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Simulating Norms, Social Inequality, and Functional Change in Artificial Societies

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Abstract

In this paper, we compare the computational and sociological study of norms, and resimulate previous simulations (Conte and Castelfranchi 1995a, Castelfranchi, Conte and Paolucci 1998) under slightly different conditions. First, we analyze the relation between norms, social inequality and functional change more closely. Due to our results, the hypothesis stating that the "finder-keeper" norm while controlling aggression efficaciously reduces social inequality holds only in quite egalitarian societies. Throughout a variety of inegalitarian societies, it instead increases social inequality. This argument which can be traced back to Marx is being investigated by use of computer simulations of artificial societies. Second, we remodel normative behaviour from a sociological point of view by implementing Haferkamp's theory of action approach to deviant behaviour. Following the game theoretic models, the computational study of norms has up to now ignored the importance of power in explaining how norms affect social behaviour, how norms emerge, become established and internalized, and change. By simulating Haferkamp and repeating the Conte and Castelfranchi experiments, we demonstrate that it is possible to integrate power into computational models of norms.

Suggested Citation

  • Nicole J. Saam & Andreas G. Harrer, 1999. "Simulating Norms, Social Inequality, and Functional Change in Artificial Societies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 2(1), pages 1-2.
  • Handle: RePEc:jas:jasssj:1998-13-1
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    1. Andreas Flache & Rainer Hegselmann, 1998. "Understanding Complex Social Dynamics: a Plea for Cellular Automata Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(3), pages 1-1.
    2. Cristiano Castelfranchi & Rosaria Conte & Mario Paolucci, 1998. "Normative Reputation and the Costs of Compliance," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(3), pages 1-3.
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    Cited by:

    1. Fischer, Ilan & Sullivan, Oriel, 2007. "Evolutionary modeling of time-use vectors," Journal of Economic Behavior & Organization, Elsevier, vol. 62(1), pages 120-143, January.
    2. Yutaka NAKAI & Masayoshi Muto, 2008. "Emergence and Collapse of Peace with Friend Selection Strategies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-6.
    3. Dirk Helbing & Anders Johansson, 2010. "Cooperation, Norms, and Revolutions: A Unified Game-Theoretical Approach," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-15, October.
    4. Christian Scheve & Daniel Moldt & Julia Fix & Rolf Luede, 2006. "My agents love to conform: Norms and emotion in the micro-macro link," Computational and Mathematical Organization Theory, Springer, vol. 12(2), pages 81-100, October.
    5. Peter Revay & Claudio Cioffi-Revilla, 2018. "Survey of evolutionary computation methods in social agent-based modeling studies," Journal of Computational Social Science, Springer, vol. 1(1), pages 115-146, January.
    6. Martin Neumann, 2008. "Homo Socionicus: a Case Study of Simulation Models of Norms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(4), pages 1-6.
    7. Christian Hahn & Bettina Fley & Michael Florian & Daniela Spresny & Klaus Fischer, 2007. "Social Reputation: a Mechanism for Flexible Self-Regulation of Multiagent Systems," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(1), pages 1-2.
    8. Francesco C. Billari & Alexia Prskawetz & Johannes Fürnkranz, 2002. "The cultural evolution of age-at-marriage norms," MPIDR Working Papers WP-2002-018, Max Planck Institute for Demographic Research, Rostock, Germany.

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