The objective of this research is to tackle bargaining problems with Evolutionary Algorithms (EA). EA have been proved effective for a wide variety of problems. In this paper, we apply EA to solve Rubinstein’s Basic Alternating-Offer Bargaining Problem whose game-theoretic solution is known. Experimental outcomes suggest that EA are able to generate convincing approximations of the theoretic so...

In the context of Evolutionary Game Theory, we have developed an evolutionary algorithm without an explicit fitness function or selection function. Instead players obtain energy by playing games. Clonal reproduction subject to mutation occurs when a player’s energy exceeds some threshold. To avoid exponential growth of the population there is a death event that depends on population size. By tw...

acidity and k the cost of a glycolytic metabolism. The proportion of invasive cells increase as we increase the cost of living in an acid environment and decrease the cost of glycolysis, factors that should ease the emergent of glycolytic cells. Abstract— Cancers arise from genetic abberations but also consistently display high levels of intra-tumor heterogeneity and evolve according to Darwini...

This thesis applies evolutionary algorithms to tackle bargaining games. Evolutionary algorithms can discover efficient and stationary strategies for various bargaining games. Game-theoretic method requires a substantial amount of mathematical reasoning. Thus this method restricts to simple problems. Moreover, game-theoretic solutions rest on the crucial assumption that every player is perfectly...

Free riding experiments have generated many anomalous results that cannot be explained with standard Nash equilibrium models of public goods. This paper examines the experiments within the context of evolutionary game theory. This approach models the decision process of agents by an adaptive learning algorithm. The algorithm ‘strengthens’ strategies that do relatively well and ‘weakens’ strateg...

Multi–agent learning is a challenging open task in artificial intelligence. It is known an interesting connection between multi–agent learning algorithms and evolutionary game theory, showing that the learning dynamics of some algorithms can be modeled as replicator dynamics with a mutation term. Inspired by the recent sequence–form replicator dynamics, we develop a new version of theQ–learning...

The Snowdrift game is a well-known social dilemma model frequently used in evolutionary game theory to investigate the emergence of cooperative behaviour under different biologically or socially plausible conditions. In this paper, we examine a multi-player version of the Snowdrift game where (i) the agents playing the game are mapped to the nodes of a regular two-dimensional lattice, (ii) the ...