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Arrow's Impossibility Theorem as a Generalisation of Condorcet's Paradox
Ori Livson, Mikhail Prokopenko
https://arxiv.org/abs/2510.09076 https://ar…

Arrow's Impossibility Theorem as a Generalisation of Condorcet's Paradox
Arrow's Impossibility Theorem is a seminal result of Social Choice Theory that demonstrates the impossibility of ranked-choice decision-making processes to jointly satisfy a number of intuitive and seemingly desirable constraints. The theorem is often described as a generalisation of Condorcet's Paradox, wherein pairwise majority voting may fail to jointly satisfy the same constraints due to the occurrence of elections that result in contradictory preference cycles. However, a formal proof of t…

A Category Theoretic Approach to Approximate Game Theory
Neil Ghani (MSP Group, University of Strathclyde)
https://arxiv.org/abs/2509.20932 https://arxiv.o…

A Category Theoretic Approach to Approximate Game Theory
This paper uses category theory to develop an entirely new approach to approximate game theory. Game theory is the study of how different agents within a multi-agent system take decisions. At its core, game theory asks what an optimal decision is in a given scenario. Thus approximate game theory asks what is an approximately optimal decision in a given scenario. This is important in practice as -- just like in much of computing -- exact answers maybe too difficult to compute or even impossible …

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Causal Coordination for Distributed Decision-Making
Mengyuan Zhao, Tobias J. Oechtering, Ma\"el Le Treust

Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees
Daniel Koutas, Daniel Straub
https://arxiv.org/abs/2509.20145 https://

Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees
We propose a general method for deriving prognostics-based predictive maintenance policies. The method takes into account the available decision options at hand, the information on the future state of the system provided by a prognostic model, as well as the costs of the underlying renewal-reward process. It results in heuristic policies with only a few parameters, which can be determined based on theoretical considerations or by optimization from run-to-failure data. We show the potential of t…

Extreme Value Theory-enhanced Radio Maps for Handovers in Ultra-reliable Communications
Dian Echevarr\'ia P\'erez, Onel L. Alcaraz L\'opez, Hirley Alves
https://arxiv.org/abs/2509.22547

Extreme Value Theory-enhanced Radio Maps for Handovers in Ultra-reliable Communications
Efficient handover (HO) strategies are essential for maintaining the stringent performance requirements of ultra-reliable communication (URC) systems. This work introduces a novel HO framework designed from a physical-layer perspective, where the decision-making process focuses on determining the optimal time and location for performing HOs. Leveraging extreme value theory (EVT) and statistical radio maps, the proposed method predicts signal behaviour and enables efficient resource allocation. …

Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov
https://arxiv.org/abs/2509.26610

Uncertainty Quantification for Regression using Proper Scoring Rules
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantification (UQ) theory has advanced significantly, building on a firm basis of learning with proper scoring rules. However, these advances were focused on classification, while extending these ideas to regression remains challenging. In this work, we introduce a unified UQ framework for regression based on proper scoring r…

Downside Risk-Aware Equilibria for Strategic Decision-Making
Oliver Slumbers, Benjamin Patrick Evans, Sumitra Ganesh, Leo Ardon
https://arxiv.org/abs/2510.03446 https://

Downside Risk-Aware Equilibria for Strategic Decision-Making
Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently, a new game-theoretic approach provides a more holistic view of risk also considering the reward-variance. However, these variance-based approaches measure variance of the reward on both the upside and downside. In many domains, such as finance, downside risk only is of key importance, as this represents the potentia…

A decision-theoretic framework for uncertainty quantification in epidemiological modelling
Nicholas Steyn, Freddie Bickford Smith, Cathal Mills, Vik Shirvaikar, Christl A Donnelly, Kris V Parag
https://arxiv.org/abs/2509.20013

A decision-theoretic framework for uncertainty quantification in epidemiological modelling
Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. However, sources of uncertainty are rarely formalised, and existing classifications are often defined inconsistently. This lack of structure hampers interpretation, model comparison, and targeted data collection. Connecting ideas from machine learning, information theory, experimental design, and health economics, we present a fi…

RoboSeek: You Need to Interact with Your Objects
Yibo Peng, Jiahao Yang, Shenhao Yan, Ziyu Huang, Shuang Li, Shuguang Cui, Yiming Zhao, Yatong Han
https://arxiv.org/abs/2509.17783

RoboSeek: You Need to Interact with Your Objects
Optimizing and refining action execution through exploration and interaction is a promising way for robotic manipulation. However, practical approaches to interaction driven robotic learning are still underexplored, particularly for long-horizon tasks where sequential decision-making, physical constraints, and perceptual uncertainties pose significant chal lenges. Motivated by embodied cognition theory, we propose RoboSeek, a framework for embodied action execution that leverages in…

On Dynamic Programming Theory for Leader-Follower Stochastic Games
Jilles Steeve Dibangoye, Thibaut Le Marre, Ocan Sankur, Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2512.05667 https://arxiv.org/pdf/2512.05667 https://arxiv.org/html/2512.05667
arXiv:2512.05667v1 Announce Type: new
Abstract: Leader-follower general-sum stochastic games (LF-GSSGs) model sequential decision-making under asymmetric commitment, where a leader commits to a policy and a follower best responds, yielding a strong Stackelberg equilibrium (SSE) with leader-favourable tie-breaking. This paper introduces a dynamic programming (DP) framework that applies Bellman recursion over credible sets-state abstractions formally representing all rational follower best responses under partial leader commitments-to compute SSEs. We first prove that any LF-GSSG admits a lossless reduction to a Markov decision process (MDP) over credible sets. We further establish that synthesising an optimal memoryless deterministic leader policy is NP-hard, motivating the development of {\epsilon}-optimal DP algorithms with provable guarantees on leader exploitability. Experiments on standard mixed-motive benchmarks-including security games, resource allocation, and adversarial planning-demonstrate empirical gains in leader value and runtime scalability over state-of-the-art methods.
toXiv_bot_toot

Trust Region Algorithm for Stochastic Minimax Problems with Decision-Dependent Distributions
Yan Gao, Yongchao Liu, Zili Luo
https://arxiv.org/abs/2509.12868 https://

Trust Region Algorithm for Stochastic Minimax Problems with Decision-Dependent Distributions
Stochastic minimax optimization has drawn much attention over the past decade due to its broad applications in machine learning, signal processing and game theory. In some applications, the probability distribution of uncertainty depends on decision variables, as the environment may respond to decisions. In this paper, we propose a trust region algorithm for finding the stationary points of stochastic minimax problems with decision-dependent distributions. At each iteration, the algorithm local…

Confirmation Bias as a Cognitive Resource in LLM-Supported Deliberation
Sander de Jong, Rune M{\o}berg Jacobsen, Niels van Berkel
https://arxiv.org/abs/2509.14824 https://

Confirmation Bias as a Cognitive Resource in LLM-Supported Deliberation
Large language models (LLMs) are increasingly used in group decision-making, but their influence risks fostering conformity and reducing epistemic vigilance. Drawing on the Argumentative Theory of Reasoning, we argue that confirmation bias, often seen as detrimental, can be harnessed as a resource when paired with critical evaluation. We propose a three-step process in which individuals first generate ideas independently, then use LLMs to refine and articulate them, and finally engage with LLMs…

Quantum-like representation of neuronal networks' activity: modeling "mental entanglement"
Andrei Khrennikov, Makiko Yamada
https://arxiv.org/abs/2509.16253 https:…

Quantum-like representation of neuronal networks' activity: modeling "mental entanglement"
Quantum-like modeling (QLM) - quantum theory applications outside of physics - are intensively developed with applications in biology, cognition, psychology, and decision-making. For cognition, QLM should be distinguished from quantum reductionist models in the spirit of Hameroff and Penrose and well as Umezawa and Vitiello. QLM is not concerned with just quantum physical processes in the brain but also QL information processing by macroscopic neuronal structures. Although QLM of cognition and …

Traffic jams and driver behavior archetypes
Shawn Berry
https://arxiv.org/abs/2510.04740 https://arxiv.org/pdf/2510.04740

Traffic jams and driver behavior archetypes
Traffic congestion represents a complex urban phenomenon that has been the subject of extensive research employing various modeling techniques grounded in the principles of physics and molecular theory. Although factors such as road design, accidents, weather conditions, and construction activities contribute to traffic congestion, driver behavior and decision-making are primary determinants of traffic flow efficiency. This study introduces a driver behavior archetype model that quantifies the …

Evaluating the Infinite
Toby Ord
https://arxiv.org/abs/2509.19389 https://arxiv.org/pdf/2509.19389

Evaluating the Infinite
I present a novel mathematical technique for dealing with the infinities arising from divergent sums and integrals. It assigns them fine-grained infinite values from the set of hyperreal numbers in a manner that refines the standard theories of summation and integration. This has implications in statistics (helping us work with distributions whose mean or variance is infinite), decision theory (allowing comparison of options with infinite expected values), economics (allowing evaluation of infi…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Shengbo Wang, Nian Si
https://arxiv.org/abs/2509.14203 https://a…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. The average-reward formulation, while natural in many operations research and management contexts, remains underexplored. This is primarily because the dynamic programming foundations are technically challenging and only partially understood, with several fundamental questions remaining…

Distributed Functional Optimization and Learning on Banach Spaces: Generic Frameworks
Zhan Yu, Zhongjie Shi, Deming Yuan, Daniel W. C. Ho
https://arxiv.org/abs/2509.17554 https:…

Distributed Functional Optimization and Learning on Banach Spaces: Generic Frameworks
In this paper, we establish a distributed functional optimization (DFO) theory based on time-varying multi-agent networks. The vast majority of existing distributed optimization theories are developed based on Euclidean decision variables. However, for many scenarios in machine learning and statistical learning, such as reproducing kernel spaces or probability measure spaces that use functions or probability measures as fundamental variables, the development of existing distributed optimization…