Danil Dmitriev

📄 CV ✉️ ddmitrie@ucsd.edu 📱1 (310) 466-8461 📍Economics Building 125


Welcome to my website! I am a Ph.D. candidate in Economics at the University of California, San Diego. I am on the academic job market during 2022-2023.

I am a microeconomic theorist with a focus on learning, organizational economics, and political economy. You can find my research statement here.


Job Market Paper

Motivating Creativity 📄 PDF

How should a principal motivate an agent to frequently experiment with new ideas?

Show Abstract

How should one incentivize creativity when being creative is costly? We analyze a model of delegated bandit experimentation where the principal desires the agent to constantly switch to new arms to maximize the chance of success. The agent faces a fixed cost of switching. We show that the principal's optimal reward scheme is maximally uncertain---the agent receives transfers for success, but their distribution has an extreme variance. Despite being stationary, the optimal reward scheme achieves the principal's first-best outcome provided that the agent's outside option is sufficiently valuable. Our results shed light on the non-transparent incentives used by online platforms, such as YouTube, and guide how to design incentives for creativity in such applications.

Working Papers

Learning from Shared News: When Abundant Information Leads to Belief Polarization (with Renee Bowen and Simone Galperti)
📄 PDF (conditionally accepted at the Quarterly Journal of Economics)

Can polarization of opinions arise purely due to how people share verifiable news?

Show Abstract

We study learning via shared news. Each period agents receive the same quantity

and quality of first-hand information and can share it with friends. Some friends

(possibly few) share selectively, generating heterogeneous news diets across agents akin

to echo chambers. Agents are aware of selective sharing and update beliefs by Bayes’

rule. Contrary to standard learning results, we show that beliefs can diverge in this

environment leading to polarization. This requires that (i) agents hold misperceptions

(even minor) about friends’ sharing and (ii) information quality is sufficiently low.

Polarization can worsen when agents’ social connections expand. When the quantity of

first-hand information becomes large, agents can hold opposite extreme beliefs resulting

in severe polarization. We find that news aggregators can curb polarization caused by

news sharing. Our results hold without media bias or fake news, so eliminating these

is not sufficient to reduce polarization. When fake news is included, it can lead to

polarization but only through misperceived selective sharing. We apply our theory to

shed light on the evolution of public opinions about climate change in the US.

Dynamic Inconsistency and Convex Commitment Devices (with Adrian Wolanski) 📄 PDF

Is the use of commitment devices correlated with actual dynamic inconsistency in the supply of effort over time?

Show Abstract

We present a laboratory experiment designed to measure both actual and perceived dynamic inconsistency using a novel convex commitment device. Participants supply effort in the form of unpleasant tasks over time, and can commit to future effort at a cost. We find that participants demand a great deal of commitment, implying they believe they are significantly dynamically inconsistent, despite evidence of little to no dynamic inconsistency. The results suggest caution when employing commitment devices, as their usage may be unrelated to the problem they are trying to solve.

Work in Progress

Sabotage-Proof Mechanism Design (with Freddie Papazyan)

How does one keep a voting mechanism from being hijacked by external saboteurs?

Show Abstract

To request the abstract and working draft, please click here.

Frederick Papazyan