Tootfinder

Encoding and Reasoning About Arrays in Set Theory
Maximiliano Cristi\'a, Gianfranco Rossi
https://arxiv.org/abs/2508.11447 https://arxiv.org/pdf/2508.1…

Encoding and Reasoning About Arrays in Set Theory
We encode arrays as functions which, in turn, are encoded as sets of ordered pairs. The set cardinality of each of these functions coincides with the length of the array it is representing. Then we define a fragment of set theory that is used to give the specifications of a non-trivial class of programs with arrays. In this way, array reasoning becomes set reasoning. Furthermore, a decision procedure for this fragment is also provided and implemented as part of the {log} (read 'setlog') tool. {…

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
Amin Masoumi, Mert Korkali
https://arxiv.org/abs/2507.13265

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive deep learning (DL) model with information gap decision theory (IGDT) to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of post-fault dynamics to proactively redispatch resourc…

Geometric Theory of Ising Machines
Andrew G. Moore, Zachary Richey, Isaac K. Martin
https://arxiv.org/abs/2507.12626 https://arxiv.or…

Geometric Theory of Ising Machines
We contribute to the mathematical theory of the design of low temperature Ising machines, a type of experimental probabilistic computing device implementing the Ising model. Encoding the output of a function in the ground state of a physical system allows efficient and distributed computation, but the design of the energy function is a difficult puzzle. We introduce a diagrammatic device that allows us to visualize the decision boundaries for Ising circuits. It is then used to prove two results…

Informative Post-Hoc Explanations Only Exist for Simple Functions
Eric G\"unther, Bal\'azs Szabados, Robi Bhattacharjee, Sebastian Bordt, Ulrike von Luxburg
https://arxiv.org/abs/2508.11441

Informative Post-Hoc Explanations Only Exist for Simple Functions
Many researchers have suggested that local post-hoc explanation algorithms can be used to gain insights into the behavior of complex machine learning models. However, theoretical guarantees about such algorithms only exist for simple decision functions, and it is unclear whether and under which assumptions similar results might exist for complex models. In this paper, we introduce a general, learning-theory-based framework for what it means for an explanation to provide information about a deci…

A Decision Procedure for Probabilistic Kleene Algebra with Angelic Nondeterminism
Shawn Ong, Dexter Kozen
https://arxiv.org/abs/2507.10980 https://

A Decision Procedure for Probabilistic Kleene Algebra with Angelic Nondeterminism
We give a decision procedure and proof of correctness for the equational theory of probabilistic Kleene algebra with angelic nondeterminism introduced in Ong, Ma, and Kozen (2025).

Research on Optimal Control Problem Based on Reinforcement Learning under Knightian Uncertainty
Ziyu Li, Chen Fei, Weiyin Fei
https://arxiv.org/abs/2506.13207

Research on Optimal Control Problem Based on Reinforcement Learning under Knightian Uncertainty
Considering that the decision-making environment faced by reinforcement learning (RL) agents is full of Knightian uncertainty, this paper describes the exploratory state dynamics equation in Knightian uncertainty to study the entropy-regularized relaxed stochastic control problem in a Knightian uncertainty environment. By employing stochastic analysis theory and the dynamic programming principle under nonlinear expectation, we derive the Hamilton-Jacobi-Bellman (HJB) equation and solve for the …

Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
Jia Xiao
https://arxiv.org/abs/2507.09233 https://…

Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
AI-driven recruitment systems, while promising efficiency and objectivity, often perpetuate systemic inequalities by encoding cultural and social capital disparities into algorithmic decision making. This article develops and defends a novel theory of secondary bounded rationality, arguing that AI systems, despite their computational power, inherit and amplify human cognitive and structural biases through technical and sociopolitical constraints. Analyzing multimodal recruitment frameworks, we …

Minmax Exclusivity Classes for Power-Type Loss Functions
Stanis{\l}aw M. S. Halkiewicz
https://arxiv.org/abs/2507.12447 https://arxiv…

Minmax Exclusivity Classes for Power-Type Loss Functions
In statistical decision theory, the choice of loss function fundamentally shapes which estimators qualify as optimal. This paper introduces and develops the general concept of exclusivity classes of loss functions: subsets of loss functions such that no estimator can be optimal (according to a specified notion) for losses lying in different classes. We focus on the case of minmax optimality and define minmax exclusivity classes, demonstrating that the classical family of power-type loss functio…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
Takashi Izumo
https://arxiv.org/abs/2507.12475 https://arxiv.…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
The St. Petersburg paradox presents a longstanding challenge in decision theory. It describes a game whose expected value is infinite, yet for which no rational finite stake can be determined. Traditional solutions introduce auxiliary assumptions, such as diminishing marginal utility, temporal discounting, or extended number systems. These methods often involve mathematical refinements that may not correspond to how people actually perceive or process numerical information. This paper explores …

Unified Field-integral Thermodynamics of Bose Mixtures: Stability and Critical Behavior
Yuan-Hong Chen, Renyuan Liao
https://arxiv.org/abs/2508.11263 https://

Unified Field-integral Thermodynamics of Bose Mixtures: Stability and Critical Behavior
We establish a unified thermodynamic framework for Bose mixtures at finite temperatures based on the functional field integral, within which the decision on whether to discard the anomalous densities, when determining the density configuration and stability matrix, yields distinct theories. Beyond the existing Hartree-Fock approximation and Ota-Giorgini-Stringari theory, retaining the anomalous densities throughout will provide a completely self-consistent thermodynamic description, requiring t…

A Non-Wellfounded and Labelled Sequent Calculus for Bimodal Provability Logic
Justus Becker
https://arxiv.org/abs/2506.14307 https://…

A Non-Wellfounded and Labelled Sequent Calculus for Bimodal Provability Logic
We present a labelled and non-wellfounded calculus for the bimodal provability logic CS. The system is obtained by modelling the Kripke-like semantics of this logic. As in arXiv:2309.00532, we enforce the second-order property of converse wellfoundedness by using techniques from cyclic proof theory. We will prove soundness and completeness of this system with respect to the semantics and provide a primitive decision procedure together with a way to extract countermodels.

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
Ece Gumusel
https://arxiv.org/abs/2506.10042 https://a…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferenc…

Solving the Decision Principal Ideal Problem with Pre-processing
Jincheng Zhuang, Qi Cheng
https://arxiv.org/abs/2506.09605 https://a…

Solving the Decision Principal Ideal Problem with Pre-processing
The principal ideal problem constitutes a fundamental problem in algebraic number theory and has attracted significant attention due to its applications in ideal lattice based cryptosystems. Efficient quantum algorithm has been found to address this problem. The situation is different in the classical computational setting. In this work, we delve into the relationship between the principal ideal problem and the class field computation. We show that the decision version of the problem can be sol…

A Characterization Framework for Stable Sets and Their Variants
Athanasios Andrikopoulos, Nikolaos Sampanis
https://arxiv.org/abs/2508.09798 https://arxiv.…

A Characterization Framework for Stable Sets and Their Variants
The theory of optimal choice sets offers a well-established solution framework in social choice and game theory. In social choice theory, decision-making is typically modeled as a maximization problem. However, when preferences are cyclic -- as can occur in economic processes -- the set of maximal elements may be empty, raising the key question of what should be considered a valid choice. To address this issue, several approaches -- collectively known as general solution theories -- have been p…

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
Thomas J. Ringstrom, Paul R. Schrater
https://arxiv.org/abs/2506.09499

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
We introduce Option Kernel Bellman Equations (OKBEs) for a new reward-free Markov Decision Process. Rather than a value function, OKBEs directly construct and optimize a predictive map called a state-time option kernel (STOK) to maximize the probability of completing a goal while avoiding constraint violations. STOKs are compositional, modular, and interpretable initiation-to-termination transition kernels for policies in the Options Framework of Reinforcement Learning. This means: 1) STOKs can…

Encoding and inference on separable effects for sustained treatments
Ignacio Gonzalez-Perez, Kerollos Nashat Wanis, Aaron Leor Sarvet, Mats Julius Stensrud
https://arxiv.org/abs/2508.10702

Encoding and inference on separable effects for sustained treatments
Sustained treatment strategies are common in many domains, particularly in medicine, where many treatment are delivered repeatedly over time. The effects of adherence to a treatment strategy throughout follow-up are often more relevant to decision-makers than effects of treatment assignment or initiation. Here we consider the separable effect of sustained use of a time-varying treatment. Despite the potential usefulness of this estimand, the theory of separable effects has yet to be extended to…

This https://arxiv.org/abs/2502.17701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation dec…

Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making
Carlos Andr\'es Ram\'irez Cata\~no, Makoto Itoh
https://arxiv.org/abs/2508.04408

Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making
Software defect prediction using code metrics has been extensively researched over the past five decades. However, prediction harnessing non-software metrics is under-researched. Considering that the root cause of software defects is often attributed to human error, human factors theory might offer key forecasting metrics for actionable insights. This paper explores automated software defect prediction at the method level based on the developers' coding habits. First, we propose a framework for…

This https://arxiv.org/abs/2505.20111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Preference Disaggregation Analysis with Criteria Selection in a Regularization Framework
Limited by cognitive abilities, decision-makers (DMs) may struggle to evaluate decision alternatives based on all criteria in multiple criteria decision-making problems. This paper proposes an embedded criteria selection method derived from preference disaggregation technique and regularization theory. The method aims to infer the criteria and value functions used by the DM to evaluate decision alternatives. It measures the quality of criteria subsets by investigating both the empirical error (…

This https://arxiv.org/abs/2503.01112 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Nonreciprocal field theory for decision-making in multi-agent control systems
Field theories for complex systems traditionally focus on collective behaviors emerging from simple, reciprocal pairwise interaction rules. However, many natural and artificial systems exhibit behaviors driven by microscopic decision-making processes that introduce both nonreciprocity and many-body interactions, challenging these conventional approaches. We develop a theoretical framework to incorporate decision-making into field theories using the shepherding control problem as a paradigmatic …

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04947

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents whose preferences deviate from traditional expected utility theory (EUT), specifically incorporating cumulative prospect theory (CPT) preferences. We develop a comprehensive analytical framework that captures human-centric aspects of decision-making and risky…

Cat Royale: An Artistic Inquiry into Trust in Robots
Matt Adams, Nick Tandavanitj, Steve Benford, Ayse Kucukyilmaz, Victor Ngo, Simon Castle-Green, Guido Salimberi, Pepita Bernard, Joel Fischer, Alan Chamberlain, Eike Schneiders, Clara Mancini
https://arxiv.org/abs/2507.04970

Cat Royale: An Artistic Inquiry into Trust in Robots
Cat Royale is an artwork created by the artists Blast Theory to explore the question of whether we should trust robots to care for our loved ones. The artists endeavoured to create a `Cat Utopia', a luxurious environment that was inhabited by a family of three cats for six hours a day for twelve days, at the centre of which a robot arm played with them by wielding toys. Behind the scenes, the decision engine recommended games based on ongoing assessment of their happiness. A video installation …

Constructing a bridge between functioning of oscillatory neuronal networks and quantum-like cognition along with quantum-inspired computation and AI
Andrei Khrennikov, Atsushi Iriki, Irina Basieva
https://arxiv.org/abs/2506.00040

Constructing a bridge between functioning of oscillatory neuronal networks and quantum-like cognition along with quantum-inspired computation and AI
Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, explori…

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Ryan Warnick
https://arxiv.org/abs/2508.01988 https://

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Aleatoric and Epistemic uncertainty have achieved recent attention in the literature as different sources from which uncertainty can emerge in stochastic modeling. Epistemic being intrinsic or model based notions of uncertainty, and aleatoric being the uncertainty inherent in the data. We propose a novel decision theoretic framework for outlier detection in the context of aleatoric uncertainty; in the context of Bayesian modeling. The model incorporates bayesian false discovery rate control for…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Dhruv S. Kushwaha, Zoleikha A. Biron
https://arxiv.org/abs/2508.09128 https://…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Reinforcement learning (RL) has proven to be particularly effective in solving complex decision-making problems for a wide range of applications. From a control theory perspective, RL can be considered as an adaptive optimal control scheme. Lyapunov and barrier functions are the most commonly used certificates to guarantee system stability for a proposed/derived controller and constraint satisfaction guarantees, respectively, in control theoretic approaches. However, compared to theoretical gua…

Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
https://arxiv.org/abs/2507.13447 https://arx…

Theory-informed neural networks for particle physics
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process. A transformer-based Deep Q-Network, rewarded at each step by the logarithmic change in the tree-level matrix element, learns to map final-state particles to partons. Because the reward derives solely from first-principles theory, the resulting policy is label-free and fully interpretable, allowing every reconstru…

Causality and Decision-making: A Logical Framework for Systems and Security Modelling
Pinaki Chakraborty, Tristan Caulfield, David Pym
https://arxiv.org/abs/2508.01758 https://

Causality and Decision-making: A Logical Framework for Systems and Security Modelling
Causal reasoning is essential for understanding decision-making about the behaviour of complex `ecosystems' of systems that underpin modern society, with security -- including issues around correctness, safety, resilience, etc. -- typically providing critical examples. We present a theory of strategic reasoning about system modelling based on minimal structural assumptions and employing the methods of transition systems, supported by a modal logic of system states in the tradition of van Benthe…

Cooperation and the Design of Public Goods
J. Carlos Mart\'inez Mori, Alejandro Toriello
https://arxiv.org/abs/2506.05251 https://

Cooperation and the Design of Public Goods
We consider the cooperative elements that arise in the design of public goods, such as transportation policies and infrastructure. These involve a variety of stakeholders: governments, businesses, advocates, and users. Their eventual deployment depends on the decision maker's ability to garner sufficient support from each of these groups; we formalize these strategic requirements from the perspective of cooperative game theory. Specifically, we introduce non-transferable utility, linear product…

This https://arxiv.org/abs/2503.07219 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Bag Semantics Query Containment: The CQ vs. UCQ Case and Other Stories
Query Containment Problem (QCP) is a fundamental decision problem in query processing and optimization. While QCP has for a long time been completely understood for the case of set semantics, decidability of QCP for conjunctive queries under multi-set semantics ($QCP_{\text{CQ}}^{\text{bag}}$) remains one of the most intriguing open problems in database theory. Certain effort has been put, in last 30 years, to solve this problem and some decidable special cases of $QCP_{\text{CQ}}^{\text{bag}}$…

Explanations are a means to an end
Jessica Hullman, Ziyang Guo, Berk Ustun
https://arxiv.org/abs/2506.22740 https://arxiv.org/pdf/250…

Explanations are a means to an end
Modern methods for explainable machine learning are designed to describe how models map inputs to outputs--without deep consideration of how these explanations will be used in practice. This paper argues that explanations should be designed and evaluated with a specific end in mind. We describe how to formalize this end in a framework based in statistical decision theory. We show how this functionally-grounded approach can be applied across diverse use cases, such as clinical decision support, …

Perceptual Rationality: An Evolutionary Game Theory of Perceptually Rational Decision-Making
Mohammad Salahshour
https://arxiv.org/abs/2506.17724 https://

Perceptual Rationality: An Evolutionary Game Theory of Perceptually Rational Decision-Making
Understanding how biological organisms make decisions is of fundamental importance in understanding behavior. Such an understanding within evolutionary game theory so far has been sought by appealing to bounded rationality. Here, we present a perceptual rationality framework in the context of group cooperative interactions, where individuals make rational decisions based on their evolvable perception of the environment. We show that a simple public goods game accounts for power law distributed …

Decision algorithms for fragments of real analysis. III: A theory of differentiable functions with (semi-)open intervals
G. Buriola, D. Cantone, G. Cincotti, E. G. Omodeo, G. T. Spart\`a
https://arxiv.org/abs/2507.02742

Decision algorithms for fragments of real analysis. III: A theory of differentiable functions with (semi-)open intervals
This paper enriches preexisting satisfiability tests for unquantified languages, which in turn augment a fragment of Tarski's elementary algebra with unary real functions possessing a continuous first derivative. Two sorts of individual variables are available, one ranging over real numbers and the other one ranging over the functions of interest. Numerical terms are built from real variables through constructs designating the four basic arithmetic operations and through the function-applicat…

Enriching the Felsenthal index with a priori unions for decision-making processes
Alicia Mascare\~nas-Pazos, Silvia Lorenzo-Freire, Jose Maria Alonso-Meijide
https://arxiv.org/abs/2507.01621

Enriching the Felsenthal index with a priori unions for decision-making processes
Within the domain of game theory, power indexes are defined as functions that quantify the influence of individual participants in collective decision-making processes. Felsenthal [D. Felsenthal. A Well-Behaved Index of a Priori P-Power for Simple N-Person Games. Homo Oeconomicus, 33, 2016] proposed a power index with a focus on least size winning coalitions, i.e., those coalitions capable of determining the final outcome and with the smallest number of players among all winning coalitions. How…

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques
Zhong Zhang, Xiang Guo, Di Wu, Hexi Baoyin, Junfeng Li, Francesco Topputo
https://arxiv.org/abs/2508.02904

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques
Designing optimal trajectories for multi-flyby asteroid missions is scientifically critical but technically challenging due to nonlinear dynamics, intermediate constraints, and numerous local optima. This paper establishes a method that approaches global optimality for multi-flyby trajectory optimization under a given sequence. The original optimal control problem with interior-point equality constraints is transformed into a multi-stage decision formulation. This reformulation enables direct a…

Verification Cost Asymmetry in Cognitive Warfare: A Complexity-Theoretic Framework
Joshua Luberisse
https://arxiv.org/abs/2507.21258 https://arxiv.org/pdf/…

Verification Cost Asymmetry in Cognitive Warfare: A Complexity-Theoretic Framework
Human verification under adversarial information flow operates as a cost-bounded decision procedure constrained by working memory limits and cognitive biases. We introduce the Verification Cost Asymmetry (VCA) coefficient, formalizing it as the ratio of expected verification work between populations under identical claim distributions. Drawing on probabilistically checkable proofs (PCP) and parameterized complexity theory, we construct dissemination protocols that reduce verification for truste…

MichelangeRoll: Sculpting Rational Distributions Exactly and Efficiently
Jui-Hsiang Shao, Hsin-Po Wang
https://arxiv.org/abs/2507.00915 https://

MichelangeRoll: Sculpting Rational Distributions Exactly and Efficiently
Simulating an arbitrary discrete distribution $D \in [0, 1]^n$ using fair coin tosses incurs trade-offs between entropy complexity and space and time complexity. Shannon's theory suggests that $H(D)$ tosses are necessary and sufficient, but does not guarantee exact distribution. Knuth and Yao showed that a decision tree consumes fewer than $H(D) + 2$ tosses for one exact sample. Drapper and Saad's recent work addresses the space and time aspect, showing that $H(D) + 2$ tosses, $O(n \log(n) \log…

Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality
Yuansheng Lian, Ke Zhang, Meng Li, Shen Li
https://arxiv.org/abs/2507.03002 …

Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality
Modeling the decision-making behavior of vehicles presents unique challenges, particularly during unprotected left turns at intersections, where the uncertainty of human drivers is especially pronounced. In this context, connected autonomous vehicle (CAV) technology emerges as a promising avenue for effectively managing such interactions while ensuring safety and efficiency. Traditional approaches, often grounded in game theory assumptions of perfect rationality, may inadequately capture the co…

Leave No One Undermined: Policy Targeting with Regret Aversion
Toru Kitagawa, Sokbae Lee, Chen Qiu
https://arxiv.org/abs/2506.16430 https://

Leave No One Undermined: Policy Targeting with Regret Aversion
While the importance of personalized policymaking is widely recognized, fully personalized implementation remains rare in practice. We study the problem of policy targeting for a regret-averse planner when training data gives a rich set of observable characteristics while the assignment rules can only depend on its subset. Grounded in decision theory, our regret-averse criterion reflects a planner's concern about regret inequality across the population, which generally leads to a fractional opt…

Probabilistic Graphical Models: A Concise Tutorial
Jacqueline Maasch, Willie Neiswanger, Stefano Ermon, Volodymyr Kuleshov
https://arxiv.org/abs/2507.17116

Probabilistic Graphical Models: A Concise Tutorial
Probabilistic graphical modeling is a branch of machine learning that uses probability distributions to describe the world, make predictions, and support decision-making under uncertainty. Underlying this modeling framework is an elegant body of theory that bridges two mathematical traditions: probability and graph theory. This framework provides compact yet expressive representations of joint probability distributions, yielding powerful generative models for probabilistic reasoning. This tut…

Profiling Frailty: A parsimonious Frailty Index from health administrative data based on POSET theory
Margherita Silan, Maurizio Nicolaio, Giovanna Boccuzzo
https://arxiv.org/abs/2506.23158

Profiling Frailty: A parsimonious Frailty Index from health administrative data based on POSET theory
Frailty assessment is crucial for stratifying populations and addressing healthcare challenges associated with ageing. This study proposes a Frailty Index based on administrative health data, with the aim of facilitating informed decision-making and resource allocation in population health management. The aim of this work is to develop a Frailty Index that 1) accurately predicts multiple adverse health outcomes, 2) comprises a parsimonious set of variables, 3) aggregates variables without prede…

This https://arxiv.org/abs/2502.13697 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Linear programming for finite-horizon vector-valued Markov decision processes
We propose a vector linear programming formulation for a non-stationary, finite-horizon Markov decision process with vector-valued rewards. Pareto efficient policies are shown to correspond to efficient solutions of the linear program, and vector linear programming theory allows us to fully characterize deterministic efficient policies. An algorithm for enumerating all efficient deterministic policies is presented then tested numerically in an engineering application.

Beyond Scalars: Zonotope-Valued Utility for Representation of Multidimensional Incomplete Preferences
Behrooz Moosavi Ramezanzadeh
https://arxiv.org/abs/2507.05844

Beyond Scalars: Zonotope-Valued Utility for Representation of Multidimensional Incomplete Preferences
In this paper, I propose a new framework for representing multidimensional incomplete preferences through zonotope-valued utilities, addressing the shortcomings of traditional scalar and vector-based models in decision theory. Traditional approaches assign single numerical values to alternatives, failing to capture the complexity of preferences where alternatives remainmain incomparable due to conflicting criteria across multiple dimensions. Our method maps each alternative to a zonotope, a con…

Agentic Markets: Game Dynamics and Equilibrium in Markets with Learning Agents
Martin Bichler, Julius Durmann, Matthias Oberlechner
https://arxiv.org/abs/2506.18571

Agentic Markets: Game Dynamics and Equilibrium in Markets with Learning Agents
Autonomous and learning agents increasingly participate in markets - setting prices, placing bids, ordering inventory. Such agents are not just aiming to optimize in an uncertain environment; they are making decisions in a game-theoretical environment where the decision of one agent influences the profit of other agents. While game theory usually predicts outcomes of strategic interaction as an equilibrium, it does not capture how repeated interaction of learning agents arrives at a certain out…

Reconfiguring Digital Accountability: AI-Powered Innovations and Transnational Governance in a Postnational Accounting Context
Claire Li, David Freeborn
https://arxiv.org/abs/2507.00288

Reconfiguring Digital Accountability: AI-Powered Innovations and Transnational Governance in a Postnational Accounting Context
This study explores how AI-powered digital innovations are reshaping organisational accountability in a transnational governance context. As AI systems increasingly mediate decision-making in domains such as auditing and financial reporting, traditional mechanisms of accountability, based on control, transparency, and auditability, are being destabilised. We integrate the Technology Acceptance Model (TAM), Actor-Network Theory (ANT), and institutional theory to examine how organisations adopt A…

Reasoning about Bounded Reasoning
Shuige Liu, Gabriel Ziegler
https://arxiv.org/abs/2506.19737 https://arxiv.org/pdf/2506.19737

Reasoning about Bounded Reasoning
Interactive decision-making relies on strategic reasoning. Two prominent frameworks are (1) models of bounded reasoning, exemplified by level-$k$ models, which keep reasoning implicit, and (2) epistemic game theory, which makes reasoning explicit. We connect these approaches by "lifting" static complete-information games into incomplete-information settings where payoff types reflect players' reasoning depths as in level-$k$ models. We introduce downward rationalizability, defined via minimal b…