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Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we employ an axiomatic framework for bounded rational decision-making based on a…

Artificial Intelligence · Computer Science 2011-07-29 Pedro A. Ortega , Daniel A. Braun

Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we propose an information-theoretic formalization of bounded rational decision-making…

Statistics Theory · Mathematics 2015-06-04 Pedro A. Ortega , Daniel A. Braun

A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of…

Artificial Intelligence · Computer Science 2013-12-25 Jordi Grau-Moya , Daniel A. Braun

The von Neumann and Morgenstern theory postulates that rational choice under uncertainty is equivalent to maximization of expected utility (EU). This view is mathematically appealing and natural because of the affine structure of the space…

Optimization and Control · Mathematics 2014-12-22 Roman V. Belavkin

Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics. This paper…

Machine Learning · Statistics 2015-12-22 Pedro A. Ortega , Daniel A. Braun , Justin Dyer , Kee-Eung Kim , Naftali Tishby

A model for decision making that generalizes Expected Utility Maximization is presented. This model, Expected Qualitative Utility Maximization, encompasses the Maximin criterion. It relaxes both the Independence and the Continuity…

Computer Science and Game Theory · Computer Science 2007-05-23 Daniel Lehmann

The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be…

Machine Learning · Statistics 2012-05-18 Pedro A. Ortega , Daniel A. Braun

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

Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e.…

Machine Learning · Statistics 2016-10-07 Pedro A. Ortega , Alan A. Stocker

Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded…

Artificial Intelligence · Computer Science 2015-11-06 Jordi Grau-Moya , Daniel A. Braun

Although many investigators affirm a desire to build reasoning systems that behave consistently with the axiomatic basis defined by probability theory and utility theory, limited resources for engineering and computation can make a complete…

Artificial Intelligence · Computer Science 2013-04-11 Eric J. Horvitz

Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate…

Machine Learning · Statistics 2019-10-29 Tomasz Kuśmierczyk , Joseph Sakaya , Arto Klami

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

A central challenge for intelligent agents in an uncertain world is striking the right balance between utility maximization and resource use, not only for external movement but also for internal computation. Existing theories of control…

Artificial Intelligence · Computer Science 2026-05-19 Itzel Olivos-Castillo , Paul Schrater , Xaq Pitkow

Motivated by the analysis of a general optimal portfolio selection problem, which encompasses as special cases an optimal consumption and an optimal debt-arrangement problem, we are concerned with the questions of how a personality trait…

Theoretical Economics · Economics 2023-11-14 Francesco Ruscitti , Ram Sewak Dubey , Giorgio Laguzzi

Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power. In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative…

Artificial Intelligence · Computer Science 2018-09-07 Heinke Hihn , Sebastian Gottwald , Daniel A. Braun

Many evaluation metrics have been defined to evaluate the effectiveness ad-hoc retrieval and search result diversification systems. However, it is often unclear which evaluation metric should be used to analyze the performance of retrieval…

Information Retrieval · Computer Science 2018-08-21 Enrique Amigó , Damiano Spina , Jorge Carrillo-de-Albornoz

Active inference, a corollary of the free energy principle, is a formal way of describing the behavior of certain kinds of random dynamical systems that have the appearance of sentience. In this chapter, we describe how active inference…

Machine Learning · Statistics 2021-10-11 Noor Sajid , Lancelot Da Costa , Thomas Parr , Karl Friston

This paper presents a comprehensive formalization of the von Neumann-Morgenstern (vNM) expected utility theorem using the Lean 4 interactive theorem prover. We implement the classical axioms of preference-completeness, transitivity,…

Theoretical Economics · Economics 2025-06-10 Li Jingyuan

A formalized and quantifiable responsibility score is a crucial component in many aspects of the development and application of multi-agent systems and autonomous agents. We can employ it to inform decision making processes based on ethical…

Computer Science and Game Theory · Computer Science 2022-09-16 Sarah Hiller , Jonas Israel , Jobst Heitzig
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