Automated mechanism design with co-evolutionary hierarchical genetic programming techniques

We present a novel form of automated game theoretic mechanism design in which mechanisms and players co-evolve. We also model the memetic propagation of strategies through a population of players, and argue that this process represents a more accurate depiction of human behavior than conventional economic models. The resulting model is evaluated by evolving mechanisms for the ultimatum game, and replicates the results of empirical studies of human economic behaviors, as well as demonstrating the ability to evaluate competing hypothesizes for the creation of economic incentives.

[1]  Richard C. Jeffrey,et al.  The Dynamics of Norms , 2009 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[4]  Tuomas Sandholm,et al.  Automated Mechanism Design: A New Application Area for Search Algorithms , 2003, CP.

[5]  G. Williams.  Adaptation and Natural Selection. (Book Reviews: Adaptation and Natural Selection: A Critique of Some Current Evolutionary Thought) , 2018 .

[6]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[7]  Victor Ciesielski,et al.  Linear genetic programming , 2008, Genetic Programming and Evolvable Machines.

[8]  M. Satterthwaite.  Strategy-proofness and Arrow's conditions: Existence and correspondence theorems for voting procedures and social welfare functions , 1975 .

[9]  Jordan B. Pollack,et al.  Emergent geometric organization and informative dimensions in coevolutionary algorithms , 2007 .

[10]  W. Güth,et al.  An experimental analysis of ultimatum bargaining , 1982 .

[11]  Tuomas Sandholm,et al.  Better with Byzantine: Manipulation-Optimal Mechanisms , 2009, SAGT.

[12]  Nagel,et al.  Experimental Results on the Centipede Game in Normal Form: An Investigation on Learning. , 1998, Journal of mathematical psychology.

[13]  H. Gintis.  Beyond Homo economicus: evidence from experimental economics , 2000 .

[14]  Malcolm I. Heywood,et al.  Revisiting the Acrobot ‘height’ task: An example of efficient evolutionary policy search under an episodic goal seeking task , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[15]  Pattie Maes,et al.  Dynamics of Co-evolutionary Learning , 1996 .

[16]  Malcolm I. Heywood,et al.  Symbiosis, complexification and simplicity under GP , 2010, GECCO '10.

[17]  C. Starmer.  Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk , 2000 .

[18]  H. Fawcett.  Manual of Political Economy , 1995 .

[19]  Peter McBurney,et al.  Evolutionary mechanism design: a review , 2010, Autonomous Agents and Multi-Agent Systems.

[20]  Vincent Conitzer,et al.  Incremental Mechanism Design , 2007, IJCAI.

[21]  T. Sandholm,et al.  Applications of Automated Mechanism Design , 2003 .

[22]  Colin Camerer,et al.  Foundations of Human Sociality - Economic Experiments and Ethnographic: Evidence From Fifteen Small-Scale Societies , 2004 .

[23]  M. Nowak,et al.  Fairness versus reason in the ultimatum game. , 2000, Science.

[24]  Elizabeth Sklar,et al.  Co-evolutionary Auction Mechanism Design: A Preliminary Report , 2002, AMEC.

[25]  K. Arrow.  A Difficulty in the Concept of Social Welfare , 1950, Journal of Political Economy.

[26]  Karl Tuyls,et al.  Human-inspired computational fairness , 2010, Autonomous Agents and Multi-Agent Systems.

[27]  R. Lewontin.  ‘The Selfish Gene’ , 1977, Nature.

[28]  J. M. Smith,et al.  The Logic of Animal Conflict , 1973, Nature.

[29]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.