Michael J. North and Charles M. Macal
- Published in print:
- 2007
- Published Online:
- September 2007
- ISBN:
- 9780195172119
- eISBN:
- 9780199789894
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195172119.003.0007
- Subject:
- Business and Management, Strategy
This chapter provides an overview of ways to create agent-based models, including agent-based modeling and simulation architectures and implementation tools. It also discusses model growth paths for ...
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This chapter provides an overview of ways to create agent-based models, including agent-based modeling and simulation architectures and implementation tools. It also discusses model growth paths for enhancing systems and related issues.Less
This chapter provides an overview of ways to create agent-based models, including agent-based modeling and simulation architectures and implementation tools. It also discusses model growth paths for enhancing systems and related issues.
Jeannette A. Colyvas and Spiro Maroulis
- Published in print:
- 2012
- Published Online:
- October 2017
- ISBN:
- 9780691148670
- eISBN:
- 9781400845552
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691148670.003.0016
- Subject:
- Sociology, Economic Sociology
This chapter extends previous work analyzing the origins of academic entrepreneurship at Stanford with an agent-based model that simulates the rise and spread of patenting by research faculty, ...
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This chapter extends previous work analyzing the origins of academic entrepreneurship at Stanford with an agent-based model that simulates the rise and spread of patenting by research faculty, drawing on archival analysis of divergent approaches taken by different lab directors. In so doing, this chapter builds on the formal model of autocatalysis developed in Chapter 3, which enables this chapter to disentangle competing explanations. The results are quite surprising. Incentives or mimicry alone are less likely to account for academic embrace of patenting, whereas preemptive efforts to preserve scientific autonomy do play a large role. The pursuit of safeguards from commercial co-optation by other researchers has the transformative effect of making the emergence of proprietary science more likely.Less
This chapter extends previous work analyzing the origins of academic entrepreneurship at Stanford with an agent-based model that simulates the rise and spread of patenting by research faculty, drawing on archival analysis of divergent approaches taken by different lab directors. In so doing, this chapter builds on the formal model of autocatalysis developed in Chapter 3, which enables this chapter to disentangle competing explanations. The results are quite surprising. Incentives or mimicry alone are less likely to account for academic embrace of patenting, whereas preemptive efforts to preserve scientific autonomy do play a large role. The pursuit of safeguards from commercial co-optation by other researchers has the transformative effect of making the emergence of proprietary science more likely.
Michael Laver and Ernest Sergenti
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691139036
- eISBN:
- 9781400840328
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691139036.003.0001
- Subject:
- Political Science, Comparative Politics
This chapter begins with a brief discussion of the need for a new approach to modeling party competition. It then makes a case for the use of agent-based modeling to study multiparty competition in ...
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This chapter begins with a brief discussion of the need for a new approach to modeling party competition. It then makes a case for the use of agent-based modeling to study multiparty competition in an evolving dynamic party system, given the analytical intractability of the decision-making environment, and the resulting need for real politicians to rely on informal decision rules. Agent-based models (ABMs) are “bottom-up” models that typically assume settings with a fairly large number of autonomous decision-making agents. Each agent uses some well-specified decision rule to choose actions, and there may be considerable diversity in the decision rules used by different agents. Given the analytical intractability of the decision-making environment, the decision rules that are specified and investigated in ABMs are typically based on adaptive learning rather than forward-looking strategic analysis, and agents are assumed to have bounded rather than perfect rationality. An overview of the subsequent chapters is also presented.Less
This chapter begins with a brief discussion of the need for a new approach to modeling party competition. It then makes a case for the use of agent-based modeling to study multiparty competition in an evolving dynamic party system, given the analytical intractability of the decision-making environment, and the resulting need for real politicians to rely on informal decision rules. Agent-based models (ABMs) are “bottom-up” models that typically assume settings with a fairly large number of autonomous decision-making agents. Each agent uses some well-specified decision rule to choose actions, and there may be considerable diversity in the decision rules used by different agents. Given the analytical intractability of the decision-making environment, the decision rules that are specified and investigated in ABMs are typically based on adaptive learning rather than forward-looking strategic analysis, and agents are assumed to have bounded rather than perfect rationality. An overview of the subsequent chapters is also presented.
Michael Laver and Ernest Sergenti
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691139036
- eISBN:
- 9781400840328
- Item type:
- book
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691139036.001.0001
- Subject:
- Political Science, Comparative Politics
Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to ...
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Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to the subject leave many important questions unanswered. This book offers the first comprehensive treatment of party competition using the computational techniques of agent-based modeling. This exciting new technology enables researchers to model competition between several different political parties for the support of voters with widely varying preferences on many different issues. The book models party competition as a true dynamic process in which political parties rise and fall, a process where different politicians attack the same political problem in very different ways, and where today's political actors, lacking perfect information about the potential consequences of their choices, must constantly adapt their behavior to yesterday's political outcomes. This book shows how agent-based modeling can be used to accurately reflect how political systems really work. It demonstrates that politicians who are satisfied with relatively modest vote shares often do better at winning votes than rivals who search ceaselessly for higher shares of the vote. It reveals that politicians who pay close attention to their personal preferences when setting party policy often have more success than opponents who focus solely on the preferences of voters, that some politicians have idiosyncratic “valence” advantages that enhance their electability—and much more.Less
Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to the subject leave many important questions unanswered. This book offers the first comprehensive treatment of party competition using the computational techniques of agent-based modeling. This exciting new technology enables researchers to model competition between several different political parties for the support of voters with widely varying preferences on many different issues. The book models party competition as a true dynamic process in which political parties rise and fall, a process where different politicians attack the same political problem in very different ways, and where today's political actors, lacking perfect information about the potential consequences of their choices, must constantly adapt their behavior to yesterday's political outcomes. This book shows how agent-based modeling can be used to accurately reflect how political systems really work. It demonstrates that politicians who are satisfied with relatively modest vote shares often do better at winning votes than rivals who search ceaselessly for higher shares of the vote. It reveals that politicians who pay close attention to their personal preferences when setting party policy often have more success than opponents who focus solely on the preferences of voters, that some politicians have idiosyncratic “valence” advantages that enhance their electability—and much more.
John F. Padgett, Peter McMahan, and Xing Zhong
- Published in print:
- 2012
- Published Online:
- October 2017
- ISBN:
- 9780691148670
- eISBN:
- 9781400845552
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691148670.003.0003
- Subject:
- Sociology, Economic Sociology
This chapter further develops an agent-based model of economic production from the previous chapter. It shows that certain limitations intrinsic to the original hypercycle model—in particular, ...
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This chapter further develops an agent-based model of economic production from the previous chapter. It shows that certain limitations intrinsic to the original hypercycle model—in particular, complexity barriers and vulnerability to parasites—are overcome once autocatalysis takes place in a spatial context, rather than in random-topology liquids. Localized heterogeneity in spatial interaction induces the inscription of path dependencies into cells. This explains why life becomes enhanced once it is embodied. The model also demonstrates why altruism and stigmergy produce more complex rule-chemistries. Altruistic reproduction and stigmergy are superior to selfish reproduction and fixed environments, respectively, because of their superior capacities for self-repair. Beyond suggestive specifics, the hypercycle model and its extensions show how chemistry and economic production and trading in markets can be mapped onto each other, sparking insights for both sides.Less
This chapter further develops an agent-based model of economic production from the previous chapter. It shows that certain limitations intrinsic to the original hypercycle model—in particular, complexity barriers and vulnerability to parasites—are overcome once autocatalysis takes place in a spatial context, rather than in random-topology liquids. Localized heterogeneity in spatial interaction induces the inscription of path dependencies into cells. This explains why life becomes enhanced once it is embodied. The model also demonstrates why altruism and stigmergy produce more complex rule-chemistries. Altruistic reproduction and stigmergy are superior to selfish reproduction and fixed environments, respectively, because of their superior capacities for self-repair. Beyond suggestive specifics, the hypercycle model and its extensions show how chemistry and economic production and trading in markets can be mapped onto each other, sparking insights for both sides.
Akira Namatame and Shu-Heng Chen
- Published in print:
- 2016
- Published Online:
- March 2016
- ISBN:
- 9780198708285
- eISBN:
- 9780191779404
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198708285.001.0001
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with ...
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The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the given foundation, the second part reviews three primary forms of network dynamics, i.e., diffusions, cascades, and influences. These primary dynamics are further extended and enriched by practical networks in goods-and-service markets, labor markets, and international trade. The book ends with two challenging issues using agent-based models of networks, i.e., network risks and economic growth.Less
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the given foundation, the second part reviews three primary forms of network dynamics, i.e., diffusions, cascades, and influences. These primary dynamics are further extended and enriched by practical networks in goods-and-service markets, labor markets, and international trade. The book ends with two challenging issues using agent-based models of networks, i.e., network risks and economic growth.
Michael Laver and Ernest Sergenti
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691139036
- eISBN:
- 9781400840328
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691139036.003.0004
- Subject:
- Political Science, Comparative Politics
This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the ...
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This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.Less
This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.
Michael Laver and Ernest Sergenti
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691139036
- eISBN:
- 9781400840328
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691139036.003.0012
- Subject:
- Political Science, Comparative Politics
This concluding chapter summarizes key themes and presents some final thoughts. This book started with the twin premises that understanding multiparty competition is a core concern for everyone ...
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This concluding chapter summarizes key themes and presents some final thoughts. This book started with the twin premises that understanding multiparty competition is a core concern for everyone interested in representative democracy and that multiparty competition should be understood as an evolving dynamic system, not a stationary state. Given these premises, it investigated the dynamics of multiparty competition using computational agent-based modeling, a new technology that is ideally suited to providing systematic answers to the types of question we want to ask. This allows the modeling of decision making by party leaders, in what is clearly an analytically intractable setting, in terms of the informal rules of thumb that might be used by real human beings, rather than the formally provable best response strategies used by traditional formal theorists. Whether people use the dynamic model of multiparty competition or some better model of this vital but complex political process, there is no doubt that the computational approach deployed in this book offers vast potential to ask and answer interesting and important questions.Less
This concluding chapter summarizes key themes and presents some final thoughts. This book started with the twin premises that understanding multiparty competition is a core concern for everyone interested in representative democracy and that multiparty competition should be understood as an evolving dynamic system, not a stationary state. Given these premises, it investigated the dynamics of multiparty competition using computational agent-based modeling, a new technology that is ideally suited to providing systematic answers to the types of question we want to ask. This allows the modeling of decision making by party leaders, in what is clearly an analytically intractable setting, in terms of the informal rules of thumb that might be used by real human beings, rather than the formally provable best response strategies used by traditional formal theorists. Whether people use the dynamic model of multiparty competition or some better model of this vital but complex political process, there is no doubt that the computational approach deployed in this book offers vast potential to ask and answer interesting and important questions.
A. Maurits Van Der Veen and David D. Laitin
- Published in print:
- 2012
- Published Online:
- January 2013
- ISBN:
- 9780199893157
- eISBN:
- 9780199980079
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199893157.003.0007
- Subject:
- Political Science, Comparative Politics
This chapter introduces agent-based modeling as a tool for studying the dynamics of ethnic demography. Computer simulations using agent-based models allow us to advance the static Boulet and Chandra ...
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This chapter introduces agent-based modeling as a tool for studying the dynamics of ethnic demography. Computer simulations using agent-based models allow us to advance the static Boulet and Chandra framework. This chapter demonstrates how core concepts in the constructivist agenda such as “stickiness” of attributes and “salience” of dimensions can be modeled as continuous variables that are mutually constitutive of identity shifts. It first presents simulations that investigate the relationship between the stickiness of a salient attribute and the longevity of leadership of ethnic entrepreneurs who build a constituency based upon identification with that attribute. Next, it applies the model to examine the relationship of electoral thresholds to the concentration of attributes in both ethnic and non-ethnic demographies. The simulations reveal that the electoral system is a significant intervening factor in both of these applications. The chapter concludes by discussing the advantages of agent-based models for analyzing multi-layered and complex interactions.Less
This chapter introduces agent-based modeling as a tool for studying the dynamics of ethnic demography. Computer simulations using agent-based models allow us to advance the static Boulet and Chandra framework. This chapter demonstrates how core concepts in the constructivist agenda such as “stickiness” of attributes and “salience” of dimensions can be modeled as continuous variables that are mutually constitutive of identity shifts. It first presents simulations that investigate the relationship between the stickiness of a salient attribute and the longevity of leadership of ethnic entrepreneurs who build a constituency based upon identification with that attribute. Next, it applies the model to examine the relationship of electoral thresholds to the concentration of attributes in both ethnic and non-ethnic demographies. The simulations reveal that the electoral system is a significant intervening factor in both of these applications. The chapter concludes by discussing the advantages of agent-based models for analyzing multi-layered and complex interactions.
Max H. Boisot, Ian C. MacMillan, and Kyeong Seok Han
- Published in print:
- 2007
- Published Online:
- January 2008
- ISBN:
- 9780199250875
- eISBN:
- 9780191719509
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199250875.003.0008
- Subject:
- Business and Management, Knowledge Management
The conceptual framework that this book gradually develops across the various chapters — I-Space — has been used on numerous occasions as conceptual aids both in research and in consulting ...
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The conceptual framework that this book gradually develops across the various chapters — I-Space — has been used on numerous occasions as conceptual aids both in research and in consulting interventions. It has proven its worth as a sensemaking device in complex situations. But does it have any predictive value? At present, it helps to understand rather than predict. To develop a capacity to predict, the framework would have to be further researched. This chapter therefore first briefly recapitulates the key points presented in the book's previous chapters, and then looks ahead at the kind of research agenda that these points imply. It then identifies several possible avenues of research, from mapping knowledge, cultural and institutional structures, and learning processes to further developing the agent-based simulation model that implements the main provisions of the theorizing underpinning the framework. The research agenda keeps growing. To engage with it, the I-Space Institute whose mission is to carry out I-Space-related research and consulting, has been founded.Less
The conceptual framework that this book gradually develops across the various chapters — I-Space — has been used on numerous occasions as conceptual aids both in research and in consulting interventions. It has proven its worth as a sensemaking device in complex situations. But does it have any predictive value? At present, it helps to understand rather than predict. To develop a capacity to predict, the framework would have to be further researched. This chapter therefore first briefly recapitulates the key points presented in the book's previous chapters, and then looks ahead at the kind of research agenda that these points imply. It then identifies several possible avenues of research, from mapping knowledge, cultural and institutional structures, and learning processes to further developing the agent-based simulation model that implements the main provisions of the theorizing underpinning the framework. The research agenda keeps growing. To engage with it, the I-Space Institute whose mission is to carry out I-Space-related research and consulting, has been founded.
Akira Namatame and Shu-Heng Chen
- Published in print:
- 2016
- Published Online:
- March 2016
- ISBN:
- 9780198708285
- eISBN:
- 9780191779404
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198708285.003.0001
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
This book is about the integration of agent-based modeling and network science. The leading chapter gives a brief historical review as a background and motivation of the book. The historical review ...
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This book is about the integration of agent-based modeling and network science. The leading chapter gives a brief historical review as a background and motivation of the book. The historical review begins with the network origin of agent-based models, the rising of autonomous agents and network sciences, the game-theoretic formulation of networks, and the agent-based formulation of networks, and ends with networks as an alternative manifestation of markets. Along this series of development, we see how network work becomes an indispensable element of agent-based models, and how agents-based models can be a powerful tool for modeling the dynamics on and of networks. With this large picture, the book is divided into three parts: foundations, primary dynamics on and of networks, and applications.Less
This book is about the integration of agent-based modeling and network science. The leading chapter gives a brief historical review as a background and motivation of the book. The historical review begins with the network origin of agent-based models, the rising of autonomous agents and network sciences, the game-theoretic formulation of networks, and the agent-based formulation of networks, and ends with networks as an alternative manifestation of markets. Along this series of development, we see how network work becomes an indispensable element of agent-based models, and how agents-based models can be a powerful tool for modeling the dynamics on and of networks. With this large picture, the book is divided into three parts: foundations, primary dynamics on and of networks, and applications.
Joshua M. Epstein
- Published in print:
- 2014
- Published Online:
- October 2017
- ISBN:
- 9780691158884
- eISBN:
- 9781400848256
- Item type:
- book
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691158884.001.0001
- Subject:
- Mathematics, Applied Mathematics
This book introduces a new theoretical entity: Agent_Zero. This software individual, or “agent,” is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in ...
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This book introduces a new theoretical entity: Agent_Zero. This software individual, or “agent,” is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics. The book weaves a computational tapestry with threads from Plato, David Hume, Charles Darwin, Ivan Pavlov, Adam Smith, Leo Tolstoy, Karl Marx, William James, and Fyodor Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Computer programs are provided in the book or available online. This book is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, this book presents a ground-breaking vision and the tools to realize it.Less
This book introduces a new theoretical entity: Agent_Zero. This software individual, or “agent,” is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics. The book weaves a computational tapestry with threads from Plato, David Hume, Charles Darwin, Ivan Pavlov, Adam Smith, Leo Tolstoy, Karl Marx, William James, and Fyodor Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Computer programs are provided in the book or available online. This book is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, this book presents a ground-breaking vision and the tools to realize it.
Brenda Heaton, Abdulrahman El-Sayed, and Sandro Galea
- Published in print:
- 2018
- Published Online:
- April 2018
- ISBN:
- 9780190843496
- eISBN:
- 9780190843533
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190843496.003.0005
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions ...
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Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities, such as organizations or groups) with a view to assessing their effects on the system as a whole. Neighborhood characteristics and resources evolve and adapt as the individuals living within them change and vice versa. In this way, neighborhoods reflect a complex adaptive system. In this chapter, we introduce agent-based models as a tool for modeling these interactive and adaptive processes that occur within a system, such as a neighborhood. The chapter provides a basic introduction to this method, drawing on examples from the neighborhoods and health literature.Less
Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities, such as organizations or groups) with a view to assessing their effects on the system as a whole. Neighborhood characteristics and resources evolve and adapt as the individuals living within them change and vice versa. In this way, neighborhoods reflect a complex adaptive system. In this chapter, we introduce agent-based models as a tool for modeling these interactive and adaptive processes that occur within a system, such as a neighborhood. The chapter provides a basic introduction to this method, drawing on examples from the neighborhoods and health literature.
Michael Laver and Ernest Sergenti
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691139036
- eISBN:
- 9781400840328
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691139036.003.0003
- Subject:
- Political Science, Comparative Politics
This chapter specifies the “baseline” agent-based model of dynamic multiparty competition, which derives from an article published by (Laver 2005). This assumes that each voter has in mind some ...
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This chapter specifies the “baseline” agent-based model of dynamic multiparty competition, which derives from an article published by (Laver 2005). This assumes that each voter has in mind some personal ideal “package” of policy positions and supports the political party that offers the policy package closest to this. The dynamic system at the heart of the model is as follows: voters support their “closest” party in this sense; party leaders adapt the policy packages they offer in light of the revealed pattern of voter support; voters reconsider which party they support in light of the revealed pattern of party policy packages; and this process continues forever. This recursive model describes policy-based party competition as a complex system, and the baseline model specifies three decision rules that party leaders may deploy when they choose party policy positions in such a setting. These rules are Sticker (always keep the same position), Aggregator (move policy to the centroid of the ideal policy positions of your current supporters), and Hunter (if your last policy move increased your support, make another move in the same direction; or else change heading and move in a different direction).Less
This chapter specifies the “baseline” agent-based model of dynamic multiparty competition, which derives from an article published by (Laver 2005). This assumes that each voter has in mind some personal ideal “package” of policy positions and supports the political party that offers the policy package closest to this. The dynamic system at the heart of the model is as follows: voters support their “closest” party in this sense; party leaders adapt the policy packages they offer in light of the revealed pattern of voter support; voters reconsider which party they support in light of the revealed pattern of party policy packages; and this process continues forever. This recursive model describes policy-based party competition as a complex system, and the baseline model specifies three decision rules that party leaders may deploy when they choose party policy positions in such a setting. These rules are Sticker (always keep the same position), Aggregator (move policy to the centroid of the ideal policy positions of your current supporters), and Hunter (if your last policy move increased your support, make another move in the same direction; or else change heading and move in a different direction).
Douglas A. Luke, Alexandra B. Morshed, Virginia R. McKay, and Todd B. Combs
- Published in print:
- 2017
- Published Online:
- November 2017
- ISBN:
- 9780190683214
- eISBN:
- 9780190683245
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190683214.003.0010
- Subject:
- Public Health and Epidemiology, Public Health
As we have seen, numerous analysis and modeling tools that take into account the natural complexity of systems and dissemination and implementation processes are available, and the use of them is ...
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As we have seen, numerous analysis and modeling tools that take into account the natural complexity of systems and dissemination and implementation processes are available, and the use of them is increasing over time. This chapter summarizes the characteristics, potential insights, and limitations of each modeling approach. It is important to note that modeling from a systems perspective, like all modeling approaches, requires assumptions about variables to include (or exclude), and hypothesized relationships dictate the quality of the model and the utility of the results. As such, using theory and empirical data to inform model design is paramount. Systems thinking and methods remain underutilized in dissemination and implementation despite demonstrations of the utility of incorporating systems thinking and methods into dissemination and implementation studies.Less
As we have seen, numerous analysis and modeling tools that take into account the natural complexity of systems and dissemination and implementation processes are available, and the use of them is increasing over time. This chapter summarizes the characteristics, potential insights, and limitations of each modeling approach. It is important to note that modeling from a systems perspective, like all modeling approaches, requires assumptions about variables to include (or exclude), and hypothesized relationships dictate the quality of the model and the utility of the results. As such, using theory and empirical data to inform model design is paramount. Systems thinking and methods remain underutilized in dissemination and implementation despite demonstrations of the utility of incorporating systems thinking and methods into dissemination and implementation studies.
Akira Namatame and Shu-Heng Chen
- Published in print:
- 2016
- Published Online:
- March 2016
- ISBN:
- 9780198708285
- eISBN:
- 9780191779404
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198708285.003.0002
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
Chapter 2 reviews the development of the network-based agent-based models. From the behavioral and decision-making perspective of agents, the network-based agent-based model is accompanied by the ...
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Chapter 2 reviews the development of the network-based agent-based models. From the behavioral and decision-making perspective of agents, the network-based agent-based model is accompanied by the neighborhood-based decision rules. The chapter divides the literature into two parts. The one developed before the advent of modern network science normally relies on the one-dimensional or two-dimensional lattices (cellular automata). The one developed with the advent of modern network science relies on the newly proposed network generation algorithms. In a chronological order, the chapter demonstrates the two-generation network-based agent-based models via a number of pioneering works. The purpose of these demonstrations is to show how network topologies can affect the operation of various economic and social systems, including residential segregation, pro-social behavior, oligopolistic competition, market sentiment, sharing of public resources, market mechanism, marketing, and macroeconomic stability. Cellular automata as the theoretical underpinning of undecidability and unpredictability for the dynamics on networks are also introduced.Less
Chapter 2 reviews the development of the network-based agent-based models. From the behavioral and decision-making perspective of agents, the network-based agent-based model is accompanied by the neighborhood-based decision rules. The chapter divides the literature into two parts. The one developed before the advent of modern network science normally relies on the one-dimensional or two-dimensional lattices (cellular automata). The one developed with the advent of modern network science relies on the newly proposed network generation algorithms. In a chronological order, the chapter demonstrates the two-generation network-based agent-based models via a number of pioneering works. The purpose of these demonstrations is to show how network topologies can affect the operation of various economic and social systems, including residential segregation, pro-social behavior, oligopolistic competition, market sentiment, sharing of public resources, market mechanism, marketing, and macroeconomic stability. Cellular automata as the theoretical underpinning of undecidability and unpredictability for the dynamics on networks are also introduced.
Michael Wolf-Branigin
- Published in print:
- 2013
- Published Online:
- January 2013
- ISBN:
- 9780199829460
- eISBN:
- 9780199315895
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199829460.003.0006
- Subject:
- Social Work, Research and Evaluation
Agent-based modeling (ABM) provides a viable approach for investigating complex phenomena. ABM computationally simulates the interactions of autonomous agents in order to assess their effect on whole ...
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Agent-based modeling (ABM) provides a viable approach for investigating complex phenomena. ABM computationally simulates the interactions of autonomous agents in order to assess their effect on whole systems and aids in visualizing that clients self-organize at the grassroots level. ABMs create a "social reality" generated from several inputs. This chapter provides the reader with essential background on developing an agent-based model, and instructions for downloading and installing the freely distributed NetLogo software. This supplementary material includes using the tab features to write, debug, and run code. Although more advanced software is available, NetLogo has the advantage of being user-friendly. Computational models are computer programs that we develop.Less
Agent-based modeling (ABM) provides a viable approach for investigating complex phenomena. ABM computationally simulates the interactions of autonomous agents in order to assess their effect on whole systems and aids in visualizing that clients self-organize at the grassroots level. ABMs create a "social reality" generated from several inputs. This chapter provides the reader with essential background on developing an agent-based model, and instructions for downloading and installing the freely distributed NetLogo software. This supplementary material includes using the tab features to write, debug, and run code. Although more advanced software is available, NetLogo has the advantage of being user-friendly. Computational models are computer programs that we develop.
Brandon D. L. Marshall
- Published in print:
- 2017
- Published Online:
- March 2017
- ISBN:
- 9780190492397
- eISBN:
- 9780190492427
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190492397.003.0008
- Subject:
- Public Health and Epidemiology, Epidemiology, Public Health
This chapter conveys the key concepts, overarching methods, and common applications of agent-based modeling in population health science. This chapter will also provide the reader with a foundational ...
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This chapter conveys the key concepts, overarching methods, and common applications of agent-based modeling in population health science. This chapter will also provide the reader with a foundational understanding of how and why agent-based models (ABMs) are increasingly employed to address pressing public health challenges of the 21st century. It demonstrates these concepts with an example that adopts ABM to simulate HIV transmission dynamics in high-risk populations. The chapter concludes with an in-depth discussion of two of the most promising avenues for the continued adoption of agent-based modeling approaches to improve population health: the evaluation of policy experiments and evidence synthesis.Less
This chapter conveys the key concepts, overarching methods, and common applications of agent-based modeling in population health science. This chapter will also provide the reader with a foundational understanding of how and why agent-based models (ABMs) are increasingly employed to address pressing public health challenges of the 21st century. It demonstrates these concepts with an example that adopts ABM to simulate HIV transmission dynamics in high-risk populations. The chapter concludes with an in-depth discussion of two of the most promising avenues for the continued adoption of agent-based modeling approaches to improve population health: the evaluation of policy experiments and evidence synthesis.
Michael Wolf-Branigin
- Published in print:
- 2013
- Published Online:
- January 2013
- ISBN:
- 9780199829460
- eISBN:
- 9780199315895
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199829460.001.0001
- Subject:
- Social Work, Research and Evaluation
Complexity theory provides a promising framework for organizing and conducting social work research and evaluation. This book explores the history and roots of complexity and related concepts by ...
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Complexity theory provides a promising framework for organizing and conducting social work research and evaluation. This book explores the history and roots of complexity and related concepts by garnering an understanding of the components that comprise complex systems. These components include being agent-based, being sensitive to initial conditions, having attraction, being heterogeneous, operating as an iterative process, having boundaries, using feedback, and creating a self-organizing emergent behavior. Readers will learn to frame their research using the components found in complex systems by using their existing knowledge of research methods and applying basic mathematical concepts. Several concepts related to complexity theory are introduced and applied to social work research studies, including bordering between chaos and equilibrium, diverse perspectives, diverse heuristics, robustness, and the wisdom of crowds. Many of the theoretical and mathematical concepts underlying complexity are introduced, such as game theory, graph theory, Boolean logic, decision theory, and network science. Using this background, the reader will gain an understanding of the interconnectedness and networking that this approach provides. Statistical methods familiar to many readers are reviewed and applied to complexity. Readers will gain an understanding of agent-based modeling as a new and evolving computational approach for creating simulations to represent and forecast complex systems. To advance this line of inquiry, a complexity research agenda for social work is developed.Less
Complexity theory provides a promising framework for organizing and conducting social work research and evaluation. This book explores the history and roots of complexity and related concepts by garnering an understanding of the components that comprise complex systems. These components include being agent-based, being sensitive to initial conditions, having attraction, being heterogeneous, operating as an iterative process, having boundaries, using feedback, and creating a self-organizing emergent behavior. Readers will learn to frame their research using the components found in complex systems by using their existing knowledge of research methods and applying basic mathematical concepts. Several concepts related to complexity theory are introduced and applied to social work research studies, including bordering between chaos and equilibrium, diverse perspectives, diverse heuristics, robustness, and the wisdom of crowds. Many of the theoretical and mathematical concepts underlying complexity are introduced, such as game theory, graph theory, Boolean logic, decision theory, and network science. Using this background, the reader will gain an understanding of the interconnectedness and networking that this approach provides. Statistical methods familiar to many readers are reviewed and applied to complexity. Readers will gain an understanding of agent-based modeling as a new and evolving computational approach for creating simulations to represent and forecast complex systems. To advance this line of inquiry, a complexity research agenda for social work is developed.
Paul Box
- Published in print:
- 2002
- Published Online:
- November 2020
- ISBN:
- 9780195143362
- eISBN:
- 9780197561812
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195143362.003.0009
- Subject:
- Computer Science, Mathematical Theory of Computation
Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the ...
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Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.
Less
Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.
