Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).

Economies are complicated systems encompassing micro behaviors, interaction patterns, and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition,...

The standard approach to modelling consumption/saving problems is to assume that the decisionmaker is solving a dynamic stochastic optimization problem However under realistic descriptions of utility and uncertainty the optimal consumption/saving decision is so difficult that only recently...

The paper outlines a static equilibrium model, which analyses the economic development in a two-country case by considering interregional migration in R&D-sectors. The effects of migration and firm decisions on both industrial agglomeration and economic development will be shown: lock-in-effects...

This work, which shall contribute to the Fest "A Just Society: Honouring Joseph Stiglitz", discusses a major unifying theme in Joe Stiglitz monumental work, namely, the analysis of economies characterised by persistent learning and coordination hurdles. In his analysis Joe is in many respects a...

I study the effects of improved public information on equilibrium welfare and price dispersion, providing sufficient conditions for negative and positive effects. Public information affects welfare by reducing excessive (though rational) pessimism induced by sequential learning. Reduced...

Consider a multi-sector general equilibrium model where firms have incomplete information about the returns to scale of their production and where that information is sequentially updated once real production is observed. What is the impact of this learning dynamics on the market-wise...

Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but...

By inverting the optimal portfolios of mutual fund managers in a fairly general setting, which allows us to partial out the effect of risk aversion and hedging demands, we provide an estimate of perceived expected excess returns and show that they are significantly affected by experienced...

Players may categorize the strategies available to them. In many games there are different ways to categorize one's strategies (different frames) and which ones players use has implications for the outcomes realized. This paper proposes a model of agents who learn which frames to use through...