Danil Dmitriev   

📄 CV    ✉️ danil.dmitriev@yale.edu    📱1 (310) 466-8461 

Welcome to my website! I am a postdoctoral associate at the Democratic Innovations program of Institution for Social and Policy Studies at Yale University during the 2023-2024 academic year.

I am a microeconomic theorist with a focus on learning, organizational economics, and political economy. Here are my research statement and teaching statement.

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 acting creatively is costly? We address this question using a model of delegated bandit experimentation. A principal wants an agent to constantly switch to new arms to maximize the chance of success, while the agent faces a fixed cost of switching. We show that the principal's optimal bonus scheme is maximally uncertain---the agent receives transfers for success, but their distribution has extreme variance. Despite being stationary, this bonus scheme achieves the principal's first-best outcome. We also show that the joint surplus is strictly increasing in the agent's outside value if that value is low. To illuminate the value of opaque incentives in practice, we apply our results to the YouTube Partner Program. We argue that it uses inefficiently transparent bonuses and that better experimentation and larger profit can be achieved by using appropriate opaque bonuses.


Learning from Shared News: When Abundant Information Leads to Belief Polarization (with Renee Bowen and Simone Galperti)
📄 PDF  (Quarterly Journal of Economics, Vol. 138, n. 2, (May 2023))

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.

Working Papers

Sabotage-Proof Mechanism Design (with Freddie Papazyan) 📄 PDF

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

Show Abstract

Online voting mechanisms (e.g. polls) are a potentially powerful, cost-effective means of collecting {large} amounts of data about preferences. However, such large-scale data collection has proven to be vulnerable to sabotage (e.g. by internet trolls) if proper precautions are not taken. We consider the problem of designing a voting mechanism that is robust to derailment by external groups. We show that plurality voting and other standard mechanisms are often not robust to sabotage; in fact it is sometimes preferable to not run any poll at all. The optimal voting mechanism is found to make saboteurs indifferent between each alternative they can vote for, since this undermines their ability to adversely affect the designer's predictions of other voters' preferences.

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

Policy Experimentation under Disagreement

How do governments experiment with new policies when parties disagree about them?

Influence of Money in Elections

How does the size of an election affect its susceptibility to wealthy interests?