Haas School of Business
University of California - Berkeley
I use laboratory settings and data collected from prediction markets within firms to answer questions about information aggregation.
During the course of my time at Berkeley I became involved with setting up corporate prediction market games inside firms. My research to date has focused on two broad questions:
Information Inside the Firm - Evidence From a Prediction Market. I assemble a novel data set gathered from a corporate prediction market in which managers at a software firm allowed employees to place bets on key variables. I use these data to examine the static and dynamic properties of information within the firm. I find that employees are privately informed about project outcomes. However, information is not evenly distributed across the firm - some groups appear to know more than other about products and sales. To examine the flow of information within the firm, I focus on a subset of bets that were later revised by employees. Revised bets preform well relative to bets pre-revision, suggesting that employees acquire private information over time. Again, the quality of information flow appears related to job function. This study lends insight into the source of managers' private information in a corporate finance setting.
Shifting Incentives in Forecasting Contests. I focus on the motivations of market participants. I examine a betting market which experienced an exogenous shock to incentives. I use data on bets placed by participants to assess whether tournament prizes elicit exaggerated forecasts. I also study whether changing the value of monetary incentives has bearing on the participants' willingness to make forecasts. I find that, in line with predictions, reducing the proportion of prize winners, appears to increase the riskiness of their bets with no measurable increase in information content. However, contrary to expectations, I do not find that smaller prize values lead to lower participation. This study complements the theoretical literature on forecasting contests that suggests professional analysts have incentives to exaggerate their claims.
Menu-Based Complexity: Experiments on Choice Over Lotteries. I examine the question on design experimentally. I examine the effects of different types of complexity on decision making and information transmission. I focus on two types of complexity: the number of alternatives and the organization of information about risks and rewards. Each of these can prevent subjects from making appropriate choices. My results suggest that simplification of financial decisions, within limits, may improve information transmission while helping individuals make better choices. Though individuals make poor choices in very complex environments, constraining their choices too much also makes it difficult for them to choose well. My experimental setup also enables me to construct forecasts by aggregating information from agents' choices. I show how these complex choice environments can lead to inefficient forecasts.