Research

Publications:

An Equivalence Between Rational Inattention Problems and Complete-Information Conformity Games (with Ole Jann), 2022, Economic Letters

We consider two types of models: (i) a rational inattention problem (as known from the literature) and (ii) a conformity game, in which fully informed players find it costly to deviate from average behavior. We show that these problems are equivalent to each other both from the perspective of the participant and the outside observer: Each individual faces identical trade-offs in both situations, and an observer would not be able to distinguish the two models from the choice data they generate. We also establish when individual behavior in the conformity game maximizes welfare.

Working Papers:

Optimally Biased Expertise (with Andrei Matveenko, Maxim Senkov, and Egor Starkov)

This paper shows that in delegation problems, a biased principal can strictly benefit from hiring an agent with misaligned preferences or beliefs. We consider a “delegated expertise” problem in which the agent has an advantage in acquiring information relative to the principal.  We show that it is optimal for a principal who is ex ante biased towards one action to select an agent who is less biased. Such an agent is more uncertain ex ante about what the best course of action is and would acquire more information. The benefit to the principal from a more informed decision always outweighs the cost of a small misalignment. We show that selecting an optimally misaligned agent is a valuable tool, which performs on par with optimal contracting (while imposing no additional constraint on the principal) and outperforms restricted delegation. All results continue to hold when delegation is replaced by communication

Sequential Search with Flexible Information (with Andrei Matveenko, Salil Sharma, Elias Tsakas, and Mark Voorneveld)

We consider a model of sequential search in which a decision-maker (DM) has to choose one alternative from a fixed set. All available alternatives are iid random variables and ex-ante unknown to the DM. Before making a choice, contrary to the standard search literature, we allow the DM to decide how much and what kind of information to acquire about each alternative, e.g., design different job market interviews for candidates with different arrival ranks. We find that optimal inteviews have an intuitive property - the first arriving candidates are treated harshly, and their interviews are harder to pass, while later candidates' interviews are easier to pass. We compare unconditional probabilites of choice and study the discrimination the order of inspection can case. We argue that discrimination is sensitive to the functional form of the cost of learning. We consider several extensions, and we show that it may be optimal for the DM to interview an inferior candidate first and that a naive affirmative action policy can increase discrimination.