arXiv subjects

Anuj S. Vora

Publications and source records attributed to Anuj S. Vora.

5 recordsLinked to original sources

arXiv2026

Adaptive Incentive Design in Dynamic Principal-Agent Problem via Kernelized Bandits

We consider the dynamic principal-agent problem under asymmetric information, wherein a principal sequentially designs contracts to incentivize an agent with unknown preferences and hidden actions. A fundamental bottleneck in the existing literature is the assumption of deterministic agent utility, which renders the principal's expected utility discontinuous and forces computationally intractable discretizations of the contract space. In this paper, we address this limitation by introducing a stochastic counterpart into the agent's utility model, capturing the inherent physical and behavioral variations in realistic subsystems. We formally prove that this stochastic formulation restores the continuity of the principal's expected utility. Leveraging this continuous geometric structure, we formulate the interaction as a structured multi-armed bandit problem subject to heteroscedastic noise. We propose a \texttt{Heteroscedastic GP-UCB} algorithm that utilizes a Neural Network (Arcsin) kernel, chosen to capture the non-stationary, sigmoidal geometry of the utility landscape. For an $m$-dimensional compact contract space, we establish a high-probability cumulative regret bound of $O\left(\sqrt{T}(\log T)^{m+1}\right)$. Finally, we demonstrate the practical efficacy of our theoretical framework by formulating the Vehicle-to-Grid (V2G) incentive design problem, proving its equivalence to a dynamic principal-agent problem, and showing superior economic performance for grid aggregators.

cs.MA↗

arXiv2025

Achievable Rates for Information Extraction from a Strategic Sender

We consider a setting of non-cooperative communication where a receiver wants to recover randomly generated sequences of symbols that are observed by a strategic sender. The sender aims to maximize an average utility that may not align with the recovery criterion of the receiver, whereby the signals it sends may not be truthful. The rate of communication is defined as the number of reconstructions corresponding to the sequences recovered correctly while communicating with the sender. We pose this problem as a sequential game between the sender and the receiver with the receiver as the leader and determine strategies for the receiver that attain vanishing probability of error and compute the rates of such strategies. We show the existence of such strategies under a condition on the utility of the sender. For the case of the binary alphabet, this condition is also necessary, in the absence of which, the probability of error goes to one for all choices of strategies of the receiver. We show that for reliable recovery, the receiver chooses to correctly decode only a $\textit{subset}$ of messages received from the sender and deliberately makes an error on messages outside this subset. Despite a clean channel, our setting exhibits a non-trivial $\textit{maximum}$ rate of communication, which is in general strictly less than the capacity of the channel. This implies the impossibility of strategies that correctly decode sequences of rate greater than the maximum rate while also achieving reliable communication. This is a key point of departure from the usual setting of cooperative communication.

cs.IT↗

arXiv2022

Shannon meets Myerson: Information Extraction from a Strategic Sender

We study a setting where a receiver must design a questionnaire to recover a sequence of symbols known to strategic sender, whose utility may not be incentive compatible. We allow the receiver the possibility of selecting the alternatives presented in the questionnaire, and thereby linking decisions across the components of the sequence. We show that, despite the strategic sender and the noise in the channel, the receiver can recover exponentially many sequences, but also that exponentially many sequences are unrecoverable even by the best strategy. We define the growth rate of the number of recovered sequences as the information extraction capacity. A generalization of the Shannon capacity, it characterizes the optimal amount of communication resources required. We derive bounds leading to an exact evaluation of the information extraction capacity in many cases. Our results form the building blocks of a novel, noncooperative regime of communication involving a strategic sender.

cs.IT↗

arXiv2020

Optimal Questionnaires for Screening of Strategic Agents

During the COVID-$19$ pandemic the health authorities at airports and train stations try to screen and identify the travellers possibly exposed to the virus. However, many individuals avoid getting tested and hence may misreport their travel history. This is a challenge for the health authorities who wish to ascertain the truly susceptible cases in spite of this strategic misreporting. We investigate the problem of questioning travellers to classify them for further testing when the travellers are strategic or are unwilling to reveal their travel histories. We show there are fundamental limits to how many travel histories the health authorities can recover.% can be correctly classified by any probing mechanism.

cs.IR↗

arXiv2019

Minimax Theorems for Finite Blocklength Lossy Joint Source-Channel Coding over an AVC

Motivated by applications in the security of cyber-physical systems, we pose the finite blocklength communication problem in the presence of a jammer as a zero-sum game between the encoder-decoder team and the jammer, by allowing the communicating team as well as the jammer only locally randomized strategies. The communicating team's problem is non-convex under locally randomized codes, and hence, in general, a minimax theorem need not hold for this game. However, we show that approximate minimax theorems hold in the sense that the minimax and maximin values of the game approach each other asymptotically. In particular, for rates strictly below a critical threshold, both the minimax and maximin values approach zero, and for rates strictly above it, they both approach unity. We then show a second order minimax theorem, i.e., for rates exactly approaching the threshold with along a specific scaling, the minimax and maximin values approach the same constant value, that is neither zero nor one. Critical to these results is our derivation of finite blocklength bounds on the minimax and maximin values of the game and our derivation of second order dispersion-based bounds.

cs.IT↗