Research Output
The institutional approach for modeling the evolution of human societies
  Artificial Life is concerned with understanding the dynamics of human societies. A defining feature of any society is its institutions. However, defining exactly what an institution is has proven difficult, with authors often talking past each other. This paper presents a dynamic model of institutions, which views institutions as political game forms that generate the rules of a group's economic interactions. Unlike most prior work, the framework presented here allows for the construction of explicit models of the evolution of institutional rules. It takes account of the fact that group members are likely to try to create rules that benefit themselves. Following from this, it allows us to determine the conditions under which self-interested individuals will create institutional rules that support cooperation, e.g. that prevent a Tragedy of the Commons. The paper finishes with an example of how a model of the evolution of institutional rewards and punishments for promoting cooperation can be created. It is intended that this framework will allow Artificial Life researchers to examine how human groups can themselves create conditions for cooperation. This will help provide a better understanding of historical human social evolution, and facilitate the resolution of pressing societal social dilemmas.

  • Type:

    Article

  • Date:

    09 February 2018

  • Publication Status:

    Published

  • DOI:

    10.1162/ARTL_a_00251

  • ISSN:

    1064-5462

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

  • Funders:

    Edinburgh Napier Funded

Citation

Powers, S. T. (2018). The institutional approach for modeling the evolution of human societies. Artificial Life, 24(1), 10-28. https://doi.org/10.1162/ARTL_a_00251

Authors

Keywords

cooperation; institutions; human evolution; exchange; evolutionary economics; tragedy of the commons

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