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...

This chapter surveys the state-of-art of heterogeneous agent models (HAMs) in finance using a jointly theoretical and empirical analysis, combined with numerical analysis from the latest development in computational finance. It provides supporting evidence on the explanatory power of HAMs to...

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....

Adaptive learning introduces persistence in the evolution of agents' beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which...

Mechanism design theory strongly relies on the concept of Nash equilibrium. However, studies of experimental games show that Nash equilibria are rarely played and that subjects may be thinking only a finite number of iterations. We study one of the most influential benchmarks of mechanism design...