Human and Artificial Agents in a Crash-Prone Financial Market

Authored by Todd Feldman, Daniel Friedman

Date Published: 2010-10

DOI: 10.1007/s10614-010-9227-x

Sponsors: No sponsors listed

Platforms: NetLogo

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

We introduce human traders into an agent based financial market simulation prone to bubbles and crashes. We find that human traders earn lower profits overall than do the simulated agents (”robots”) but earn higher profits in the most crash-intensive periods. Inexperienced human traders tend to destabilize the smaller (10 trader) markets, but have little impact on bubbles and crashes in larger (30 trader) markets and when they are more experienced. Humans' buying and selling choices respond to the payoff gradient in a manner similar to the robot algorithm. Similarly, following losses, humans' choices shift towards faster selling.

Tags

Agent-based models financial markets experimental economics