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People rationalize their past choices, even those that were mistakes in hindsight. We propose a formal theory of this behavior. The theory predicts that sunk costs affect later choices. Its model primitives are identified by choice behavior…

Theoretical Economics · Economics 2022-03-21 Erik Eyster , Shengwu Li , Sarah Ridout

We consider the problem of prediction with expert advice for ``easy'' sequences. We show that a variant of NormalHedge enjoys a second-order $\epsilon$-quantile regret bound of $O\big(\sqrt{V_T \log(V_T/\epsilon)}\big) $ when $V_T > \log…

Machine Learning · Computer Science 2026-02-10 Yoav Freund , Nicholas J. A. Harvey , Victor S. Portella , Yabing Qi , Yu-Xiang Wang

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review…

Artificial Intelligence · Computer Science 2018-01-17 Olivier Cailloux , Sébastien Destercke

We study repeated bilateral trade where an adaptive $\sigma$-smooth adversary generates the valuations of sellers and buyers. We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback…

Machine Learning · Computer Science 2024-02-20 Nicolò Cesa-Bianchi , Tommaso Cesari , Roberto Colomboni , Federico Fusco , Stefano Leonardi

This survey reviews recent developments in revealed preference theory. It discusses the testable implications of theories of choice that are germane to specific economic environments. The focus is on expected utility in risky environments;…

Theoretical Economics · Economics 2019-12-04 Federico Echenique

In this paper, we show that the presence of the Archimedean and the mixture-continuity properties of a binary relation, both empirically non-falsifiable in principle, foreclose the possibility of consistency (transitivity) without…

Theoretical Economics · Economics 2019-05-07 Tsogbadral Galaabaatar , M. Ali Khan , Metin Uyanık

We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspective of \cite{RvR16} to the contextual setting…

Machine Learning · Computer Science 2023-03-07 Gergely Neu , Julia Olkhovskaya , Matteo Papini , Ludovic Schwartz

We consider a bandit recommendations problem in which an agent's preferences (representing selection probabilities over recommended items) evolve as a function of past selections, according to an unknown $\textit{preference model}$. In each…

Machine Learning · Computer Science 2024-02-07 Arpit Agarwal , William Brown

We study the power of different types of adaptive (nonoblivious) adversaries in the setting of prediction with expert advice, under both full-information and bandit feedback. We measure the player's performance using a new notion of regret,…

Machine Learning · Computer Science 2013-06-04 Nicolo Cesa-Bianchi , Ofer Dekel , Ohad Shamir

In this note we present a characterisation of all unary and binary patterns that do not only contain variables, but also reversals of their instances. These types of variables were studied recently in either more general or particular…

Formal Languages and Automata Theory · Computer Science 2015-08-20 Robert Mercaş

We study the robustness of Bayesian persuasion to uncertainty about the receiver's preferences. We analyze two conceptually distinct notions: continuity, in which only the modeler lacks precise knowledge, but where the model's predictions…

Theoretical Economics · Economics 2026-05-28 Ronen Gradwohl , Fengming Hu , Rann Smorodinsky

We extend Berge's Maximum Theorem to allow for incomplete preferences. We first provide a simple version of the Maximum Theorem for convex feasible sets and a fixed preference. Then, we show that if, in addition to the traditional…

Theoretical Economics · Economics 2021-11-17 Leandro Gorno , Alessandro Rivello

We consider model selection for sequential decision making in stochastic environments with bandit feedback, where a meta-learner has at its disposal a pool of base learners, and decides on the fly which action to take based on the policies…

Machine Learning · Computer Science 2024-01-24 Aldo Pacchiano , Christoph Dann , Claudio Gentile

Existing online learning algorithms for adversarial Markov Decision Processes achieve ${O}(\sqrt{T})$ regret after $T$ rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the…

Machine Learning · Computer Science 2023-10-27 Tiancheng Jin , Junyan Liu , Chloé Rouyer , William Chang , Chen-Yu Wei , Haipeng Luo

We study the problem of prediction with expert advice with adversarial corruption where the adversary can at most corrupt one expert. Using tools from viscosity theory, we characterize the long-time behavior of the value function of the…

Machine Learning · Computer Science 2021-03-02 Erhan Bayraktar , Ibrahim Ekren , Xin Zhang

Counterfactual utilities evaluate decisions not only by the realized outcome under a given decision, but also by the counterfactual outcomes that would arise under alternative decisions. By generalizing standard utility frameworks, they…

Theoretical Economics · Economics 2026-05-08 Benedikt Koch , Kosuke Imai , Tomasz Strzalecki

This study proposes a new efficiency requirement, a minimal almost weak Pareto principle, which says that x is socially better than y whenever the only one individual never prefers y to x, and all the others prefers x to y. Then, I show…

Theoretical Economics · Economics 2025-01-20 Norihito Sakamoto

We consider the problem of rationalizing choice data by a preference satisfying an arbitrary collection of invariance axioms. Examples of such axioms include quasilinearity, homotheticity, independence-type axioms for mixture spaces,…

Theoretical Economics · Economics 2024-08-09 Peter Caradonna , Christopher P. Chambers

We consider Thompson Sampling (TS) for linear combinatorial semi-bandits and subgaussian rewards. We propose the first known TS whose finite-time regret does not scale exponentially with the dimension of the problem. We further show the…

Machine Learning · Statistics 2024-10-10 Raymond Zhang , Richard Combes

The biduality and reflexivity theorems are known to hold for projective varieties defined over fields of characteristic zero, and to fail in positive characteristic. In this article, we construct a notion of reflexivity and biduality in…

Algebraic Geometry · Mathematics 2020-04-08 Aristides Kontogeorgis , Georgios Petroulakis
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