We consider a financial market model with a large number of interacting agents. Investors are heterogeneous in their expectations about the future evolution of an asset price process. Their current expectation is based on the previous states of their "neighbors" and on a random signal about the...

In Rubinstein's (1989) E-mail game there exists no Nash equilibrium where players use strategies that condition on the E-mail communication. In this paper I restrict the utilizable information for one player. I show that in contrast to Rubinstein's result, in a payoff dominant Nash equilibrium...

The general framework of decision emergence (Güth, 2000a) is applied to the specific decision task of a proposer in ultimatum bargaining, i.e. to choosing how much the responder should be offered. For this purpose the "Master Module" as well as its submodules "New Problem Solver", "Adaptation...

Robust learning experiments confront participants with structurally different decision environments which they encounter, furthermore, repeatedly. Since the decision format does not depend on the rules (of game), forward looking deliberation (the shadow of the future) can be detected by...

Deriving advice that can in fact be utilized by boundedly rational decision makers is a central function of modeling choice making. We illustrate why this role is not being fulfilled well by standard models of full rationality and that theories of bounded rationality are needed not only for...

In this paper we investigate four hypotheses which are inconsistent with expected utility theory, but may well be explained by prospect theory. It deals with framing, the non-linearity of subjective probabilities, the disposition effect, and the correspondence of different experimental risk...

We have analyzed the impact of agents and their trading strategies on an experimental electronic market. Therefore, we added an XML-interface to an existing electronic market and implemented artificial agents which acted as elements of disturbance in the trading process. These artificial traders...