Learning from Others: An Experimental Test of Brownian Motion Uncertainty Models

With David Glick


Models of decision-making with outcome uncertainty are common in political science and related fields. Recent work in flagship journals has challenged canonical work by modeling outcome uncertainty as Brownian motion. This theoretical innovation has resonated because it is highly tractable and captures intuitively important realities of many decisions in ways that earlier models cannot. As theoretically attractive as the new models are, they have not yet been evaluated empirically. This is especially important because Brownian motion models place actors in more cognitively demanding situations than previous models. We offer what we believe to be the first experimental test of actors’ ability to behave in ways consistent with the Brownian motion model by evaluating subjects’ ability to rationally learn from another actor’s experiences. We show that subjects adjust their actions in response to the Brownian motion uncertainty. However, they deviate from optimal behavior in important ways, particularly in more complex situations.