About

Krishna Dasaratha is an Assistant Professor of Economics at Boston University. His research is primarily in microeconomic theory with a focus on social and economic networks, including diffusion processes, social learning, and network formation. He received his PhD in economics from Harvard University in March 2021. His work involves studying the mechanisms and dynamics of social and economic networks, contributing to a deeper understanding of how information and behaviors spread within these networks.

Research topics

• Computer science
• Mathematics
• Artificial intelligence
• Combinatorics
• Machine learning

Selected publications

• Network Interventions: Targeting Agents or Targeting Links?

  Open MIND · 2026-02-13

  preprint1st authorCorresponding

  Consider a network game with linear best responses and spillovers between players, and let agents endogenously choose their links. A planner considers interventions to subsidize actions and/or links between players, aiming to maximize a welfare function depending on equilibrium actions. The structure of the optimal intervention depends on whether links provide non-negative intrinsic value to agents. When they do, it is optimal to focus only on subsidizing actions. When the intrinsic value of links is negative, we give conditions for including link subsidies to be optimal. This reverses the basic structure of the optimal intervention in settings with exogenous links.

  DOI

• Network Interventions: Targeting Agents or Targeting Links?

  ArXiv.org · 2026-02-13

  articleOpen access1st authorCorresponding

  Consider a network game with linear best responses and spillovers between players, and let agents endogenously choose their links. A planner considers interventions to subsidize actions and/or links between players, aiming to maximize a welfare function depending on equilibrium actions. The structure of the optimal intervention depends on whether links provide non-negative intrinsic value to agents. When they do, it is optimal to focus only on subsidizing actions. When the intrinsic value of links is negative, we give conditions for including link subsidies to be optimal. This reverses the basic structure of the optimal intervention in settings with exogenous links.

  PublisherOA PDF

• Markets for Models

  ArXiv.org · 2025-03-04

  preprintOpen access1st authorCorresponding

  Motivated by the prevalence of prediction problems in the economy, we study markets in which firms sell models to a consumer to help improve their prediction. Firms decide whether to enter, choose models to train on their data, and set prices. The consumer can purchase multiple models and use a weighted average of the models bought. Market outcomes can be expressed in terms of the \emph{bias-variance decompositions} of the models that firms sell. We give conditions when symmetric firms will choose different modeling techniques, e.g., each using only a subset of available covariates. We also show firms can choose inefficiently biased models or inefficiently costly models to deter entry by competitors.

  PublisherOA PDFDOI

• Markets for Models

  2025-07-02

  articleOpen access1st authorCorresponding

  Prediction problems are ubiquitous in the economy. To give a few examples, firms selling products often want to predict customers' willingness to pay and may use business analytics tools to do so. In science and engineering, researchers want to predict the viability of compounds in domains ranging from drug discovery to materials science. Campaigns and observers want to predict elections, and may commission polls to do so.

  PublisherOA PDFDOI

• Incentive Design With Spillovers

  2025-07-02 · 1 citations

  articleOpen access1st authorCorresponding

  Performance incentives tied to joint outcomes — such as equity for startup executives or bonuses for marketing teams — are a common tool for motivating teams. How should such incentive schemes be designed and how should they take into account the team's production function? We examine these questions in a simple non-parametric model of a team working on a joint project. Each member of the team chooses a costly effort level. These actions jointly determine a real-valued team performance according to a sufficiently smooth, increasing function of the efforts, which may entail interactions such as complementarities among agents' efforts. Any performance level determines a probability distribution over observable project outcomes.

  PublisherOA PDFDOI

• Incentive Design With Spillovers

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Optimal Bailouts in Diversified Financial Networks

  arXiv (Cornell University) · 2024-06-18 · 1 citations

  preprintOpen access1st authorCorresponding

  Widespread default involves substantial deadweight costs which could be countered by injecting capital into failing firms. Injections have positive spillovers that can trigger a repayment cascade. But which firms should a regulator bailout so as to minimize the total injection of capital while ensuring solvency of all firms? While the problem is, in general, NP-hard, for a wide range of networks that arise from a stochastic block model, we show that the optimal bailout can be implemented by a simple policy that targets firms based on their characteristics and position in the network. Specific examples of the setting include core-periphery networks.

  PublisherOA PDFDOI

• Learning from Viral Content

  2024-07-08

  article1st authorCorresponding

  In recent years, viral content on social media platforms has become a major source of news and information for many people. Which stories go viral is jointly determined by the algorithms generating platform news feeds and users' actions on the platforms. We study this process with an equilibrium model of users interacting with shared news stories, focusing on how the design of news feeds affects how users learn. In our model, rational users arrive sequentially, observe an original story (i.e., a private signal) and a sample of predecessors' stories in a news feed, and then decide which stories to share. The observed sample of stories depends on what predecessors share as well as the sampling algorithm generating news feeds.

  PublisherDOI

• Incentive Design with Spillovers

  arXiv (Cornell University) · 2024-11-12

  preprintOpen access1st authorCorresponding

  A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives. We specialize the model to explore several applied questions, including whether compensation should reward individual ability or collaborativeness and how the strength of complementarities shapes pay dispersion.

  PublisherOA PDFDOI

• Equity Pay In Networked Teams

  arXiv (Cornell University) · 2023-08-28

  preprintOpen access1st authorCorresponding

  A group of agents each exert effort to produce a joint output, with the complementarities between their efforts represented by a (weighted) network. Under equity compensation, a principal motivates the agents to work by giving them shares of the output. We describe the optimal equity allocation. It is characterized by a neighborhood balance condition: any two agents receiving equity have the same (weighted) total equity assigned to their neighbors. We also study the problem of selecting the team of agents who receive positive equity, and show this team must form a tight-knit subset of the complementarity network, with any pair being complementary to one another or jointly to another team member. Finally, we give conditions under which the amount of equity used for compensation is increasing in the strength of a team's complementarities and discuss several other applications.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Learning, Behavior, and Design in Diffusion Processes

  NSF · $159k · 2022–2026

Frequent coauthors

• Benjamin Golub

  12 shared

• Laure Flapan

  9 shared

• Nicholas Neumann-Chun

  9 shared

• Sarah Peluse

  9 shared

• Chansoo Lee

  9 shared

• Kevin He

  University of Pennsylvania

  9 shared

• Cornelia Mihaila

  Saint Michael's College

  9 shared

• Nir Hak

  Uber AI (United States)

  8 shared

Education

• Ph.D.

  Harvard University

  2021