We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators’ strategy/market selections are repeated at each time step and depend on predisposition effects, herding...

Cyclical or chaotic competitive equilibria that do not exist under perfect foresight are shown to occur in a decentralized growth model under constant gain adaptive learning. This paper considers an economy populated by boundedly rational households making one-period ahead constant gain adaptive...

We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators' strategy/market selections are repeated at each time step and depend on predisposition effects, herding...

We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators' strategy/market selections are repeated at each time step and depend on predisposition effects, herding...

Within the seminal asset-pricing model by Brock and Hommes (1998), heterogeneous boundedly rational agents choose between a fixed number of expectation rules to forecast asset prices. However, agents' heterogeneity is limited in the sense that they typically switch between a representative...

This exercise offers an innovative learning mechanism to model economic agent's decision-making process using a deep reinforcement learning algorithm. In particular, this AI agent is born in an economic environment with no information on the underlying economic structure and its own preference....

We study the implications of biased consumer beliefs for search market outcomes in the seminal framework due to Diamond (1971). Biased consumers base their search strategy on a belief function which specifies for any (true) distribution of utility offers in the market a possibly incorrect...