Working Papers

Evolution of Cooperation: Role of Costly Strategy Adjustments

(with Julian Romero)

We study the evolution of cooperation and strategies in the indefinitely repeated prisoner's dilemma when it is costly for players to change their strategies. In standard repeated game experiments, players directly choose an action each period. Our experimental interface allows subjects to design a comprehensive strategy, which then selects actions for them in every period. Subjects are also able to adjust their strategies continuously within the supergames. We conduct lab experiments and find that cooperation is lower when strategy adjustment is costly than when it is not. The main difference is due to the evolution of cooperative strategies when it is costless to adjust strategies within supergames, and prevalence of less cooperative strategies when it is costly to do so. These results highlight that within-game experimentation and learning about strategies is critical to the rise of cooperative behavior. We provide simulations based on an evolutionary algorithm to support this result.


Constructing Strategies in the Indefinitely Repeated Prisoner's Dilemma

(with Julian Romero)

We propose a new approach for running lab experiments on indefinitely repeated games with high continuation probability. The approach has two main advantages. First, it allows us to run multiple long repeated games per session. Second, it allows us to incorporate the strategy method with minimal restrictions on the types of strategies that can be constructed. This gives us insight into what happens in long repeated games and into the types of strategies that subjects use. We report results obtained from the indefinitely repeated prisoner's dilemma with a continuation probability of δ = 0.99. We find rates of cooperation that are lower than expected, given that the indefinitely repeated games are long and the setting is approaching the continuous time form. However, when we analyze the constructed strategies our results are largely similar to those found in the literature, specifically that the most common strategies are memory-1 strategies such as Tit-For-Tat, Grim Trigger, and Always Defect.


Learning Under Compound Risk vs. Learning Under Ambiguity: An Experiment

(with Othon Moreno)

We design and conduct an economic experiment to investigate the learning process of the agents under compound risk and under ambiguity. We gather data for subjects choosing between lotteries involving risky and ambiguous urns. Decisions are made in conjunction with a sequence of random draws with replacement, allowing us to estimate the beliefs of the agents at different moments in time. For each of the urn types we estimate the initial prior and a general updating model for which the standard Bayesian updating model is a particular case. Our findings suggest an important difference in updating behavior between risky and ambiguous environments. Specifically, after controlling for the initial prior, when updating under ambiguity subjects significantly overweight the new signal, while when updating under compound risk subjects are essentially Bayesian.


Uncertainty about Informed Trading in Dealer Markets - An Experiment

(with Chi Sheh)

We use an economic experiment to examine the impact of an ambiguous level of asymmetric information on the behavior of security dealers. Specifically, we distinguish three types of uncertainty with respect to informed trading -- risk, compound risk, and ambiguity -- for both a monopoly and a duopoly market setting. We find that dealer's bidding behavior is less aggressive under an ambiguous level of informed trading than a risky level of informed trading. Additionally, we find that the stochastic nature of choice can hinder our ability to observe a difference in dealer behavior between the risky versus ambiguous level of asymmetric information in dealer markets.


Work in Progress

Tolerance for Failure & Willingness to Persist: Experimental Evidence on How Negative Knowledge Affects the Balance between Exploration and Exploitation

(with Kenneth Younge )

We examine how individuals behave in the face of failure. Whereas research suggests that there may be organizational benefits in tolerating a certain degree of failure, we investigate individual reactions to negative feedback -- for tolerating failure presumes a willingness of individuals to persist. Although there is a deep literature on the escalation of commitment towards failure, little is known about how individuals learn from negative feedback and use that information in future decisions about exploring new opportunities versus exploiting known solutions. We develop an online, open-source platform to run randomized experiments with individuals playing a board game to shed light on how negative feedback affects their willingness to explore. We examine three effects: 1) the structure of gains versus losses, 2) the relative weighting of alternatives, and 3) the level of stochastic uncertainty in rewards. Our results -- across subjects online, subjects in the lab, and reinforcement learning algorithms -- suggest that humans vary widely in their willingness to persist in the face of failure. Our methods extend beyond two-period Bayesian decision models to account for repeated play and learning within exploration and exploitation. We discuss the implications of our findings for future research on the tolerance for failure.


Forthcoming

Humans Are Not Machines: The Behavioral Impact of Queueing Design on Service Time

(with Masha Shunko and Julie Niederhoff)

Using behavioral experiments, we study the impact of queue design on worker pro- ductivity in service systems that involve human servers. Specifically, we consider two queue design features: queue structure, which can either be parallel queues (multiple queues with a dedicated server per queue) or a single queue (a pooled queue served by multiple servers); and queue-length visibility, which can provide either full or blocked visibility. We find that 1) the single-queue structure slows down the servers, illustrating a drawback of pooling; and 2) poor visibility of the queue length slows down the servers; however, this effect may be mitigated, or even reversed, by pay schemes that incentivize the servers for fast performance. We pro- vide additional managerial insights by isolating two behavioral drivers behind these results{task interdependence and saliency of feedback.


Research Grants

Russell Sage Foundation Small Grants Program in Behavioral Economics