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Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of \emph{insider information}. We assume that, since the…

Mathematical Finance · Quantitative Finance 2019-12-05 Bernardo D'Auria , José Antonio Salmerón

People care about decision outcomes and how decisions get made, both when making decisions and reflecting on decisions. But formalizing the full range of normative concerns that drive decisions is an open challenge. We introduce Axiomatic…

Artificial Intelligence · Computer Science 2026-02-11 Ben Abramowitz , Nicholas Mattei

We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based…

Artificial Intelligence · Computer Science 2013-03-08 Eric J. Horvitz , Adrian Klein

In this paper, we formulate a qualitative "linear" utility theory for lotteries in which uncertainty is expressed qualitatively using a Spohnian disbelief function. We argue that a rational decision maker facing an uncertain decision…

Artificial Intelligence · Computer Science 2013-01-18 Phan H. Giang , Prakash P. Shenoy

We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective. In general, such approximations arise in applications such as reinforcement…

Machine Learning · Statistics 2016-11-16 Matthias Poloczek , Jialei Wang , Peter I. Frazier

Option prices encode the market's collective outlook through implied density and implied volatility. An explicit link between implied density and implied volatility translates the risk-neutrality of the former into conditions on the latter…

Computational Finance · Quantitative Finance 2026-03-19 Jimin Lin

We analyze characteristics' joint predictive information through the lens of out-of-sample power utility functions. Linking weights to characteristics to form optimal portfolios suffers from estimation error which we mitigate by maximizing…

General Finance · Quantitative Finance 2024-02-05 Christopher G. Lamoureux , Huacheng Zhang

We propose an efficient algorithm for estimation of possibility based qualitative expected utility. It is useful for decision making mechanisms where each possible decision is assigned a multi-attribute possibility distribution. The…

Artificial Intelligence · Computer Science 2012-07-09 Jakub Brzostowski , Ryszard Kowalczyk

Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The option-critic framework has been demonstrated to learn temporally extended actions, represented as…

Machine Learning · Computer Science 2025-11-21 Anand Kamat , Doina Precup

We propose a class of discrete-time stochastic models for the pricing of inflation-linked assets. The paper begins with an axiomatic scheme for asset pricing and interest rate theory in a discrete-time setting. The first axiom introduces a…

General Finance · Quantitative Finance 2008-12-02 Lane P. Hughston , Andrea Macrina

The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries. The axiomatic framework used is analogous to von Neumann-Morgenstern's utility theory for probabilistic lotteries as…

Artificial Intelligence · Computer Science 2020-07-16 Thierry Denoeux , Prakash P. Shenoy

We consider an arbitrage-free, discrete time and frictionless market. We prove that an investor maximising the expected utility of her terminal wealth can always find an optimal investment strategy provided that her dissatisfaction of…

Portfolio Management · Quantitative Finance 2014-09-09 Miklos Rasonyi

In this short paper we introduce a new class of performance measures based on certainty equivalents defined via scaled utility functions. We analyse their properties, show that the corresponding portfolio optimization problem is well-posed…

Risk Management · Quantitative Finance 2023-10-04 Marcin Pitera , Miklós Rásonyi

Approaches to decision-making under uncertainty in the belief function framework are reviewed. Most methods are shown to blend criteria for decision under ignorance with the maximum expected utility principle of Bayesian decision theory. A…

Artificial Intelligence · Computer Science 2019-12-13 Thierry Denoeux

Gambles are random variables that model possible changes in monetary wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes.…

Economics · Quantitative Finance 2016-02-03 Ole Peters , Murray Gell-Mann

The notion of utility maximising entropy (u-entropy) of a probability density, which was introduced and studied by Slomczynski and Zastawniak (Ann. Prob 32 (2004) 2261-2285, arXiv:math.PR/0410115 v1), is extended in two directions. First,…

Probability · Mathematics 2008-12-02 Grzegorz Harańczyk , Wojciech Słomczyński , Tomasz Zastawniak

Instead of testing for unanimous agreement, I propose learning how broad of a consensus favors one distribution over another (of earnings, productivity, asset returns, test scores, etc.). Specifically, given a sample from each of two…

Econometrics · Economics 2024-08-27 David M. Kaplan

The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing the expectation of some utility function. We extend these axioms to increasingly structured…

Artificial Intelligence · Computer Science 2022-06-29 Mehran Shakerinava , Siamak Ravanbakhsh

This paper shows how we can combine logical representations of actions and decision theory in such a manner that seems natural for both. In particular we assume an axiomatization of the domain in terms of situation calculus, using what is…

Artificial Intelligence · Computer Science 2013-02-18 David L. Poole

A variety of statistical graphical models have been defined to represent the conditional independences underlying a random vector of interest. Similarly, many different graphs embedding various types of preferential independences, as for…

Artificial Intelligence · Computer Science 2016-10-26 Manuele Leonelli , Jim Q. Smith