(As Yet) Unpublished Papers:

Non-Common Priors, Incentives, and Promotions: The Role of Learning (with Matthias Fahn)

Abstract: We analyze a repeated principal-agent setting in which the principal cares about the agent's verifiable effort as well as an extra profit that can be generated only if the agent is talented. The agent is overconfident about his talent and updates beliefs using Bayes' rule. An exploitation contract in which the agent is only compensated for his effort if the extra profit materializes maximizes the principal's profits. In this optimal contract, the agent's principal-expected compensation decreases over time and learning exacerbates his exploitation, unless he has been revealed to be talented. Therefore, the principal's profits may increase with failures, and the agent may only be employed if his perceived talent is sufficiently low. As an application of these results, we analyse a firm's optimal promotion policy, and show that promotion to a new job may optimally be based on the agent being successful in a previous job, even if the agent's talent across jobs is entirely uncorrelated. This provides a novel explanation for the so-called Peter Principle, for which Benson et al., 2019 have recently provided evidence in a setting with verifiable performance and highly confident workers.

New version (July 2023) Do Stronger Patents Lead to Faster Innovation? The Effect of Clustered Search (with Kaustav Das)

Abstract: We analyse a model of two firms that are engaged in a patent race. Firms have to choose in continuous time between an established and an innovative method of pursuing a decisive breakthrough. They share a common belief about the likelihood of the innovative method being good. The unique Markov perfect equilibrium coincides with the cartel solution if and only if firms are symmetric in their abilities of leveraging a good innovative method or there is no patent protection. Otherwise, equilibrium will entail excessive clustering of efforts in the innovative method, as compared to the cartel benchmark, for any level of patent protection. We show that the expected time to a breakthrough is minimised at an interior level of patent protection, providing a novel possible explanation for the decrease in R&D productivity sometimes associated with a greater concentration of research efforts in riskier areas and stronger patent protections.

New Strategic Experimentation with asymmetric safe options (with Kaustav Das and Katharina Schmid)

Abstract: We study a two-player game of strategic experimentation with exponential bandits à la Keller, Rady and Cripps (2005) where the safe-arm payoff is different across players. We show that, as in Das, Klein and Schmid (2020), there exists an equilibrium in cutoff strategies if and only if the difference in safe-arm payoffs is large enough. In the equilibrium in cutoff strategies, the player with the higher safe-arm payoff conducts less experimentation. This feature of the equilibrium offers an explanation for the fact that oftentimes technological innovations are due to startups rather than established market leaders.

Over-and Under-Experimentation in a Patent Race with Private Learning (with Kaustav Das)

Abstract: This paper analyses a two-player game with two-armed exponential bandits. A player experiences publicly observable arrivals by pulling the safe arm. On the other hand, a player operating a good risky arm experiences publicly observable arrivals at an intensity greater than that in the safe arm. In addition, a player pulling the risky arm can also privately learn about its quality. With direct payoff externalities and private learning, we construct a symmetric Markov equilibrium where, depending on the initial optimism about the quality of the risky arm, we can have either too much or too little experimentation.

Work in Its Earlier Gestational Stages:

* Objects in the Rearview Mirror—Information Acquisition with Payoff Rivalries and Observation Lags (with Chantal Marlats and Lucie Ménager)

* Over-Cautious or Trigger-Happy Advisors---When Best to Stop (with Sidartha Gordon)