Evolution of Communication

  • Jun Tanimoto
Part of the Evolutionary Economics and Social Complexity Science book series (EESCS, volume 6)


In this chapter, we discuss several interesting applications of evolutionary game theory. The chapter first takes up one possible scenario for why and how animal communication evolves. A series of numerical experiments based on an evolutionary game elucidates that one of the key points is time flexibility in the evolutionary trail. A social dilemma situation in a static environment only requires time-constant -reciprocity that can be emulated by Prisoner’s Dilemma (PD) games, which does not give rise to any communication at all. On the other hand, a dynamic environment needs -reciprocity to solve a social dilemma. This compels communication to emerge among agents so that they can obtain a high payoff, leading to Fair Pareto optimum. This kind of constructivist approach suggests that a PD game seems less appropriate as an argument for the inception of communication, but Leader or Hero might be better.


Action Strategy Information Rate Alarm Call Social Dilemma Image Score 
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  1. Buzing, P.C., Eiben, A.E., and Schut, M.C. 2005. Emerging communication and cooperation in evolving agent societies. Journal of Artificial Societies and Social Simulation 8(1).
  2. Chalub, F.A.C.C., F.C. Santos, and J.M. Pacheco. 2006. The evolution of norms. Journal of Theoretical Biology 241: 233–240.MathSciNetCrossRefPubMedGoogle Scholar
  3. Dorigo, M., V. Maniezzo, and A. Colorni. 1996. The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics – Part B 26(1): 29–41.CrossRefGoogle Scholar
  4. Grim, P., T. Kokalis, A. Alai-Tafti, N. Klib, and P. St Denis. 2004. Making meaning happen. Journal of Experimental & Theoretical Artificial Intelligence 16(4): 209–243.CrossRefGoogle Scholar
  5. Kawamura, H., M. Yamamoto, T. Mitamura, K. Suzuki, and A. Ohuchi. 1998. Cooperation search based on pheromone communication for vehicle routing problems. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E81-A-6: 1089–1096.Google Scholar
  6. Nowak, M.A. 2006. Five rules for the evolution of cooperation. Science 314: 1560–1563.PubMedCentralCrossRefPubMedADSGoogle Scholar
  7. Nowak, M.A., and K. Sigmund. 1998. Evolution of indirect reciprocity by image scoring. Nature 393: 573–577.CrossRefPubMedADSGoogle Scholar
  8. Ohtsuki, H., and Y. Iwasa. 2004. How should we define goodness?—reputation dynamics in indirect reciprocity. Journal of Theoretical Biology 231: 107–120.MathSciNetCrossRefPubMedGoogle Scholar
  9. Sato, T., Uchida, E., and Doya, K. 2007. Learning how, what, and whether to communicate: Emergence of protocommunication in reinforcement learning agents. Proceeding of The Twelfth International Symposium on Artificial Life and Robotics 2007 (AROB 12th '07). Beppu (Japan).Google Scholar
  10. Tanimoto, J. 2008. What initially brought about communications? Biosystems 92(1): 82–90.CrossRefPubMedGoogle Scholar
  11. Van Baalen, M., and V. Jansen. 2003. Common language or tower of Tower of Babel? On the evolutionary dynamics of signals and their meanings. Proceedings of the Royal Society B 270: 69–76.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  • Jun Tanimoto
    • 1
  1. 1.Graduate School of Engineering SciencesKyushu University InterdisciplinaryFukuokaJapan

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