This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash...

This paper revisits the Kareken-Wallace model of exchange rate formation in a two-country overlapping generations world. Following the seminal paper by Arifovic (Journal of Political Economy, 104, 1996, 510 – 541) we investigate a dynamic version of the model in which agents? decision rules...

This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The decision process of individuals is described by an Evolutionary Algorithm. We find that the contribution level converges towards the Nash...

The flexible job-shop scheduling problem (FJSP) is one of the most complex problems (Meriem and Ghédira, 2004). Thus, exact methods are not effective for solving the FJSP and heuristic approaches are generally used to find near optimal solutions within reasonable computation times. In our...

The agile New Product Development (NPD) process is a complex and integrated process wherein all of its main factors have cause-and-effect relationships. A Fuzzy Cognitive Map (FCM) can be successfully applied to show the cause-and-effect relationships between the essential components in this...

Multi-Agent Based Simulation is a branch of Distributed Artificial Intelligence that builds the base for computer simulations which connect the micro and macro level of social and economic scenarios. This paper presents a new method of modelling the formation and change of patterns of action in...

This paper revisits the Kareken-Wallace model of exchange rate formation in a two-country overlapping generations world. Following the seminal paper by Arifovic (Journal of Political Economy, 104, 1996, 510 – 541) we investigate a dynamic version of the model in which agents? decision rules...

The Santa Fe Artificial Stock Market [13, 4] is an agent-based artificial model in which agents continually explore and develop expectational models, buy and sell assets based on the predictions of those models that perform best, and confirm or discard these models based on their performance...

In this paper we study a simple learning paradigm for iterated normal form games in an evolutionary context. Following the decision theoretic concept of satisficing we design players with a certain aspiration level. If their payoff is below this level, they change their current action, otherwise...

We introduce learning based on genetic algorithms in a principal-agent model of optimal contracting under moral hazard. Applications of this setting abound in finance (credit under moral hazard), public finance (optimal taxation, information-constrained insurance), development (sharecropping),...