: An agent-based model is presented that aims to capture the involvement of inequality and trust in collective action in a classic commons dilemma before, during, and after communication. The model assumptions are based on the behavioral theory of collective action of Elinor Ostrom and the ‘humanistic rational choice theory’. The commons dilemma is represented as a spatially explicit renewable resource. Agent’s trust in others has an impact on the harvesting of shared resources, and trust is influenced by observed harvesting behavior and cheap talk. We calibrated the model using data from a prior set of lab experiments on inequality, trust, and communication. The best fit to the data consists of a population with a small share of altruistic and selfishagentsandamajorityofconditionalcooperativeagentssensitivetoinequalityandwhowouldcooperateifothersdid.Communicationincreasedtrustexplainingthebettergroupperformancewhencommunication wasintroduced.Themodelingresultscomplementpriorcommunicationresearchandclarifythedynamicsof reciprocalcooperationcommonlyobservedinrobustresourcegovernancesystems.
{"title":"An Agent-Based Model of the Interaction Between Inequality, Trust, and Communication in Common Pool Experiments","authors":"M. Janssen, D. DeCaro, Allen Lee","doi":"10.18564/jasss.4922","DOIUrl":"https://doi.org/10.18564/jasss.4922","url":null,"abstract":": An agent-based model is presented that aims to capture the involvement of inequality and trust in collective action in a classic commons dilemma before, during, and after communication. The model assumptions are based on the behavioral theory of collective action of Elinor Ostrom and the ‘humanistic rational choice theory’. The commons dilemma is represented as a spatially explicit renewable resource. Agent’s trust in others has an impact on the harvesting of shared resources, and trust is influenced by observed harvesting behavior and cheap talk. We calibrated the model using data from a prior set of lab experiments on inequality, trust, and communication. The best fit to the data consists of a population with a small share of altruistic and selfishagentsandamajorityofconditionalcooperativeagentssensitivetoinequalityandwhowouldcooperateifothersdid.Communicationincreasedtrustexplainingthebettergroupperformancewhencommunication wasintroduced.Themodelingresultscomplementpriorcommunicationresearchandclarifythedynamicsof reciprocalcooperationcommonlyobservedinrobustresourcegovernancesystems.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"195 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78139701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: A reduction in the production and consumption of meat and dairy across much of the world is critical for climate change mitigation, the alleviation of ecological stress, and improved health. We update an agent-based model (ABM) of historic UK milk consumption and apply it to scenarios of dairy reduction and adoption of plant-based milk (PBM) out to 2050. The updated model is comprised of a cognitive function, where agents perceive the physical, health and environmental characteristics of milk choice, which is modified by habit and social influence. We use European Social Survey 2018 and British Social Attitudes 2008 survey data to empir-ically inform the model. Taking a backcasting approach, we calibrate parameters against published UK dairy reduction targets (2030 and 2050), and test how different price relationships, and characterisations of environmental concern, may affect simulated milk consumption from 2020 to 2050. Scenarios for core targets (20% less dairy by 2030 and 35% by 2050) largely produced plausible consumption trajectories. However, at current pricing of dairy and PBM, simulated consumption was mostly unable to deliver on desired core targets, but this improved markedly with dairy prices set to organic levels. The influence of changing environmental concern on milk choice resulted in higher levels of dairy milk reduction. When modelled as transient, intense shocks to public concern, consumption patterns did not fundamentally change. However, small, incremental but permanent changes to concern did produce structural changes to consumption patterns, with dairy falling below plant-based alternatives at around 2030. This study is the first to apply an ABM in the context of scenarios for dairy reduction and PBM adoption in service to UK climate-related consumption targets. It can serve as valu-able bottom-up, alternative, evidence on the feasibility of dietary shift targets, and poses policy implications for how to address impediments to behavioural change. different representative price relationships between dairy and PBM; and modelled different mechanisms for changes to agent environmental concern and milk choice influence.
{"title":"Agent-Based Modelling of Future Dairy and Plant-Based Milk Consumption for UK Climate Targets","authors":"Matthew Gibson, J. Pereira, R. Slade, J. Rogelj","doi":"10.18564/jasss.4801","DOIUrl":"https://doi.org/10.18564/jasss.4801","url":null,"abstract":": A reduction in the production and consumption of meat and dairy across much of the world is critical for climate change mitigation, the alleviation of ecological stress, and improved health. We update an agent-based model (ABM) of historic UK milk consumption and apply it to scenarios of dairy reduction and adoption of plant-based milk (PBM) out to 2050. The updated model is comprised of a cognitive function, where agents perceive the physical, health and environmental characteristics of milk choice, which is modified by habit and social influence. We use European Social Survey 2018 and British Social Attitudes 2008 survey data to empir-ically inform the model. Taking a backcasting approach, we calibrate parameters against published UK dairy reduction targets (2030 and 2050), and test how different price relationships, and characterisations of environmental concern, may affect simulated milk consumption from 2020 to 2050. Scenarios for core targets (20% less dairy by 2030 and 35% by 2050) largely produced plausible consumption trajectories. However, at current pricing of dairy and PBM, simulated consumption was mostly unable to deliver on desired core targets, but this improved markedly with dairy prices set to organic levels. The influence of changing environmental concern on milk choice resulted in higher levels of dairy milk reduction. When modelled as transient, intense shocks to public concern, consumption patterns did not fundamentally change. However, small, incremental but permanent changes to concern did produce structural changes to consumption patterns, with dairy falling below plant-based alternatives at around 2030. This study is the first to apply an ABM in the context of scenarios for dairy reduction and PBM adoption in service to UK climate-related consumption targets. It can serve as valu-able bottom-up, alternative, evidence on the feasibility of dietary shift targets, and poses policy implications for how to address impediments to behavioural change. different representative price relationships between dairy and PBM; and modelled different mechanisms for changes to agent environmental concern and milk choice influence.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89842434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sedigh, M. Purvis, Bastin Tony Roy Savarimuthu, Christopher K. Frantz, Maryam A. Purvis
: In this paper, we investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. Apprenticeship is one of the major means to transfer skills in a society. Weconsiderfivesocieties: theoldBritainsystem(AD 1300 s − 1600 s ), theBritishEastIndiaCompany(AD 1600 s − 1800 s ), Armenian merchants of New-Julfa (AD 1600 s − 1700 s ), contemporary German apprenticeship ( 1990 s ), and the “ Modern Apprenticeship ” in Britain ( 2001 ). In comparing these systems, using an agent-based simulationmodel,weidentifiedsixcharacteristicswhichimpactthesuccessofanapprenticeshipprogrammeinasociety,whichwemeasuredbyconsideringthreeparameters,namelythenumberofskilledagentsproducedbytheapprenticeships,programmecompletion,andthecontributionofprogrammestotheGrossDomesticIncome(GDI)ofthesociety.Weinvestigatedifferentdefinitionsforsuccessofanapprenticeshipandsomehy-potheticalsocietiestotestsomecommonbeliefsaboutapprenticeships’performance.Thesimulationssuggestthata)itisbettertoinvestinapubliceducationalsystemratherthansubsidisingprivatecontractorstotrainapprentices,b)havingahighercompletionratioforapprenticeshipprogrammedoesnotnecessarilyresultinahighercontributionintheGDI,andc)governors(e.g.mayorsorgovernment)thatfacesignificantemigrationshouldalsoconsideremployingpoliciesthatpersuadeapprenticestocompletetheirprogrammeandstayinthesocietyaftercompletiontoimproveapprenticeshipefficacy.
{"title":"A Comparative Study on Apprenticeship Systems Using Agent-Based Simulation","authors":"A. Sedigh, M. Purvis, Bastin Tony Roy Savarimuthu, Christopher K. Frantz, Maryam A. Purvis","doi":"10.18564/jasss.4733","DOIUrl":"https://doi.org/10.18564/jasss.4733","url":null,"abstract":": In this paper, we investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. Apprenticeship is one of the major means to transfer skills in a society. Weconsiderfivesocieties: theoldBritainsystem(AD 1300 s − 1600 s ), theBritishEastIndiaCompany(AD 1600 s − 1800 s ), Armenian merchants of New-Julfa (AD 1600 s − 1700 s ), contemporary German apprenticeship ( 1990 s ), and the “ Modern Apprenticeship ” in Britain ( 2001 ). In comparing these systems, using an agent-based simulationmodel,weidentifiedsixcharacteristicswhichimpactthesuccessofanapprenticeshipprogrammeinasociety,whichwemeasuredbyconsideringthreeparameters,namelythenumberofskilledagentsproducedbytheapprenticeships,programmecompletion,andthecontributionofprogrammestotheGrossDomesticIncome(GDI)ofthesociety.Weinvestigatedifferentdefinitionsforsuccessofanapprenticeshipandsomehy-potheticalsocietiestotestsomecommonbeliefsaboutapprenticeships’performance.Thesimulationssuggestthata)itisbettertoinvestinapubliceducationalsystemratherthansubsidisingprivatecontractorstotrainapprentices,b)havingahighercompletionratioforapprenticeshipprogrammedoesnotnecessarilyresultinahighercontributionintheGDI,andc)governors(e.g.mayorsorgovernment)thatfacesignificantemigrationshouldalsoconsideremployingpoliciesthatpersuadeapprenticestocompletetheirprogrammeandstayinthesocietyaftercompletiontoimproveapprenticeshipefficacy.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89915584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: From the viewpoint of urban administration, simulation is regarded as a policy tool that provides administrators with information about the current urban situation and enables them to verify the effectiveness of urban policies. This study proposes a traffic simulation model for a real city named Sejong in South Korea. Our proposed model employs agent-based simulation with the city-level real data, which mainly focuses on describing the movement behavior of individuals using urban traffics in the real city. By aggregating the agents’ decisions and interactions during the movement, the proposed model can discover a demand for the city’s transportation system. To do this, this study validated the proposed model so that the modeled traffic system was similar to the real one, and then we conducted a case study to compare and analyze the effects of traffic dispersion led by the upcoming bridge construction in the real city. The case study showed that the proposed model can provide policy evaluation on the optimal location of the bridge construction considering the city traffic flow. Furthermore, the case study presented that the agent-based modeling enables micro-level analysis on the city traffic flow to understand on the policy implications.
{"title":"Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect","authors":"Tae-Sub Yun, Dongjun Kim, Il-Chul Moon, J. Bae","doi":"10.18564/jasss.4923","DOIUrl":"https://doi.org/10.18564/jasss.4923","url":null,"abstract":": From the viewpoint of urban administration, simulation is regarded as a policy tool that provides administrators with information about the current urban situation and enables them to verify the effectiveness of urban policies. This study proposes a traffic simulation model for a real city named Sejong in South Korea. Our proposed model employs agent-based simulation with the city-level real data, which mainly focuses on describing the movement behavior of individuals using urban traffics in the real city. By aggregating the agents’ decisions and interactions during the movement, the proposed model can discover a demand for the city’s transportation system. To do this, this study validated the proposed model so that the modeled traffic system was similar to the real one, and then we conducted a case study to compare and analyze the effects of traffic dispersion led by the upcoming bridge construction in the real city. The case study showed that the proposed model can provide policy evaluation on the optimal location of the bridge construction considering the city traffic flow. Furthermore, the case study presented that the agent-based modeling enables micro-level analysis on the city traffic flow to understand on the policy implications.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"159 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77554092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We use reinforcement learning models to investigate the role of cognitive mechanisms in the emergence of conventions in the repeated volunteer’s dilemma (VOD). The VOD is amulti-person, binary choice collective goods game in which the contribution of only one individual is necessary and su icient to produce a benefit for the entire group. Behavioral experiments show that in the symmetric VOD,where all groupmembers have the same costs of volunteering, a turn-taking convention emerges, whereas in the asymmetric VOD,where one “strong” group member has lower costs of volunteering, a solitary-volunteering convention emerges with the strong member volunteering most of the time. We compare three di erent classes of reinforcement learningmodels in their ability to replicate these empirical findings. Our results confirm that reinforcement learning models canprovide aparsimonious account of howhumans tacitly agreeonone course of actionwhenencountering each other repeatedly in the same interaction situation. We find that considering contextual clues (i.e., reward structures) for strategy design (i.e., sequences of actions) and strategy selection (i.e., favoring equal distribution of costs) facilitate coordinationwhenoptimaare less salient. Furthermore, ourmodels producebetter fits with the empirical datawhen agents actmyopically (favoring current over expected future rewards) and the rewards for adhering to conventions are not delayed.
{"title":"The Role of Reinforcement Learning in the Emergence of Conventions: Simulation Experiments with the Repeated Volunteer's Dilemma","authors":"H. Nunner, W. Przepiorka, Chris Janssen","doi":"10.18564/jasss.4771","DOIUrl":"https://doi.org/10.18564/jasss.4771","url":null,"abstract":"We use reinforcement learning models to investigate the role of cognitive mechanisms in the emergence of conventions in the repeated volunteer’s dilemma (VOD). The VOD is amulti-person, binary choice collective goods game in which the contribution of only one individual is necessary and su icient to produce a benefit for the entire group. Behavioral experiments show that in the symmetric VOD,where all groupmembers have the same costs of volunteering, a turn-taking convention emerges, whereas in the asymmetric VOD,where one “strong” group member has lower costs of volunteering, a solitary-volunteering convention emerges with the strong member volunteering most of the time. We compare three di erent classes of reinforcement learningmodels in their ability to replicate these empirical findings. Our results confirm that reinforcement learning models canprovide aparsimonious account of howhumans tacitly agreeonone course of actionwhenencountering each other repeatedly in the same interaction situation. We find that considering contextual clues (i.e., reward structures) for strategy design (i.e., sequences of actions) and strategy selection (i.e., favoring equal distribution of costs) facilitate coordinationwhenoptimaare less salient. Furthermore, ourmodels producebetter fits with the empirical datawhen agents actmyopically (favoring current over expected future rewards) and the rewards for adhering to conventions are not delayed.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73397054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles Retzlaff, Laura Burbach, Lilian Kojan, Patrick Halbach, Johannes Nakayama, M. Ziefle, André Calero Valdez
Modeling infectious diseases has been shown to be of great importance and utility during the ongoing COVID-19 pandemic. From today's globalized information landscape, however, a plethora of new factors arise that have not been covered in previous models. In this paper, we present an agent-based model that reflects the complex interplay between the spread of a pathogen and individual protective behaviors under the influence of media messaging. We use the Rescorla-Wagner model of associative learning for the growth and extinction of fear, a factor that has been proposed as a major contributor in the determination of protective behavior. The model space, as well as heterogeneous social structures among the agents, are created from empirical data. We incorporate factors like age, gender, wealth, and attitudes towards public health institutions. The model is able to reproduce the empirical trends of fear and protective behavior in Germany but struggles to simulate the accurate scale of disease spread. The decline of fear seems to promote a second wave of disease and the model suggests that individual protective behavior has a significant impact on the outcome of the epidemic. The influence of media in the form of messages promoting protective behavior is negligible in the model. Further research regarding factors influencing long-term protective behavior is recommended to improve communication and mitigation strategies.
{"title":"Fear, Behaviour, and the COVID-19 Pandemic: A City-Scale Agent-Based Model Using Socio-Demographic and Spatial Map Data","authors":"Charles Retzlaff, Laura Burbach, Lilian Kojan, Patrick Halbach, Johannes Nakayama, M. Ziefle, André Calero Valdez","doi":"10.18564/jasss.4723","DOIUrl":"https://doi.org/10.18564/jasss.4723","url":null,"abstract":"Modeling infectious diseases has been shown to be of great importance and utility during the ongoing COVID-19 pandemic. From today's globalized information landscape, however, a plethora of new factors arise that have not been covered in previous models. In this paper, we present an agent-based model that reflects the complex interplay between the spread of a pathogen and individual protective behaviors under the influence of media messaging. We use the Rescorla-Wagner model of associative learning for the growth and extinction of fear, a factor that has been proposed as a major contributor in the determination of protective behavior. The model space, as well as heterogeneous social structures among the agents, are created from empirical data. We incorporate factors like age, gender, wealth, and attitudes towards public health institutions. The model is able to reproduce the empirical trends of fear and protective behavior in Germany but struggles to simulate the accurate scale of disease spread. The decline of fear seems to promote a second wave of disease and the model suggests that individual protective behavior has a significant impact on the outcome of the epidemic. The influence of media in the form of messages promoting protective behavior is negligible in the model. Further research regarding factors influencing long-term protective behavior is recommended to improve communication and mitigation strategies.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"26 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83089947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R 2 ≃ 0 . 7 .
{"title":"Calibrating Agent-Based Models of Innovation Diffusion with Gradients","authors":"Florian Kotthoff, T. Hamacher","doi":"10.18564/jasss.4861","DOIUrl":"https://doi.org/10.18564/jasss.4861","url":null,"abstract":": Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R 2 ≃ 0 . 7 .","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89875657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves -- rather than with their mathematical representations (as in formal models). This paper uses the natural-language ABMA to test the robustness of formal reason-balancing models of argumentation [Maes&Flache 2013, Singer et al. 2019]: First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from formal models. However, once ADAs start to actively generate new contributions, the evolution of a conservation is dominated by properties of the agents *as authors*. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research.
本文开发了一种基于自然语言主体的论证模型(ABMA)。它的人工审议代理(ADAs)是在人工智能和计算语言学最近发展起来的所谓神经语言模型的帮助下构建的。助理助理配备了一个极简主义的信念系统，并可能产生和提交新的对话贡献。自然语言ABMA允许我们用英语模拟集体审议，即用论点、理由和主张本身——而不是用它们的数学表示(如在正式模型中)。本文使用自然语言ABMA来测试论证的形式推理平衡模型的鲁棒性[Maes&Flache 2013, Singer et al. 2019]:首先，只要ADAs保持被动状态，确认偏差和同质性更新就会触发极化，这与形式模型的结果一致。然而，一旦ADAs开始积极地产生新的贡献，一个守恒的进化就被作为作者的agent *的属性所主导。这表明，创造新的论点、理由和主张对谈话有重要影响，对理解集体审议的动态至关重要。论文最后指出了该模型的进一步富有成效的应用和未来研究的挑战。
{"title":"Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics","authors":"Gregor Betz","doi":"10.18564/jasss.4725","DOIUrl":"https://doi.org/10.18564/jasss.4725","url":null,"abstract":"This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves -- rather than with their mathematical representations (as in formal models). This paper uses the natural-language ABMA to test the robustness of formal reason-balancing models of argumentation [Maes&Flache 2013, Singer et al. 2019]: First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from formal models. However, once ADAs start to actively generate new contributions, the evolution of a conservation is dominated by properties of the agents *as authors*. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78983653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Policymakers decide on alternative policies facing restricted budgets and uncertain, ever-changing future. Designing public policies is further difficult due to the need to decide on priorities and handle effects across policies. Housing policies, specifically, involve heterogeneous characteristics of properties themselves and the intricacy of housing markets and the spatial context of cities. We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks to integrate economic, spatial and transport scholarship. PS2 is applied to a comparison among three competing public policies aimed at reducing inequality and alleviating poverty: (a) house acquisition by the government and distribution to lower income households, (b) rental vouchers, and (c) monetary aid. Within the model context, the monetary aid, that is, smaller amounts of help for a larger number of households, makes the economy perform better in terms of production, consumption, reduction of inequality, and maintenance of financial duties. PS2 as such is also a framework that may be further adapted to a number of related research questions.
{"title":"PolicySpace2: Modeling Markets and Endogenous Public Policies","authors":"Bernardo Alves Furtado","doi":"10.18564/jasss.4742","DOIUrl":"https://doi.org/10.18564/jasss.4742","url":null,"abstract":"Policymakers decide on alternative policies facing restricted budgets and uncertain, ever-changing future. Designing public policies is further difficult due to the need to decide on priorities and handle effects across policies. Housing policies, specifically, involve heterogeneous characteristics of properties themselves and the intricacy of housing markets and the spatial context of cities. We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks to integrate economic, spatial and transport scholarship. PS2 is applied to a comparison among three competing public policies aimed at reducing inequality and alleviating poverty: (a) house acquisition by the government and distribution to lower income households, (b) rental vouchers, and (c) monetary aid. Within the model context, the monetary aid, that is, smaller amounts of help for a larger number of households, makes the economy perform better in terms of production, consumption, reduction of inequality, and maintenance of financial duties. PS2 as such is also a framework that may be further adapted to a number of related research questions.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86947817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Madeira, Bernardo Alves Furtado, Alan Rafael Dill
Violence against women occurs predominantly in the family and domestic context. The COVID-19 pan-demic has led Brazil to recommend and at times, impose social distancing, with the partial closure of economic activities, schools, and restrictions on events and public services. Preliminary evidence shows that intense co-existence increases domestic violence, while social distancing measures may have prevented access to public services and networks, information, and help. We propose an agent-based model (ABM), called VIDA, to for-malize and illustrate a multitude of factors that influence events which could trigger violence. A central part of the model is the construction of a stress indicator, created as a probability trigger of domestic violence occur-ring within the family environment. Having a formal model that replicates observed patterns of violence based on internal familial characteristics enables us to experiment with altering dynamics. We first tested the (a) ab-sence or presence of the deterrence system of domestic violence against women and then (b) the existence of measures to increase social distancing. VIDA presents comparative results for metropolitan regions and neigh-borhoods considered in the experiments. Results suggest that social distancing measures, particularly those encouraging staying at home, may have increased domestic violence against women by about 10%. VIDA sug-gests further that more populated areas have comparatively fewer cases per hundred thousand women than less populous capitals or rural areas of urban concentrations. This paper contributes to the literature by formal-izing, to the best of our knowledge, the first model of domestic violence through agent-based modeling, using empirical detailed socioeconomic, demographic, educational, gender, and race data at the intraurban (census sectors) and household level.
{"title":"VIDA: A simulation model of domestic VIolence in times of social DistAncing","authors":"L. Madeira, Bernardo Alves Furtado, Alan Rafael Dill","doi":"10.18564/jasss.4612","DOIUrl":"https://doi.org/10.18564/jasss.4612","url":null,"abstract":"Violence against women occurs predominantly in the family and domestic context. The COVID-19 pan-demic has led Brazil to recommend and at times, impose social distancing, with the partial closure of economic activities, schools, and restrictions on events and public services. Preliminary evidence shows that intense co-existence increases domestic violence, while social distancing measures may have prevented access to public services and networks, information, and help. We propose an agent-based model (ABM), called VIDA, to for-malize and illustrate a multitude of factors that influence events which could trigger violence. A central part of the model is the construction of a stress indicator, created as a probability trigger of domestic violence occur-ring within the family environment. Having a formal model that replicates observed patterns of violence based on internal familial characteristics enables us to experiment with altering dynamics. We first tested the (a) ab-sence or presence of the deterrence system of domestic violence against women and then (b) the existence of measures to increase social distancing. VIDA presents comparative results for metropolitan regions and neigh-borhoods considered in the experiments. Results suggest that social distancing measures, particularly those encouraging staying at home, may have increased domestic violence against women by about 10%. VIDA sug-gests further that more populated areas have comparatively fewer cases per hundred thousand women than less populous capitals or rural areas of urban concentrations. This paper contributes to the literature by formal-izing, to the best of our knowledge, the first model of domestic violence through agent-based modeling, using empirical detailed socioeconomic, demographic, educational, gender, and race data at the intraurban (census sectors) and household level.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81202945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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