About

Sushil Bikhchandani is a Professor of Decisions, Operations, and Technology Management, as well as a Professor of Strategy, holding the Howard Noble Chair in Management at UCLA Anderson School of Management. He has been teaching at UCLA Anderson since 1985. His research interests include auctions, market institutions, herd behavior, and decision making. He has published widely cited work on informational cascades, and his articles on topics such as auctions, bargaining, and decision theory have appeared in numerous professional journals, including Econometrica, Review of Economic Studies, Journal of Economic Theory, and Operations Research.

Research topics

• Artificial Intelligence
• Machine Learning
• Computer Science
• Psychology
• Social psychology
• Engineering
• Knowledge management
• Data science
• Physics
• Cognitive science

Selected publications

• Strategy-proof and Efficient Job Matching with Participation Constraints

  ArXiv.org · 2026-05-03

  articleOpen access1st authorCorresponding

  We study the design of strategy-proof and efficient mechanisms satisfying participation constraints in the job-matching problem. Each firm can hire multiple workers and each worker can be employed at only one firm. While firm utilities over subsets of workers are common knowledge, worker disutilities for working at each firm are private information. The VCG mechanism is the unique mechanism that is strategy-proof, efficient, and individually rational for workers; however, it may not be individual rational for firms. We show that the VCG mechanism is individually rational for firms if and only if firm utilities satisfy a condition called weak substitutes. We then strengthen participation constraints of firms to {\sl strong individual rationality}, which requires that each firm has no incentive to fire some of the workers assigned to it. The VCG mechanism is strongly individual rational if and only if firm utilities satisfy submodularity.

  PublisherOA PDF

• Strategy-proof and Efficient Job Matching with Participation Constraints

  arXiv (Cornell University) · 2026-05-03

  preprintOpen access1st authorCorresponding

  We study the design of strategy-proof and efficient mechanisms satisfying participation constraints in the job-matching problem. Each firm can hire multiple workers and each worker can be employed at only one firm. While firm utilities over subsets of workers are common knowledge, worker disutilities for working at each firm are private information. The VCG mechanism is the unique mechanism that is strategy-proof, efficient, and individually rational for workers; however, it may not be individual rational for firms. We show that the VCG mechanism is individually rational for firms if and only if firm utilities satisfy a condition called weak substitutes. We then strengthen participation constraints of firms to {\sl strong individual rationality}, which requires that each firm has no incentive to fire some of the workers assigned to it. The VCG mechanism is strongly individual rational if and only if firm utilities satisfy submodularity.

  PublisherDOI

• Rank-Preserving Multidimensional Mechanisms

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Rank-preserving multidimensional mechanisms: An equivalence between identical-object and heterogeneous-object models

  Journal of Economic Theory · 2024-10-09

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Continuity and Monotonicity of Preferences and Probabilistic Equivalence

  arXiv (Cornell University) · 2024-09-26

  preprintOpen access1st authorCorresponding

  We show that probabilistic equivalence of a regret-based preference relationship over random variables is implied by a weak form of continuity and monotonicity.

  PublisherOA PDFDOI

• Information Cascades and Social Learning

  Journal of Economic Literature · 2024-09-01 · 54 citations

  articleOpen access1st authorCorresponding

  Social learning is the updating of beliefs based on observation of others. Such observation can lead to efficient aggregation of information, but also to inaccurate decisions, fragility of mass behaviors, and, in the case of information cascades, to complete blockage of learning. We review the theory of information cascades and social learning and discuss important themes, insights, and applications of this literature as it has developed over the last 30 years. We also highlight open questions and promising directions for further theoretical and empirical exploration. (JEL D71, D82, D83, D91, Z13)

  PublisherOA PDFDOI

• Rank-preserving Multidimensional Mechanisms: an equivalence between identical-object and heterogeneous-object models

  arXiv (Cornell University) · 2022-09-21

  preprintOpen access1st authorCorresponding

  We show that the mechanism-design problem for a monopolist selling multiple, heterogeneous objects to a buyer with ex ante symmetric and additive values is equivalent to the mechanism-design problem for a monopolist selling identical objects to a buyer with decreasing marginal values. We derive three new results for the identical-objects model: (i) a new condition for revenue monotonicity of stochastic mechanisms, (ii) a sufficient condition on priors, such that prices in optimal deterministic mechanism are not increasing, and (iii) a simplification of incentive constraints for deterministic mechanisms. We use the equivalence to establish corresponding results in the heterogeneous-objects model.

  PublisherOA PDFDOI

• Rank-Preserving Multidimensional Mechanisms

  SSRN Electronic Journal · 2022-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Information Cascades and Social Learning

  SSRN Electronic Journal · 2021-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Information Cascades and Social Learning

  SSRN Electronic Journal · 2021-01-01 · 14 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Ivo Welch

  68 shared

• David Hirshleifer

  University of Southern California

  54 shared

• Omer Tamuz

  California Institute of Technology

  32 shared

• John G. Riley

  BioVectra (Canada)

  24 shared

• Sunil Sharma

  20 shared

• Uzi Segal

  18 shared

• James Schummer

  Northwestern University

  14 shared

• Rakesh Vohra

  13 shared