Shaoni Wang, Kees Zoethout, W. Jager, Yanzhong Dang
1Institute of Systems Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China 2Institute of Business Studies, Hanze University of Applied Science, Zernikeplein 23, Groningen 9747 AS, Netherlands 3Groningen Center for Social Complexity Studies, University of Groningen, Hoendiepskade 23/24, Groningen 9718 BG, Netherlands Correspondence should be addressed tow.jager@rug.nl
1大连理工大学系统工程研究所，大连甘景子区灵工路2号，大连116024 2汉斯应用科技大学商业研究所，荷兰格罗宁根9747 AS Zernikeplein 23; 3格罗宁根大学格罗宁根社会复杂性研究中心，荷兰格罗宁根9718 BG Hoendiepskade 23/24
{"title":"The Dynamical Relation Between Individual Needs and Group Performance: A Simulation of the Self-Organising Task Allocation Process","authors":"Shaoni Wang, Kees Zoethout, W. Jager, Yanzhong Dang","doi":"10.18564/jasss.4715","DOIUrl":"https://doi.org/10.18564/jasss.4715","url":null,"abstract":"1Institute of Systems Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China 2Institute of Business Studies, Hanze University of Applied Science, Zernikeplein 23, Groningen 9747 AS, Netherlands 3Groningen Center for Social Complexity Studies, University of Groningen, Hoendiepskade 23/24, Groningen 9718 BG, Netherlands Correspondence should be addressed tow.jager@rug.nl","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73859451","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}
G. T. Broeke, G. Voorn, A. Ligtenberg, J. Molenaar
: The utility of Agent Based Models (ABMs) for decision making support as well as for scientific applications can be increased considerably by the availability and use of methodologies for thorough model behaviour analysis. In view of their intrinsic construction, ABMs have to be analysed numerically. Furthermore, ABM behaviour is often complex, featuring strong non-linearities, tipping points, and adaptation. This easily leads to high computational costs, presenting a serious practical limitation. Model developers and users alike would benefit from methodologies that can explore large parts of parameter space at limited computational costs. In this paper we present a methodology that makes this possible. The essence of our approach is to develop a cost-effective surrogate model based on ABM output using machine learning to approximate ABM simulation data. The development consists of two steps, both with iterative loops of training and cross-validation. In the first part, a Support Vector Machine (SVM) is developed to split behaviour space into regions of qualitatively different behaviour. In the second part, a Support Vector Regression (SVR) is developed to cover the quantitative behaviour within these regions. Finally, sensitivity indices are calculated to rank the importance of parameters for describing the boundaries between regions, and for the quantitative dynamics within regions. The methodology is demonstrated in three case studies, a differential equation model of predator-prey interaction, a common-pool resource ABM and an ABM representing the Philippine tuna fishery. In all cases, the model and the corresponding surrogate model show a good match. Furthermore, different parameters are shown to influence the quantitative outcomes, compared to those that influence the underlying qualitative behaviour. Thus, the method helps to distinguish which parameters determine the boundaries in parameter space between regions that are separated by tipping points, or by any criterion of interest to the user. sensitivity analysis surrogate models. we start with a training set of 100 sample points, set
{"title":"The Use of Surrogate Models to Analyse Agent-Based Models","authors":"G. T. Broeke, G. Voorn, A. Ligtenberg, J. Molenaar","doi":"10.18564/jasss.4530","DOIUrl":"https://doi.org/10.18564/jasss.4530","url":null,"abstract":": The utility of Agent Based Models (ABMs) for decision making support as well as for scientific applications can be increased considerably by the availability and use of methodologies for thorough model behaviour analysis. In view of their intrinsic construction, ABMs have to be analysed numerically. Furthermore, ABM behaviour is often complex, featuring strong non-linearities, tipping points, and adaptation. This easily leads to high computational costs, presenting a serious practical limitation. Model developers and users alike would benefit from methodologies that can explore large parts of parameter space at limited computational costs. In this paper we present a methodology that makes this possible. The essence of our approach is to develop a cost-effective surrogate model based on ABM output using machine learning to approximate ABM simulation data. The development consists of two steps, both with iterative loops of training and cross-validation. In the first part, a Support Vector Machine (SVM) is developed to split behaviour space into regions of qualitatively different behaviour. In the second part, a Support Vector Regression (SVR) is developed to cover the quantitative behaviour within these regions. Finally, sensitivity indices are calculated to rank the importance of parameters for describing the boundaries between regions, and for the quantitative dynamics within regions. The methodology is demonstrated in three case studies, a differential equation model of predator-prey interaction, a common-pool resource ABM and an ABM representing the Philippine tuna fishery. In all cases, the model and the corresponding surrogate model show a good match. Furthermore, different parameters are shown to influence the quantitative outcomes, compared to those that influence the underlying qualitative behaviour. Thus, the method helps to distinguish which parameters determine the boundaries in parameter space between regions that are separated by tipping points, or by any criterion of interest to the user. sensitivity analysis surrogate models. we start with a training set of 100 sample points, set","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89132481","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}
Ngan Nguyen, Hongfei Chen, B. Jin, Walker Quinn, C. Tyler, A. Landsberg
We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. Thismodification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrodmodel, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the di erences in behavior of the twomodels over time, but also provides amore comprehensive view into the behavior of Axelrod’s originalmodel. Thequantitativemetrics introduced in this paperhavebroadpotential applicability across a large variety of agent-based cultural dissemination models.
{"title":"Cultural Dissemination: An Agent-Based Model with Social Influence","authors":"Ngan Nguyen, Hongfei Chen, B. Jin, Walker Quinn, C. Tyler, A. Landsberg","doi":"10.18564/jasss.4633","DOIUrl":"https://doi.org/10.18564/jasss.4633","url":null,"abstract":"We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. Thismodification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrodmodel, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the di erences in behavior of the twomodels over time, but also provides amore comprehensive view into the behavior of Axelrod’s originalmodel. Thequantitativemetrics introduced in this paperhavebroadpotential applicability across a large variety of agent-based cultural dissemination models.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"319 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74996757","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}
: In this paper, we have estimated the parameters of 41 simulation models to find which of 9 estimation algorithms performs better. Unfortunately, no single algorithm was the best for all or even most of the models. Rather, five main results emerge from this research. First, each algorithm was the best estimator for at least one parameter. Second, the best estimation algorithm varied not only between models but even between parameters of the same model. Third, each estimation algorithm failed to estimate at least one identifiable parameter. Fourth, choosing the right algorithm improved estimation performance by more than quadrupling the number of model runs. Fifth, half of the agent-based models tested could not be fully identified. We therefore argue that the testing performed here should be done in other applied work and to facilitate this we would like to share the R package freelunch .
{"title":"No Free Lunch when Estimating Simulation Parameters","authors":"Ernesto Carrella","doi":"10.18564/jasss.4572","DOIUrl":"https://doi.org/10.18564/jasss.4572","url":null,"abstract":": In this paper, we have estimated the parameters of 41 simulation models to find which of 9 estimation algorithms performs better. Unfortunately, no single algorithm was the best for all or even most of the models. Rather, five main results emerge from this research. First, each algorithm was the best estimator for at least one parameter. Second, the best estimation algorithm varied not only between models but even between parameters of the same model. Third, each estimation algorithm failed to estimate at least one identifiable parameter. Fourth, choosing the right algorithm improved estimation performance by more than quadrupling the number of model runs. Fifth, half of the agent-based models tested could not be fully identified. We therefore argue that the testing performed here should be done in other applied work and to facilitate this we would like to share the R package freelunch .","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77142260","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}
: The behavior of a heterogeneous population of individuals during an emergency, such as epidemics, natural disasters, terrorist attacks, is dynamic, emergent and complex. In this situation, reducing uncertainty about the event is crucial in order to identify and pursue the best possible course of action. People depend on experts, government sources, the media and fellow community members as potentially valid sources of information to reduce uncertainty, but their messages can be ambiguous, misleading or contradictory. Effective risk prevention depends on the way in which the population receives, elaborates and spread the message, and together these elements result in a collective perception of risk. The interaction between individuals’ attitudes toward risk and institutions, the more or less alarmist way in which the information is reported and the role of the media can lead to risk perception that differs from the original message, as well as to contrasting opinions about risk within the same population. The aim of this study is to bridge a model of opinion dynamics with the issue of uncertainty and trust in the sources, in order to understand the determinants of collective risk assessment. Our results show that alarming information spreads more easily than reassuring one, and that the media plays a key role in this. Concerning the role of internal variables, our simulation results show that risk sensitiveness has more influence on the final opinion than trust towards the institutional message. Furthermore, the role of different network structures seems to be negligible, even on two empirically calibrated network topologies, thus suggesting that knowing beforehand how much the public trusts their institutional representatives and how reactive they are to a certain risk might provide useful indications to design more effective communication strategies during crises.
{"title":"Opinion Dynamics and Collective Risk Perception: An Agent-Based Model of Institutional and Media Communication About Disasters","authors":"F. Giardini, Daniele Vilone","doi":"10.18564/jasss.4479","DOIUrl":"https://doi.org/10.18564/jasss.4479","url":null,"abstract":": The behavior of a heterogeneous population of individuals during an emergency, such as epidemics, natural disasters, terrorist attacks, is dynamic, emergent and complex. In this situation, reducing uncertainty about the event is crucial in order to identify and pursue the best possible course of action. People depend on experts, government sources, the media and fellow community members as potentially valid sources of information to reduce uncertainty, but their messages can be ambiguous, misleading or contradictory. Effective risk prevention depends on the way in which the population receives, elaborates and spread the message, and together these elements result in a collective perception of risk. The interaction between individuals’ attitudes toward risk and institutions, the more or less alarmist way in which the information is reported and the role of the media can lead to risk perception that differs from the original message, as well as to contrasting opinions about risk within the same population. The aim of this study is to bridge a model of opinion dynamics with the issue of uncertainty and trust in the sources, in order to understand the determinants of collective risk assessment. Our results show that alarming information spreads more easily than reassuring one, and that the media plays a key role in this. Concerning the role of internal variables, our simulation results show that risk sensitiveness has more influence on the final opinion than trust towards the institutional message. Furthermore, the role of different network structures seems to be negligible, even on two empirically calibrated network topologies, thus suggesting that knowing beforehand how much the public trusts their institutional representatives and how reactive they are to a certain risk might provide useful indications to design more effective communication strategies during crises.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77938272","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}
Alejandro Platas-López, A. Guerra-Hernández, F. Grimaldo
: This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the scarce data about this criminal activity resulting from its hidden nature. The main idea is to simulate both a healthy economy without extortion and the same economy under the influence of extortion, comparing then the macroeconomic signals produced in both cases. For this, the Bottom-up Adaptive Macroeconomics (BAM) model was implemented and validated in order to simulate an economy with healthy macroeconomic signals, i.e., moderate inflation, as well as a reasonable unemployment rate. The BAM model defines the usual interactions among workers, firms, and banks in labor, goods and credit markets. Then, crime is introduced by defining the propensity of the poorest workers to become extortionists, as well the efficiency of the police in terms of the probability of capturing them. The definition of the BAM under Extortion Racket Systems (BAMERS) model is completed with a threshold for the firms rejecting the extortion. These parameters are explored exhaustively and independently. Results show that even low levels of propensity towards extortion are enough to notice considerable negative effects as a marked contraction of the Gross Domestic Product and an increase of the unemployment rate, consistent with the few data known about the macroeconomic effect of extortion. Effects on consume, Gini index, inflation, and wealth distribution are also reported. Interestingly, our results suggest that it is more convenient to prevent extortion, rather than combat it once deployed, i.e., there is no police efficiency level that achieves the healthy macroeconomic signals observed in the absence of extortion.
{"title":"On the Macroeconomic Effect of Extortion: An Agent-Based Approach","authors":"Alejandro Platas-López, A. Guerra-Hernández, F. Grimaldo","doi":"10.18564/jasss.4496","DOIUrl":"https://doi.org/10.18564/jasss.4496","url":null,"abstract":": This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the scarce data about this criminal activity resulting from its hidden nature. The main idea is to simulate both a healthy economy without extortion and the same economy under the influence of extortion, comparing then the macroeconomic signals produced in both cases. For this, the Bottom-up Adaptive Macroeconomics (BAM) model was implemented and validated in order to simulate an economy with healthy macroeconomic signals, i.e., moderate inflation, as well as a reasonable unemployment rate. The BAM model defines the usual interactions among workers, firms, and banks in labor, goods and credit markets. Then, crime is introduced by defining the propensity of the poorest workers to become extortionists, as well the efficiency of the police in terms of the probability of capturing them. The definition of the BAM under Extortion Racket Systems (BAMERS) model is completed with a threshold for the firms rejecting the extortion. These parameters are explored exhaustively and independently. Results show that even low levels of propensity towards extortion are enough to notice considerable negative effects as a marked contraction of the Gross Domestic Product and an increase of the unemployment rate, consistent with the few data known about the macroeconomic effect of extortion. Effects on consume, Gini index, inflation, and wealth distribution are also reported. Interestingly, our results suggest that it is more convenient to prevent extortion, rather than combat it once deployed, i.e., there is no police efficiency level that achieves the healthy macroeconomic signals observed in the absence of extortion.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72869250","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}
Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streamsends. Regarding the current societal need for a shi towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the e ect of these behaviours on ISN robustness byusing anagent-basedmodel (ABM). The constructedmodel is basedon insights fromboth literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchangeandcompostproduction. TheTheoryofPlannedBehaviour (TPB)wasused tomodel agentbehaviour in time-dependent bilateral negotiations and synergy evaluationprocesses. Weexploredmodel behaviourwith andwithoutTPB logic across a rangeof possible TPB input variables. The simulation results showhowthemodelled planned behaviour a ects the cash flowoutcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by o ering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
工业共生网络(ISNs)由在当地交换剩余材料和能源的公司组成，以获得经济、环境和/或社会优势。在实践中，当伙伴离开和剩余流发送恢复时，isn通常会失败。考虑到当前社会对可持续发展的需求，isn失败是不可取的。isn的故障可能是由于行动者的行为导致意外的经济损失造成的。在本文中，我们利用基于管理的模型(ABM)来探讨这些行为对系统鲁棒性的影响。构建的模型是基于对三个现实世界案例的文献和参与式建模的见解。它模拟了本地废物交换和堆肥生产的协同效应的实施。利用计划行为理论(theory of planned behavior, TPB)对时间依赖性双边谈判和协同评价过程中的代理行为进行建模。我们在一系列可能的TPB输入变量中探索了具有和不具有TPB逻辑的模型行为。仿真结果表明，所建立的计划行为会影响社会主体的现金流量结果和网络的鲁棒性。本研究为工业共生研究的理论发展提供了一个量化模型，其中包括了企业家的各种行为模式。通过深入了解网络动力学和鲁棒性结果如何不仅与上下文和ISN设计相关，而且与参与者行为相关，这也有助于实践。
{"title":"Actor Behaviour and Robustness of Industrial Symbiosis Networks: An Agent-Based Modelling Approach","authors":"Kasper Lange, G. Korevaar, I. Nikolic, P. Herder","doi":"10.18564/jasss.4635","DOIUrl":"https://doi.org/10.18564/jasss.4635","url":null,"abstract":"Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streamsends. Regarding the current societal need for a shi towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the e ect of these behaviours on ISN robustness byusing anagent-basedmodel (ABM). The constructedmodel is basedon insights fromboth literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchangeandcompostproduction. TheTheoryofPlannedBehaviour (TPB)wasused tomodel agentbehaviour in time-dependent bilateral negotiations and synergy evaluationprocesses. Weexploredmodel behaviourwith andwithoutTPB logic across a rangeof possible TPB input variables. The simulation results showhowthemodelled planned behaviour a ects the cash flowoutcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by o ering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90696382","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}
Processes of individual attitude formation and their macroscopic consequences have become an intriguing research topic, and agent-based models of opinion formation have been proposed to understand this phenomenon. This study conducted an agent-based simulation and examined the role of mass media in a noisy opinion formation process, where opinion heterogeneity is preserved by a weak intensity of assimilation and errors accompanying opinion modifications. In a computational model, agents conformed to their neighbours' opinions in social networks. In addition, each agent tended to be influenced by one of a two external agents with fixed opinions, that is, mass media that take opposite positions on an opinion spectrum. The simulation results demonstrated that a small probability of interactions with mass media reduces opinion heterogeneity even with extreme mass media position values. However, a large frequency of interactions with mass media increases opinion heterogeneity. Accordingly, intermediate assimilation strength achieves the least heterogeneous opinion distribution. The influence of mass media dampens the effects of network topology. Our simulation implies that mass media can play qualitatively different roles depending on their positions and intensity of influence.
{"title":"A Noisy Opinion Formation Model with Two Opposing Mass Media","authors":"Hirofumi Takesue","doi":"10.18564/jasss.4666","DOIUrl":"https://doi.org/10.18564/jasss.4666","url":null,"abstract":"Processes of individual attitude formation and their macroscopic consequences have become an intriguing research topic, and agent-based models of opinion formation have been proposed to understand this phenomenon. This study conducted an agent-based simulation and examined the role of mass media in a noisy opinion formation process, where opinion heterogeneity is preserved by a weak intensity of assimilation and errors accompanying opinion modifications. In a computational model, agents conformed to their neighbours' opinions in social networks. In addition, each agent tended to be influenced by one of a two external agents with fixed opinions, that is, mass media that take opposite positions on an opinion spectrum. The simulation results demonstrated that a small probability of interactions with mass media reduces opinion heterogeneity even with extreme mass media position values. However, a large frequency of interactions with mass media increases opinion heterogeneity. Accordingly, intermediate assimilation strength achieves the least heterogeneous opinion distribution. The influence of mass media dampens the effects of network topology. Our simulation implies that mass media can play qualitatively different roles depending on their positions and intensity of influence.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"51 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77331745","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}
Christine Boshuijzen-van Burken, Ross Gore, F. Dignum, Lambèr M. M. Royakkers, P. Wozny, F. LeRon Shults
We have used value sensitive design as a method to develop an agent-based model of values in humanitarian logistics for refugees. Schwartz's theory of universal values is implemented in the model in such a way that agents can make value trade-offs, which are operationalized into a measure of refugee wellbeing and a measure of public opinion about how the refugee logistics is being handled. By trying out different ‘value-scenarios’, stakeholders who are responsible for, or involved in refugee logistics can have insights into the effects of various value choices. The model is visualized and made usable as a platform (interactive website) for decision-makers to understand the trade-offs in policies for government and non-government organizations.
{"title":"Agent-Based Modelling of Values: The Case of Value Sensitive Design for Refugee Logistics","authors":"Christine Boshuijzen-van Burken, Ross Gore, F. Dignum, Lambèr M. M. Royakkers, P. Wozny, F. LeRon Shults","doi":"10.18564/JASSS.4411","DOIUrl":"https://doi.org/10.18564/JASSS.4411","url":null,"abstract":"We have used value sensitive design as a method to develop an agent-based model of values in humanitarian logistics for refugees. Schwartz's theory of universal values is implemented in the model in such a way that agents can make value trade-offs, which are operationalized into a measure of refugee wellbeing and a measure of public opinion about how the refugee logistics is being handled. By trying out different ‘value-scenarios’, stakeholders who are responsible for, or involved in refugee logistics can have insights into the effects of various value choices. The model is visualized and made usable as a platform (interactive website) for decision-makers to understand the trade-offs in policies for government and non-government organizations.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82958384","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}
Elizabeth Hunter, Brian Mac Namee, John D. Kelleher
Both agent-based models and equation-based models can be used to model the spread of an infectious disease. Equation-based models have been shown to capture the overall dynamics of a disease outbreak while agent-based models are able to capture heterogeneous characteristics of agents that drive the spread of an outbreak. However, agent-based models are computationally intensive. To capture the advantages of both the equation-based and agent-based models, we create a hybrid model where the disease component of the hybrid model switches between agent-based and equation-based. The switch is determined using the number of agents infected. We first test the model at the town level and then the county level investigating different switch values and geographic levels of switching. We find that a hybrid model is able to save time compared to a fully agent-based model without losing a significant amount of fidelity.
{"title":"A Hybrid Agent-Based and Equation Based Model for the Spread of Infectious Diseases","authors":"Elizabeth Hunter, Brian Mac Namee, John D. Kelleher","doi":"10.18564/jasss.4421","DOIUrl":"https://doi.org/10.18564/jasss.4421","url":null,"abstract":"Both agent-based models and equation-based models can be used to model the spread of an infectious disease. Equation-based models have been shown to capture the overall dynamics of a disease outbreak while agent-based models are able to capture heterogeneous characteristics of agents that drive the spread of an outbreak. However, agent-based models are computationally intensive. To capture the advantages of both the equation-based and agent-based models, we create a hybrid model where the disease component of the hybrid model switches between agent-based and equation-based. The switch is determined using the number of agents infected. We first test the model at the town level and then the county level investigating different switch values and geographic levels of switching. We find that a hybrid model is able to save time compared to a fully agent-based model without losing a significant amount of fidelity.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88164343","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：