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Related papers: A Contextualist Decision Theory

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The influence of additional information on the decision making of agents, who are interacting members of a society, is analyzed within the mathematical framework based on the use of quantum probabilities. The introduction of social…

Physics and Society · Physics 2015-10-14 V. I. Yukalov , D. Sornette

We investigate how the choice of decision makers can be varied under the presence of risk and uncertainty. Our analysis is based on the approach we have previously applied to individual decision makers, which we now generalize to the case…

Physics and Society · Physics 2014-09-03 V. I. Yukalov , D. Sornette

Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost). With the advent of crowd-sensing systems, there is a need to redesign binary hypothesis testing rules for behavioral…

Artificial Intelligence · Computer Science 2016-10-07 V. Sriram Siddhardh Nadendla , Swastik Brahma , Pramod K. Varshney

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 develop a model to study the role of rationality in economics and biology. The model's agents differ continuously in their ability to make rational choices. The agents' objective is to ensure their individual survival over time or,…

Physics and Society · Physics 2015-07-20 Wolfgang Kuhle

We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask…

Computation and Language · Computer Science 2019-11-04 Daniel Kasenberg , Antonio Roque , Ravenna Thielstrom , Meia Chita-Tegmark , Matthias Scheutz

We study the performance of different methods for processing information, incorporating narrative selection within an evolutionary model. All agents update their beliefs according to Bayes' Rule, but some strategically choose the narrative…

Theoretical Economics · Economics 2025-08-06 Federico Innocenti , Roberto Rozzi

We provide a formal, simple and intuitive theory of rational decision making including sequential decisions that affect the environment. The theory has a geometric flavor, which makes the arguments easy to visualize and understand. Our…

Machine Learning · Computer Science 2012-02-10 Peter Sunehag , Marcus Hutter

Traditional models of rational action treat the agent as though it is cleanly separated from its environment, and can act on that environment from the outside. Such agents have a known functional relationship with their environment, can…

Artificial Intelligence · Computer Science 2020-10-08 Abram Demski , Scott Garrabrant

Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make and retract (tentative) conclusions from inconclusive evidence. This paper gives a possible-worlds interpretation of the nonmonotonic reasoning problem based on…

Artificial Intelligence · Computer Science 2013-04-10 Carl Kadie

A researcher observes a finite sequence of choices made by multiple agents in a binary-state environment. Agents maximize expected utilities that depend on their chosen alternative and the unknown underlying state. Agents learn about the…

Theoretical Economics · Economics 2021-05-11 Rahul Deb , Ludovic Renou

The principle that rational agents should maximize expected utility or choiceworthiness is intuitively plausible in many ordinary cases of decision-making under uncertainty. But it is less plausible in cases of extreme, low-probability risk…

Theoretical Economics · Economics 2020-08-11 Christian Tarsney

The influence of additional information on the decision making of agents, who are interacting members of a society, is analyzed within the mathematical framework based on the use of quantum probabilities. The introduction of social…

Physics and Society · Physics 2015-10-12 V. I. Yukalov , D. Sornette

Causality has been often confused with the notion of determinism. It is mandatory to separate the two notions in view of the debate about quantum foundations. Quantum theory provides an example of causal not-deterministic theory. Here we…

Quantum Physics · Physics 2015-01-15 Giacomo M. D'Ariano , Franco Manessi , Paolo Perinotti

As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical results show that optimal control can be *implemented* using belief states or world…

Machine Learning · Computer Science 2026-04-03 Aran Nayebi

We develop a model-independent framework to study the dynamics of decision-making in opinion networks for an arbitrary number of agents and an arbitrary number of options. Model-independence means that the analysis is not performed on a…

Optimization and Control · Mathematics 2020-09-10 Alessio Franci , Martin Golubitsky , Anastasia Bizyaeva , Naomi Ehrich Leonard

After two decades of research on indefinite causality, a philosophical lesson emerges: the tension between operational quantum theory and dynamical spacetime physics is unbridgeable if one believes both types of theories to be fundamental.…

Quantum Physics · Physics 2025-12-30 Alexei Grinbaum

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

We present a general logical framework for reasoning about agents' cognitive attitudes of both epistemic type and motivational type. We show that it allows us to express a variety of relevant concepts for qualitative decision theory…

Artificial Intelligence · Computer Science 2023-06-22 Emiliano Lorini

We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it. Firstly, while much of prior work focuses on studying a fixed pool of agents that remains…

Artificial Intelligence · Computer Science 2024-02-06 Kiet Q. H. Vo , Muneeb Aadil , Siu Lun Chau , Krikamol Muandet