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Growing Artificial Societies: Social Science from the Bottom Up

TL;DR: Artificial Society as discussed by the authors models life and death on the sugarcane, sex, culture and conflict, the emergence of history sugar and spice -trade comes to the sugarscane disease agents a society is born artificial societies versus traditional models artificial society versus a life toward generative social science - can you grow it?.
Abstract: Part I Introduction: "Artificial Society" models life and death on the sugarscape sex, culture and conflict - the emergence of history sugar and spice - trade comes to the sugarscape disease agents a society is born artificial societies versus traditional models artificial societies versus a life toward generative social science - can you grow it?. Part II Life and death on the sugarscape: in the beginning - there was sugar the agents artificial society on the sugarscape wealth and its distribution in the agent population social networks of neighbours migration summary. Part III Sex, culture and conflict - the emergence of history: sexual reproduction cultural processes combat the proto-history. Part IV Sugar and spice - trade comes to the sugarscape: spice - a second commodity trade rules markets of bilateral traders emergent economic networks social computation, emergent computation summary and conclusions. Part V Disease processes: models of disease transmission and immune response immune system response disease transmission digital diseases on the sugarscape disease transmission networks. Part VI Conclusions: summary some extensions of the current model other artificial societies formal analysis of artificial societies generative social science looking ahead. Appendices: software engineering aspects of artificial societies summary of rule notation state-dependence on the welfare function.
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Journal ArticleDOI
TL;DR: In this article, a wide list of topics ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading are reviewed and connections between these problems and other, more traditional, topics of statistical physics are highlighted.
Abstract: Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

3,840 citations


Cites background from "Growing Artificial Societies: Socia..."

  • ...In (Epstein and Axtell, 1996), by focusing on a bottom-up approach, the first large scale agent model, the Sugarscape, has been introduced to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, trade and transmission of disease and…...

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Journal ArticleDOI
Elinor Ostrom1
TL;DR: The Logic of Collective Action (LCA) as mentioned in this paper was a seminal work in modern democratic thought that challenged the assumption that groups would tend to form and take collective action in democratic societies.
Abstract: With the publication of The Logic of Collective Action in 1965, Mancur Olson challenged a cherished foundation of modern democratic thought that groups would tend to form and take collective action...

3,231 citations

Journal ArticleDOI
TL;DR: In this paper, an agent-based adaptive model is proposed to reveal the effects of a mechanism of convergent social influence, where actors are placed at fixed sites and the basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits.
Abstract: Despite tendencies toward convergence, differences between individuals and groups continue to exist in beliefs, attitudes, and behavior. An agent-based adaptive model reveals the effects of a mechanism of convergent social influence. The actors are placed at fixed sites. The basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits. Unlike previous models of social influence or cultural change that treat features one at a time, the proposed model takes into account the interaction between different features. The model illustrates how local convergence can generate global polarization. Simulations show that the number of stable homogeneous regions decreases with the number of features, increases with the number of alternative traits per feature, decreases with the range of interaction, and (most surprisingly) decreases when the geographic territory grows beyond a certain size. MAINTENANCE OF DIFFERENCES If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear? Social scientists have proposed many mechanisms to answer this question. The purpose of this article is to explore one more mechanism. The mechanism proposed here deals with how people do indeed become more similar as they interact, but also provides an explanation of why the tendency to converge stops before it reaches completion. It therefore provides a new type of explanation of why we do not all become alike. Because the proposed mechanism can exist alongside other mechanisms, it can be regarded as complementary with older explanations rather than necessarily competing with them. Unfortunately, no good term describes the range of things about which people can influence each other. Although beliefs, attitudes, and behavior cover a wide range indeed, there are still more things over which interpersonal influence extends, such as language, art, technical standards, and social norms. The most generic term for the

1,754 citations

Journal ArticleDOI
TL;DR: Agent-based models (ABMs) as mentioned in this paper have been widely used in computational sociology to model social life as interactions among adaptive agents who influence one another in response to the influence they receive, such as diffusion of information, emergence of norms, coordination of conventions or participation in collective action.
Abstract: ■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.

1,354 citations

Journal ArticleDOI
01 Jul 2005
TL;DR: This paper describes the MASON system, its motivation, and its basic architectural design, and compares MASON to related multi-agent libraries in the public domain, and discusses six applications of the system built over the past year which suggest its breadth of utility.
Abstract: MASON is a fast, easily extensible, discrete-event multi-agent simulation toolkit in Java, designed to serve as the basis for a wide range of multi-agent simulation tasks ranging from swarm robotics to machine learning to social complexity environments. MASON carefully delineates between model and visualization, allowing models to be dynamically detached from or attached to visualizers, and to change platforms mid-run. This paper describes the MASON system, its motivation, and its basic architectural design. It then compares MASON to related multi-agent libraries in the public domain, and discusses six applications of the system built over the past year which suggest its breadth of utility.

956 citations