Tootfinder

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…

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…

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 …

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…

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…

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…

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 $QCP_{\text{CQ}}^{\text{bag}}$…

Finding signatures of low-dimensional geometric landscapes in high-dimensional cell fate transitions
Maria Yampolskaya, Laertis Ikonomou, Pankaj Mehta
https://arxiv.org/abs/2506.04219

Finding signatures of low-dimensional geometric landscapes in high-dimensional cell fate transitions
Multicellular organisms develop a wide variety of highly-specialized cell types. The consistency and robustness of developmental cell fate trajectories suggests that complex gene regulatory networks effectively act as low-dimensional cell fate landscapes. A complementary set of works draws on the theory of dynamical systems to argue that cell fate transitions can be categorized into universal decision-making classes. However, the theory connecting geometric landscapes and decision-making classe…

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.