Mithun Chakraborty's research while affiliated with Washington University in St. Louis and other places
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Publications (8)
In this paper, we introduce a multi-agent multi-armed bandit-based model for ad hoc teamwork with expensive communication. The goal of the team is to maximize the total reward gained from pulling arms of a bandit over a number of epochs. In each epoch, each agent decides whether to pull an arm, or to broadcast the reward it obtained in the previous...
The logarithmic market scoring rule (LMSR), the most common automated market making rule for prediction markets, is typically studied in the framework of dealer markets, where the market maker takes one side of every transaction. The continuous double auction (CDA) is a much more widely used microstructure for general financial markets in practice....
We present a simple theoretical framework, and corresponding practical
procedures, for comparing probabilistic models on real data in a traditional
machine learning setting. This framework is based on the theory of proper
scoring rules, but requires only basic algebra and probability theory to
understand and verify. The theoretical concepts present...
Prediction markets are often used as mechanisms to aggregate information
about a future event, for example, whether a candidate will win an election.
The event is typically assumed to be exogenous. In reality, participants may
influence the outcome, and therefore (1) running the prediction market could
change the incentives of participants in the p...
We describe the design of Instructor Rating Markets in which students trade on the ratings that will be received by instructors, with new ratings revealed every two weeks. The markets provide useful dynamic feedback to instructors on the progress of their class, while at the same time enabling the controlled study of prediction markets where trader...
Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are loss-making in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to reac...
We study learning in a noisy bisection model: specifically, Bayesian
algorithms to learn a target value V given access only to noisy realizations of
whether V is less than or greater than a threshold theta. At step t = 0, 1, 2,
..., the learner sets threshold theta t and observes a noisy realization of
sign(V - theta t). After T steps, the goal is...
Citations
... Distributed/federated multi-armed bandits. There is a vast literature on distributed/federated multi-armed bandits (MABs) (Liu and Zhao, 2010;Szorenyi et al., 2013;Landgren et al., 2016;Chakraborty et al., 2017;Landgren et al., 2018;Martínez-Rubio et al., 2019;Sankararaman et al., 2019;Wang et al., 2019Wang et al., , 2020Zhu et al., 2021), to mention a few. However, none of these algorithms can be directly applied to linear bandits, needless to say contextual linear bandits with infinite decision sets. ...
... A similar market 2 was later launched at the University of Texas at Austin, using a liquidity-sensitive variation of LMSR (Othman, Pennock, Reeves, and Sandholm 2013). Moreover, LMSR has been deployed to predict product-sales levels (Plott and Chen 2002), instructor ratings (Chakraborty et al. 2013), and political events (Hanson 1999). ...
... A significant body of literature, in particular in AI, has studied the market making problem for prediction markets [29,121,122]. In this setting, the agent's main goal is to elicit information from informed participants in the market. ...
... It represents a natural generalization of allocation problems. Chakraborty, Das, and Magdon-Ismail (2011) and Karp and Kleinberg (2007) mention various important scenarios. In strategic planning, the resource b represents the various levels of investment in risky development and one seeks to find the minimal investment level that assures certain probability of success. ...
