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The general use of subjective probabilities to model belief has been justified using many axiomatic schemes. For example, ?consistent betting behavior' arguments are well-known. To those not already convinced of the unique fitness and…

Artificial Intelligence · Computer Science 2013-03-25 Paul Snow

This paper presents an axiomatic framework for qualitative decision under uncertainty in a finite setting. The corresponding utility is expressed by a sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking, Sugeno…

Artificial Intelligence · Computer Science 2013-02-01 Didier Dubois , Henri Prade , Regis Sabbadin

The aim of this work is to provide a unified framework for ordinal representations of uncertainty lying at the crosswords between possibility and probability theories. Such confidence relations between events are commonly found in monotonic…

Artificial Intelligence · Computer Science 2012-08-07 Didier Dubois , Helene Fargier

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

Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action…

General Economics · Economics 2022-05-03 Stefan Rass , Sandra König , Stefan Schauer

We implement nonparametric revealed-preference tests of subjective expected utility theory and its generalizations. We find that a majority of subjects' choices are consistent with the maximization of some utility function. They respond to…

General Economics · Economics 2021-05-04 Federico Echenique , Taisuke Imai , Kota Saito

Analyzing decision problems under uncertainty commonly relies on idealizing assumptions about the describability of the world, with the most prominent examples being the closed world and the small world assumption. Most assumptions are…

Methodology · Statistics 2025-12-08 Christoph Jansen , Georg Schollmeyer , Thomas Augustin , Julian Rodemann

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable,…

Machine Learning · Statistics 2018-10-30 Dimitris Bertsimas , Christopher McCord

We propose a quantum expected value theory for decision-making under uncertainty. Quantum density operator as value operator is proposed to simulate people's subjective beliefs. Value operator guides people to choose corresponding actions…

Physics and Society · Physics 2023-03-22 Lizhi Xin , Houwen Xin

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound…

Artificial Intelligence · Computer Science 2013-03-08 Judea Pearl

We live in a world brimming with uncertainty, where we constantly have to make a lot of decisions under incomplete information. We are firm believers that our subjective belief cannot be computed by rigorous mathematical formula; instead…

Physics and Society · Physics 2023-02-08 Lizhi Xin , Kevin Xin , Houwen Xin

I consider decision-making constrained by considerations of morality, rationality, or other virtues. The decision maker (DM) has a true preference over outcomes, but feels compelled to choose among outcomes that are top-ranked by some…

Theoretical Economics · Economics 2020-03-17 Sarah Ridout

This paper proposes a decision theory for a symbolic generalization of probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the requirement of using numbers for uncertainty while preserving desirable patterns of…

Artificial Intelligence · Computer Science 2012-08-01 Phan H. Giang , Sathyakama Sandilya

Preferences among acts are analyzed in the style of L. Savage, but as partially ordered. The rationality postulates considered are weaker than Savage's on three counts. The Sure Thing Principle is derived in this setting. The postulates are…

Artificial Intelligence · Computer Science 2014-08-08 Daniel Lehmann

This paper studies preference aggregation under uncertainty in the multi-profile framework and characterizes a new class of aggregation rules that address classical concerns about Harsanyi's (1955) utilitarian rules. Our aggregation rules,…

Theoretical Economics · Economics 2026-05-12 Leo Kurata , Kensei Nakamura

Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision…

Computer Science and Game Theory · Computer Science 2023-06-22 Philippe Mongin

Unaided human decision making appears to systematically violate consistency constraints imposed by normative theories; these biases in turn appear to justify the application of formal decision-analytic models. It is argued that both claims…

Artificial Intelligence · Computer Science 2013-04-08 Marvin S. Cohen

Mutually exclusive decisions have been studied for decades. Many well-known decision theories have been defined to help people either to make rational decisions or to interpret people's behaviors, such as expected utility theory, regret…

Economics · Quantitative Finance 2018-01-09 Pengyu Zhu

We study intertemporal decision making under uncertainty. We fully characterize discounted expected utility in a framework \`a la Savage. Despite the popularity of this model, no characterization is available in this setting. The concept of…

Theoretical Economics · Economics 2020-03-13 Lorenzo Bastianello , José Heleno Faro

This paper explains why internal and external validity cannot be simultaneously maximised. It introduces "evidential states" to represent the information available for causal inference and shows that routine study operations (restriction,…

Applications · Statistics 2025-12-01 Daniel D. Reidpath