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In stochastic decision problems, one often wants to estimate the underlying probability measure statistically, and then to use this estimate as a basis for decisions. We shall consider how the uncertainty in this estimation can be…

Statistics Theory · Mathematics 2017-05-24 Samuel N. Cohen

We introduce a simple stochastic dynamics for game theory. It assumes ``local'' rationality in the sense that any player climbs the gradient of his utility function in the presence of a stochastic force which represents deviation from…

Statistical Mechanics · Physics 2008-11-23 Matteo Marsili , Yi-Cheng Zhang

Much of the theoretical work on strategic voting makes strong assumptions about what voters know about the voting situation. A strategizing voter is typically assumed to know how other voters will vote and to know the rules of the voting…

Multiagent Systems · Computer Science 2019-07-23 Wesley H. Holliday , Eric Pacuit

In this paper we focus on noncooperative games with uncertain constraints coupling the agents' decisions. We consider a setting where bounded deviations of agents' decisions from the equilibrium are possible, and uncertain constraints are…

Optimization and Control · Mathematics 2023-11-28 George Pantazis , Filiberto Fele , Kostas Margellos

We show that under plausible levels of background risk, no theory of choice under risk -- such as expected utility theory, prospect theory, or rank dependent utility -- can simultaneously satisfy the following three economic postulates: (i)…

Theoretical Economics · Economics 2021-03-09 Xiaosheng Mu , Luciano Pomatto , Philipp Strack , Omer Tamuz

We study the problem of assigning indivisible objects to agents where each is to receive at most one. To ensure fairness in the absence of monetary compensation, we consider random assignments. Random Priority, also known as Random Serial…

Theoretical Economics · Economics 2025-06-24 Christian Basteck

When robots share the same workspace with other intelligent agents (e.g., other robots or humans), they must be able to reason about the behaviors of their neighboring agents while accomplishing the designated tasks. In practice,…

Robotics · Computer Science 2022-10-18 Junhong Xu , Durgakant Pushp , Kai Yin , Lantao Liu

We establish an equivalence between two seemingly different theories: one is the traditional axiomatisation of incomplete preferences on horse lotteries based on the mixture independence axiom; the other is the theory of desirable gambles…

Artificial Intelligence · Computer Science 2018-01-03 Marco Zaffalon , Enrique Miranda

Bayesian rationality in strategic games presumes that it is possible to translate strategic uncertainty into imperfect information. Correlated equilibrium is guided by the idea that players are Bayes rational, have a common prior, and…

Computer Science and Game Theory · Computer Science 2016-02-02 Gabriel Frahm

In this work we generalize standard Decision Theory by assuming that two outcomes can also be incomparable. Two motivating scenarios show how incomparability may be helpful to represent those situations where, due to lack of information,…

Computer Science and Game Theory · Computer Science 2014-04-04 Piero A. Bonatti , Marco Faella , Luigi Sauro

The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in the process of skill acquisition. With the aid of artificial curiosity, we could equip current techniques for control, such as Reinforcement…

Machine Learning · Computer Science 2022-02-24 Pietro Mazzaglia , Ozan Catal , Tim Verbelen , Bart Dhoedt

Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to…

Physics and Society · Physics 2020-04-28 Sergio Cobo-Lopez , Antonia Godoy-Lorite , Jordi Duch , Marta Sales-Pardo , Roger Guimera

The rationalizability concept was introduced in \cite{Ber84} and \cite{Pea84} to assess what can be inferred by rational players in a non-cooperative game in the presence of common knowledge. However, this notion can be defined in a number…

Computer Science and Game Theory · Computer Science 2007-06-13 Krzysztof R. Apt

The aim of this of this paper is to study infinite games and to prove formally some properties in this framework. As a consequence we show that the behavior (the madness) of people which leads to speculative crashes or escalation can be…

Computer Science and Game Theory · Computer Science 2012-02-10 Pierre Lescanne

We propose a general framework for strategic voting when a voter may lack knowledge about other votes or about other voters' knowledge about her own vote. In this setting we define notions of manipulation and equilibrium. We also model…

Computer Science and Game Theory · Computer Science 2013-10-28 Hans van Ditmarsch , Jerome Lang , Abdallah Saffidine

This paper presents a plausible reasoning system to illustrate some broad issues in knowledge representation: dualities between different reasoning forms, the difficulty of unifying complementary reasoning styles, and the approximate nature…

Artificial Intelligence · Computer Science 2013-03-26 Wray L. Buntine

The systematic biases seen in people's probability judgments are typically taken as evidence that people do not reason about probability using the rules of probability theory, but instead use heuristics which sometimes yield reasonable…

Data Analysis, Statistics and Probability · Physics 2014-05-01 Fintan Costello , Paul Watts

Learning models do not in general imply that weakly dominated strategies are irrelevant or justify the related concept of "forward induction," because rational agents may use dominated strategies as experiments to learn how opponents play,…

Theoretical Economics · Economics 2022-11-15 Daniel Clark , Drew Fudenberg , Kevin He

This paper studies a new and more general axiomatization than one presented previously for preference on likelihood gambles. Likelihood gambles describe actions in a situation where a decision maker knows multiple probabilistic models and a…

Artificial Intelligence · Computer Science 2012-07-02 Phan H. Giang

Probability forecasts are intended to account for the uncertainties inherent in forecasting. It is suggested that from an end-user's point of view probability is not necessarily sufficient to reflect uncertainties that are not simply the…

Statistics Theory · Mathematics 2015-01-22 Kevin Judd