Research

WORKING PAPERS

  • Strategic Risk-taking in Dynamic Contests [Job Market Paper][under review][Working paper (pdf)]

We investigate a process of decision-making in a multi-period winner-take-all contest, in which competing players simultaneously choose among actions with different levels of risk every period. Strategic risk-taking is analyzed in isolation from effort choices, and, according to expected utility theory, risk preferences are irrelevant. We derive a closed form solution of the dynamic game for any number of periods. In equilibrium, a leading player chooses the lowest level of risk, a trailing player chooses the highest level of risk, and all elements of the action space with intermediate levels of risk are irrelevant. We design a laboratory experiment to test various comparative statics of the model and explore behavioral deviations. Our findings are consistent with theoretical predictions – subjects tend to choose riskier lotteries when they are behind and safer lotteries when they are ahead, while the magnitude of the advantage does not seem to affect the risk-taking levels. We also observe some behavioral deviations such as a decline in risk-taking in the absence of the safe option, which only occurs while being behind. We find the quantal response equilibrium of our dynamic game and explain some of behavioral deviations; incorporating learning, subject types, and the probability weighting function into the behavioral model allows us to achieve a 40% improvement in fit and explain the rest of behavioral findings obtained from the data.

We investigate a dynamic development process which involves heterogeneous agents making location choices. In our spatial model agents differ from each other by the impact they have on the development dynamics. In equilibrium, a high impact agent, the pioneer, sacrifices some short-term benefits and chooses an underdeveloped location. The pioneer improves that location and creates incentives for other agents to choose it later in the game. We design a laboratory experiment to test various comparative statics of the model and analyze the role of pioneers as well as the effect that early investments in public goods have on long-term outcomes. Our findings are consistent with theoretical predictions – high impact subjects tend to choose pioneering more frequently than other agents. As predicted, improvements in initial conditions through early investments in public goods significantly affect the dynamics of the system and can lead to substantial welfare improvements. We also observe behavioral deviations such as when some low impact subjects consistently act as lesser pioneers and also choose an underdeveloped location. Learning and experimentation play a significant role in our experiments and help subjects’ behavior to match point predictions of the model.

WORK IN PROGRESS

  • Dynamics of Specialization Process: Experimental Evidence (with Luke Boosey and Sebastian Goerg) [Data collection in progress]

We study the way people adjust their specialization under ambiguity in future earnings, and there is a lot of evidence that workers in declining industries, such as coal miners, reject retraining, even with the state assistance. We design a laboratory experiment and focus on informational interactions between workers in a dynamic game, in which workers simultaneously choose occupations. We implement the learning-by-doing component as well as shocks to occupations. Thus, we introduce a trade-off between switching to a potentially more beneficial new occupation and sticking to an occupation which might be in crisis, while being more skillful in that occupation. Our results provide an insight on observed cyclical fluctuations of supply and demand of specialists in a given profession as well as technology-based changes such as industries dying off or new specializations appearing.

  • Dynamic Model of Crowdfunding: Campaign Goals and Design (with Arthur Nelson) [Completing the manuscript]
  • Endogenous Action Space: Experimental Results (with Dmitry Ryvkin)
  • Information Disclosure in Pure Risk-taking Dynamic Contests
  • Mobility Advantage in Dynamic Multi-battle Contests
  • Suspense and Surprise in Competitive Settings