Community formation in agent based models of societies
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/43/43134/tde-20082020-140035/ |
Resumo: | In this work we present an agent based model for community for- mation on societies from the dynamics for opinion exchange and dis- trust between agents. The development framework relies on Proba- bility Theory, Machine Learning and MaxEnt principles, from which we derive a new form of Entropic Dynamics for Information Process- ing Systems, in particular for simple Neural Networks, the Entropic Learning Dynamics. The resulting theory and model for agents interactions are an- alyzed in a few scenarios, chosen due to their intuitive nature and connection with possible real scenarios. We started the analysis with the properties of systems with 2 agents interacting under different trust and opinion initial conditions, and showed that the dynamics is not trivial nor leads to results with absurd interpretations. Then, we analyzed the properties of societies with many agents, varying the distribution of opinions and distrust, as well as the subjects they could discuss, and found different situations leading to consensus, polarization and even frustrated state like a spin glass. Finally, we applied the model to study the behavior of judges due the availability of data regarding the influence of political party ideology in the voting patterns of judges in the U.S Court of Appeals. In this application, although just a caricature aiming just to provide a quantitative tool for experts in the field, we tried to mimic the typical situations a panel of three judges would be submitted, attributing to agents representing judges a common knowledge of the Law, a Party bias, a Personality and exposing them to different distrust scenarios. The only scenario capable of reproducing the available data had to consider similar contributions of the Law, Party bias and Personality, as well as having Courteous and Certain judges, who extended the courtesy of attributing low distrust to agents of the opposing political party. |
id |
USP_2b26a64cfdd1cdb11354bbc9d609babe |
---|---|
oai_identifier_str |
oai:teses.usp.br:tde-20082020-140035 |
network_acronym_str |
USP |
network_name_str |
Biblioteca Digital de Teses e Dissertações da USP |
repository_id_str |
2721 |
spelling |
Community formation in agent based models of societiesFormação de comunidades em modelos de sociedades baseados em agentesAgent ModelsAprendizado de MáquinaMachine Learning, SociologyMecânica EstatísticaModelos de AgentesNeural NetworksRedes NeuraisSociologiaStatistical MechanicsIn this work we present an agent based model for community for- mation on societies from the dynamics for opinion exchange and dis- trust between agents. The development framework relies on Proba- bility Theory, Machine Learning and MaxEnt principles, from which we derive a new form of Entropic Dynamics for Information Process- ing Systems, in particular for simple Neural Networks, the Entropic Learning Dynamics. The resulting theory and model for agents interactions are an- alyzed in a few scenarios, chosen due to their intuitive nature and connection with possible real scenarios. We started the analysis with the properties of systems with 2 agents interacting under different trust and opinion initial conditions, and showed that the dynamics is not trivial nor leads to results with absurd interpretations. Then, we analyzed the properties of societies with many agents, varying the distribution of opinions and distrust, as well as the subjects they could discuss, and found different situations leading to consensus, polarization and even frustrated state like a spin glass. Finally, we applied the model to study the behavior of judges due the availability of data regarding the influence of political party ideology in the voting patterns of judges in the U.S Court of Appeals. In this application, although just a caricature aiming just to provide a quantitative tool for experts in the field, we tried to mimic the typical situations a panel of three judges would be submitted, attributing to agents representing judges a common knowledge of the Law, a Party bias, a Personality and exposing them to different distrust scenarios. The only scenario capable of reproducing the available data had to consider similar contributions of the Law, Party bias and Personality, as well as having Courteous and Certain judges, who extended the courtesy of attributing low distrust to agents of the opposing political party.Neste trabalho apresentamos um modelo para a formação de comu- nidades em sociedades a partir da dinâmica de trocas de opinião e de- sconfiança entre agentes. A teoria é desnvolvida com base na Teoria de Probabilidades, Aprendizado de Máquina no princípio de Máx- ima Entropia (MaxEnt), dos quais deduzimos uma nova forma de Dinâmica Entrópica para Sistemas de Processamento de Informação, em particular para Redes Neurais simples, a Dinâmica Entrópica de Aprendizado. A teoria e o modelo para a interação de agentes foram analisa- dos em alguns cenários, escolhidos pela natureza intuitiva e de pos- sível associação com circunstâncias reais. Começamos com a análise de sistemas com 2 agentes interagindo em diferentes condições de opinião e desconfiança iniciais, mostrando que a dinâmica deduzina não apresenta apenas fases triviais e não leva a interpretações ab- surdas. Em seguida, analisamos as propriedades de sociedades com muitos agentes, variando a distribuição de opiniões e desconfianças iniciais, bem como os assuntos que poderiam ser discutidos pelos agentes, mostrando que diferentes condições levam a consenso, po- larização ou até mesmo a uma fase frustrada como um video de spin. Finalizamos com uma aplicação do modelo para o comporta- mento dos juízes, dada a disponibilidade de dados a respeito da influência ideológico-partidária nos padrões de decisão judicial da Corte de Apelações dos EUA. Nesta aplicação, apesar de se apresen- tar como uma caricatura com o objetivo de dispor uma ferramenta quantitativa para especialistas na área, tentamos imitar as situações típicas às quais um colégio judicial composto por três juízes estaria submetido, atribuindo aos agentes representantes dos juízes um con- hecimento da Lei, um viés do Partido, uma Personalidade e os ex- pondo a diferentes cenários de desconfiança. O único cenárioi capaz de reproduzir o padrão empírico de votações requer que os juízes se- jam representados por agentes que atribuem pesos similares à Lei, ao viés Paridário e à Pesonalidade, bem como que extendam a Cortesia Certeira de confiar em juízes com vieses políticos opostos.Biblioteca Digitais de Teses e Dissertações da USPAlfonso, Nestor Felipe CatichaPereira, Felippe Alves2020-07-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/43/43134/tde-20082020-140035/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2020-09-16T03:06:40Zoai:teses.usp.br:tde-20082020-140035Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212020-09-16T03:06:40Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Community formation in agent based models of societies Formação de comunidades em modelos de sociedades baseados em agentes |
title |
Community formation in agent based models of societies |
spellingShingle |
Community formation in agent based models of societies Pereira, Felippe Alves Agent Models Aprendizado de Máquina Machine Learning, Sociology Mecânica Estatística Modelos de Agentes Neural Networks Redes Neurais Sociologia Statistical Mechanics |
title_short |
Community formation in agent based models of societies |
title_full |
Community formation in agent based models of societies |
title_fullStr |
Community formation in agent based models of societies |
title_full_unstemmed |
Community formation in agent based models of societies |
title_sort |
Community formation in agent based models of societies |
author |
Pereira, Felippe Alves |
author_facet |
Pereira, Felippe Alves |
author_role |
author |
dc.contributor.none.fl_str_mv |
Alfonso, Nestor Felipe Caticha |
dc.contributor.author.fl_str_mv |
Pereira, Felippe Alves |
dc.subject.por.fl_str_mv |
Agent Models Aprendizado de Máquina Machine Learning, Sociology Mecânica Estatística Modelos de Agentes Neural Networks Redes Neurais Sociologia Statistical Mechanics |
topic |
Agent Models Aprendizado de Máquina Machine Learning, Sociology Mecânica Estatística Modelos de Agentes Neural Networks Redes Neurais Sociologia Statistical Mechanics |
description |
In this work we present an agent based model for community for- mation on societies from the dynamics for opinion exchange and dis- trust between agents. The development framework relies on Proba- bility Theory, Machine Learning and MaxEnt principles, from which we derive a new form of Entropic Dynamics for Information Process- ing Systems, in particular for simple Neural Networks, the Entropic Learning Dynamics. The resulting theory and model for agents interactions are an- alyzed in a few scenarios, chosen due to their intuitive nature and connection with possible real scenarios. We started the analysis with the properties of systems with 2 agents interacting under different trust and opinion initial conditions, and showed that the dynamics is not trivial nor leads to results with absurd interpretations. Then, we analyzed the properties of societies with many agents, varying the distribution of opinions and distrust, as well as the subjects they could discuss, and found different situations leading to consensus, polarization and even frustrated state like a spin glass. Finally, we applied the model to study the behavior of judges due the availability of data regarding the influence of political party ideology in the voting patterns of judges in the U.S Court of Appeals. In this application, although just a caricature aiming just to provide a quantitative tool for experts in the field, we tried to mimic the typical situations a panel of three judges would be submitted, attributing to agents representing judges a common knowledge of the Law, a Party bias, a Personality and exposing them to different distrust scenarios. The only scenario capable of reproducing the available data had to consider similar contributions of the Law, Party bias and Personality, as well as having Courteous and Certain judges, who extended the courtesy of attributing low distrust to agents of the opposing political party. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-16 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/43/43134/tde-20082020-140035/ |
url |
https://www.teses.usp.br/teses/disponiveis/43/43134/tde-20082020-140035/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
_version_ |
1809090898329337856 |