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Suraj Malladi
PhD Program, Economics
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Graduate School of Business
Stanford University
655 Knight Way
Stanford, CA 94305

Suraj Malladi

Faculty Advisors
Andrzej Skrzypacz Michael Ostrovsky Matthew Jackson Mohammad Akbarpour
Research Statement
I study decision problems where information and actions spaces are realistically much more limited than what is assumed in standard optimization or Bayesian mechanism design models. My research belongs to the literature on social learning, networks, delegation and auctions; it is methodologically related to prior-free decision making and robust mechanism design.
Research Interests
Networks
Prior-Free Decision Making
Robust Mechanism Design
Diffusion and Learning
Working Papers
Just a Few Seeds More: Value of Network Information for Diffusion
(R&R at the American Economic Review; with Mohammad Akbarpour and Amin Saberi) Identifying the optimal set of individuals to first receive information in a social network is a widely-studied problem in settings such as the diffusion of information, microfinance programs, and new technologies. We show that, for some frequently studied diffusion processes, randomly seeding S + X individuals can prompt a larger cascade than optimally targeting the best S individuals, for a small X. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.
Learning Through the Grapevine: The Impact of Message Mutation, Transmission Failure, and Deliberate Bias
(With Matthew O. Jackson and David McAdams) We examine how well agents learn when information reaches them through chains of noisy person-to-person relay. If noise only takes the form of random mutations and transmission failures, then there is a sharp threshold such that a receiver learns fully if she has access to more chains than the threshold and nothing with fewer. Moreover, simple information processing rules can perform as well as fully Bayesian learning. However, if some agents deliberately distort message content, learning may be impossible with any number of chains, even if the fraction of such biased individuals is small.
Work in Progress
Fair Auctions with Asymmetrically Informed Bidders
(With Aranyak Mehta and Uri Nadav) Agents often arrive to auctions with different levels of informations about their own value for the object sold. In such asymmetric settings, it may be optimal to charge different reservation prices to discriminate between bidders. However, it is often infeasible to expressly treat different bidders in the same auction differently, particularly in on-line settings. We characterize optimal nondiscriminatory mechanisms in the presence of informational asymmetries and compares them to the revenue of unconstrained optimal auctions. We find the revenue of the unconstrained optimal auctions never exceeds between twice to four-thirds of the optimal nondiscriminatory auction’s revenue.
Last Updated 9 Oct 2019