Top

Published in:

2015 | OriginalPaper | Chapter

4. Evolution of Communication

Author : Jun Tanimoto

Published in: Fundamentals of Evolutionary Game Theory and its Applications

Publisher: Springer Japan

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Network Reciprocity

next chapter Traffic Flow Analysis Dovetailed with Evolutionary Game Theory

The Information Entropy H _act|sense[bit] of action outputting x _k under information inputting y _i can be defined as;

$$ {H}_{act\Big| sense}=-{\displaystyle \sum_{j\kern0.5em =\kern0.5em 1}{\displaystyle \sum_{k\kern0.5em =\kern0.5em 1}p\left({x}_k,{y}_j\right)\cdot { \log}_2p\left({x}_k\Big|{y}_j\right)}}, $$

where p(x _k, y _j) is the compounded probability of x _k and y _i, and $ p\left({x}_k\Big|{y}_j\right) $ is the conditional probability of x _k under y _i. The Information Rate I _sense[bit] is defined as the difference between information entropy without any information input H _act [bit] and H _act|sense,

$$ {I}_{sense}={H}_{act}-{H}_{act\Big| sense}. $$

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). http://jasss.soc.surrey.ac.uk/8/1/2.html.

Chalub, F.A.C.C., F.C. Santos, and J.M. Pacheco. 2006. The evolution of norms. Journal of Theoretical Biology 241: 233–240.MathSciNetCrossRefPubMed

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.CrossRef

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.CrossRef

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.

Nowak, M.A. 2006. Five rules for the evolution of cooperation. Science 314: 1560–1563.PubMedCentralCrossRefPubMedADS

Nowak, M.A., and K. Sigmund. 1998. Evolution of indirect reciprocity by image scoring. Nature 393: 573–577.CrossRefPubMedADS

Ohtsuki, H., and Y. Iwasa. 2004. How should we define goodness?—reputation dynamics in indirect reciprocity. Journal of Theoretical Biology 231: 107–120.MathSciNetCrossRefPubMed

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).

Tanimoto, J. 2008. What initially brought about communications? Biosystems 92(1): 82–90.CrossRefPubMed

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.PubMedCentralCrossRefPubMed

Title: Evolution of Communication
Author: Jun Tanimoto
Publisher: Springer Japan
Book: Fundamentals of Evolutionary Game Theory and its Applications
Print ISBN: 978-4-431-54961-1

Electronic ISBN: 978-4-431-54962-8

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-4-431-54962-8_4