This article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in mathematics and closely related sciences in order to find out more about the properties of genetic algorithms as stochastic processes. On the other...

We present a simple two-steps procedure for a within-subject test of the inequity aversion model of Fehr and Schmidt (1999). In the first step, subjects played modified ultimatum and dictator games and were classified according to their preferences. In the second step, subjects with specific...

The book is dedicated to the use of genetic algorithms in theoretical economic research. Genetic algorithms offer the chance of overcoming the limitations traditional mathematical tractability puts on economic research and thus open new horzions for economic theory. The book reveals close...

This paper puts three of the most prominent specifications of ‘other-regarding’ preferences to the experimental test, namely the theories developed by Charness and Rabin, by Fehr and Schmidt, and by Andreoni and Miller. In a series of experiments based on various dictator and prisoner’s...

Non-monotone incentive structures, which - according to theory - are able to induce optimal behavior, are often regarded as empirically less relevant for labor relationships. We compare the performance of a theoretically optimal non-monotone contract with a monotone one under controlled...

The problem of coordination failure, particularly in "team production" situations, is central to a large number of mircroeconomic as well as macroeconomic models. As this type of inefficient coordination poses a severe economic problem, there is a need for institutions fostering efficient...

This paper presents a model of individual behavior in minimum effort coordination games, focusing primarily on the effects of the number of players and the introduction of inter-group competition. It is shown that independent of the number of players and the number of competing groups, the most...