02. November 2017, 16.15 p.m. 

Ernst-Abbe-Platz 2, seminar room 3423

Dynamic landscape models of coevolutionary games

Prof. Dr.-Ing. Hendrik Richter
(HTWK Leipzig, Institut Mess-, Steuerungs- und Regelungstechnik, Leipzig)

Studying (co)-evolutionary games can be seen as an attempt to address a long-standing and fundamental problem in Darwinian evolution. How can the two seemingly contradictory observations be reconciled that we have selective pressure entailing competition in order to be successful in survival and reproduction (and thus fit), but at the same time wide-spread cooperative and even altruistic behavior between individuals (and also groups of individuals or even species)?

(Co)-evolutionary games set up mathematical models to discuss the question of whether, when and under what circumstances cooperation may be more advantageous than competition [1, 2, 5]. Such games define individuals having behavioral choices to be players selecting and executing a strategy. By linking the relative costs and benefits of strategies to payoff (and subsequently fitness), we obtain a measure of how profitable a given choice is in evolutionary terms. This discussion crystallizes most prominently at considering so-called social dilemma games, for instance prisoner’s dilemma (PD), or snowdrift (SD) games.

Recently, an approach of modeling and analyzing coevolutionary games by dynamic game landscapes has been proposed [3, 4]. The main advantage of such a game landscape approach is that a strategy-to-payoff map can be defined for every combination of strategies and networks of interaction. Thus, a systematic and quantitative evaluation of the expectable evolutionary dynamics becomes possible. The talk introduces dynamic landscape models of coevolutionary games, discusses the modelling procedure and addresses the question of how landscape measures and fixation properties can be linked.

[1] E.  Lieberman, C. Hauert, M. A. Nowak, Evolutionary dynamics on graphs. Nature 433: 312-316, 2005.

[2] M. Perc, A. Szolnoki, Coevolutionary games-A mini review. BioSystems 99, 109-125, 2010.

[3] H. Richter, Analyzing coevolutionary games with dynamic fitness landscapes. In: Ong, Y. S. (Ed.), Proc. IEEE Congress on Evolutionary Computation, IEEE CEC 2016, IEEE Press, Piscataway, NJ, 610-616, 2016.

[4] H. Richter, Dynamic landscape models of coevolutionary games. BioSystems 153-154: 26-44, 2017.

[5] G. Szabo, G. Fath, Evolutionary games on graphs. Phys. Rep. 446: 97-216, 2007.