Outline Mini-Course Evolutionary Game Theory and Learning at QUT Brisbane, November 2012 by Friederike Mengel Summary: In this course we will study the most standard concepts used in Evolutionary Game Theory. We will start by studying ``classic’’ approaches, such as the static concept of evolutionary stability or deterministic dynamic systems like the replicator dynamics (Lecture 1). We will move to learning models, which are more explicit about the underlying reasoning and decision making process of the decision maker. We will see that some stochastic learning models are closely related to deterministic evolutionary concepts like the Replicator Dynamics (Lecture 3). Finally we will study the concept of stochastic stability (Lecture 4). For each of the different concepts we will review some applications they have had in recent research. From the vast array of possibilities we selected three topics. We will apply the Replicator Dynamics to study the cultural transmission of preferences and social norms (Lecture 2). We will study the problem of learning across games and categorization (Lecture 3). Finally we will apply the concept of stochastic stability to some examples of learning in games played on social networks (Lecture 5).

Overview of Sessions Session 1: Introduction: What is evolutionary GT, what is its purpose? and the Classics: Evolutionary Stability, (Noisy) Replicator Dynamics, Quasi-species equation References: Textbooks • Nowak, M. A. (2006), Evolutionary Dynamics, Belknap/Harvard. • Vega Redondo, F. (2003), Economics and the Theory of Games, Cambridge University Press. • Weibull, J. (1996), Evolutionary Game Theory, MIT Press. Classics • Eigen, M. J. McCaskill and P. Schuster (1989), ``The molecular quasi-species'', Adv. Chem.Phys.75: 149-263. • Maynard Smith, J. and G.R. Price (1973), ``The logic of animal conflict'', Nature 246: 15-18. • Maynard Smith, J. (1974), ``The theory of games and the evolution of animal conflicts'', Journal of Theoretical Biology 47, 209-221. • Taylor, P.D. and L.B. Jonker (1978), ``Evolutionary stable strategies and game dynamics'', Math.Biosci. 40: 145-156.

Session 2: The Classics: (Recent) Applications References: •

Bester, H. And W. Gueth, (1998). Is altruism evolutionary stable?, Journal of Economic Behavior and Organization 34, 193–209.

• • •



Bisin, A., G. Topa and T. Verdier (2004), ``Cooperation as a transmitted cultural trait’’, Rationality and Society 16(4): 477-507. Mengel, F. (2008), ``Matching Structure and the Cultural Transmission of Social Norms’’, Journal of Economic Behavior and Organization 67, 608-623. Mengel, F. (2012), ``On the Evolution of Coarse Categories’’, Journal of Theoretical Biology 307, 117-124. Nowak, M., R. May and R. Anderson, ``The evolutionary dynamics of HIV quasispecies and the development of immunodeficiency disease’’, AIDS 4, 1095–1103.

Session 3: Learning Models and Stochastic Approximation References: • • • • • • • •

Benveniste, A., Metevier, M., Priouret, P., (1990). Adaptive Algorithms and Stochastic Approximation. Springer-Verlag, Berlin. Boergers, T., Sarin, R., (1997). Learning through reinforcement and replicator dynamics, Journal of Economic Theory 77, 1–14. Grimm, V, and F. Mengel (2012), An Experiment on Learning in a Multiple Games Environment, Journal of Economic Theory 147, 2220-2259. Hopkins, E., (2002), Two competing models of how people learn in games, Econometrica 70 (6), 2141–2166. Kushner, H.J., Yin, G.G. (2003), Stochastic Approximation and Recursive Algorithms and Applications. Springer, New York. Mengel, F. (2012), Learning Across Games, Games and Economic Behavior 74, 601-619. Roth, A.E. and Erev, I., (1995), Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term, Games and Economic Behavor 8, 164–212. Vega-Redondo, F. (2000), Economics and the Theory of Games. Cambridge University Press.

Session 4: Stochastic Models: Markov chains, Stochastic Stability References: • • •

Ellison, G. (2000), Basins of Attraction, Long Run Stability and the Speed of Step-by-Step Evolution, Review of Economic Studies, 67(1), 17-45. Kandori, M., G. Mailath and R. Rob (1993), Learning, Mutation and Long Run Equilibria in Games, Econometrica, 61 (1), 29-56. Young, P. (2003), Individual Strategy and Social Structure, Princeton University Press.

Session 5: Stochastic Models: Applications (focus on Networks) References: • • • • • • •

Alos-Ferrer, C. and S. Weidenholzer (2008), Contagion and Efficiency, Journal of Economic Theory 143, 251-274. Ellison, G. (1993), Learning, Local Interaction and Coordination, Econometrica, 61(5),10471071. Ellison, G. (2000), Basins of Attraction, Long Run Stability and the Speed of Step-by-Step Evolution, Review of Economic Studies, 67(1), 17-45 Fosco, C. and F. Mengel (2011), Cooperation through Imitation and Exclusion in Networks, Journal of Economic Dynamics and Control 35, 641-658. Goyal, S. and F. Vega Redondo (2005), Network formation and social coordination, Games and Economic Behavior 50, 178-207. Jackson, M. and A. Watts (2002), On the formation of interaction networks in social coordination games, Games and Economic Behavior 41, 265-291. Jackson, M. (2010), Social and Economic Networks, Princeton University Press.

Outline Mini-Course Evolutionary Game Theory and ...

we will study the concept of stochastic stability (Lecture 4). For each of ... some examples of learning in games played on social networks (Lecture 5). Overview ...

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