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Digested Information as an Information Theoretic Motivation for Social Interaction

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Abstract

Within a universal agent-world interaction framework, based on Information Theory and Causal Bayesian Networks, we demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment. We introduce the concept of 'Digested Information' which both quantifies, and explains this phenomenon. Based on the properties of digested information, especially the high density of relevant information in other agents actions, we outline how this could motivate the development of low level social interaction mechanisms, such as the ability to detect other agents.

Suggested Citation

  • Christoph Salge & Daniel Polani, 2011. "Digested Information as an Information Theoretic Motivation for Social Interaction," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(1), pages 1-5.
    • Handle: RePEc:jas:jasssj:2010-27-2
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    References listed on IDEAS

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    1. N. Ay & N. Bertschinger & R. Der & F. Güttler & E. Olbrich, 2008. "Predictive information and explorative behavior of autonomous robots," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(3), pages 329-339, June.
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    Cited by:

    1. Goodman, James, 2014. "Evidence for ecological learning and domain specificity in rational asset pricing and market efficiency," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 48(C), pages 27-39.
    2. Ross Gore & Saikou Diallo & Christopher Lynch & Jose Padilla, 2017. "Augmenting Bottom-up Metamodels with Predicates," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-4.

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