Bubbles and Crashes: a Cyborg Approach

In joint work since 2004 we have created a family of agent-based models for financial markets in which bubbles and crashes occur in imitation of real markets. The evolution of behavioral rules in these models has shed light on some possible mechanisms used by human account managers or traders. Our programming environment, NetLogo, has proved ideal for this work, and also offers a feature, HubNet, capable of extending simulations to include human as well as robot traders. Recently we have used this feature to test a bubbles and crash model in a controlled laboratory environment. The experiment uses agent-based modeling to create a virtual financial market where human subjects act as stock market traders alongside automated robots. We use the experimental data to first test whether humans adjust their exposure to risk in response to a payoff gradient and to test second whether humans perceive risk by responding to an exponential average of their losses. We find that humans do not exactly follow a gradient but are very close. We also find that humans strongly respond to losses putting more weight on the most current losses. However, how they respond to losses depends on the frequency and predictability of crashes.