This editorial paper reviews the state of the science about agent-basedmodeling (ABM), pointing out the strengths and weaknesses of ABM. This paper also highlights several impending tasks that warrant special attention inorder to improve the scienceandapplicationof ABM:Modelinghumandecisions, ABM transparency and reusability, validation of ABM, ABM so ware and big data ABM, and ABM theories. Six innovative papers that are included in the special issue are summarized, and their connections to the ABM impending tasks are brought toattention. Theauthorshope that this special issuewill helpprioritize specific resourcesandactivities in relation to ABM advances, leading to coordinated, joint e orts and initiatives to advance the science and technology behind ABM.
{"title":"Editorial: Meeting Grand Challenges in Agent-Based Models","authors":"Li An, V. Grimm, B. Turner","doi":"10.18564/jasss.4012","DOIUrl":"https://doi.org/10.18564/jasss.4012","url":null,"abstract":"This editorial paper reviews the state of the science about agent-basedmodeling (ABM), pointing out the strengths and weaknesses of ABM. This paper also highlights several impending tasks that warrant special attention inorder to improve the scienceandapplicationof ABM:Modelinghumandecisions, ABM transparency and reusability, validation of ABM, ABM so ware and big data ABM, and ABM theories. Six innovative papers that are included in the special issue are summarized, and their connections to the ABM impending tasks are brought toattention. Theauthorshope that this special issuewill helpprioritize specific resourcesandactivities in relation to ABM advances, leading to coordinated, joint e orts and initiatives to advance the science and technology behind ABM.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87303069","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}
S. Manson, Li An, K. Clarke, A. Heppenstall, J. Koch, B. Krzyzanowski, Fraser J. Morgan, David O’Sullivan, Bryan C. Runck, Eric Shook, L. Tesfatsion
Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.
{"title":"Methodological Issues of Spatial Agent-Based Models","authors":"S. Manson, Li An, K. Clarke, A. Heppenstall, J. Koch, B. Krzyzanowski, Fraser J. Morgan, David O’Sullivan, Bryan C. Runck, Eric Shook, L. Tesfatsion","doi":"10.18564/jasss.4174","DOIUrl":"https://doi.org/10.18564/jasss.4174","url":null,"abstract":"Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90523033","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}
Multi-Level Agent-Based Modeling (ML-ABM) has been receiving increasing attention in recent years. In this paper we present LevelSpace, an extension that allows modelers to easily build ML-ABMs in the popular and widely used NetLogo language. We present the LevelSpace framework and its associated programming primitives. Based on three common use-cases of ML-ABM – coupling of heterogeneous models, dynamic adaptation of detail, and cross-level interaction - we show how easy it is to build ML-ABMs with LevelSpace. We argue that it is important to have a unified conceptual language for describing LevelSpace models, and present six dimensions along which models can differ, and discuss how these can be combined into a variety of ML-ABM types in LevelSpace. Finally, we argue that future work should explore the relationships between these six dimensions, and how different configurations of them might be more or less appropriate for particular modeling tasks.
{"title":"LevelSpace: A NetLogo Extension for Multi-Level Agent-Based Modeling","authors":"A. Hjorth, Bryan Head, C. Brady, U. Wilensky","doi":"10.18564/jasss.4130","DOIUrl":"https://doi.org/10.18564/jasss.4130","url":null,"abstract":"Multi-Level Agent-Based Modeling (ML-ABM) has been receiving increasing attention in recent years. In this paper we present LevelSpace, an extension that allows modelers to easily build ML-ABMs in the popular and widely used NetLogo language. We present the LevelSpace framework and its associated programming primitives. Based on three common use-cases of ML-ABM – coupling of heterogeneous models, dynamic adaptation of detail, and cross-level interaction - we show how easy it is to build ML-ABMs with LevelSpace. We argue that it is important to have a unified conceptual language for describing LevelSpace models, and present six dimensions along which models can differ, and discuss how these can be combined into a variety of ML-ABM types in LevelSpace. Finally, we argue that future work should explore the relationships between these six dimensions, and how different configurations of them might be more or less appropriate for particular modeling tasks.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82812092","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}
Land use and land cover change has been recognized to have significant environmental impacts in a watershed, such as regulation of water quality. However, the identification of potential regions that are sensitive to land change activities for the protection of water quality poses a grand challenge particularly in a large watershed. These potential regions are o en associated with critical thresholds in terms of, for example, water quality. In this study, we developed an agent-based land changemodel to investigate the relationship between land development activities and water quality in eight North Carolina counties that cover the lower High Rock Lake Watershed area. This agent-based model, which is empirically calibrated, is used to identify space-time locations of those regions at critical thresholds of water quality in this study area. Our experimental results suggest that land development as a form of system stress is of pivotal importance in a ecting water quality at sub watershed level and the state transition of water quality. The agent-based model developed in this study provides solid support for investigations on the impact of land development under alternative scenarios in a large watershed.
{"title":"Agent-Based Land Change Modeling of a Large Watershed: Space-Time Locations of Critical Threshold","authors":"Wenwu Tang, Jianxin Yang","doi":"10.18564/jasss.4226","DOIUrl":"https://doi.org/10.18564/jasss.4226","url":null,"abstract":"Land use and land cover change has been recognized to have significant environmental impacts in a watershed, such as regulation of water quality. However, the identification of potential regions that are sensitive to land change activities for the protection of water quality poses a grand challenge particularly in a large watershed. These potential regions are o en associated with critical thresholds in terms of, for example, water quality. In this study, we developed an agent-based land changemodel to investigate the relationship between land development activities and water quality in eight North Carolina counties that cover the lower High Rock Lake Watershed area. This agent-based model, which is empirically calibrated, is used to identify space-time locations of those regions at critical thresholds of water quality in this study area. Our experimental results suggest that land development as a form of system stress is of pivotal importance in a ecting water quality at sub watershed level and the state transition of water quality. The agent-based model developed in this study provides solid support for investigations on the impact of land development under alternative scenarios in a large watershed.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73635264","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}
Li An, Judy Mak, Shuang Yang, R. Lewison, D. Stow, H. Chen, Weihua Xu, Lei Shi, Y. Tsai
The theory and practice associated with payments for ecosystem services (PES) feature a variety of piecemeal studies related to impacts of socioeconomic, demographic, and environmental variables, lacking e orts in understanding their mutual relationships in a spatially and temporally explicit manner. In addition, PES literature is short of ecological metrics that document the consequences of PES other than land use and land cover and its change. Building on detailed survey data from Fanjingshan National Nature Reserve (FNNR), China, we developed and tested an agent-basedmodel to study the complex interactions among human livelihoods (migration and resource extraction in particular), PES, and the Guizhou golden monkey habitat occupancy over 20 years. We then performed simulation-based experiments testing social and ecological impacts of PES payments as well as human population pressures. The results show that with a steady increase in outmigration, the number of land parcels enrolled in one of China’s major PES programs tends to increase, reach a peak, and then slowly decline, showing a convex trend that converges to a stable number of enrolled parcels regardless of payment levels. Simulated monkey occupancy responds to changes in PES payment levels substantially in edge areas of FNNR. Our model is not only useful for FNNR, but also applicable as a platform to study and further understand human and ecological roles of PES in many other complex human-environment systems, shedding light into key elements, interactions, or relationships in the systems that PES researchers and practitioners should bear inmind. Our research contributes to establishing a scientific basis of PES science that incorporates features in complex systems, o eringmore realistic, spatially and temporally explicit insights related to PES policy or related interventions.
{"title":"Cascading Impacts of Payments for Ecosystem Services in Complex Human-Environment Systems","authors":"Li An, Judy Mak, Shuang Yang, R. Lewison, D. Stow, H. Chen, Weihua Xu, Lei Shi, Y. Tsai","doi":"10.18564/jasss.4196","DOIUrl":"https://doi.org/10.18564/jasss.4196","url":null,"abstract":"The theory and practice associated with payments for ecosystem services (PES) feature a variety of piecemeal studies related to impacts of socioeconomic, demographic, and environmental variables, lacking e orts in understanding their mutual relationships in a spatially and temporally explicit manner. In addition, PES literature is short of ecological metrics that document the consequences of PES other than land use and land cover and its change. Building on detailed survey data from Fanjingshan National Nature Reserve (FNNR), China, we developed and tested an agent-basedmodel to study the complex interactions among human livelihoods (migration and resource extraction in particular), PES, and the Guizhou golden monkey habitat occupancy over 20 years. We then performed simulation-based experiments testing social and ecological impacts of PES payments as well as human population pressures. The results show that with a steady increase in outmigration, the number of land parcels enrolled in one of China’s major PES programs tends to increase, reach a peak, and then slowly decline, showing a convex trend that converges to a stable number of enrolled parcels regardless of payment levels. Simulated monkey occupancy responds to changes in PES payment levels substantially in edge areas of FNNR. Our model is not only useful for FNNR, but also applicable as a platform to study and further understand human and ecological roles of PES in many other complex human-environment systems, shedding light into key elements, interactions, or relationships in the systems that PES researchers and practitioners should bear inmind. Our research contributes to establishing a scientific basis of PES science that incorporates features in complex systems, o eringmore realistic, spatially and temporally explicit insights related to PES policy or related interventions.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90142908","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}
Peer-Olaf Siebers, Zhi En Lim, G. Figueredo, James Hey
Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisationmodels. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, o en used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stockswith a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybridmodelling approach; we do not claim that it has predictive powers.
{"title":"An Innovative Approach to Multi-Method Integrated Assessment Modelling of Global Climate Change","authors":"Peer-Olaf Siebers, Zhi En Lim, G. Figueredo, James Hey","doi":"10.18564/jasss.4209","DOIUrl":"https://doi.org/10.18564/jasss.4209","url":null,"abstract":"Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisationmodels. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, o en used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stockswith a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybridmodelling approach; we do not claim that it has predictive powers.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"85 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83447197","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 successful adoptionof innovationsdependson theprovisionof adequate information to farmers. In rural areas of developing countries, farmers usually rely on their social networks as an information source. Hence, policy-makers and program-implementers can benefit from social di usion processes to e ectively disseminate information. This study aims to identify the set of farmers who initially obtain information (‘seeds’) that optimises di usion through the network. It systematically evaluates di erent criteria for seed selection, number of seeds, and their interaction e ects. An empirical Agent-Based Model adjusted to a case study in rural Zambia was applied to predict di usion outcomes for varying seed sets ex ante. Simulations revealed that informing farmers with the most connections leads to highest di usion speed and reach. Also targeting village heads and farmers with high betweenness centrality, who function as bridges connecting di erent parts of the network, enhances di usion. An increased number of seeds improves reach, but the marginal e ects of additional seeds decline. Interdependencies between seed set size and selection criteria highlight the importance of considering both seed selection criteria and seed set size for optimising seeding strategies to enhance information di usion.
{"title":"Seed Selection Strategies for Information Diffusion in Social Networks: An Agent-Based Model Applied to Rural Zambia","authors":"Beatrice Nöldeke, E. Winter, U. Grote","doi":"10.18564/jasss.4429","DOIUrl":"https://doi.org/10.18564/jasss.4429","url":null,"abstract":"The successful adoptionof innovationsdependson theprovisionof adequate information to farmers. In rural areas of developing countries, farmers usually rely on their social networks as an information source. Hence, policy-makers and program-implementers can benefit from social di usion processes to e ectively disseminate information. This study aims to identify the set of farmers who initially obtain information (‘seeds’) that optimises di usion through the network. It systematically evaluates di erent criteria for seed selection, number of seeds, and their interaction e ects. An empirical Agent-Based Model adjusted to a case study in rural Zambia was applied to predict di usion outcomes for varying seed sets ex ante. Simulations revealed that informing farmers with the most connections leads to highest di usion speed and reach. Also targeting village heads and farmers with high betweenness centrality, who function as bridges connecting di erent parts of the network, enhances di usion. An increased number of seeds improves reach, but the marginal e ects of additional seeds decline. Interdependencies between seed set size and selection criteria highlight the importance of considering both seed selection criteria and seed set size for optimising seeding strategies to enhance information di usion.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80497121","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}
Nicholas LaBerge, Aria Chaderjian, Victor Ginelli, Margrethe Jebsen, A. Landsberg
The process by which beliefs, opinions, and other individual, socially malleable attributes spread across a society, known as "cultural dissemination," is a broadly recognized concept among sociologists and political scientists. Yet fundamental aspects of how this process can ultimately lead to cultural divergences between rural and urban segments of society are currently poorly understood. This article uses an agent-based model to isolate and analyze one very basic yet essential facet of this issue, namely, the question of how the intrinsic differences in urban and rural population densities influence the levels of cultural homogeneity/heterogeneity that emerge within each region. Because urban and rural cultures do not develop in isolation from one another, the dynamical interplay between the two is of particular import in their evolution. It is found that, in urban areas, the relatively high number of local neighbors with whom one can interact tends to promote cultural homogeneity in both urban and rural regions. Moreover, and rather surprisingly, the higher frequency of potential interactions with neighbors within urban regions promotes homogeneity in urban regions but tends to drive rural regions towards greater levels of heterogeneity.
{"title":"Modeling Cultural Dissemination and Divergence Between Rural and Urban Regions","authors":"Nicholas LaBerge, Aria Chaderjian, Victor Ginelli, Margrethe Jebsen, A. Landsberg","doi":"10.18564/jasss.4391","DOIUrl":"https://doi.org/10.18564/jasss.4391","url":null,"abstract":"The process by which beliefs, opinions, and other individual, socially malleable attributes spread across a society, known as \"cultural dissemination,\" is a broadly recognized concept among sociologists and political scientists. Yet fundamental aspects of how this process can ultimately lead to cultural divergences between rural and urban segments of society are currently poorly understood. This article uses an agent-based model to isolate and analyze one very basic yet essential facet of this issue, namely, the question of how the intrinsic differences in urban and rural population densities influence the levels of cultural homogeneity/heterogeneity that emerge within each region. Because urban and rural cultures do not develop in isolation from one another, the dynamical interplay between the two is of particular import in their evolution. It is found that, in urban areas, the relatively high number of local neighbors with whom one can interact tends to promote cultural homogeneity in both urban and rural regions. Moreover, and rather surprisingly, the higher frequency of potential interactions with neighbors within urban regions promotes homogeneity in urban regions but tends to drive rural regions towards greater levels of heterogeneity.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"144 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86754366","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 provide an overview of the WorkSim model, an agent-based framework designed to study labor markets. The first objective of this model was to reproduce, within rigorous stock-flow accounting, the gross flows of individuals between important work-states: i.e., employment (distinguishing fixed term contracts and open-ended contracts), unemployment and inactivity. French legal institutions of the labor market are modelled in some detail and constrain the decisions of the agents on job flows and worker flows. Firms and individuals are heterogeneous and all decisions are taken on the basis of bounded rationality, yet employers as well as workers form imperfect anticipations. One important theoretical novelty of the model is that we consider multi-job firms and shocks on the individual demand of the firms. Employers consider anticipated shocks when they decide on the types of contract. Once the model was calibrated, the secondary objective was to characterize the nature of the labor market under study, and notably the differentiated roles of the two types of contracts and their impact on unemployment. This is achieved, first by examining the patterns of flows and stocks of labor and secondly by sensitivity experiments, modifying certain exogenous parameters and variables such as total demand. We then used the model as a tool for experimenting labor market policies, including changes in the labor law in France.
{"title":"WorkSim: An Agent-Based Model of Labor Markets","authors":"Jean-Daniel Kant, Gérard Ballot, Olivier Goudet","doi":"10.18564/jasss.4396","DOIUrl":"https://doi.org/10.18564/jasss.4396","url":null,"abstract":"In this paper, we provide an overview of the WorkSim model, an agent-based framework designed to study labor markets. The first objective of this model was to reproduce, within rigorous stock-flow accounting, the gross flows of individuals between important work-states: i.e., employment (distinguishing fixed term contracts and open-ended contracts), unemployment and inactivity. French legal institutions of the labor market are modelled in some detail and constrain the decisions of the agents on job flows and worker flows. Firms and individuals are heterogeneous and all decisions are taken on the basis of bounded rationality, yet employers as well as workers form imperfect anticipations. One important theoretical novelty of the model is that we consider multi-job firms and shocks on the individual demand of the firms. Employers consider anticipated shocks when they decide on the types of contract. Once the model was calibrated, the secondary objective was to characterize the nature of the labor market under study, and notably the differentiated roles of the two types of contracts and their impact on unemployment. This is achieved, first by examining the patterns of flows and stocks of labor and secondly by sensitivity experiments, modifying certain exogenous parameters and variables such as total demand. We then used the model as a tool for experimenting labor market policies, including changes in the labor law in France.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82352427","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}
Chaitanya Kaligotla, J. Ozik, Nicholson T. Collier, C. Macal, K. Boyd, Jennifer A. Makelarski, E. Huang, S. Lindau
This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.
{"title":"Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning","authors":"Chaitanya Kaligotla, J. Ozik, Nicholson T. Collier, C. Macal, K. Boyd, Jennifer A. Makelarski, E. Huang, S. Lindau","doi":"10.2139/ssrn.3429164","DOIUrl":"https://doi.org/10.2139/ssrn.3429164","url":null,"abstract":"This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83096038","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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