine Straßenbahn hinlege oder solche Sachen Genau,", "start": 184.795, "end": 197.01001 } ], "start": 184.795, "end": 197.01001, "num_words": 39.0, "speaker": 0 }, { "sentences": [ { "text": "genau, solche Sachen.", "start": 197.01001, "end": 197.19 }, { "text": "Genau, solche Sachen.", "start": 197.19, "end": 197.89 }, { "text": "Also zum Beispiel, dass 'n großer Bereich in dem Simulation eine Rolle spielen, also zum Beispiel Verkehr oder Mobilität allgemein, dass man sagt, welche Wege das hat, welche Vorteile es hat.", "start": 197.89, "end": 208.035 }, { "text": "Ist ja auch immer wieder in der Presse, in der Planung gehört man ja, jetzt hier in Berlin zum Beispiel, Mexiko Platz, ob das 'n neuer Umsteigepunkt sein soll und dann muss man natürlich gucken, wie viel Leute werden davon profitieren?", "start": 208.195, "end": 220.455 }, { "text": "Bekommen da Leute vorbei und dann wird man sowas simulieren und schauen, ob sich das lohnt.", "start": 221.23001, "end": 227.01001 } ], "start": 197.01001, "end": 227.01001, "num_words": 94.0, "speaker": 1 }, { "sentences": [ { "text": "Mhm.", "start": 227.07, "end": 227.55 }, { "text": "Also das heißt, in dem Bereich könnte man sagen, öffnet sich die Verwaltung gerade für 'n Thema, was mit Simulationen zu tun hat?", "start": 227.55, "end": 235.15001 }, { "text": "Ist es vielleicht son son Einfallstor, zu sagen, so guck mal, da nutzt ihr's, da funktioniert's, macht doch mal in anderen Bereichen auch?", "start": 235.15001, "end": 241.395 } ], "start": 227.07, "end": 241.395, "num_words": 47.0, "speaker": 0 }, { "sentences": [ { "text": "Ja, einerseits das, aber ich denke bei den Fachleuten, da werden ja Simulationen schon immer benutzt.", "start": 241.77501, "end": 248.195 }, { "text": "Eigentlich hört man immer wieder diese Modellierung, das ist doch so einfach oder mit Computertechnik heute mit immer besseren grafischen Oberflächen und so, müsste das doch eigentlich immer einfacher werden, dass jedermann sozusagen modellieren kann.", "start": 249.09001, "end": 264.495 }, { "text": "Und da haben wir geguckt, geht das eigentlich?", "start": 265.455, "end": 267.935 }, { "text": "Und unsere Idee war, kann man nicht sozusagen stärker direkt die Fachpersonen, die jetzt eine fachliche Ahnung haben, aber nicht zu viel Ahnung vom Programmieren, vom Modellieren, vom Simulieren, kann man diese Person nicht in die Lage versetzen, Modellierungen zu nutzen.", "start": 267.935, "end": 287.58 } ], "start": 241.77501, "end": 287.58, "num_words": 100.0, "speaker": 1 }, { "sentences": [ { "text": "Also Fachpersonal heißt, in der Wissenschaft, da ist es ja sehr verbreitet, ne?", "start": 288.04, "end": 292.61502 } ], "start": 288.04, "end": 292.61502, "num_words": 13.0, "speaker": 0 }, { "sentences": [ { "text": "Und Ja, genau.", "start": 292.61502, "end": 293.255 }, { "text": "Genau.", "start": 293.33502, "end": 293.415 } ], "start": 292.61502, "end": 293.415, "num_words": 4.0, "speaker": 1 }, { "sentences": [ { "text": "Genau.", "start": 293.495, "end": 293.62833 }, { "text": "Und", "start": 293.7617, "end": 293.89502 } ], "start": 293.495, "end": 293.89502, "num_words": 2.0, "speaker": 0 }, { "sentences": [ { "text": "dann aber jetzt Fachpersonal in Behörden.", "start": 293.89502, "end": 295.815 }, { "text": "Genau, jetzt meine ich dann eben Fachpersonal in Behörden.", "start": 295.815, "end": 298.475 }, { "text": "Das schließt son bisschen vielleicht auch an die Idee von den Datenlaboren an, nicht?", "start": 299.095, "end": 304.535 }, { "text": "Dass man immer mehr, das ist ja auch in der Presse, dass man so sagt, okay, wir ein Ministerium oder eine Behörde braucht eben Daten, möchte eben Datenlabore aufbauen, stellt Daten der Öffentlichkeit zur Verfügung, arbeitet verstärkt mit Daten und da ist natürlich dieses direkter Anschluss zu sagen, wir haben die Daten oder wir wollen jetzt Fragestellungen mithilfe dieser Daten beantworten.", "start": 304.535, "end": 332.85498 } ], "start": 293.89502, "end": 332.85498, "num_words": 90.0, "speaker": 1 }, { "sentences": [ { "text": "Ja, weil die Daten natürlich die Grundlage für vieles sind, aber eben auch für Simulationen.", "start": 333.07498, "end": 336.85498 } ], "start": 333.07498, "end": 336.85498, "num_words": 15.0, "speaker": 0 }, { "sentences": [ { "text": "Genau.", "start": 336.995, "end": 337.395 } ], "start": 336.995, "end": 337.395, "num_words": 1.0, "speaker": 1 }, { "sentences": [ { "text": "Ja, mhm.", "start": 337.395, "end": 338.215 }, { "text": "Mhm.", "start": 339.07, "end": 339.15002 }, { "text": "Ich hab ja versprochen in der Anmoderation, dass wir heute wirklich praktisch werden.", "start": 339.15002, "end": 342.51 }, { "text": "Jens, ihr habt für die Veröffentlichung Wirkungen verstehen und nutzen, agentenbasierte Simulationen für den öffentlichen Sektor, ja selbst eine Simulation erstellt.", "start": 342.51, "end": 351.01 }, { "text": "Was habt ihr denn da fürn Beispiel ausgewählt und warum auch?", "start": 351.51498, "end": 354.975 } ], "start": 337.395, "end": 354.975, "num_words": 48.0, "speaker": 0 }, { "sentences": [ { "text": "Wir haben uns diese verschiedenen Anwendungsfälle angeguckt oder was es da schon gibt und sind eigentlich zu dieser Idee gekommen, dass man so was wie Akzeptanz oder Verbreitung von Angeboten der Verwaltung, dass man sowas eigentlich ganz gut Mhm.", "start": 356.235, "end": 371.57 }, { "text": "Modellieren können müsste und wir sind dann auf die EID gekommen, sprich 'n elektronischer Personalausweis und haben dort eben unsere Modelle schrittweise aufgebaut, zu gucken, wie verbreitet er sich nach welchen Mechanismen und mit welchen Mechanismen könnte ich das fördern.", "start": 371.73, "end": 388.89502 } ], "start": 356.235, "end": 388.89502, "num_words": 79.0, "speaker": 1 }, { "sentences": [ { "text": "Ja, lustigerweise, da haben wir glaube ich sogar schon mal eine Folge dazu gemacht, Mike, ne?", "start": 388.89502, "end": 392.5 }, { "text": "Elektronischer Personalausweis und warum der sich nicht so richtig durchsetzt.", "start": 392.5, "end": 395.72 }, { "text": "Da haben sich ja schon viele kluge Menschen Gedanken dazu gemacht.", "start": 396.82, "end": 399.56 }, { "text": "Was meinst Du, kann jetzt eine Simulation so dazu beitragen?", "start": 399.78, "end": 403.08002 }, { "text": "Mhm.", "start": 403.14, "end": 403.64 } ], "start": 388.89502, "end": 403.64, "num_words": 48.0, "speaker": 0 }, { "sentences": [ { "text": "Also erst mal, wir wollten ganz bewusst halt von diesen klassischen Feldern, Umwelt, Verkehr und so was runter und dann zu so was, was man als Kernverwaltung bezeichnen kann oder Kerndienstleistungen der Verwaltung in der Hoffnung, dass es sich einfach simulieren lässt als Innovationsdiffusionsprozess.", "start": 403.7, "end": 417.585 }, { "text": "Der entscheidende Vorteil bei der Simulation ist, dass man sich wirklich über jeden einzelnen Schritt Gedanken machen muss.", "start": 420.86, "end": 426.94 }, { "text": "Also sonst, man kann natürlich, wie wir Sie auch gemacht haben in in dieser Podcastfolge, man kann so einzelne Punkte ganz gut identifizieren, woran es denn gescheitert sein könnte, dass diese Diffusion nicht so funktioniert hat, wie man sich das erhofft und erträumt hatte.", "start": 426.94, "end": 440.145 }, { "text": "Bei 'ner Simulation muss man ganz unten anfangen.", "start": 440.685, "end": 443.025 }, { "text": "Was ist die Grundmotivation?", "start": 443.165, "end": 444.305 } ], "start": 403.7, "end": 444.305, "num_words": 118.0, "speaker": 2 }, { "sentences": [ { "text": "Jetzt sind wir wieder bei den Agenten, also wenn man wenn man Die Agenten individuellen Themen bleibt.", "start": 444.845, "end": 448.525 } ], "start": 444.845, "end": 448.525, "num_words": 17.0, "speaker": 2 }, { "sentences": [ { "text": "Ne?", "start": 448.60498, "end": 448.845 }, { "text": "Also Bürger und Bürgerinnen.", "start": 448.845, "end": 450.15 } ], "start": 448.60498, "end": 450.15, "num_words": 5.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, also", "start": 450.69, "end": 451.11667 } ], "start": 450.69, "end": 451.11667, "num_words": 2.0, "speaker": 2 }, { "sentences": [ { "text": "Du Ja.", "start": 451.11667, "end": 451.43668 }, { "text": "Unterstützend klingt so nach eben 0 0 7, aber Agenten sind Bürger, ne?", "start": 451.86334, "end": 455.57 }, { "text": "Das ist einfach der Simulations Slang, was Ja,", "start": 455.57, "end": 457.81 } ], "start": 451.11667, "end": 457.81, "num_words": 23.0, "speaker": 0 }, { "sentences": [ { "text": "vielleicht müsste man das kurz mal erläutern.", "start": 457.81, "end": 459.81 }, { "text": "Ja.", "start": 459.81, "end": 460.21 }, { "text": "Son agentenbasierte Simulation, das kann man sich wie Spielfiguren aufm Spielfeld vorstellen, dass man so sagt, okay, man hat diese Umwelt, also das Spielfeld und man hat dort Agenten da drauf.", "start": 460.21, "end": 471.815 }, { "text": "Das sind also Einheiten in der Simulation, die eben interagieren miteinander, die Akteure sozusagen.", "start": 471.815, "end": 478.87 }, { "text": "Das sind aber nicht nur die Bürgerinnen und Bürger.", "start": 478.87, "end": 481.50998 } ], "start": 457.81, "end": 481.50998, "num_words": 62.0, "speaker": 1 }, { "sentences": [ { "text": "Bei uns sind es zum Beispiel auch Organisationen wie zum Beispiel die Verwaltung oder Firmen.", "start": 481.50998, "end": 487.77 }, { "text": "Also auch Organisationen werden als Agenten betrachtet und die interagieren untereinander oder aber mit ihrer Umwelt.", "start": 487.83, "end": 495.925 } ], "start": 481.50998, "end": 495.925, "num_words": 31.0, "speaker": 1 }, { "sentences": [ { "text": "Okay und das ist ein Ansatz, den man wählen kann, wenn man eine Simulation macht Genau, das", "start": 496.22498, "end": 500.22498 } ], "start": 496.22498, "end": 500.22498, "num_words": 17.0, "speaker": 0 }, { "sentences": [ { "text": "ist ja", "start": 500.22498, "end": 500.35834 } ], "start": 500.22498, "end": 500.35834, "num_words": 2.0, "speaker": 1 }, { "sentences": [ { "text": "1, der wir eben ausgewählt haben.", "start": 500.465, "end": 502.06497 } ], "start": 500.465, "end": 502.06497, "num_words": 6.0, "speaker": 0 }, { "sentences": [ { "text": "Genau und das ist ein sehr einfacher Ansatz, also sehr einfach verständlich.", "start": 502.06497, "end": 505.28497 }, { "text": "2 Akteure treffen sich.", "start": 506.66998, "end": 508.91 }, { "text": "Was passiert dann?", "start": 508.91, "end": 509.87 } ], "start": 502.06497, "end": 509.87, "num_words": 19.0, "speaker": 1 }, { "sentences": [ { "text": "Mhm, okay.", "start": 509.87, "end": 510.69 }, { "text": "Das heißt, ihr habt im ersten Schritt geguckt, wer sind überhaupt die Agenten?", "start": 510.75, "end": 514.20996 } ], "start": 509.87, "end": 514.20996, "num_words": 15.0, "speaker": 0 }, { "sentences": [ { "text": "Genau.", "start": 514.35, "end": 514.85 }, { "text": "Also In unserem Fall sind das erst mal die Bürger, die diese EID haben oder noch nicht haben, eine Motivation haben, diese EID zu nutzen.", "start": 514.91, "end": 524.625 }, { "text": "Es sind die Verwaltungen, bei denen ich das einsetzen kann oder noch nicht.", "start": 525.245, "end": 529.745 }, { "text": "Und wir haben das dann schrittweise aufgebaut und haben gesagt, okay, was wäre dann, wenn wir jetzt zusätzlich noch Firmen haben, Unternehmen, wo man auch die Serien mit", "start": 530.045, "end": 538.62 } ], "start": 514.35, "end": 538.62, "num_words": 67.0, "speaker": 1 }, { "sentences": [ { "text": "dem Ausweis identifizieren kann.", "start": 538.62, "end": 540.02 } ], "start": 538.62, "end": 540.02, "num_words": 4.0, "speaker": 0 }, { "sentences": [ { "text": "Weil man dann einfach sozusagen, das war unsere Annahme, die wir reingesteckt haben, dass wir gesagt haben, na ja, je öfter ich das nutze, desto mehr und das einfach funktioniert, also eine EID zu nutzen, war unsere Annahme, desto überzeugter bin ich ja, mir die zu holen oder wenn ich schon habe, zu verlängern oder wenn ich davon gehört hab, dass andere damit gut zurechtkommen, dass ich mir auch so eine EID hole.", "start": 540.14, "end": 565.98 }, { "text": "Ein Aspekt dabei sind ja die langen Zeiträume, also", "start": 566.76, "end": 569.64 } ], "start": 540.14, "end": 569.64, "num_words": 82.0, "speaker": 1 }, { "sentences": [ { "text": "Weil ich den Ausweis ja nicht Genau.", "start": 569.88, "end": 571.32 }, { "text": "Dauernd verlängere, sondern nur alle 10 Jahre.", "start": 571.4, "end": 573.08 } ], "start": 569.88, "end": 573.08, "num_words": 14.0, "speaker": 0 }, { "sentences": [ { "text": "Alle 10 Jahre, genau Und darüber kommen natürlich dann sehr lange Zeiträume in so eine Betrachtung.", "start": 573.08, "end": 579.075 }, { "text": "Mhm.", "start": 579.29504, "end": 579.79504 } ], "start": 573.08, "end": 579.79504, "num_words": 17.0, "speaker": 1 }, { "sentences": [ { "text": "Ich sag", "start": 579.935, "end": 580.335 } ], "start": 579.935, "end": 580.335, "num_words": 2.0, "speaker": 2 }, { "sentences": [ { "text": "ich mal, aus der Verkaufspsychologie oder so gibt es ja vielleicht Modelle, die sagen, was ist eine positive Erfahrung?", "start": 580.335, "end": 586.815 }, { "text": "Wie wirkt sich die aus?", "start": 586.815, "end": 588.11505 }, { "text": "Wie wirkt sich eine negative Erfahrung aus?", "start": 588.2, "end": 590.2 }, { "text": "Wie viel Leuten erzähle ich von 1 positiven oder negativen Erfahrung?", "start": 590.2, "end": 594.3 }, { "text": "Nicht und solche Sachen, dort kann man dann natürlich auch eine wissenschaftliche Grundlage suchen und finden.", "start": 594.6, "end": 601.74 } ], "start": 580.335, "end": 601.74, "num_words": 58.0, "speaker": 1 }, { "sentences": [ { "text": "Uns war wichtig, ein Schritt eigentlich vorher eben, kann ich Fachpersonen, die jetzt eben nicht täglich mit Simulationen arbeiten 'N großer Anteil an der Simulation ist ja die Modellierung.", "start": 602.095, "end": 615.15497 }, { "text": "Also die Wirklichkeit vereinfacht zu beschreiben.", "start": 615.77997, "end": 618.75995 }, { "text": "Modellierung bedeutet eben auch, dass ich mir selber Gedanken mache, wie sind Zusammenhänge.", "start": 619.62, "end": 625.16 }, { "text": "Ich kann das auch nachvollziehbar machen, weil andere meine Modellierung, die ich aufschreibe, nicht, dass ich sage, dieser oder jener Faktor, den schätze ich als sehr stark ein.", "start": 626.01996, "end": 642.935 }, { "text": "Also ist 'n großer Einfluss, das schreibe ich in meine Modellierung rein und jemand anders kann dann sagen, nee, nee, das ist aber viel geringer.", "start": 642.935, "end": 650.26 } ], "start": 602.095, "end": 650.26, "num_words": 101.0, "speaker": 1 }, { "sentences": [ { "text": "Das heißt, die Simulation und eben da im Speziellen die Modellierung ist ja auch der Weg, so eine so eine Diskussion zu Versachlichen, Sachen nachvollziehbar zu machen.", "start": 650.56, "end": 661.625 }, { "text": "Mhm.", "start": 661.765, "end": 662.16504 }, { "text": "Zu lernen und weiterzuentwickeln und so weiter.", "start": 662.325, "end": 664.72504 }, { "text": "Und auf diese Mechanismen, das wollten wir eigentlich gucken, wie gut geht das heutzutage schon?", "start": 664.72504, "end": 669.865 }, { "text": "Sind aber neben nicht tief in die fachlichen.", "start": 670.16504, "end": 672.265 } ], "start": 650.56, "end": 672.265, "num_words": 58.0, "speaker": 1 }, { "sentences": [ { "text": "Genau, weil's ja wirklich nur drum ging, das einfach mal auszuprobieren.", "start": 672.48505, "end": 674.88 }, { "text": "Genau.", "start": 675.04, "end": 675.36 } ], "start": 672.48505, "end": 675.36, "num_words": 12.0, "speaker": 0 }, { "sentences": [ { "text": "Und", "start": 675.36, "end": 675.52 } ], "start": 675.36, "end": 675.52, "num_words": 1.0, "speaker": 2 }, { "sentences": [ { "text": "ja, mhm.", "start": 675.52, "end": 676.42 }, { "text": "Aber spannender Punkt trotzdem, dass man natürlich bei dem Simulieren an sich auch Erkenntnisgewinne hat, ne.", "start": 676.56, "end": 683.04004 }, { "text": "Genau.", "start": 683.04004, "end": 683.2 }, { "text": "Also dass man eben zum Beispiel merkt, ach Mensch, ich müsste jetzt über die Agenten das und das wissen, da gibt's aber gar keine Zahlen dazu, also muss ich die irgendwo besorgen oder so was.", "start": 683.2, "end": 691.315 }, { "text": "Genau.", "start": 691.315, "end": 691.71497 } ], "start": 675.52, "end": 691.71497, "num_words": 55.0, "speaker": 0 }, { "sentences": [ { "text": "Solche Sachen können ja da auch rauskommen.", "start": 691.71497, "end": 693.175 } ], "start": 691.71497, "end": 693.175, "num_words": 7.0, "speaker": 0 }, { "sentences": [ { "text": "Ja und Simulation ist ja auch zum Beispiel ein ganz wichtiger Punkt und auch mit dieser Sprache, die wir benutzt haben, Net Logo, die frei verfügbar ist, es ist ja auch 'n sehr starker Erkenntnisgewinn.", "start": 693.315, "end": 704.56995 }, { "text": "Also zum Beispiel Mikes Lieblingsbeispiel ist eine Visualisierung, wie ein Stau auf 'ner Straße entsteht.", "start": 704.70996, "end": 710.14996 }, { "text": "Und das ist da 'n fertiges Simulationsmodul, was man sich also ausm Internet runterladen kann, wo dann kleine Autos überm Bildschirm fahren und wo man dann je nach Verkehrsdichte sehen kann, wie eben Staus aus dem Nichts entstehen.", "start": 710.14996, "end": 722.515 } ], "start": 693.315, "end": 722.515, "num_words": 88.0, "speaker": 1 }, { "sentences": [ { "text": "Also da gibt's Dinge, die kann ich nicht schnappen eigentlich.", "start": 722.735, "end": 725.05 } ], "start": 722.735, "end": 725.05, "num_words": 10.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, das ist das.", "start": 725.05, "end": 725.69 } ], "start": 725.05, "end": 725.69, "num_words": 4.0, "speaker": 1 }, { "sentences": [ { "text": "Ne, als jemand, der genau.", "start": 725.69, "end": 726.89 }, { "text": "Ja.", "start": 726.89, "end": 727.13 } ], "start": 725.69, "end": 727.13, "num_words": 6.0, "speaker": 0 }, { "sentences": [ { "text": "Und in diesem Stauseimulationsbeispiel, da ist es also eher sozusagen der Erkenntnisgewinn, dass man sich das mal praktisch angucken kann, dass man son Beispiel hat, wie man Simulationen aufbauen kann und nicht.", "start": 727.13, "end": 739.47504 }, { "text": "Oder aber wenn man eben selber sagt, selber sich ran setzt, dann wird's natürlich etwas schwieriger, dass man die richtigen Parameter auswählt.", "start": 739.47504, "end": 746.85504 } ], "start": 727.13, "end": 746.85504, "num_words": 54.0, "speaker": 1 }, { "sentences": [ { "text": "Ja, ja.", "start": 746.995, "end": 747.715 }, { "text": "Aber mit der Transparenz ist auch spannend, weil wir ja immer wenn wir über KI zum Beispiel diskutieren, diese Blackbox Geschichte haben und das ist ja bei Simulation, kann man ja dann sehr genau nachvollziehen irgendwie.", "start": 747.715, "end": 756.2 } ], "start": 746.995, "end": 756.2, "num_words": 38.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, vor allen Dingen als also diesem also ich find das sehr wichtig.", "start": 756.2, "end": 759.02 }, { "text": "Es ist ja allgemeine Forderung, dass gesagt wird, ja viel mehr Menschen müssten programmieren lernen zum Beispiel.", "start": 759.64, "end": 765.56 }, { "text": "Aber eigentlich ist es, wenn man's 'n bisschen genauer beschreibt, ist es eigentlich die Modellierung und das ist die Verbindung, dass man eine Sprache benutzt, eine formale Sprache benutzt, die sowohl Menschen als auch Computer verstehen können.", "start": 765.56, "end": 778.895 }, { "text": "Und in unserem Fall kann dann zum Beispiel ein Verwaltungsexperte, eine Verwaltungsexpertin könnte also in dieser Fachsprache eine Simulation formulieren, die dann so transparent ist, dass jemand anders sie lesen kann, dass sie aber auch vom Computer ausgeführt werden.", "start": 779.03, "end": 795.865 } ], "start": 756.2, "end": 795.865, "num_words": 106.0, "speaker": 1 }, { "sentences": [ { "text": "Ja.", "start": 795.865, "end": 796.265 }, { "text": "Da kommen wir gleich noch mal dazu, wie ihr das Ganze dann programmiert habt, da ja auch in 2 Varianten, ne?", "start": 796.265, "end": 800.105 }, { "text": "Einmal mit Genau.", "start": 800.105, "end": 800.625 }, { "text": "Und einmal ohne.", "start": 800.745, "end": 801.545 }, { "text": "Genau.", "start": 801.545, "end": 801.945 } ], "start": 795.865, "end": 801.945, "num_words": 29.0, "speaker": 0 }, { "sentences": [ { "text": "Mike, Du wolltest noch was ergänzen, glaube ich.", "start": 801.945, "end": 804.125 } ], "start": 801.945, "end": 804.125, "num_words": 8.0, "speaker": 0 }, { "sentences": [ { "text": "Ja, ich würd ganz gerne noch mal wieder zurück zu den Daten.", "start": 804.265, "end": 806.94 }, { "text": "Also es stimmt, es ist keine Simulation, die wir da gemacht haben, wo wir empirische Daten genommen haben.", "start": 807.02, "end": 811.66 }, { "text": "Das gibt's ja durchaus auch.", "start": 811.66, "end": 812.96 }, { "text": "Wettervorhersage ist Paradebeispiel, wo ein ganzer Strauß von verschiedenen Daten erfasst wird und dann in diese Simulation reingeschoben wird.", "start": 813.18, "end": 820.995 }, { "text": "Das war genau nicht unser Ansatz, sondern wir haben uns natürlich 'n paar Daten angeguckt, also so Durchdringung von EAD Funktion früher und heute und so was.", "start": 821.075, "end": 829.315 } ], "start": 804.265, "end": 829.315, "num_words": 81.0, "speaker": 2 }, { "sentences": [ { "text": "Aber wir haben dann sind dann den Weg gegangen, die Simulation so flexibel zu machen, dass man diese Daten quasi für die eigenen Simulationsdurchgänge selbst einstellen kann.", "start": 829.315, "end": 839.16003 }, { "text": "Und das ist noch mal eine Besonderheit, die bei NetLogo halt auch ganz besonders leicht umzusetzen ist.", "start": 839.16003, "end": 844.14 }, { "text": "Und dann geht es in der Tat da, also habt ihr ja schon gesagt, also dann erst mal festzustellen, welche Daten hab ich denn gar nicht?", "start": 844.36, "end": 849.715 }, { "text": "Welcher hätt ich denn gerne, diese Simulation noch mehr an die Realität dranzub bringen?", "start": 849.715, "end": 854.435 }, { "text": "Und das ist die große große Krux dabei und die große Chance, aus diesen Simulationen auch zu lernen.", "start": 854.435, "end": 859.475 } ], "start": 829.315, "end": 859.475, "num_words": 102.0, "speaker": 2 }, { "sentences": [ { "text": "Ja.", "start": 859.475, "end": 859.975 }, { "text": "Man kann ja auch wirklich damit rumspielen, weil ihr das alles online stellt und man könnte mit diesen Dingen, die ihr da erarbeitet habt, weil Du grad gesagt hast, Mike irgendwie Zahlen rauf runterstellen und sowas.", "start": 860.115, "end": 870.25 }, { "text": "Das kann man wirklich machen, ne?", "start": 870.39, "end": 871.58997 }, { "text": "Es ist jetzt eine frei verfügbares Spielmaterial sozusagen, was da entstanden ist.", "start": 871.58997, "end": 875.895 } ], "start": 859.475, "end": 875.895, "num_words": 55.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, wir haben die Modelle alle online gestellt, also alle Zwischenschritte, die wir gemacht haben und auch den Weg dahin und jetzt kann sich jede und jeder, der genauer gucken will, was wir dafür Annahmen implizit und explizit drin haben, kann sich diesen Code angucken, kann sich auch erklären lassen durch entsprechende KI Tools oder durch entsprechende Expertinnen und Experten und kann dann genau sehen, welche Annahmen da drin stecken.", "start": 876.055, "end": 899.07 }, { "text": "Mhm.", "start": 899.07, "end": 899.23004 }, { "text": "Und natürlich dann auch widersprechen an jedem Einkaufszentrum.", "start": 899.23004, "end": 901.33 } ], "start": 876.055, "end": 901.33, "num_words": 79.0, "speaker": 2 }, { "sentences": [ { "text": "Okay, zu wie viel Prozent tritt das jetzt ein oder nicht, ne?", "start": 902.19, "end": 905.925 }, { "text": "Ist es eine exakte Vorhersage der Zukunft?", "start": 905.925, "end": 908.425 }, { "text": "Wie genau kommt es daran?", "start": 908.805, "end": 909.845 }, { "text": "Was spielen denn da für Faktoren eine Rolle?", "start": 909.845, "end": 911.865 } ], "start": 902.19, "end": 911.865, "num_words": 32.0, "speaker": 0 }, { "sentences": [ { "text": "Also ich glaub, da muss man noch 'n halben Schritt zurückgehen, Ja.", "start": 913.925, "end": 916.405 }, { "text": "Was für unterschiedliche Typen von Simulationen es gibt.", "start": 916.565, "end": 918.885 }, { "text": "Wie eben schon gesagt, das Wetterbeispiel oder auch solche Simulationen im Verkehrsbereich sind sehr nah daran, Vorhersagen treffen zu wollen.", "start": 918.885, "end": 926.08 }, { "text": "Mhm.", "start": 926.22003, "end": 926.54 }, { "text": "Also", "start": 926.54, "end": 926.94 } ], "start": 913.925, "end": 926.94, "num_words": 42.0, "speaker": 2 }, { "sentences": [ { "text": "Und auch zu können?", "start": 927.26, "end": 928.32 } ], "start": 927.26, "end": 928.32, "num_words": 4.0, "speaker": 0 }, { "sentences": [ { "text": "Ja, beim Wetter funktioniert's ganz gut, oder?", "start": 928.7, "end": 930.62 } ], "start": 928.7, "end": 930.62, "num_words": 7.0, "speaker": 2 }, { "sentences": [ { "text": "Ja.", "start": 930.62, "end": 931.12 }, { "text": "Wird", "start": 931.18, "end": 931.68 } ], "start": 930.62, "end": 931.68, "num_words": 2.0, "speaker": 0 }, { "sentences": [ { "text": "vor allem immer besser und eher auch kleinräumig, was früher völlig illusogen", "start": 931.74, "end": 935.565 } ], "start": 931.74, "end": 935.565, "num_words": 12.0, "speaker": 2 }, { "sentences": [ { "text": "ist.", "start": 935.565, "end": 935.805 }, { "text": "Regenradar.", "start": 936.20496, "end": 936.70496 } ], "start": 935.565, "end": 936.70496, "num_words": 2.0, "speaker": 0 }, { "sentences": [ { "text": "Ja.", "start": 937.005, "end": 937.505 }, { "text": "Nach meiner subjektiven Wahrnehmung.", "start": 937.725, "end": 939.58496 }, { "text": "Wir haben da eher 'n anderen Simulationsansatz.", "start": 940.20496, "end": 941.985 }, { "text": "Also eher, der in Richtung Verstehen geht.", "start": 943.08496, "end": 945.565 }, { "text": "Also wir wollen verstehen, wenn wir genau im Sinne der Wirkungsorientierung, was passiert, wenn ich jetzt eine bestimmte politische Maßnahme oder eine Kommunikationsmaßnahme oder irgendetwas, wenn ich das jetzt in dieses modellierte System reinbringe?", "start": 945.565, "end": 960.38 } ], "start": 937.005, "end": 960.38, "num_words": 53.0, "speaker": 2 }, { "sentences": [ { "text": "Was passiert dann?", "start": 960.945, "end": 961.905 }, { "text": "Steigt dann die oder beschleunigt sich die Diffusion oder geht sie zurück oder wie auch immer?", "start": 961.905, "end": 966.885 }, { "text": "Und da sind wir dann im Bereich, wo's gar nicht so sehr darum geht, vorherzusagen, sind es jetzt in Zukunft 80 Prozent, 90", "start": 967.185, "end": 974.945 } ], "start": 960.945, "end": 974.945, "num_words": 42.0, "speaker": 2 }, { "sentences": [ { "text": "Prozent Leute, die das nutzen.", "start": 974.945, "end": 975.89746 }, { "text": "Mhm.", "start": 975.89746, "end": 976.05 } ], "start": 974.945, "end": 976.05, "num_words": 6.0, "speaker": 0 }, { "sentences": [ { "text": "Das interessante an der Vorhersage ist ja, dass wir als Menschen lineare Zusammenhänge supergut abschätzen können, nicht?", "start": 976.45, "end": 983.25 }, { "text": "Also wenn wir wissen, was weiß ich, wir machen Wasserhahn auf und das Waschbecken, das ist halbvoll.", "start": 983.25, "end": 988.14996 }, { "text": "In 2 Minuten, dann wissen wir in 4 Minuten ist es ganz voll oder sowas.", "start": 988.875, "end": 992.715 }, { "text": "Das kriegen wir ja gut hin.", "start": 992.715, "end": 994.255 }, { "text": "Aber exponentielles Wachstum zum Beispiel kriegen wir als Menschen nicht hin, dieses berühmte Seerosenbeispiel auf dem Teich oder aber auch sowas wie Sättigung.", "start": 994.715, "end": 1004.42004 } ], "start": 976.45, "end": 1004.42004, "num_words": 78.0, "speaker": 1 }, { "sentences": [ { "text": "Also wenn irgendwas natürlich gesättigt ist, wenn ich einfach keine Steigerung mehr drin habe.", "start": 1004.88, "end": 1009.38 }, { "text": "Oder wenn ich Kipppunkte drin", "start": 1010.0, "end": 1011.44 } ], "start": 1004.88, "end": 1011.44, "num_words": 19.0, "speaker": 1 }, { "sentences": [ { "text": "habe, was Aber das kennt", "start": 1011.44, "end": 1012.24005 } ], "start": 1011.44, "end": 1012.24005, "num_words": 5.0, "speaker": 2 }, { "sentences": [ { "text": "man ja vom Klima.", "start": 1012.24005, "end": 1012.72003 }, { "text": "Genau.", "start": 1012.72003, "end": 1013.22003 } ], "start": 1012.24005, "end": 1013.22003, "num_words": 5.0, "speaker": 0 }, { "sentences": [ { "text": "Das kennt man jetzt vom Klima, ist das jetzt bekannt, aber solche Kipppunkte.", "start": 1013.28, "end": 1016.955 }, { "text": "Das bedeutet ja, dass ich nichtlineare Zusammenhänge habe und aber auch plötzlich ganze Systeme umkippen oder sich Sachen ganz fundamental ändern.", "start": 1017.41504, "end": 1028.795 }, { "text": "Und das ist sehr schwer sozusagen mit seinem Alltagsverstand oder mit 'nem einfachen Draufgucken, jetzt ohne Computerhilfe zu sagen, was passiert da eigentlich?", "start": 1029.5, "end": 1040.7001 }, { "text": "Wann tritt so ein Effekt ein?", "start": 1040.7001, "end": 1042.4801 }, { "text": "Was ist eine Auswirkung von exponentiellem Wachstum zum Beispiel?", "start": 1043.145, "end": 1046.2051 } ], "start": 1013.28, "end": 1046.2051, "num_words": 72.0, "speaker": 1 }, { "sentences": [ { "text": "Ja und dafür sind Simulationen durch, ne?", "start": 1046.425, "end": 1048.105 } ], "start": 1046.425, "end": 1048.105, "num_words": 7.0, "speaker": 0 }, { "sentences": [ { "text": "Ach so, genau.", "start": 1048.105, "end": 1048.265 } ], "start": 1048.105, "end": 1048.265, "num_words": 3.0, "speaker": 1 }, { "sentences": [ { "text": "Es könnte halt sein, erst sind 20 Prozent mit dem Ja.", "start": 1048.265, "end": 1050.9451 }, { "text": "Elektronischen Personalausweis unterwegs und dann auf einmal, wir haben eine gute Werbemaßnahme gemacht, auf einmal sind's 80 oder?", "start": 1051.145, "end": 1056.64 } ], "start": 1048.265, "end": 1056.64, "num_words": 29.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, das haben wir ja son bisschen gesehen.", "start": 1056.64, "end": 1058.24 }, { "text": "Ich glaube, in den letzten Jahren bei Kartenzahlen, beim kontaktlosen Zahlen mit mit mit Karte, was ja auch durch verschiedene Maßnahmen gefördert wurde und so weiter, wo aber dann auch irgendwo plötzlich eine Akzeptanz überall da war und das dann auch genutzt wird.", "start": 1058.24, "end": 1074.275 } ], "start": 1056.64, "end": 1074.275, "num_words": 51.0, "speaker": 1 }, { "sentences": [ { "text": "Zum Beispiel schalte ich Werbung?", "start": 1074.495, "end": 1077.155 }, { "text": "Mache ich Kommunikationsmaßnahmen?", "start": 1077.2151, "end": 1078.115 }, { "text": "Ne?", "start": 1078.895, "end": 1079.055 }, { "text": "Das war eine Sache, die ihr untersucht habt.", "start": 1079.055, "end": 1080.5751 }, { "text": "Ich finde es ganz interessant, weil wir ja gerade gesagt haben, uns geht's gar nicht darum zu prognostizieren, sind's irgendwie 87 Prozent, sind's 89 Prozent, sondern wir wollen ja wissen, bringt's was, bringt's nichts, ne?", "start": 1080.5751, "end": 1090.56 } ], "start": 1074.495, "end": 1090.56, "num_words": 52.0, "speaker": 0 }, { "sentences": [ { "text": "Das ist eigentlich die Frage, die wir wissen wollen und da habt ihr untersucht, zum Beispiel Kommunikationsmaßnahmen, was was noch?", "start": 1090.56, "end": 1097.14 } ], "start": 1090.56, "end": 1097.14, "num_words": 20.0, "speaker": 0 }, { "sentences": [ { "text": "So ein Akteur, also Bürger geht aufs Amt und dann ist die Frage, ist das Angebot überhaupt da?", "start": 1097.6849, "end": 1104.345 }, { "text": "Dann hat er's positiv auf sone Stimmung eingezahlt sozusagen, auf sone Bereitschaft, die EID gut zu finden, sage ich mal, war es einfach und dadurch stieg dann dieser Wert von diesem Agenten.", "start": 1105.205, "end": 1120.93 }, { "text": "Ja.", "start": 1120.99, "end": 1121.49 }, { "text": "Und im Laufe der Zeit, wenn man keine Interaktionen hat, dann vergisst man das wieder, dann sinkt dieser Wert wieder.", "start": 1122.03, "end": 1127.175 }, { "text": "Das war unsere ganz einfache Modellannahme, zum Beispiel ein Zusammenhang, den wir drin hatten.", "start": 1127.255, "end": 1132.935 } ], "start": 1097.6849, "end": 1132.935, "num_words": 85.0, "speaker": 1 }, { "sentences": [ { "text": "Ja.", "start": 1132.935, "end": 1133.435 }, { "text": "Okay und also genau, ums nochmal zu sagen, es ging ja wirklich nur darum zu versuchen, kann man mit so 'ner Agentenbasierten Simulation überhaupt so was untersuchen.", "start": 1133.5751, "end": 1140.79 }, { "text": "Gibt's trotzdem irgendwie Erkenntnisse, wo ihr sagt, ach Mensch, das hat uns überrascht, der hätten wir gar nicht gesagt gedacht, dass das so wirkt oder so.", "start": 1141.57, "end": 1149.43 }, { "text": "Hätten wir so gar nicht abschätzen können.", "start": 1149.49, "end": 1151.27 } ], "start": 1132.935, "end": 1151.27, "num_words": 61.0, "speaker": 0 }, { "sentences": [ { "text": "Bei 1 Berechnung haben wir sehr häufig nachgeguckt, ob's wirklich richtig ist.", "start": 1151.33, "end": 1154.655 }, { "text": "Also wir haben ja Kommunikationsmaßnahmen untersucht und sind auch zu dem Ergebnis gekommen, dass es dass sie positiv wirken im Grunde.", "start": 1154.975, "end": 1161.635 }, { "text": "Positiv wirken auf die tatsächliche Nutzung und haben dann auch noch mal differenziert, sollte man das lieber aufm Block am Anfang kann schnell oder lieber über Zeitstrecken?", "start": 1162.095, "end": 1170.63 }, { "text": "Das war alles im Ergebnis relativ gleichwertig.", "start": 1170.63, "end": 1174.25 }, { "text": "Da gab's keine großen Unterschiede.", "start": 1174.79, "end": 1176.3301 } ], "start": 1151.33, "end": 1176.3301, "num_words": 72.0, "speaker": 2 }, { "sentences": [ { "text": "Aber aber und jetzt kommt das große aber, die Einstellung zur eID ist dabei abgefallen.", "start": 1176.63, "end": 1183.685 }, { "text": "Also die die Leute standen der EID negativer gegenüber.", "start": 1183.685, "end": 1187.285 } ], "start": 1176.63, "end": 1187.285, "num_words": 24.0, "speaker": 2 }, { "sentences": [ { "text": "Je mehr drüber geredet wurde sozusagen.", "start": 1187.285, "end": 1188.885 }, { "text": "Je mehr sie drüber", "start": 1188.885, "end": 1189.4451 } ], "start": 1187.285, "end": 1189.4451, "num_words": 10.0, "speaker": 0 }, { "sentences": [ { "text": "Und mehr Kommunikationspartner es gibt und der der Hebel dahinter war einfach, dass wir ein Modell genommen haben, wo's sehr wenig Angebote gab.", "start": 1189.4851, "end": 1197.54 }, { "text": "Mhm.", "start": 1197.7001, "end": 1197.86 }, { "text": "Und deswegen waren die Leute einfach frustriert.", "start": 1197.86, "end": 1200.18 }, { "text": "Und je mehr sie durch die Kommunikationsmaßnahmen darauf hingewiesen worden sind, desto frustrierter waren sie.", "start": 1200.18, "end": 1204.98 }, { "text": "Ja.", "start": 1204.98, "end": 1205.4601 } ], "start": 1189.4851, "end": 1205.4601, "num_words": 47.0, "speaker": 2 }, { "sentences": [ { "text": "Also dann, wenn das Angebot nicht stimmt und man in Anführungszeichen nur kommuniziert, dann hat es tatsächlich mittelfristig negative Effekte.", "start": 1205.4601, "end": 1213.16 } ], "start": 1205.4601, "end": 1213.16, "num_words": 20.0, "speaker": 2 }, { "sentences": [ { "text": "Ja, ja, macht auch Sinn, ne, wenn man sich so überlegt, die standen quasi in Startlöchern und dann konnten sie nicht loslegen, ja.", "start": 1213.405, "end": 1219.025 } ], "start": 1213.405, "end": 1219.025, "num_words": 23.0, "speaker": 0 }, { "sentences": [ { "text": "Genau.", "start": 1219.085, "end": 1219.325 }, { "text": "Und das ist sehr auch eine, der neben den nicht linearen Zusammenhängen ist das ja eine der besonderen Reize an der Simulation, solche Sachen, die erstmals sehr kontraintuitiv sind, dabei rauszufinden.", "start": 1219.325, "end": 1230.5701 }, { "text": "Also erst mal, wenn man so drauf guckt, wie ich mache Kommunikationsmaßnahmen, bewerbe das das Ganze und trotzdem geht die Einstellung runter, ist irgendwie komisch.", "start": 1230.71, "end": 1238.73 }, { "text": "Und dann, wenn man dahinter guckt, wird es dann ganz einfach.", "start": 1239.11, "end": 1241.51 } ], "start": 1219.085, "end": 1241.51, "num_words": 68.0, "speaker": 2 }, { "sentences": [ { "text": "Ah, okay.", "start": 1241.51, "end": 1242.25 }, { "text": "Jetzt lass uns noch mal gerne über das technische Vorgehen reden.", "start": 1243.605, "end": 1246.745 }, { "text": "Ist grad schon der Name gefallen von 1 Programmiersprache, die da genutzt wird, ne, Simulationsumgebung?", "start": 1247.685, "end": 1253.0651 } ], "start": 1241.51, "end": 1253.0651, "num_words": 28.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, das ist sone Simulationsumgebung, die heißt NetLogo.", "start": 1253.2051, "end": 1256.425 }, { "text": "Ja.", "start": 1256.645, "end": 1257.09 }, { "text": "Die ist frei verfügbar, ist gut dokumentiert.", "start": 1257.09, "end": 1259.75 }, { "text": "Wichtig war für uns, dass man natürlich Beispiele findet, Tutorials, da einzusteigen, loslegen zu können, dass es auch nichts kostet.", "start": 1260.21, "end": 1269.9099 }, { "text": "War jetzt auch eine unserer Bedingungen, dass wir gesagt haben, alle Sachen, die wir machen, müssen frei verfügbar sein.", "start": 1270.0249, "end": 1275.3049 } ], "start": 1253.2051, "end": 1275.3049, "num_words": 55.0, "speaker": 1 }, { "sentences": [ { "text": "Dann haben wir ja schon gesagt, hinterher stellen wir das ja auch wieder zur Verfügung.", "start": 1275.3049, "end": 1278.845 }, { "text": "Also wir wollen dann schon so einsteigen in einen Zyklus, dass man die Tools einfach zur Verfügung hat und auch wenn man damit arbeitet, seine Ergebnisse weitergibt.", "start": 1279.225, "end": 1289.14 } ], "start": 1275.3049, "end": 1289.14, "num_words": 42.0, "speaker": 1 }, { "sentences": [ { "text": "Und wie hoch oder niedrigschwellig ist das, würdet ihr sagen?", "start": 1290.4, "end": 1294.48 }, { "text": "Also wenn ich jetzt sage, ich hab jetzt nicht Zeit, mich 2 Wochen fortzubilden, die IT Abteilung ist auch nicht hilfreich, ich bin total auf mich alleine gestellt und hab eher so Erfahrungen mit MS Office Paketen.", "start": 1294.48, "end": 1306.6151 }, { "text": "Wie schnell finde ich deinen Zugang?", "start": 1306.8351, "end": 1308.4551 } ], "start": 1290.4, "end": 1308.4551, "num_words": 53.0, "speaker": 0 }, { "sentences": [ { "text": "Müsste Maike jetzt 'n bisschen antworten.", "start": 1309.39, "end": 1310.99 }, { "text": "Ich bin Hab vielleicht gemerkt.", "start": 1310.99, "end": 1312.99 } ], "start": 1309.39, "end": 1312.99, "num_words": 11.0, "speaker": 1 }, { "sentences": [ { "text": "Ist natürlich für euch schwer, sich vorzustellen.", "start": 1312.99, "end": 1314.59 } ], "start": 1312.99, "end": 1314.59, "num_words": 7.0, "speaker": 0 }, { "sentences": [ { "text": "Nein, das ist Also ich bin mehr so Ich bin Hab mehr Programmierkenntnisse und man muss sagen, dass es ganz wichtig ist, das erste Beispiel durchzuarbeiten.", "start": 1314.59, "end": 1323.735 }, { "text": "Also es gibt ja in den Tutorials oder so was gibt es immer so Einführung.", "start": 1324.115, "end": 1327.415 }, { "text": "Mhm.", "start": 1327.555, "end": 1327.875 }, { "text": "Und das ist unglaublich wichtig.", "start": 1327.875, "end": 1329.395 }, { "text": "Einmal zu verstehen, was ist der Ansatz von so 1 Programmiersprache?", "start": 1329.395, "end": 1333.895 } ], "start": 1314.59, "end": 1333.895, "num_words": 58.0, "speaker": 1 }, { "sentences": [ { "text": "Wie blickt die auf die Welt sozusagen?", "start": 1334.435, "end": 1336.215 }, { "text": "Wie soll ich damit umgehen?", "start": 1336.275, "end": 1337.735 }, { "text": "Und dann, wenn man das mal so verstanden hat, also zum Beispiel in diesem Fall eben auch die Agenten.", "start": 1338.25, "end": 1342.99 }, { "text": "Ja.", "start": 1343.21, "end": 1343.53 }, { "text": "Nicht?", "start": 1343.53, "end": 1343.77 } ], "start": 1334.435, "end": 1343.77, "num_words": 33.0, "speaker": 1 }, { "sentences": [ { "text": "Was ist das?", "start": 1343.77, "end": 1344.33 }, { "text": "Also ich hab die Plan", "start": 1344.33, "end": 1345.53 } ], "start": 1343.77, "end": 1345.53, "num_words": 8.0, "speaker": 1 }, { "sentences": [ { "text": "da bitten Genau.", "start": 1345.53, "end": 1345.9299 }, { "text": "Leute, ja.", "start": 1346.1699, "end": 1346.73 } ], "start": 1345.53, "end": 1346.73, "num_words": 5.0, "speaker": 0 }, { "sentences": [ { "text": "Da wegen sich Leute, genau.", "start": 1346.73, "end": 1347.9299 }, { "text": "Wie möchte ich das aufsetzen?", "start": 1347.9299, "end": 1349.9299 }, { "text": "Das muss man einmal verstanden haben.", "start": 1349.9299, "end": 1351.7899 }, { "text": "Dann ist es eben eine spezielle Sprache, die jetzt besonders geeignet ist für diese für diese Art von Simulation.", "start": 1353.075, "end": 1361.735 }, { "text": "Man wird also ganz viel zum Beispiel Zufallsfunktion, das ist alles da, nicht?", "start": 1361.7949, "end": 1367.36 } ], "start": 1346.73, "end": 1367.36, "num_words": 48.0, "speaker": 1 }, { "sentences": [ { "text": "Dass man sozusagen zufällig irgendwelchen Agenten irgendwelche zufälligen Werte geben kann, dass die irgendwie handeln können.", "start": 1367.36, "end": 1374.3201 }, { "text": "Da nimmt man oft den Zufall, dass die also durch die Gegend rennen sozusagen, jetzt im Bild und dann zufällig aneinanderstoßen, aufeinanderstoßen.", "start": 1374.3201, "end": 1383.665 }, { "text": "Mhm.", "start": 1384.285, "end": 1384.785 }, { "text": "Und da nimmt man Zufallsfunktion und da gibt es dann halt sehr viele Programmunterstützung oder aber auch für die grafische Auswertung, dass man schöne Grafik machen kann.", "start": 1384.925, "end": 1394.385 }, { "text": "Da gibt es dann auch eben viele Elemente.", "start": 1394.51, "end": 1396.8501 } ], "start": 1367.36, "end": 1396.8501, "num_words": 74.0, "speaker": 1 }, { "sentences": [ { "text": "Und das muss man einmal verstanden haben sozusagen, welche Elemente es gibt, was überhaupt der Ansatz ist und dann denke ich, ist das schon eine Ausbildungssprache, mit der man gut reinkommt.", "start": 1398.1101, "end": 1406.37 } ], "start": 1398.1101, "end": 1406.37, "num_words": 31.0, "speaker": 1 }, { "sentences": [ { "text": "Diese Modelle, die schon bereits verfügbar sind, die sind immer so aufgebaut, dass man eine Oberfläche hat, mit der man die bedient.", "start": 1407.205, "end": 1413.3049 }, { "text": "Dann dahinter eine Beschreibung, was dieses Modell erreichen will und auch mit son paar Beispielen, was man vielleicht mal einstellen sollte, 'n Gespür dafür zu kriegen, wie das Ding funktioniert.", "start": 1413.445, "end": 1422.93 }, { "text": "Und dahinter dann der Code.", "start": 1423.47, "end": 1425.01 }, { "text": "Also", "start": 1425.71, "end": 1426.03 } ], "start": 1407.205, "end": 1426.03, "num_words": 58.0, "speaker": 2 }, { "sentences": [ { "text": "Okay, also in so 3 man kann so 3 Schichten abziehen und das nach und nach verstehen son bisschen.", "start": 1426.35, "end": 1431.47 } ], "start": 1426.35, "end": 1431.47, "num_words": 19.0, "speaker": 0 }, { "sentences": [ { "text": "Genau und das ist natürlich 'n wunderbarer Einstieg.", "start": 1431.47, "end": 1433.81 }, { "text": "Zum Beispiel mein kleines Verkehrsbeispiel, mein Staubeispiel.", "start": 1434.1901, "end": 1436.6901 }, { "text": "Das ist ganz, ganz simpel und dann, wenn man sich da einmal durchgearbeitet hat, son bisschen durcharbeiten, muss man sich wohl dann auch sehr gut nachvollziehbar.", "start": 1437.245, "end": 1444.625 }, { "text": "Die Sprache ist 'n bisschen ruckelig, aber natürlich auch speziell für diese Simulationsanforderung Ja.", "start": 1445.165, "end": 1450.38 }, { "text": "Gebaut.", "start": 1450.7, "end": 1451.02 } ], "start": 1431.47, "end": 1451.02, "num_words": 56.0, "speaker": 2 }, { "sentences": [ { "text": "Also man wenn man 'n bisschen drin ist, kann man sich fast lesen wie normalen Text.", "start": 1451.02, "end": 1455.04 } ], "start": 1451.02, "end": 1455.04, "num_words": 16.0, "speaker": 2 }, { "sentences": [ { "text": "Okay und also jetzt für die Leute, die aber noch nicht so überzeugt sind und immer noch Hürden haben, habt ihr ja noch 'n anderen Einsatz probiert, nämlich das Ganze mit KI zu machen,", "start": 1455.74, "end": 1464.72 } ], "start": 1455.74, "end": 1464.72, "num_words": 34.0, "speaker": 0 }, { "sentences": [ { "text": "ne?", "start": 1464.945, "end": 1465.105 }, { "text": "Genau, zu dem Zeitpunkt, wo wir die Arbeiten gemacht haben, war das natürlich eine neue Möglichkeit Programmieren mithilfe von generativer KI mit Chatbots wie ChatGPT und so weiter zu unterstützen.", "start": 1465.105, "end": 1477.205 }, { "text": "Auch hier haben wir wieder gesagt, wir wollen jetzt keine speziellen Programme benutzen, sondern wir wollen die kostenlosen öffentlichen Angebote nutzen, weil im Zweifel ist es ja so, dass dann spezialisierte Sachen irgendwie besser sind.", "start": 1477.48, "end": 1490.12 }, { "text": "Wir wollten ja mal gucken, was geht überhaupt?", "start": 1490.12, "end": 1492.2999 }, { "text": "Auch möglichst geringe Hürden und haben das eben dann unser Simulationsproblem beschrieben und haben dann die KI sozusagen aufgefordert, uns eine Simulation dazu zu schreiben.", "start": 1493.0549, "end": 1506.195 } ], "start": 1464.945, "end": 1506.195, "num_words": 99.0, "speaker": 1 }, { "sentences": [ { "text": "Und diese Programmiersprache Ja.", "start": 1506.66, "end": 1508.02 }, { "text": "Über die wir gerade gesprochen haben, kennen die üblichen Chat Bots sozusagen.", "start": 1508.1001, "end": 1512.02 }, { "text": "Also das heißt, die sprechen miteinander auf eine Art, ne?", "start": 1512.02, "end": 1514.5801 }, { "text": "Also man hat wie son Dolmetscher.", "start": 1514.5801, "end": 1515.86 }, { "text": "Kann man sich das so vorstellen vielleicht?", "start": 1515.86, "end": 1517.64 } ], "start": 1506.66, "end": 1517.64, "num_words": 39.0, "speaker": 0 }, { "sentences": [ { "text": "Es ist so, die die Programmiersprache net Logo, die hat da 'n kleines Problem.", "start": 1518.18, "end": 1523.255 }, { "text": "Diese grafische Oberfläche, es ist eigentlich gemacht sozusagen, dass man auf der einen Seite seine Simulationsbeschreibung hat.", "start": 1523.255, "end": 1530.375 }, { "text": "Das ist 'n klassischer Programmiercode, aber diese grafische Oberfläche dazu, das sind also grafische Elemente wie Knöpfe oder Schieber, Werte einzustellen.", "start": 1530.375, "end": 1539.9601 }, { "text": "In den Normalfall ordnet man die per Hand an sozusagen.", "start": 1540.3401, "end": 1543.64 }, { "text": "Und da hat jetzt die KI leichte Schwierigkeiten, jetzt leichte Schwierigkeiten, jetzt im allgemeinen Fall dieses diese komplette Simulation zu machen.", "start": 1543.86, "end": 1549.599 } ], "start": 1518.18, "end": 1549.599, "num_words": 83.0, "speaker": 1 }, { "sentences": [ { "text": "Was man aber machen kann, ist halt, man ordnet selber diese Schieber an und sagt, was man machen möchte und kann sich dann den Code schreiben lassen, die Simulation durchzuführen.", "start": 1549.599, "end": 1559.0386 }, { "text": "Okay, also wenn man jetzt in diesem durchzuführen.", "start": 1559.0386, "end": 1561.99 } ], "start": 1549.599, "end": 1561.99, "num_words": 38.0, "speaker": 1 }, { "sentences": [ { "text": "Okay, also wenn man jetzt in diesem Ebenenmodell bleibt von Mike gerade, also die kann die unterste und zweitunterste Ebene kriegt die gut hin und dann das oberste, das was man sieht.", "start": 1562.05, "end": 1571.03 } ], "start": 1562.05, "end": 1571.03, "num_words": 32.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, da das das wird man sicherlich auch lösen können und da gibt's auch spezielle Ansätze, sowas zu lösen.", "start": 1571.41, "end": 1577.8251 }, { "text": "Aber in diesem Fall, wenn man jetzt da so ganz naiv rangeht, dann muss man sich 'n Weg überlegen.", "start": 1578.125, "end": 1583.265 }, { "text": "Wenn das nicht funktioniert, dann kann man auch immer die KI fragen, wenn's dann gibt's eine Fehlermeldung und dann kann man die KI fragen,", "start": 1583.5651, "end": 1589.745 } ], "start": 1571.41, "end": 1589.745, "num_words": 62.0, "speaker": 1 }, { "sentences": [ { "text": "was", "start": 1590.43, "end": 1590.67 } ], "start": 1590.43, "end": 1590.67, "num_words": 1.0, "speaker": 2 }, { "sentences": [ { "text": "ist denn mit diesem Fehler und dann wird die KI diesen Fehler korrigieren.", "start": 1590.67, "end": 1594.2101 } ], "start": 1590.67, "end": 1594.2101, "num_words": 13.0, "speaker": 1 }, { "sentences": [ { "text": "Also aber es jetzt noch mal zusammenzufassen, also die KI macht schon noch mal ein Stück zugänglicher Auf", "start": 1594.91, "end": 1600.43 } ], "start": 1594.91, "end": 1600.43, "num_words": 18.0, "speaker": 0 }, { "sentences": [ { "text": "jeden Fall, ja.", "start": 1600.43, "end": 1601.0701 } ], "start": 1600.43, "end": 1601.0701, "num_words": 3.0, "speaker": 1 }, { "sentences": [ { "text": "Im Falle, ja.", "start": 1601.23, "end": 1601.79 } ], "start": 1601.23, "end": 1601.79, "num_words": 3.0, "speaker": 0 }, { "sentences": [ { "text": "Also die KI ist für uns glaube ich, 'n ganz großer, also wirklich eine Möglichkeit, das dieses Gebiet zu öffnen.", "start": 1601.79, "end": 1607.375 }, { "text": "Man muss immer noch dieses Grundverständnis muss man haben, das ist ganz wichtig.", "start": 1607.375, "end": 1611.155 }, { "text": "Man muss verstehen auch die ersten Ansätze, die die KI zum Beispiel in diesem Netlogob Beispiel bringt.", "start": 1612.4149, "end": 1619.5499 }, { "text": "Da hab ich ja gesagt, es gibt eben das Spielfeld und es gibt die Spielfiguren.", "start": 1619.5499, "end": 1622.9299 }, { "text": "Und nun könnte man sagen zum Beispiel im Spielfeld sind schon die Behörden sind schon im Spielfeld oder sind fest sozusagen.", "start": 1623.63, "end": 1631.405 } ], "start": 1601.79, "end": 1631.405, "num_words": 86.0, "speaker": 1 }, { "sentences": [ { "text": "Die sind keine Agenten.", "start": 1631.405, "end": 1632.365 } ], "start": 1631.405, "end": 1632.365, "num_words": 4.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, die sind keine Agenten, sondern die sind Teil des Spielfelds.", "start": 1632.365, "end": 1635.105 }, { "text": "Nicht und da müsste man muss man sich überlegen, was will ich denn später simulieren?", "start": 1635.645, "end": 1639.1849 }, { "text": "Wo will ich hin?", "start": 1639.245, "end": 1640.3049 }, { "text": "Welche Vorstellung hab ich?", "start": 1640.92, "end": 1642.54 }, { "text": "Und wenn die KI dann einen Weg vorschlägt, dann kann ich ganz zu Anfang schon sehen, ist es der Weg, den ich gehen möchte?", "start": 1642.76, "end": 1650.22 } ], "start": 1632.365, "end": 1650.22, "num_words": 58.0, "speaker": 1 }, { "sentences": [ { "text": "Oder aber möchte ich noch mal fragen und krieg eine neue Antwort von der KI.", "start": 1651.0, "end": 1657.975 }, { "text": "Was aber toll ist an der Stelle ist, ich brauche natürlich nicht so wahnsinnig viel beschreiben.", "start": 1658.6749, "end": 1664.375 }, { "text": "Ich muss nur die Grundzüge beschreiben und dann wird mir die KI ganz viel von der Routinearbeit abnehmen und wird die genaue Bezeichnung von den Funktionen, wie ich die Zufallsfunktion aufrufen muss und so weiter, wie eine Grafik gemacht werden muss.", "start": 1664.4349, "end": 1679.2101 }, { "text": "Das wird mir die KI machen.", "start": 1679.35, "end": 1680.9501 } ], "start": 1651.0, "end": 1680.9501, "num_words": 78.0, "speaker": 1 }, { "sentences": [ { "text": "Mhm.", "start": 1680.9501, "end": 1681.395 }, { "text": "Okay.", "start": 1681.555, "end": 1681.875 } ], "start": 1680.9501, "end": 1681.875, "num_words": 2.0, "speaker": 0 }, { "sentences": [ { "text": "Ich hatte in meinem Beispiel, wie ich das noch dazu führen darf, innere Einstellung der Bürgerinnen und Bürger zur EID.", "start": 1681.875, "end": 1689.335 }, { "text": "Und ich hab dann gesagt, okay, diese Einstellung kann sich ändern und die KI hatte von selber vorgeschlagen, dass die Farbe des Agenten auf dem Spielfeld von dieser Einstellung abhängig gemacht wird.", "start": 1689.715, "end": 1700.8401 }, { "text": "Mhm.", "start": 1700.8401, "end": 1701.16 }, { "text": "Dass man also schon gleich eine neue Funktion hat, über die ich ja noch gar nicht nachgedacht hab in meiner Simulation.", "start": 1701.16, "end": 1706.14 }, { "text": "In meiner Simulation wollte ich erst mal die Simulation laufen lassen und wollte mir hinterher Gedanken machen, wie ich das denn darstellen kann.", "start": 1706.3601, "end": 1713.465 } ], "start": 1681.875, "end": 1713.465, "num_words": 97.0, "speaker": 1 }, { "sentences": [ { "text": "Und die KI hat aber gleich dieses Farbschema auf son kleines Männchen übertragen.", "start": 1714.005, "end": 1718.405 }, { "text": "Also hat 'n Männchensymbol für Bürger ausgewählt.", "start": 1718.405, "end": 1720.745 }, { "text": "Und hat dann dem Männchen noch eine Farbe gegeben Also das ist ja", "start": 1721.49, "end": 1724.21 } ], "start": 1714.005, "end": 1724.21, "num_words": 33.0, "speaker": 1 }, { "sentences": [ { "text": "zum Beispiel grün, wenn's voll heiß ist auf die Erde.", "start": 1724.21, "end": 1726.21 }, { "text": "Genau.", "start": 1726.21, "end": 1726.71 }, { "text": "Und rot, wenn's total Genau.", "start": 1726.77, "end": 1727.5701 }, { "text": "Spannend ist.", "start": 1727.73, "end": 1728.21 }, { "text": "Mhm.", "start": 1728.21, "end": 1728.61 } ], "start": 1724.21, "end": 1728.61, "num_words": 19.0, "speaker": 0 }, { "sentences": [ { "text": "Okay.", "start": 1728.61, "end": 1729.11 }, { "text": "Mike, kannst Du vielleicht noch mal so sagen, wie könnte denn zum Beispiel son prompt lauten?", "start": 1729.49, "end": 1733.51 }, { "text": "Also ich müsste sagen, mach mir eine agentenbasierte Simulation mit dem und dem Programm.", "start": 1733.875, "end": 1738.195 }, { "text": "Also was sage ich da?", "start": 1738.195, "end": 1739.7949 }, { "text": "Was sind so die wichtigsten Inhalte, die ich da liefern muss, damit die KI eben in die richtige Richtung losläuft?", "start": 1739.7949, "end": 1746.775 } ], "start": 1728.61, "end": 1746.775, "num_words": 56.0, "speaker": 0 }, { "sentences": [ { "text": "Ich kann's jetzt ganz leicht machen.", "start": 1747.81, "end": 1749.3301 }, { "text": "Ja.", "start": 1749.3301, "end": 1749.8301 }, { "text": "Das haben wir alles veröffentlicht, inklusive der PONTS, Okay.", "start": 1750.37, "end": 1752.97 }, { "text": "Wie man das am besten beschreibt.", "start": 1753.01, "end": 1755.1901 }, { "text": "Aber ja, natürlich muss muss die KI wissen, in welchem Umfeld, also mit welchem Programm, NetLogo oder Python, mit welcher Bibliothek ich das machen will, an welcher Sprache ich das machen will.", "start": 1755.41, "end": 1765.6649 } ], "start": 1747.81, "end": 1765.6649, "num_words": 54.0, "speaker": 2 }, { "sentences": [ { "text": "Und dann muss ich aber ganz normal alltagssprachlich beschreiben, was ich denn da gerne simuliert haben möchte.", "start": 1766.0449, "end": 1772.225 }, { "text": "Dann die Aufforderung, mach mir 'n Code.", "start": 1773.27, "end": 1775.77 } ], "start": 1766.0449, "end": 1775.77, "num_words": 24.0, "speaker": 2 }, { "sentences": [ { "text": "Genau.", "start": 1775.91, "end": 1776.39 }, { "text": "Interessant wird das dann an der Stelle, wo man das nicht explizit formuliert, sondern wenn jetzt Agenten aufeinandertreffen, was dann passieren soll, hab ich natürlich ganz vorne was im Kopf, was dann passieren soll und das beschreibe ich.", "start": 1776.39, "end": 1786.785 }, { "text": "Aber die anderen Fälle, die es da auch noch gibt, die beschreibe ich jetzt vielleicht gar nicht.", "start": 1787.025, "end": 1791.765 }, { "text": "Aber wenn das technisch umgesetzt wird, dann muss ja eine Entscheidung getroffen werden, ne?", "start": 1791.985, "end": 1796.4651 }, { "text": "Oder es wird eben bewusst keine Entscheidung getroffen, was aber aus technischer Sicht wieder eine Entscheidung ist.", "start": 1796.4651, "end": 1803.1 } ], "start": 1775.91, "end": 1803.1, "num_words": 87.0, "speaker": 1 }, { "sentences": [ { "text": "Und dann sehe ich ja plötzlich auch, was in meiner Beschreibung fehlt.", "start": 1803.3201, "end": 1808.92 }, { "text": "Ob ich unpräzise bin, die KI etwas vereinfacht hat, was ich aber gar nicht vereinfacht haben möchte und dort muss ich dann präziser werden.", "start": 1808.92, "end": 1818.705 } ], "start": 1803.3201, "end": 1818.705, "num_words": 36.0, "speaker": 1 }, { "sentences": [ { "text": "Gut.", "start": 1819.005, "end": 1819.405 }, { "text": "Also alles ist veröffentlicht.", "start": 1819.405, "end": 1820.465 }, { "text": "Man kann direkt loslegen, Mike.", "start": 1820.525, "end": 1822.145 } ], "start": 1819.005, "end": 1822.145, "num_words": 10.0, "speaker": 0 }, { "sentences": [ { "text": "Ein Aspekt, der noch ganz wichtig ist, die Syntax wird abgenommen von der KI und die KI kann die richtige Syntax, das ist Gold wert.", "start": 1823.565, "end": 1830.4601 }, { "text": "Okay.", "start": 1830.6, "end": 1831.0801 }, { "text": "Auch so Kleinigkeiten, aber genau da auf dem Punkt wollt ich noch mal eingehen.", "start": 1831.0801, "end": 1834.2001 }, { "text": "Das ist natürlich auch absolutes Gold wert, weil man weiß, also man kann dann auch anfangen, die KI zu hinterfragen, was sie da in den Beschreibungslücken, die geblieben sind, was sie da automatisch einsetzt.", "start": 1834.2001, "end": 1843.6849 }, { "text": "Macht das denn überhaupt Sinn, was sie da einsetzt?", "start": 1844.065, "end": 1846.005 } ], "start": 1823.565, "end": 1846.005, "num_words": 83.0, "speaker": 2 }, { "sentences": [ { "text": "Und stellt sich dann die nächste Frage, wo sich das dann vielleicht noch mal irgendwie inhaltlich hinterfragen und ganz anders bauen?", "start": 1846.065, "end": 1851.6 }, { "text": "Also so gesehen ist das wie bei anderen bei Textfragen oder bei Brainstorming Prozessen ist der KI da auch 'n Schilderspartner, wenn man dann weiterkommen kann.", "start": 1851.76, "end": 1860.08 } ], "start": 1846.065, "end": 1860.08, "num_words": 47.0, "speaker": 2 }, { "sentences": [ { "text": "Mhm.", "start": 1860.08, "end": 1860.48 }, { "text": "Okay.", "start": 1860.48, "end": 1860.98 }, { "text": "Wo jetzt zum Abschluss seht ihr denn heiße Anwendungsfelder, Low hanging fruits?", "start": 1862.24, "end": 1868.375 }, { "text": "Wer wer sollte jetzt sofort sich euer Beispiel aufrufen und damit loslegen?", "start": 1868.675, "end": 1873.255 } ], "start": 1860.08, "end": 1873.255, "num_words": 26.0, "speaker": 0 }, { "sentences": [ { "text": "Ich glaube, diese Felder werden immer größer.", "start": 1874.995, "end": 1877.895 }, { "text": "Swan City hatten wir ja schon, da ist halt Mhm.", "start": 1878.0, "end": 1879.6799 }, { "text": "Das der enorme Vorteil, dass die Daten oder sehr, sehr viele Daten halt einfach schon da sind.", "start": 1879.84, "end": 1884.48 }, { "text": "Das ist natürlich immer Gold wert, darauf Simulationen aufzusetzen.", "start": 1884.48, "end": 1887.38 }, { "text": "Aber in dem Bereich passiert ja auch schon 'n bisschen was.", "start": 1887.52, "end": 1890.02 } ], "start": 1874.995, "end": 1890.02, "num_words": 54.0, "speaker": 2 }, { "sentences": [ { "text": "Vielleicht nicht ganz so low hanging, aber ich glaube, ganz, ganz wichtig, ganz zentral für die Modernisierung von Staat und Verwaltung ist halt wirklich diese Wirkungsorientierung und das wirklich eigentlich überall sich Gedanken zu machen, wie wirkt das, was ich tue?", "start": 1890.985, "end": 1905.81 }, { "text": "Weil da kann man dann anekdotisch sehr schön einige kontraintuitive Effekte mal beobachten, aber wenn man's dann in der Simulation hat mit diesem gesamten Reflexionsprozess, das ist dann noch mal sehr viel wert.", "start": 1905.9701, "end": 1918.29 } ], "start": 1890.985, "end": 1918.29, "num_words": 74.0, "speaker": 2 }, { "sentences": [ { "text": "Ja.", "start": 1918.29, "end": 1918.575 }, { "text": "Und so wie ich das verstehe, also es funktioniert auch auf allen Ebenen, ne.", "start": 1918.575, "end": 1921.215 }, { "text": "Also man könnte sagen auf Bundesebene zu sagen, erhöhe ich das Elterngeld, erhöhe ich's nicht, aber genauso wie führ ich in meiner Kleinstadt eine Mikrowährung lokal ein oder so was.", "start": 1921.215, "end": 1931.5349 }, { "text": "Also man könnte, ne, so wie ich's versteh", "start": 1931.5349, "end": 1934.51 } ], "start": 1918.29, "end": 1934.51, "num_words": 53.0, "speaker": 0 }, { "sentences": [ { "text": "Die Methode ist komplett offen.", "start": 1934.75, "end": 1936.11 }, { "text": "Ja.", "start": 1936.11, "end": 1936.4299 }, { "text": "Es ist son bisschen Fluch und Segen der Methode.", "start": 1936.4299, "end": 1938.45 }, { "text": "Es ist komplett offen.", "start": 1938.59, "end": 1939.5499 }, { "text": "Man kann letztlich jede Fragestellung damit simulieren.", "start": 1939.5499, "end": 1942.37 } ], "start": 1934.75, "end": 1942.37, "num_words": 26.0, "speaker": 2 }, { "sentences": [ { "text": "Mal angemessen und mal weniger angemessen.", "start": 1942.91, "end": 1944.77 }, { "text": "Manchmal tut's vielleicht auch eine Befragung, aber im Prinzip ist die Methode für fast alles geeignet.", "start": 1945.07, "end": 1950.395 } ], "start": 1942.91, "end": 1950.395, "num_words": 22.0, "speaker": 2 }, { "sentences": [ { "text": "Ja.", "start": 1950.4551, "end": 1950.935 }, { "text": "Okay.", "start": 1950.935, "end": 1951.435 }, { "text": "Gut, dann hoffen wir, dass ganz viele Lust bekommen haben, da einfach mal mit rumzuspielen mit diesen Reglern, die man da auch bedienen kann und sich das hoffentlich 'n bisschen mehr durchsetzt.", "start": 1951.735, "end": 1960.535 }, { "text": "Vielen Dank euch beiden auch fürs Ausprobieren.", "start": 1960.535, "end": 1963.4401 }, { "text": "So jemand muss ja den Anfang machen.", "start": 1963.7401, "end": 1965.68 } ], "start": 1950.4551, "end": 1965.68, "num_words": 48.0, "speaker": 0 }, { "sentences": [ { "text": "Wir danken dir.", "start": 1966.3, "end": 1967.26 }, { "text": "Ja, Dankeschön.", "start": 1967.26, "end": 1968.0801 } ], "start": 1966.3, "end": 1968.0801, "num_words": 5.0, "speaker": 1 }, { "sentences": [ { "text": "Simulationen als digitales Werkzeug.", "start": 1970.2201, "end": 1972.7001 }, { "text": "Das war unser Thema heute bei restart digital.", "start": 1972.7001, "end": 1975.6001 }, { "text": "Meine Gesprächspartner heute waren Mike Weber und Jens Tiemann vom Kompetenzzentrum öffentlicher IT am Fraunhofer Institut Focus in Berlin.", "start": 1975.865, "end": 1984.4451 }, { "text": "Wir verlinken euch die Publikation zu dem Thema und auch alle anderen Ressourcen, über die wir gesprochen haben, als Link in den Shownotes zu dem Podcast.", "start": 1985.145, "end": 1993.9199 }, { "text": "Ihr findet sie auch online unter ö fit Punkt d e mit o e geschrieben.", "start": 1994.46, "end": 1999.0399 } ], "start": 1970.2201, "end": 1999.0399, "num_words": 72.0, "speaker": 0 }, { "sentences": [ { "text": "Diesen Podcast findet ihr kostenfrei auf Apple Podcasts, Spotify und vielen weiteren Plattformen.", "start": 1999.82, "end": 2005.6 }, { "text": "Wenn ihr unsere Themen spannend findet und keine Folge verpassen wollt, dann könnt ihr uns da auch abonnieren.", "start": 2006.295, "end": 2011.5951 }, { "text": "Schön, dass ihr dabei wart.", "start": 2012.295, "end": 2013.5751 }, { "text": "Ich bin Franziska Walser.", "start": 2013.5751, "end": 2014.9551 }, { "text": "Tschüs und bis bald.", "start": 2015.0951, "end": 2016.3151 } ], "start": 1999.82, "end": 2016.3151, "num_words": 44.0, "speaker": 0 } ] }, "entities": null, "translations": null, "topics": null } ], "detected_language": "" } ], "utterances": [ { "start": 1.4399999, "end": 1.9399999, "confidence": 0.99853516, "channel": 0, "transcript": "Simulationen", "words": [ { "word": "simulationen", "start": 1.4399999, "end": 1.9399999, "confidence": 0.99853516, "punctuated_word": "Simulationen", "speaker": 0, "speaker_confidence": 0.74526256 } ], "speaker": 0, "id": "4f643d73-c131-47a6-ad6e-dcaac7493dd0" }, { "start": 2.3999999, "end": 5.8599997, "confidence": 0.98876953, "channel": 0, "transcript": "sind nur 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Was ist die Grundmotivation?", "words": [ { "word": "bei", "start": 440.685, "end": 440.845, "confidence": 0.99609375, "punctuated_word": "Bei", "speaker": 2, "speaker_confidence": 0.86555064 }, { "word": "'ner", "start": 440.845, "end": 441.085, "confidence": 0.984375, "punctuated_word": "'ner", "speaker": 2, "speaker_confidence": 0.86555064 }, { "word": "simulation", "start": 441.085, "end": 441.485, "confidence": 0.9980469, "punctuated_word": "Simulation", "speaker": 2, "speaker_confidence": 0.86555064 }, { "word": "muss", "start": 441.485, "end": 441.805, "confidence": 0.9970703, "punctuated_word": "muss", "speaker": 2, "speaker_confidence": 0.86555064 }, { "word": "man", "start": 441.805, "end": 441.885, "confidence": 0.99121094, "punctuated_word": "man", "speaker": 2, "speaker_confidence": 0.86555064 }, { "word": "ganz", "start": 441.885, "end": 442.205, "confidence": 0.99902344, "punctuated_word": "ganz", "speaker": 2, "speaker_confidence": 0.7720701 }, { "word": "unten", "start": 442.205, "end": 442.525, "confidence": 1.0, "punctuated_word": "unten", "speaker": 2, "speaker_confidence": 0.7720701 }, { "word": "anfangen", "start": 442.525, "end": 443.025, "confidence": 0.8857422, "punctuated_word": "anfangen.", "speaker": 2, "speaker_confidence": 0.7720701 }, { "word": "was", "start": 443.165, "end": 443.245, "confidence": 0.9970703, "punctuated_word": "Was", "speaker": 2, "speaker_confidence": 0.7720701 }, { "word": "ist", "start": 443.245, "end": 443.565, "confidence": 0.9970703, "punctuated_word": "ist", "speaker": 2, "speaker_confidence": 0.7720701 }, { "word": "die", "start": 443.565, "end": 443.805, "confidence": 0.99316406, "punctuated_word": "die", "speaker": 2, "speaker_confidence": 0.7720701 }, { "word": "grundmotivation", "start": 443.805, "end": 444.305, "confidence": 0.8338216, "punctuated_word": "Grundmotivation?", "speaker": 2, "speaker_confidence": 0.7720701 } ], "speaker": 2, "id": "f338aa27-aa25-4ee0-9a60-18662c0a0e6b" }, { "start": 444.845, "end": 450.15, "confidence": 0.88181376, "channel": 0, "transcript": "Jetzt sind wir wieder bei den Agenten, also wenn man wenn man Die Agenten individuellen Themen bleibt. 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Mhm. 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Genau, weil's ja wirklich nur drum ging, das einfach mal auszuprobieren. Genau. Und ja, mhm. 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Genau. Also dass man eben zum Beispiel merkt, ach Mensch, ich müsste jetzt über die Agenten das und das wissen, da gibt's aber gar keine Zahlen dazu, also muss ich die irgendwo besorgen oder so was. Genau. Solche Sachen können ja da auch rauskommen. 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Also zum Beispiel Mikes Lieblingsbeispiel ist eine Visualisierung, wie ein Stau auf 'ner Straße entsteht. 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Also da gibt's Dinge, die kann ich nicht schnappen eigentlich. Genau, das ist das. Ne, als jemand, der genau. Ja. 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Oder aber wenn man eben selber sagt, selber sich ran setzt, dann wird's natürlich etwas schwieriger,", "words": [ { "word": "und", "start": 738.995, "end": 739.075, "confidence": 0.98291016, "punctuated_word": "und", "speaker": 1, "speaker_confidence": 0.42084724 }, { "word": "nicht", "start": 739.075, "end": 739.47504, "confidence": 0.44396973, "punctuated_word": "nicht.", "speaker": 1, "speaker_confidence": 0.42084724 }, { "word": "oder", "start": 739.47504, "end": 739.635, "confidence": 0.9970703, "punctuated_word": "Oder", "speaker": 1, "speaker_confidence": 0.42084724 }, { "word": "aber", "start": 739.635, "end": 739.875, "confidence": 0.9951172, "punctuated_word": "aber", "speaker": 1, "speaker_confidence": 0.42084724 }, { "word": "wenn", "start": 739.875, "end": 740.03503, "confidence": 0.6176758, "punctuated_word": "wenn", "speaker": 1, "speaker_confidence": 0.42084724 }, { "word": "man", "start": 740.03503, "end": 740.275, "confidence": 0.9980469, "punctuated_word": "man", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "eben", "start": 740.275, "end": 740.435, "confidence": 0.9589844, "punctuated_word": "eben", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "selber", "start": 740.435, "end": 740.835, "confidence": 0.984375, "punctuated_word": "selber", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "sagt", "start": 740.835, "end": 741.315, "confidence": 0.954834, "punctuated_word": "sagt,", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "selber", "start": 741.315, "end": 741.635, "confidence": 0.99609375, "punctuated_word": "selber", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "sich", "start": 741.635, "end": 741.79504, "confidence": 1.0, "punctuated_word": "sich", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "ran", "start": 741.79504, "end": 742.03503, "confidence": 0.9194336, "punctuated_word": "ran", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "setzt", "start": 742.03503, "end": 742.515, "confidence": 0.8483887, "punctuated_word": "setzt,", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "dann", "start": 742.515, "end": 742.755, "confidence": 0.97998047, "punctuated_word": "dann", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "wird's", "start": 742.755, "end": 742.995, "confidence": 0.9343262, "punctuated_word": "wird's", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "natürlich", "start": 742.995, "end": 743.315, "confidence": 0.9970703, "punctuated_word": "natürlich", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "etwas", "start": 743.315, "end": 743.715, "confidence": 1.0, "punctuated_word": "etwas", "speaker": 1, "speaker_confidence": 0.6817039 }, { "word": "schwieriger", "start": 743.715, "end": 744.215, "confidence": 0.99658203, "punctuated_word": "schwieriger,", "speaker": 1, "speaker_confidence": 0.6817039 } ], "speaker": 1, "id": "d84dcd98-7dfd-45df-921b-611665aa3174" }, { "start": 744.755, "end": 752.615, "confidence": 0.9487915, "channel": 0, "transcript": "dass man die richtigen Parameter auswählt. 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Ja. Da kommen wir gleich noch mal dazu, wie ihr das Ganze dann programmiert habt, da ja auch in 2 Varianten, ne? Einmal mit Genau. Und einmal ohne. Genau. Mike, Du wolltest noch was ergänzen, glaube ich. Ja, ich würd ganz gerne noch mal wieder zurück zu den Daten. Also es stimmt, es ist keine Simulation, die wir da gemacht haben, wo wir empirische Daten genommen haben. Das gibt's ja durchaus auch. 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Das war genau nicht unser Ansatz, sondern wir haben uns natürlich 'n paar Daten angeguckt, also so Durchdringung von EAD Funktion früher und heute und so was. 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Und das ist noch mal eine Besonderheit, die bei NetLogo halt auch ganz besonders leicht umzusetzen ist. Und dann geht es in der Tat da, also habt ihr ja schon gesagt, also dann erst mal festzustellen, welche Daten hab ich denn gar nicht? Welcher hätt ich denn gerne, diese Simulation noch mehr an die Realität dranzub bringen? Und das ist die große große Krux dabei und die große Chance, aus diesen Simulationen auch zu lernen. Ja. Man kann ja auch wirklich damit rumspielen, weil ihr das alles online stellt und man könnte mit diesen Dingen, die ihr da erarbeitet habt, weil Du grad gesagt hast, Mike irgendwie Zahlen rauf runterstellen und sowas. Das kann man wirklich machen, ne? 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Mhm. Und natürlich dann auch widersprechen an jedem Einkaufszentrum.", "words": [ { "word": "ki", "start": 892.67004, "end": 893.07, "confidence": 0.4375, "punctuated_word": "KI", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "tools", "start": 893.07, "end": 893.39, "confidence": 0.8378906, "punctuated_word": "Tools", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "oder", "start": 893.39, "end": 893.63, "confidence": 0.98828125, "punctuated_word": "oder", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "durch", "start": 893.63, "end": 894.13, "confidence": 0.9951172, "punctuated_word": "durch", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "entsprechende", "start": 894.43, "end": 894.59, "confidence": 0.98291016, "punctuated_word": "entsprechende", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "expertinnen", "start": 894.59, "end": 894.99005, "confidence": 1.0, "punctuated_word": "Expertinnen", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "und", "start": 894.99005, "end": 895.23004, "confidence": 1.0, "punctuated_word": "und", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "experten", "start": 895.23004, "end": 895.73004, "confidence": 0.9902344, "punctuated_word": "Experten", "speaker": 2, "speaker_confidence": 0.8518956 }, { "word": "und", "start": 896.03, "end": 896.19, "confidence": 0.76123047, "punctuated_word": "und", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "kann", "start": 896.19, "end": 896.43, "confidence": 0.9970703, "punctuated_word": "kann", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "dann", "start": 896.43, "end": 896.59, "confidence": 0.99316406, "punctuated_word": "dann", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "genau", "start": 896.59, "end": 896.83, "confidence": 0.9980469, "punctuated_word": "genau", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "sehen", "start": 896.83, "end": 897.15, "confidence": 0.99194336, "punctuated_word": "sehen,", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "welche", "start": 897.15, "end": 897.65, "confidence": 1.0, "punctuated_word": "welche", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "annahmen", "start": 897.95, "end": 898.27, "confidence": 1.0, "punctuated_word": "Annahmen", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "da", "start": 898.27, "end": 898.51, "confidence": 0.9902344, "punctuated_word": "da", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "drin", "start": 898.51, "end": 898.75, "confidence": 0.97998047, "punctuated_word": "drin", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "stecken", "start": 898.75, "end": 899.07, "confidence": 0.6201172, "punctuated_word": "stecken.", "speaker": 2, "speaker_confidence": 0.7145265 }, { "word": "mhm", "start": 899.07, "end": 899.23004, "confidence": 0.9724121, "punctuated_word": "Mhm.", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "und", "start": 899.23004, "end": 899.39, "confidence": 0.5258789, "punctuated_word": "Und", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "natürlich", "start": 899.39, "end": 899.63, "confidence": 0.984375, "punctuated_word": "natürlich", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "dann", "start": 899.63, "end": 899.87, "confidence": 0.9770508, "punctuated_word": "dann", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "auch", "start": 899.87, "end": 900.03, "confidence": 1.0, "punctuated_word": "auch", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "widersprechen", "start": 900.03, "end": 900.35004, "confidence": 0.9980469, "punctuated_word": "widersprechen", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "an", "start": 900.35004, "end": 900.59, "confidence": 0.92578125, "punctuated_word": "an", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "jedem", "start": 900.59, "end": 900.83, "confidence": 0.9863281, "punctuated_word": "jedem", "speaker": 2, "speaker_confidence": 0.7627119 }, { "word": "einkaufszentrum", "start": 900.83, "end": 901.33, "confidence": 0.58496094, "punctuated_word": "Einkaufszentrum.", "speaker": 2, "speaker_confidence": 0.7627119 } ], "speaker": 2, "id": "dec17c9c-fd75-4094-ad39-cd737c2bb32a" }, { "start": 902.19, "end": 908.425, "confidence": 0.97572726, "channel": 0, "transcript": "Okay, zu wie viel Prozent tritt das jetzt ein oder nicht, ne? Ist es eine exakte Vorhersage der Zukunft?", "words": [ { "word": "okay", "start": 902.19, "end": 902.59, "confidence": 0.7753906, "punctuated_word": "Okay,", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "zu", "start": 902.59, "end": 902.83, "confidence": 0.9970703, "punctuated_word": "zu", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "wie", "start": 902.83, "end": 903.07, "confidence": 0.96435547, "punctuated_word": "wie", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "viel", "start": 903.07, "end": 903.31, "confidence": 0.9980469, "punctuated_word": "viel", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "prozent", "start": 903.31, "end": 903.63, "confidence": 0.99902344, "punctuated_word": "Prozent", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "tritt", "start": 903.63, "end": 903.87, "confidence": 1.0, "punctuated_word": "tritt", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "das", "start": 903.87, "end": 904.11005, "confidence": 0.99902344, "punctuated_word": "das", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "jetzt", "start": 904.11005, "end": 904.51, "confidence": 1.0, "punctuated_word": "jetzt", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "ein", "start": 904.51, "end": 904.83, "confidence": 1.0, "punctuated_word": "ein", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "oder", "start": 904.83, "end": 905.15, "confidence": 0.9873047, "punctuated_word": "oder", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "nicht", "start": 905.15, "end": 905.65, "confidence": 0.9675293, "punctuated_word": "nicht,", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "ne", "start": 905.765, "end": 905.925, "confidence": 0.9069824, "punctuated_word": "ne?", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "ist", "start": 905.925, "end": 906.085, "confidence": 0.9892578, "punctuated_word": "Ist", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "es", "start": 906.085, "end": 906.325, "confidence": 0.97998047, "punctuated_word": "es", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "eine", "start": 906.325, "end": 906.725, "confidence": 0.9941406, "punctuated_word": "eine", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "exakte", "start": 906.725, "end": 907.205, "confidence": 1.0, "punctuated_word": "exakte", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "vorhersage", "start": 907.205, "end": 907.685, "confidence": 1.0, "punctuated_word": "Vorhersage", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "der", "start": 907.685, "end": 907.925, "confidence": 1.0, "punctuated_word": "der", "speaker": 0, "speaker_confidence": 0.80552757 }, { "word": "zukunft", "start": 907.925, "end": 908.425, "confidence": 0.9807129, "punctuated_word": "Zukunft?", "speaker": 0, "speaker_confidence": 0.80552757 } ], "speaker": 0, "id": "6bbe4803-38ba-4570-992c-05129b8ae1f2" }, { "start": 908.805, "end": 911.865, "confidence": 0.9769569, "channel": 0, "transcript": "Wie genau kommt es daran? Was spielen denn da für Faktoren eine Rolle?", "words": [ { "word": "wie", "start": 908.805, "end": 908.965, "confidence": 1.0, "punctuated_word": "Wie", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "genau", "start": 908.965, "end": 909.285, "confidence": 1.0, "punctuated_word": "genau", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "kommt", "start": 909.285, "end": 909.445, "confidence": 1.0, "punctuated_word": "kommt", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "es", "start": 909.445, "end": 909.605, "confidence": 0.94189453, "punctuated_word": "es", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "daran", "start": 909.605, "end": 909.845, "confidence": 0.9182129, "punctuated_word": "daran?", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "was", "start": 909.845, "end": 910.085, "confidence": 1.0, "punctuated_word": "Was", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "spielen", "start": 910.085, "end": 910.245, "confidence": 0.9951172, "punctuated_word": "spielen", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "denn", "start": 910.245, "end": 910.40497, "confidence": 0.9873047, "punctuated_word": "denn", "speaker": 0, "speaker_confidence": 0.63205355 }, { "word": "da", "start": 910.40497, "end": 910.565, "confidence": 0.9267578, "punctuated_word": "da", "speaker": 0, "speaker_confidence": 0.5669061 }, { "word": "für", "start": 910.565, "end": 910.805, "confidence": 0.9941406, "punctuated_word": "für", "speaker": 0, "speaker_confidence": 0.5669061 }, { "word": "faktoren", "start": 910.805, "end": 911.205, "confidence": 1.0, "punctuated_word": "Faktoren", "speaker": 0, "speaker_confidence": 0.5669061 }, { "word": "eine", "start": 911.205, "end": 911.365, "confidence": 0.9375, "punctuated_word": "eine", "speaker": 0, "speaker_confidence": 0.5669061 }, { "word": "rolle", "start": 911.365, "end": 911.865, "confidence": 0.9995117, "punctuated_word": "Rolle?", "speaker": 0, "speaker_confidence": 0.5669061 } ], "speaker": 0, "id": "e891060d-1edc-40d6-8560-5a0359f35944" }, { "start": 913.925, "end": 920.425, "confidence": 0.9390822, "channel": 0, "transcript": "Also ich glaub, da muss man noch 'n halben Schritt zurückgehen, Ja. Was für unterschiedliche Typen von Simulationen es gibt. Wie eben schon gesagt, das Wetterbeispiel", "words": [ { "word": "also", "start": 913.925, "end": 914.40497, "confidence": 0.9980469, "punctuated_word": "Also", "speaker": 2, "speaker_confidence": 0.52687985 }, { "word": "ich", "start": 914.40497, "end": 914.645, "confidence": 0.7939453, "punctuated_word": "ich", "speaker": 2, "speaker_confidence": 0.52687985 }, { "word": "glaub", "start": 914.645, "end": 914.885, "confidence": 0.9416504, "punctuated_word": "glaub,", "speaker": 2, "speaker_confidence": 0.52687985 }, { "word": "da", "start": 914.885, "end": 914.965, "confidence": 0.9863281, "punctuated_word": "da", "speaker": 2, "speaker_confidence": 0.52687985 }, { "word": "muss", "start": 914.965, "end": 915.045, "confidence": 1.0, "punctuated_word": "muss", "speaker": 2, "speaker_confidence": 0.52687985 }, { "word": "man", "start": 915.045, "end": 915.125, "confidence": 0.99316406, "punctuated_word": "man", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "noch", "start": 915.125, "end": 915.365, "confidence": 0.9741211, "punctuated_word": "noch", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "'n", "start": 915.365, "end": 915.445, "confidence": 0.8022461, "punctuated_word": "'n", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "halben", "start": 915.445, "end": 915.765, "confidence": 1.0, "punctuated_word": "halben", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "schritt", "start": 915.765, "end": 915.925, "confidence": 1.0, "punctuated_word": "Schritt", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "zurückgehen", "start": 915.925, "end": 916.08496, "confidence": 0.71984863, "punctuated_word": "zurückgehen,", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "ja", "start": 916.245, "end": 916.405, "confidence": 0.9885254, "punctuated_word": "Ja.", "speaker": 2, "speaker_confidence": 0.55683345 }, { "word": "was", "start": 916.565, "end": 916.805, "confidence": 0.83447266, "punctuated_word": "Was", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "für", "start": 916.805, "end": 917.045, "confidence": 0.9980469, "punctuated_word": "für", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "unterschiedliche", "start": 917.045, "end": 917.545, "confidence": 0.9980469, "punctuated_word": "unterschiedliche", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "typen", "start": 917.605, "end": 917.845, "confidence": 0.99902344, "punctuated_word": "Typen", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "von", "start": 917.845, "end": 918.005, "confidence": 1.0, "punctuated_word": "von", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "simulationen", "start": 918.005, "end": 918.485, "confidence": 0.9970703, "punctuated_word": "Simulationen", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "es", "start": 918.485, "end": 918.645, "confidence": 0.93408203, "punctuated_word": "es", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "gibt", "start": 918.645, "end": 918.885, "confidence": 0.9550781, "punctuated_word": "gibt.", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "wie", "start": 918.885, "end": 919.125, "confidence": 0.9838867, "punctuated_word": "Wie", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "eben", "start": 919.125, "end": 919.365, "confidence": 0.61865234, "punctuated_word": "eben", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "schon", "start": 919.365, "end": 919.445, "confidence": 0.9770508, "punctuated_word": "schon", "speaker": 2, "speaker_confidence": 0.7843421 }, { "word": "gesagt", "start": 919.445, "end": 919.685, "confidence": 0.92578125, "punctuated_word": "gesagt,", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "das", "start": 919.685, "end": 919.925, "confidence": 0.9970703, "punctuated_word": "das", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "wetterbeispiel", "start": 919.925, "end": 920.425, "confidence": 1.0, "punctuated_word": "Wetterbeispiel", "speaker": 2, "speaker_confidence": 0.94036156 } ], "speaker": 2, "id": "61be635a-d12d-45a8-81bc-dcc601c12ba8" }, { "start": 921.1, "end": 928.32, "confidence": 0.9890747, "channel": 0, "transcript": "oder auch solche Simulationen im Verkehrsbereich sind sehr nah daran, Vorhersagen treffen zu wollen. Mhm. Also Und auch zu können?", "words": [ { "word": "oder", "start": 921.1, "end": 921.34, "confidence": 1.0, "punctuated_word": "oder", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "auch", "start": 921.34, "end": 921.42, "confidence": 0.9980469, "punctuated_word": "auch", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "solche", "start": 921.42, "end": 921.82, "confidence": 1.0, "punctuated_word": "solche", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "simulationen", "start": 921.82, "end": 922.22003, "confidence": 0.99194336, "punctuated_word": "Simulationen", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "im", "start": 922.22003, "end": 922.46, "confidence": 0.99902344, "punctuated_word": "im", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "verkehrsbereich", "start": 922.46, "end": 922.96, "confidence": 0.9995117, "punctuated_word": "Verkehrsbereich", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "sind", "start": 923.18, "end": 923.34, "confidence": 0.98828125, "punctuated_word": "sind", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "sehr", "start": 923.34, "end": 923.66, "confidence": 1.0, "punctuated_word": "sehr", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "nah", "start": 923.66, "end": 923.9, "confidence": 0.90625, "punctuated_word": "nah", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "daran", "start": 923.9, "end": 924.38, "confidence": 0.97924805, "punctuated_word": "daran,", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "vorhersagen", "start": 924.38, "end": 924.88, "confidence": 0.99902344, "punctuated_word": "Vorhersagen", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "treffen", "start": 925.02, "end": 925.34, "confidence": 0.9980469, "punctuated_word": "treffen", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "zu", "start": 925.34, "end": 925.58, "confidence": 1.0, "punctuated_word": "zu", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "wollen", "start": 925.58, "end": 926.08, "confidence": 0.96850586, "punctuated_word": "wollen.", "speaker": 2, "speaker_confidence": 0.94036156 }, { "word": "mhm", "start": 926.22003, "end": 926.54, "confidence": 0.9916992, "punctuated_word": "Mhm.", "speaker": 2, "speaker_confidence": 0.33475947 }, { "word": "also", "start": 926.54, "end": 926.94, "confidence": 0.98828125, "punctuated_word": "Also", "speaker": 2, "speaker_confidence": 0.33475947 }, { "word": "und", "start": 927.26, "end": 927.42, "confidence": 0.9790039, "punctuated_word": "Und", "speaker": 0, "speaker_confidence": 0.53665525 }, { "word": "auch", "start": 927.42, "end": 927.58, "confidence": 0.99902344, "punctuated_word": "auch", "speaker": 0, "speaker_confidence": 0.53665525 }, { "word": "zu", "start": 927.58, "end": 927.82, "confidence": 1.0, "punctuated_word": "zu", "speaker": 0, "speaker_confidence": 0.53665525 }, { "word": "können", "start": 927.82, "end": 928.32, "confidence": 0.99560547, "punctuated_word": "können?", "speaker": 0, "speaker_confidence": 0.53665525 } ], "speaker": 2, "id": "dc2a86e5-059d-4e28-ac78-e7533ed52ab9" }, { "start": 928.7, "end": 934.605, "confidence": 0.98503035, "channel": 0, "transcript": "Ja, beim Wetter funktioniert's ganz gut, oder? Ja. Wird vor allem immer besser und eher auch kleinräumig, was früher", "words": [ { "word": "ja", "start": 928.7, "end": 928.94, "confidence": 0.9794922, "punctuated_word": "Ja,", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "beim", "start": 928.94, "end": 929.1, "confidence": 0.9770508, "punctuated_word": "beim", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "wetter", "start": 929.1, "end": 929.42, "confidence": 1.0, "punctuated_word": "Wetter", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "funktioniert's", "start": 929.42, "end": 929.82, "confidence": 0.9848633, "punctuated_word": "funktioniert's", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "ganz", "start": 929.82, "end": 930.06, "confidence": 1.0, "punctuated_word": "ganz", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "gut", "start": 930.06, "end": 930.22003, "confidence": 0.8623047, "punctuated_word": "gut,", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "oder", "start": 930.22003, "end": 930.62, "confidence": 0.9946289, "punctuated_word": "oder?", "speaker": 2, "speaker_confidence": 0.53312325 }, { "word": "ja", "start": 930.62, "end": 931.12, "confidence": 0.99609375, "punctuated_word": "Ja.", "speaker": 0, "speaker_confidence": 0.31597126 }, { "word": "wird", "start": 931.18, "end": 931.68, "confidence": 0.9560547, "punctuated_word": "Wird", "speaker": 0, "speaker_confidence": 0.31597126 }, { "word": "vor", "start": 931.74, "end": 931.9, "confidence": 0.99902344, "punctuated_word": "vor", "speaker": 2, "speaker_confidence": 0.58309764 }, { "word": "allem", "start": 931.9, "end": 932.14, "confidence": 1.0, "punctuated_word": "allem", "speaker": 2, "speaker_confidence": 0.58309764 }, { "word": "immer", "start": 932.14, "end": 932.46, "confidence": 0.9980469, "punctuated_word": "immer", "speaker": 2, "speaker_confidence": 0.58309764 }, { "word": "besser", "start": 932.46, "end": 932.78, "confidence": 0.9980469, "punctuated_word": "besser", "speaker": 2, "speaker_confidence": 0.58309764 }, { "word": "und", "start": 932.78, "end": 932.94, "confidence": 0.9980469, "punctuated_word": "und", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "eher", "start": 932.94, "end": 933.18, "confidence": 0.97998047, "punctuated_word": "eher", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "auch", "start": 933.18, "end": 933.34, "confidence": 0.9970703, "punctuated_word": "auch", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "kleinräumig", "start": 933.34, "end": 933.84, "confidence": 0.99487305, "punctuated_word": "kleinräumig,", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "was", "start": 933.9, "end": 934.14, "confidence": 1.0, "punctuated_word": "was", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "früher", "start": 934.14, "end": 934.605, "confidence": 1.0, "punctuated_word": "früher", "speaker": 2, "speaker_confidence": 0.7536847 } ], "speaker": 2, "id": "1fe6ef9d-3850-442c-a287-efcba56f6ffa" }, { "start": 935.08496, "end": 936.70496, "confidence": 0.7249552, "channel": 0, "transcript": "völlig illusogen ist. Regenradar.", "words": [ { "word": "völlig", "start": 935.08496, "end": 935.40497, "confidence": 0.86865234, "punctuated_word": "völlig", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "illusogen", "start": 935.40497, "end": 935.565, "confidence": 0.39444986, "punctuated_word": "illusogen", "speaker": 2, "speaker_confidence": 0.7536847 }, { "word": "ist", "start": 935.565, "end": 935.805, "confidence": 0.7348633, "punctuated_word": "ist.", "speaker": 0, "speaker_confidence": 0.48505324 }, { "word": "regenradar", "start": 936.20496, "end": 936.70496, "confidence": 0.90185547, "punctuated_word": "Regenradar.", "speaker": 0, "speaker_confidence": 0.48505324 } ], "speaker": 2, "id": "ec5d6d2b-8ffe-4fdb-afb7-e3423e7ae15f" }, { "start": 937.005, "end": 939.58496, "confidence": 0.9043457, "channel": 0, "transcript": "Ja. Nach meiner subjektiven Wahrnehmung.", "words": [ { "word": "ja", "start": 937.005, "end": 937.505, "confidence": 0.8371582, "punctuated_word": "Ja.", "speaker": 2, "speaker_confidence": 0.5936646 }, { "word": "nach", "start": 937.725, "end": 937.96497, "confidence": 0.7006836, "punctuated_word": "Nach", "speaker": 2, "speaker_confidence": 0.5936646 }, { "word": "meiner", "start": 937.96497, "end": 938.285, "confidence": 0.99902344, "punctuated_word": "meiner", "speaker": 2, "speaker_confidence": 0.5936646 }, { "word": "subjektiven", "start": 938.285, "end": 938.785, "confidence": 0.9921875, "punctuated_word": "subjektiven", "speaker": 2, "speaker_confidence": 0.5936646 }, { "word": "wahrnehmung", "start": 939.08496, "end": 939.58496, "confidence": 0.9926758, "punctuated_word": "Wahrnehmung.", "speaker": 2, "speaker_confidence": 0.5936646 } ], "speaker": 2, "id": "3fdbde69-5d17-4c3f-8bf3-4ee49ec63a47" }, { "start": 940.20496, "end": 941.985, "confidence": 0.9837123, "channel": 0, "transcript": "Wir haben da eher 'n anderen Simulationsansatz.", "words": [ { "word": "wir", "start": 940.20496, "end": 940.365, "confidence": 0.9790039, "punctuated_word": "Wir", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "haben", "start": 940.365, "end": 940.605, "confidence": 1.0, "punctuated_word": "haben", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "da", "start": 940.605, "end": 940.76495, "confidence": 1.0, "punctuated_word": "da", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "eher", "start": 940.76495, "end": 940.925, "confidence": 1.0, "punctuated_word": "eher", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "'n", "start": 940.925, "end": 941.08496, "confidence": 0.9797363, "punctuated_word": "'n", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "anderen", "start": 941.08496, "end": 941.485, "confidence": 0.9941406, "punctuated_word": "anderen", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "simulationsansatz", "start": 941.485, "end": 941.985, "confidence": 0.93310547, "punctuated_word": "Simulationsansatz.", "speaker": 2, "speaker_confidence": 0.8711418 } ], "speaker": 2, "id": "a451aa4d-35fc-48dc-992c-3322cada58df" }, { "start": 943.08496, "end": 949.10004, "confidence": 0.96605086, "channel": 0, "transcript": "Also eher, der in Richtung Verstehen geht. Also wir wollen verstehen, wenn wir genau im Sinne der Wirkungsorientierung,", "words": [ { "word": "also", "start": 943.08496, "end": 943.40497, "confidence": 1.0, "punctuated_word": "Also", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "eher", "start": 943.40497, "end": 943.725, "confidence": 0.9621582, "punctuated_word": "eher,", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "der", "start": 943.725, "end": 943.96497, "confidence": 1.0, "punctuated_word": "der", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "in", "start": 943.96497, "end": 944.285, "confidence": 0.9980469, "punctuated_word": "in", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "richtung", "start": 944.285, "end": 944.685, "confidence": 1.0, "punctuated_word": "Richtung", "speaker": 2, "speaker_confidence": 0.8711418 }, { "word": "verstehen", "start": 944.685, "end": 945.165, "confidence": 0.9140625, "punctuated_word": "Verstehen", "speaker": 2, "speaker_confidence": 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Hätten wir so gar nicht abschätzen können. Bei 1 Berechnung haben wir sehr häufig nachgeguckt, ob's wirklich richtig ist. 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Je mehr drüber geredet wurde sozusagen. 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Mhm. 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Also wenn ich jetzt sage, ich hab jetzt nicht Zeit, mich 2 Wochen fortzubilden, die IT Abteilung ist auch nicht hilfreich,", "words": [ { "word": "hoch", "start": 1291.92, "end": 1292.16, "confidence": 0.99902344, "punctuated_word": "hoch", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "oder", "start": 1292.16, "end": 1292.4, "confidence": 0.91748047, "punctuated_word": "oder", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "niedrigschwellig", "start": 1292.4, "end": 1292.9, "confidence": 0.99902344, "punctuated_word": "niedrigschwellig", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "ist", "start": 1293.2, "end": 1293.36, "confidence": 0.99902344, "punctuated_word": "ist", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "das", "start": 1293.36, "end": 1293.76, "confidence": 0.66186523, "punctuated_word": "das,", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "würdet", "start": 1293.76, "end": 1294.08, "confidence": 0.9741211, "punctuated_word": "würdet", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "ihr", "start": 1294.08, "end": 1294.16, "confidence": 0.9301758, "punctuated_word": "ihr", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "sagen", "start": 1294.16, "end": 1294.48, "confidence": 0.8684082, "punctuated_word": "sagen?", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "also", "start": 1294.48, "end": 1294.64, "confidence": 0.9980469, "punctuated_word": "Also", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "wenn", "start": 1294.64, "end": 1294.88, "confidence": 0.8959961, "punctuated_word": "wenn", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "ich", "start": 1294.88, "end": 1294.96, "confidence": 0.99902344, "punctuated_word": "ich", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "jetzt", "start": 1294.96, "end": 1295.2, "confidence": 0.9902344, "punctuated_word": "jetzt", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "sage", "start": 1295.2, "end": 1295.44, "confidence": 0.70532227, "punctuated_word": "sage,", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "ich", "start": 1295.44, "end": 1295.6, "confidence": 0.99902344, "punctuated_word": "ich", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "hab", "start": 1295.6, "end": 1295.76, "confidence": 0.75097656, "punctuated_word": "hab", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "jetzt", "start": 1295.76, "end": 1296.08, "confidence": 0.9970703, "punctuated_word": "jetzt", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "nicht", "start": 1296.08, "end": 1296.435, "confidence": 0.99902344, "punctuated_word": "nicht", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "zeit", "start": 1296.755, "end": 1297.0751, "confidence": 0.79589844, "punctuated_word": "Zeit,", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "mich", "start": 1297.0751, "end": 1297.3151, "confidence": 1.0, "punctuated_word": "mich", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "2", "start": 1297.3151, "end": 1297.4751, "confidence": 0.99902344, "punctuated_word": "2", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "wochen", "start": 1297.4751, "end": 1297.7151, "confidence": 1.0, "punctuated_word": "Wochen", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "fortzubilden", "start": 1297.7151, "end": 1298.2151, "confidence": 0.8723958, "punctuated_word": "fortzubilden,", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "die", "start": 1298.435, "end": 1298.675, "confidence": 0.99902344, "punctuated_word": "die", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "it", "start": 1298.675, "end": 1298.9951, "confidence": 1.0, "punctuated_word": "IT", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "abteilung", "start": 1298.9951, "end": 1299.3151, "confidence": 0.9980469, "punctuated_word": "Abteilung", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "ist", "start": 1299.3151, "end": 1299.4751, "confidence": 0.9980469, "punctuated_word": "ist", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "auch", "start": 1299.4751, "end": 1299.555, "confidence": 1.0, "punctuated_word": "auch", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "nicht", "start": 1299.555, "end": 1299.795, "confidence": 1.0, "punctuated_word": "nicht", "speaker": 0, "speaker_confidence": 0.8387971 }, { "word": "hilfreich", "start": 1299.795, "end": 1300.295, "confidence": 0.75097656, "punctuated_word": "hilfreich,", "speaker": 0, "speaker_confidence": 0.8387971 } ], "speaker": 0, "id": "c28af7f0-aa76-471d-8ee2-c6acea96d5e8" }, { "start": 1300.755, "end": 1308.4551, "confidence": 0.97755015, "channel": 0, "transcript": "ich bin total auf mich alleine gestellt und hab eher so Erfahrungen mit MS Office Paketen. Wie schnell finde ich deinen Zugang?", "words": [ { "word": "ich", "start": 1300.755, "end": 1300.915, "confidence": 0.9970703, "punctuated_word": "ich", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "bin", "start": 1300.915, "end": 1301.155, "confidence": 1.0, "punctuated_word": "bin", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "total", "start": 1301.155, "end": 1301.395, "confidence": 1.0, "punctuated_word": "total", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "auf", "start": 1301.395, "end": 1301.555, "confidence": 0.99902344, "punctuated_word": "auf", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "mich", "start": 1301.555, "end": 1301.7151, "confidence": 1.0, "punctuated_word": "mich", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "alleine", "start": 1301.7151, "end": 1302.1151, "confidence": 0.9902344, "punctuated_word": "alleine", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "gestellt", "start": 1302.1151, "end": 1302.6151, "confidence": 0.9970703, "punctuated_word": "gestellt", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "und", "start": 1302.675, "end": 1303.175, "confidence": 0.9921875, "punctuated_word": "und", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "hab", "start": 1303.2351, "end": 1303.4751, "confidence": 0.9194336, "punctuated_word": "hab", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "eher", "start": 1303.4751, "end": 1303.7151, "confidence": 0.9921875, "punctuated_word": "eher", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "so", "start": 1303.7151, "end": 1303.9551, "confidence": 0.9980469, "punctuated_word": "so", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "erfahrungen", "start": 1303.9551, "end": 1304.4551, "confidence": 0.99902344, "punctuated_word": "Erfahrungen", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "mit", "start": 1304.675, "end": 1305.175, "confidence": 0.9970703, "punctuated_word": "mit", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "ms", "start": 1305.395, "end": 1305.635, "confidence": 0.9980469, "punctuated_word": "MS", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "office", "start": 1305.635, "end": 1306.1151, "confidence": 0.9790039, "punctuated_word": "Office", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "paketen", "start": 1306.1151, "end": 1306.6151, "confidence": 0.8737793, "punctuated_word": "Paketen.", "speaker": 0, "speaker_confidence": 0.866554 }, { "word": "wie", "start": 1306.8351, "end": 1307.155, "confidence": 0.99902344, "punctuated_word": "Wie", "speaker": 0, "speaker_confidence": 0.57942075 }, { "word": "schnell", "start": 1307.155, "end": 1307.4751, "confidence": 1.0, "punctuated_word": "schnell", "speaker": 0, "speaker_confidence": 0.57942075 }, { "word": "finde", "start": 1307.4751, "end": 1307.555, "confidence": 0.92822266, "punctuated_word": "finde", "speaker": 0, "speaker_confidence": 0.57942075 }, { "word": "ich", "start": 1307.555, "end": 1307.7151, "confidence": 1.0, "punctuated_word": "ich", "speaker": 0, "speaker_confidence": 0.57942075 }, { "word": "deinen", "start": 1307.7151, "end": 1307.9551, "confidence": 0.8491211, "punctuated_word": "deinen", "speaker": 0, "speaker_confidence": 0.57942075 }, { "word": "zugang", "start": 1307.9551, "end": 1308.4551, "confidence": 0.9975586, "punctuated_word": "Zugang?", "speaker": 0, "speaker_confidence": 0.57942075 } ], "speaker": 0, "id": "4ee55023-5035-4a68-91aa-2d99618b7abb" }, { "start": 1309.39, "end": 1311.65, "confidence": 0.90231323, "channel": 0, "transcript": "Müsste Maike jetzt 'n bisschen antworten. 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Wie soll ich damit umgehen?", "words": [ { "word": "wie", "start": 1334.435, "end": 1334.675, "confidence": 1.0, "punctuated_word": "Wie", "speaker": 1, "speaker_confidence": 0.8160733 }, { "word": "blickt", "start": 1334.675, "end": 1334.995, "confidence": 0.9921875, "punctuated_word": "blickt", "speaker": 1, "speaker_confidence": 0.8160733 }, { "word": "die", "start": 1334.995, "end": 1335.155, "confidence": 1.0, "punctuated_word": "die", "speaker": 1, "speaker_confidence": 0.8160733 }, { "word": "auf", "start": 1335.155, "end": 1335.315, "confidence": 1.0, "punctuated_word": "auf", "speaker": 1, "speaker_confidence": 0.8160733 }, { "word": "die", "start": 1335.315, "end": 1335.475, "confidence": 1.0, "punctuated_word": "die", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "welt", "start": 1335.475, "end": 1335.715, "confidence": 1.0, "punctuated_word": "Welt", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "sozusagen", "start": 1335.715, "end": 1336.215, "confidence": 0.9675293, "punctuated_word": "sozusagen?", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "wie", "start": 1336.275, "end": 1336.515, "confidence": 1.0, "punctuated_word": "Wie", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "soll", "start": 1336.515, "end": 1336.675, "confidence": 1.0, "punctuated_word": "soll", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "ich", "start": 1336.675, "end": 1336.835, "confidence": 1.0, "punctuated_word": "ich", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "damit", "start": 1336.835, "end": 1337.235, "confidence": 1.0, "punctuated_word": "damit", "speaker": 1, "speaker_confidence": 0.6934783 }, { "word": "umgehen", "start": 1337.235, "end": 1337.735, "confidence": 0.99658203, "punctuated_word": "umgehen?", "speaker": 1, "speaker_confidence": 0.6934783 } ], "speaker": 1, "id": "955819fe-7b1b-43bb-b674-ad785410c6e2" }, { "start": 1338.25, "end": 1348.4299, "confidence": 0.8579224, "channel": 0, "transcript": "Und dann, wenn man das mal so verstanden hat, also zum Beispiel in diesem Fall eben auch die Agenten. 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Über die wir gerade gesprochen haben, kennen die üblichen Chat Bots sozusagen. Also das heißt, die sprechen miteinander auf eine Art, ne? Also man hat wie son Dolmetscher. 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Das wird mir die KI machen. Mhm. Okay. 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Mhm. Dass man also schon gleich eine neue Funktion hat, über die ich ja noch gar nicht nachgedacht hab in meiner Simulation. In meiner Simulation wollte ich erst mal die Simulation laufen lassen und wollte mir hinterher Gedanken machen, wie ich das denn darstellen kann.", "words": [ { "word": "von", "start": 1698.52, "end": 1698.68, "confidence": 0.9951172, "punctuated_word": "von", "speaker": 1, "speaker_confidence": 0.7136496 }, { "word": "dieser", "start": 1698.68, "end": 1699.0801, "confidence": 1.0, "punctuated_word": "dieser", "speaker": 1, "speaker_confidence": 0.7136496 }, { "word": "einstellung", "start": 1699.0801, "end": 1699.5801, "confidence": 0.9902344, "punctuated_word": "Einstellung", "speaker": 1, "speaker_confidence": 0.7136496 }, { "word": "abhängig", "start": 1699.64, "end": 1700.12, "confidence": 1.0, "punctuated_word": "abhängig", "speaker": 1, "speaker_confidence": 0.7136496 }, { "word": "gemacht", "start": 1700.12, "end": 1700.4401, "confidence": 0.9980469, "punctuated_word": "gemacht", "speaker": 1, "speaker_confidence": 0.7136496 }, { "word": "wird", "start": 1700.4401, "end": 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Also hat 'n Männchensymbol für Bürger ausgewählt.", "words": [ { "word": "und", "start": 1714.005, "end": 1714.325, "confidence": 0.9980469, "punctuated_word": "Und", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "die", "start": 1714.325, "end": 1714.485, "confidence": 0.99902344, "punctuated_word": "die", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "ki", "start": 1714.485, "end": 1714.8049, "confidence": 0.8984375, "punctuated_word": "KI", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "hat", "start": 1714.8049, "end": 1715.045, "confidence": 0.85595703, "punctuated_word": "hat", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "aber", "start": 1715.045, "end": 1715.365, "confidence": 0.9980469, "punctuated_word": "aber", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "gleich", "start": 1715.365, "end": 1715.865, "confidence": 1.0, "punctuated_word": "gleich", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "dieses", "start": 1715.9249, "end": 1716.165, "confidence": 0.99609375, "punctuated_word": "dieses", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "farbschema", "start": 1716.165, "end": 1716.665, "confidence": 0.99609375, "punctuated_word": "Farbschema", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "auf", "start": 1716.8049, "end": 1717.045, "confidence": 0.99609375, "punctuated_word": "auf", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "son", "start": 1717.045, "end": 1717.205, "confidence": 0.7036133, "punctuated_word": "son", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "kleines", "start": 1717.205, "end": 1717.525, "confidence": 0.99316406, "punctuated_word": "kleines", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "männchen", "start": 1717.525, "end": 1717.9249, "confidence": 1.0, "punctuated_word": "Männchen", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "übertragen", "start": 1717.9249, "end": 1718.405, "confidence": 0.7734375, "punctuated_word": "übertragen.", "speaker": 1, "speaker_confidence": 0.9463032 }, { "word": "also", "start": 1718.405, "end": 1718.565, "confidence": 0.99902344, "punctuated_word": "Also", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "hat", "start": 1718.565, "end": 1718.725, "confidence": 0.97314453, "punctuated_word": "hat", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "'n", "start": 1718.725, "end": 1718.885, "confidence": 0.8166504, "punctuated_word": "'n", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "männchensymbol", "start": 1718.885, "end": 1719.385, "confidence": 0.91381836, "punctuated_word": "Männchensymbol", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "für", "start": 1719.605, "end": 1719.9249, "confidence": 0.9970703, "punctuated_word": "für", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "bürger", "start": 1719.9249, "end": 1720.245, "confidence": 0.65966797, "punctuated_word": "Bürger", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "ausgewählt", "start": 1720.245, "end": 1720.745, "confidence": 0.81640625, "punctuated_word": "ausgewählt.", "speaker": 1, "speaker_confidence": 0.656674 } ], "speaker": 1, "id": "152eb24f-f2ab-4861-a1fe-5dddd365e574" }, { "start": 1721.49, "end": 1729.11, "confidence": 0.6910659, "channel": 0, "transcript": "Und hat dann dem Männchen noch eine Farbe gegeben Also das ist ja zum Beispiel grün, wenn's voll heiß ist auf die Erde. Genau. Und rot, wenn's total Genau. Spannend ist. Mhm. Okay.", "words": [ { "word": "und", "start": 1721.49, "end": 1721.65, "confidence": 0.8276367, "punctuated_word": "Und", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "hat", "start": 1721.65, "end": 1721.89, "confidence": 0.9658203, "punctuated_word": "hat", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "dann", "start": 1721.89, "end": 1722.13, "confidence": 0.9770508, "punctuated_word": "dann", "speaker": 1, "speaker_confidence": 0.656674 }, { "word": "dem", "start": 1722.13, "end": 1722.53, "confidence": 0.4116211, "punctuated_word": "dem", "speaker": 1, "speaker_confidence": 0.45889056 }, { "word": "männchen", "start": 1722.53, "end": 1722.85, "confidence": 0.90283203, "punctuated_word": "Männchen", "speaker": 1, "speaker_confidence": 0.45889056 }, { "word": "noch", "start": 1722.85, "end": 1723.01, "confidence": 0.8911133, "punctuated_word": "noch", "speaker": 1, "speaker_confidence": 0.45889056 }, { "word": "eine", "start": 1723.01, "end": 1723.25, 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Aber die anderen Fälle, die es da auch noch gibt,", "words": [ { "word": "was", "start": 1781.5901, "end": 1781.75, "confidence": 0.99902344, "punctuated_word": "was", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "dann", "start": 1781.75, "end": 1781.99, "confidence": 0.7441406, "punctuated_word": "dann", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "passieren", "start": 1781.99, "end": 1782.47, "confidence": 1.0, "punctuated_word": "passieren", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "soll", "start": 1782.47, "end": 1782.97, "confidence": 0.8703613, "punctuated_word": "soll,", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "hab", "start": 1783.11, "end": 1783.35, "confidence": 0.85595703, "punctuated_word": "hab", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "ich", "start": 1783.35, "end": 1783.51, "confidence": 0.9980469, "punctuated_word": "ich", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "natürlich", "start": 1783.51, "end": 1783.75, "confidence": 0.99902344, "punctuated_word": "natürlich", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "ganz", "start": 1783.75, "end": 1784.0701, "confidence": 1.0, "punctuated_word": "ganz", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "vorne", "start": 1784.0701, "end": 1784.31, "confidence": 0.99902344, "punctuated_word": "vorne", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "was", "start": 1784.31, "end": 1784.47, "confidence": 0.9892578, "punctuated_word": "was", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "im", "start": 1784.47, "end": 1784.7101, "confidence": 0.9951172, "punctuated_word": "im", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "kopf", "start": 1784.7101, "end": 1784.9501, "confidence": 0.95874023, "punctuated_word": "Kopf,", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "was", "start": 1784.9501, "end": 1785.11, "confidence": 0.9980469, "punctuated_word": "was", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "dann", "start": 1785.11, "end": 1785.35, "confidence": 0.9941406, "punctuated_word": "dann", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "passieren", "start": 1785.35, "end": 1785.67, "confidence": 1.0, "punctuated_word": "passieren", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "soll", "start": 1785.67, "end": 1785.8301, "confidence": 0.9902344, "punctuated_word": "soll", "speaker": 1, "speaker_confidence": 0.80897284 }, { "word": "und", "start": 1785.8301, "end": 1785.91, "confidence": 0.41870117, "punctuated_word": "und", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "das", "start": 1785.91, "end": 1786.15, "confidence": 0.99121094, "punctuated_word": "das", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "beschreibe", "start": 1786.15, "end": 1786.47, "confidence": 0.97802734, "punctuated_word": "beschreibe", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "ich", "start": 1786.47, "end": 1786.785, "confidence": 0.9741211, "punctuated_word": "ich.", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "aber", "start": 1787.025, "end": 1787.265, "confidence": 0.99902344, "punctuated_word": "Aber", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "die", "start": 1787.265, "end": 1787.425, "confidence": 1.0, "punctuated_word": "die", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "anderen", "start": 1787.425, "end": 1787.8251, "confidence": 0.9951172, "punctuated_word": "anderen", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "fälle", "start": 1787.8251, "end": 1788.145, "confidence": 0.9975586, "punctuated_word": "Fälle,", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "die", "start": 1788.145, "end": 1788.305, "confidence": 0.99902344, "punctuated_word": "die", "speaker": 1, "speaker_confidence": 0.6627044 }, { "word": "es", "start": 1788.305, "end": 1788.4651, "confidence": 0.95166016, "punctuated_word": "es", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "da", "start": 1788.4651, "end": 1788.625, "confidence": 0.9970703, "punctuated_word": "da", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "auch", "start": 1788.625, "end": 1788.785, "confidence": 0.99902344, "punctuated_word": "auch", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "noch", "start": 1788.785, "end": 1789.025, "confidence": 0.99609375, "punctuated_word": "noch", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "gibt", "start": 1789.025, "end": 1789.525, "confidence": 0.9890137, "punctuated_word": "gibt,", "speaker": 1, "speaker_confidence": 0.5793025 } ], "speaker": 1, "id": "ade32036-2977-4ef5-8d57-43d9a540bb7b" }, { "start": 1789.8251, "end": 1799.285, "confidence": 0.9734644, "channel": 0, "transcript": "die beschreibe ich jetzt vielleicht gar nicht. Aber wenn das technisch umgesetzt wird, dann muss ja eine Entscheidung getroffen werden, ne? Oder es wird eben bewusst keine Entscheidung getroffen,", "words": [ { "word": "die", "start": 1789.8251, "end": 1790.145, "confidence": 0.9980469, "punctuated_word": "die", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "beschreibe", "start": 1790.145, "end": 1790.385, "confidence": 0.9863281, "punctuated_word": "beschreibe", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "ich", "start": 1790.385, "end": 1790.4651, "confidence": 0.99902344, "punctuated_word": "ich", "speaker": 1, "speaker_confidence": 0.5793025 }, { "word": "jetzt", "start": 1790.4651, "end": 1790.785, "confidence": 0.9980469, "punctuated_word": "jetzt", "speaker": 1, "speaker_confidence": 0.5889076 }, { "word": "vielleicht", "start": 1790.785, "end": 1791.025, "confidence": 1.0, "punctuated_word": "vielleicht", "speaker": 1, "speaker_confidence": 0.5889076 }, { "word": "gar", "start": 1791.025, "end": 1791.265, "confidence": 0.99902344, "punctuated_word": "gar", "speaker": 1, "speaker_confidence": 0.5889076 }, { "word": "nicht", "start": 1791.265, "end": 1791.765, "confidence": 0.98583984, "punctuated_word": "nicht.", "speaker": 1, "speaker_confidence": 0.5889076 }, { "word": "aber", "start": 1791.985, "end": 1792.305, "confidence": 1.0, "punctuated_word": "Aber", "speaker": 1, "speaker_confidence": 0.5889076 }, { "word": "wenn", "start": 1792.305, "end": 1792.385, "confidence": 0.9951172, "punctuated_word": "wenn", "speaker": 1, "speaker_confidence": 0.5889076 }, { "word": "das", "start": 1792.385, "end": 1792.7051, "confidence": 0.9951172, "punctuated_word": "das", "speaker": 1, "speaker_confidence": 0.6246691 }, { "word": "technisch", "start": 1792.7051, "end": 1793.025, "confidence": 1.0, "punctuated_word": "technisch", "speaker": 1, "speaker_confidence": 0.6246691 }, { "word": "umgesetzt", "start": 1793.025, "end": 1793.525, "confidence": 1.0, "punctuated_word": "umgesetzt", "speaker": 1, "speaker_confidence": 0.6246691 }, { "word": "wird", "start": 1793.5851, "end": 1793.8251, "confidence": 0.9995117, "punctuated_word": "wird,", "speaker": 1, "speaker_confidence": 0.6246691 }, { "word": "dann", "start": 1793.8251, "end": 1794.0651, "confidence": 0.99902344, "punctuated_word": "dann", "speaker": 1, "speaker_confidence": 0.6246691 }, { "word": "muss", "start": 1794.0651, "end": 1794.305, "confidence": 1.0, "punctuated_word": "muss", "speaker": 1, "speaker_confidence": 0.6246691 }, { "word": "ja", "start": 1794.305, "end": 1794.4651, "confidence": 0.81152344, "punctuated_word": "ja", "speaker": 1, "speaker_confidence": 0.6159859 }, { "word": "eine", "start": 1794.4651, "end": 1794.625, "confidence": 0.9550781, "punctuated_word": "eine", "speaker": 1, "speaker_confidence": 0.6159859 }, { "word": "entscheidung", "start": 1794.625, "end": 1795.105, "confidence": 1.0, "punctuated_word": "Entscheidung", "speaker": 1, "speaker_confidence": 0.6159859 }, { "word": "getroffen", "start": 1795.105, "end": 1795.505, "confidence": 1.0, "punctuated_word": "getroffen", "speaker": 1, "speaker_confidence": 0.6159859 }, { "word": "werden", "start": 1795.505, "end": 1796.005, "confidence": 0.87158203, "punctuated_word": "werden,", "speaker": 1, "speaker_confidence": 0.6159859 }, { "word": "ne", "start": 1796.145, "end": 1796.4651, "confidence": 0.81933594, "punctuated_word": "ne?", "speaker": 1, "speaker_confidence": 0.6159859 }, { "word": "oder", "start": 1796.4651, "end": 1796.7051, "confidence": 1.0, "punctuated_word": "Oder", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "es", "start": 1796.7051, "end": 1796.865, "confidence": 0.9970703, "punctuated_word": "es", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "wird", "start": 1796.865, "end": 1797.105, "confidence": 0.9980469, "punctuated_word": "wird", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "eben", "start": 1797.105, "end": 1797.605, "confidence": 0.99902344, "punctuated_word": "eben", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "bewusst", "start": 1797.745, "end": 1797.905, "confidence": 0.9941406, "punctuated_word": "bewusst", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "keine", "start": 1797.905, "end": 1798.225, "confidence": 1.0, "punctuated_word": "keine", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "entscheidung", "start": 1798.225, "end": 1798.725, "confidence": 0.99316406, "punctuated_word": "Entscheidung", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "getroffen", "start": 1798.785, "end": 1799.285, "confidence": 0.8364258, "punctuated_word": "getroffen,", "speaker": 1, "speaker_confidence": 0.82481915 } ], "speaker": 1, "id": "1c07a9c0-5ee0-481d-8ddb-e07acbbf9d13" }, { "start": 1800.28, "end": 1803.8201, "confidence": 0.9861816, "channel": 0, "transcript": "was aber aus technischer Sicht wieder eine Entscheidung ist. Und", "words": [ { "word": "was", "start": 1800.28, "end": 1800.6, "confidence": 0.95947266, "punctuated_word": "was", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "aber", "start": 1800.6, "end": 1800.84, "confidence": 0.9980469, "punctuated_word": "aber", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "aus", "start": 1800.84, "end": 1801.08, "confidence": 0.99316406, "punctuated_word": "aus", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "technischer", "start": 1801.08, "end": 1801.4, "confidence": 1.0, "punctuated_word": "technischer", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "sicht", "start": 1801.4, "end": 1801.72, "confidence": 1.0, "punctuated_word": "Sicht", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "wieder", "start": 1801.72, "end": 1801.96, "confidence": 0.9970703, "punctuated_word": "wieder", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "eine", "start": 1801.96, "end": 1802.2001, "confidence": 0.9658203, "punctuated_word": "eine", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "entscheidung", "start": 1802.2001, "end": 1802.6, "confidence": 1.0, "punctuated_word": "Entscheidung", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "ist", "start": 1802.6, "end": 1803.1, "confidence": 0.94921875, "punctuated_word": "ist.", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "und", "start": 1803.3201, "end": 1803.8201, "confidence": 0.99902344, "punctuated_word": "Und", "speaker": 1, "speaker_confidence": 0.82481915 } ], "speaker": 1, "id": "33af08d1-9089-4df3-969e-cf28f015c8a3" }, { "start": 1804.68, "end": 1806.06, "confidence": 0.984375, "channel": 0, "transcript": "dann sehe ich ja", "words": [ { "word": "dann", "start": 1804.68, "end": 1805.08, "confidence": 0.9980469, "punctuated_word": "dann", "speaker": 1, "speaker_confidence": 0.82481915 }, { "word": "sehe", "start": 1805.08, "end": 1805.3201, "confidence": 0.9404297, "punctuated_word": "sehe", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "ich", "start": 1805.3201, "end": 1805.56, "confidence": 1.0, "punctuated_word": "ich", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "ja", "start": 1805.56, "end": 1806.06, "confidence": 0.99902344, "punctuated_word": "ja", "speaker": 1, "speaker_confidence": 0.8144393 } ], "speaker": 1, "id": "568f2e3b-68af-4774-8229-447450cb67d3" }, { "start": 1806.52, "end": 1810.54, "confidence": 0.95389444, "channel": 0, "transcript": "plötzlich auch, was in meiner Beschreibung fehlt. Ob ich unpräzise bin,", "words": [ { "word": "plötzlich", "start": 1806.52, "end": 1807.02, "confidence": 0.9902344, "punctuated_word": "plötzlich", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "auch", "start": 1807.16, "end": 1807.4, "confidence": 0.7302246, "punctuated_word": "auch,", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "was", "start": 1807.4, "end": 1807.56, "confidence": 1.0, "punctuated_word": "was", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "in", "start": 1807.56, "end": 1807.72, "confidence": 1.0, "punctuated_word": "in", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "meiner", "start": 1807.72, "end": 1807.96, "confidence": 1.0, "punctuated_word": "meiner", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "beschreibung", "start": 1807.96, "end": 1808.46, "confidence": 0.9980469, "punctuated_word": "Beschreibung", "speaker": 1, "speaker_confidence": 0.8144393 }, { "word": "fehlt", "start": 1808.52, 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Also alles ist veröffentlicht. Man kann direkt loslegen, Mike.", "words": [ { "word": "gut", "start": 1819.005, "end": 1819.405, "confidence": 0.50219727, "punctuated_word": "Gut.", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "also", "start": 1819.405, "end": 1819.645, "confidence": 0.9980469, "punctuated_word": "Also", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "alles", "start": 1819.645, "end": 1819.8049, "confidence": 0.9941406, "punctuated_word": "alles", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "ist", "start": 1819.8049, "end": 1819.965, "confidence": 0.9819336, "punctuated_word": "ist", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "veröffentlicht", "start": 1819.965, "end": 1820.465, "confidence": 0.8635254, "punctuated_word": "veröffentlicht.", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "man", "start": 1820.525, "end": 1820.6849, "confidence": 1.0, "punctuated_word": "Man", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "kann", "start": 1820.6849, "end": 1820.9249, "confidence": 1.0, "punctuated_word": "kann", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "direkt", "start": 1820.9249, "end": 1821.165, "confidence": 1.0, "punctuated_word": "direkt", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "loslegen", "start": 1821.165, "end": 1821.645, "confidence": 0.7507324, "punctuated_word": "loslegen,", "speaker": 0, "speaker_confidence": 0.7074945 }, { "word": "mike", "start": 1821.645, "end": 1822.145, "confidence": 0.7236328, "punctuated_word": "Mike.", "speaker": 0, "speaker_confidence": 0.7074945 } ], "speaker": 0, "id": "09a78324-13b4-44d0-8725-0e30aa312e37" }, { "start": 1823.565, "end": 1841.0449, "confidence": 0.9410308, "channel": 0, "transcript": "Ein Aspekt, der noch ganz wichtig ist, die Syntax wird abgenommen von der KI und die KI kann die richtige Syntax, das ist Gold wert. Okay. Auch so Kleinigkeiten, aber genau da auf dem Punkt wollt ich noch mal eingehen. 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"460eaddd-1dd4-45af-977f-938954d6b7c7" }, { "start": 1867.155, "end": 1868.375, "confidence": 0.73876953, "channel": 0, "transcript": "Low hanging fruits?", "words": [ { "word": "low", "start": 1867.155, "end": 1867.475, "confidence": 0.5678711, "punctuated_word": "Low", "speaker": 0, "speaker_confidence": 0.5937486 }, { "word": "hanging", "start": 1867.475, "end": 1867.875, "confidence": 0.81884766, "punctuated_word": "hanging", "speaker": 0, "speaker_confidence": 0.5937486 }, { "word": "fruits", "start": 1867.875, "end": 1868.375, "confidence": 0.82958984, "punctuated_word": "fruits?", "speaker": 0, "speaker_confidence": 0.5937486 } ], "speaker": 0, "id": "cdcdd753-3b67-49b5-b576-abfcb42e0d79" }, { "start": 1868.675, "end": 1871.5751, "confidence": 0.961792, "channel": 0, "transcript": "Wer wer sollte jetzt sofort sich euer Beispiel", "words": [ { "word": "wer", "start": 1868.675, "end": 1868.915, "confidence": 0.9951172, "punctuated_word": "Wer", "speaker": 0, "speaker_confidence": 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Swan City hatten wir ja schon, da ist halt Mhm. Das der enorme Vorteil, dass die Daten oder sehr, sehr viele Daten halt einfach schon da sind. Das ist natürlich immer Gold wert, darauf Simulationen aufzusetzen. Aber in dem Bereich passiert ja auch schon 'n bisschen was.", "words": [ { "word": "ich", "start": 1874.995, "end": 1875.495, "confidence": 1.0, "punctuated_word": "Ich", "speaker": 2, "speaker_confidence": 0.7004486 }, { "word": "glaube", "start": 1875.555, "end": 1876.035, "confidence": 0.9753418, "punctuated_word": "glaube,", "speaker": 2, "speaker_confidence": 0.7004486 }, { "word": "diese", "start": 1876.035, "end": 1876.355, "confidence": 1.0, "punctuated_word": "diese", "speaker": 2, "speaker_confidence": 0.7004486 }, { "word": "felder", "start": 1876.355, "end": 1876.755, "confidence": 1.0, "punctuated_word": "Felder", "speaker": 2, "speaker_confidence": 0.7004486 }, { "word": "werden", "start": 1876.755, "end": 1877.0751, "confidence": 1.0, "punctuated_word": "werden", "speaker": 2, "speaker_confidence": 0.7004486 }, { "word": "immer", "start": 1877.0751, "end": 1877.395, "confidence": 1.0, "punctuated_word": "immer", "speaker": 2, "speaker_confidence": 0.7004486 }, { "word": 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