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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2014: Simulated Evolution and Learning pp 443–454Cite as

Adaptive Risk Aversion in Social Dilemmas

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

Abstract

Risks are ubiquitous in many social dilemmas. A decision to cooperate (or invest in a common pool) is inherently “risky” when the reward received depends on the frequency of actions displayed by other social group members. In this paper, we study the evolutionary dynamics of the multiple-group public goods game where the population of mobile individuals is divided into fixed-sized groups. Agents use continuous investment strategies. This approach allows for the evolution of an intermediate level of investments correlated with specific risk aversion levels. Agents are also presented with an opportunity to switch groups at the end of each round based on an environmental trigger. Detailed simulation experiments using an evolutionary game theory framework show that investment levels can be maintained within groups. Over time, the mean level of the risk aversion trait increases especially for larger groups. In the conditional migration scenarios, levels of investment consistent with risk aversion emerge.

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Author information

Authors and Affiliations

  1. Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Michael Kirley & Friedrich Burkhard von der Osten

Authors
  1. Michael Kirley
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  2. Friedrich Burkhard von der Osten
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Kirley, M., von der Osten, F.B. (2014). Adaptive Risk Aversion in Social Dilemmas. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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