Tootfinder

Scenario-based Decision-making Using Game Theory for Interactive Autonomous Driving: A Survey
Zhihao Lin, Zhen Tian
https://arxiv.org/abs/2509.05777 https://

Scenario-based Decision-making Using Game Theory for Interactive Autonomous Driving: A Survey
Game-based interactive driving simulations have emerged as versatile platforms for advancing decision-making algorithms in road transport mobility. While these environments offer safe, scalable, and engaging settings for testing driving strategies, ensuring both realism and robust performance amid dynamic and diverse scenarios remains a significant challenge. Recently, the integration of game-based techniques with advanced learning frameworks has enabled the development of adaptive decision-mak…

Chapter 8 Multi-Criteria Decision-Making: Aggregation-Type Methods
Zhiyuan Wang, Gade Pandu Rangaiah
https://arxiv.org/abs/2509.06388 https://arxiv.org/pdf…

Chapter 8 Multi-Criteria Decision-Making: Aggregation-Type Methods
This chapter describes selected aggregation-type multi-criteria decision-making (MCDM) methods that convert an alternatives-criteria matrix (ACM) into a single performance score per alternative through additive, multiplicative or hybrid manipulations, for ranking the alternatives. The 8 methods are: Simple Additive Weighting (SAW), Multiplicative Exponent Weighting (MEW), Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Complex Proportional Assessment (COPRAS), Multi-Objective …

Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India
Swapnil Bhattacharyya, Shrey Ganatra, Harshvivek Kashid, Spandan Anaokar, Shruti Nair, Reshma Sekhar, Siddharth Manohar, Rahul Hemrajani, Pushpak Bhattacharyya
https://arxiv.org/abs/2507.06090

Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India
AI-based judicial assistance and case prediction have been extensively studied in criminal and civil domains, but remain largely unexplored in consumer law, especially in India. In this paper, we present Nyay-Darpan, a novel two-in-one framework that (i) summarizes consumer case files and (ii) retrieves similar case judgements to aid decision-making in consumer dispute resolution. Our methodology not only addresses the gap in consumer law AI tools but also introduces an innovative approach to e…

LLMs in Cybersecurity: Friend or Foe in the Human Decision Loop?
Irdin Pekaric, Philipp Zech, Tom Mattson
https://arxiv.org/abs/2509.06595 https://arxiv.or…

LLMs in Cybersecurity: Friend or Foe in the Human Decision Loop?
Large Language Models (LLMs) are transforming human decision-making by acting as cognitive collaborators. Yet, this promise comes with a paradox: while LLMs can improve accuracy, they may also erode independent reasoning, promote over-reliance and homogenize decisions. In this paper, we investigate how LLMs shape human judgment in security-critical contexts. Through two exploratory focus groups (unaided and LLM-supported), we assess decision accuracy, behavioral resilience and reliance dynamics…

What's Coming Next? Short-Term Simulation of Business Processes from Current State
Maksym Avramenko, David Chapela-Campa, Marlon Dumas, Fredrik Milani
https://arxiv.org/abs/2509.07747

What's Coming Next? Short-Term Simulation of Business Processes from Current State
Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary use-case is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and sh…

AI Darwin Awards
Honouring Those Who Asked "Can We?" Without Ever Asking "Should We?"
Named after Charles Darwin's theory of natural selection, the original Darwin Awards celebrated those who "improved the gene pool by removing themselves from it" through spectacularly stupid acts. Well, guess what? Humans have evolved! We're now so advanced that we've outsourced our poor decision-making to machines. […]
🤖

AI Darwin Awards 2025 - Celebrating Spectacularly Bad AI Decisions
Honouring those who use artificial intelligence in spectacularly reckless ways. Submit nominations for the most catastrophically ill-advised AI deployments of 2025.

MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs
Libo Ren, Yee Man Ng, Lifeng Han
https://arxiv.org/abs/2509.07622

MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs
Efficient communication between patients and clinicians plays an important role in shared decision-making. However, clinical reports are often lengthy and filled with clinical jargon, making it difficult for domain experts to identify important aspects in the document efficiently. This paper presents the methodology we applied in the MultiClinSUM shared task for summarising clinical case documents. We used an Iterative Self-Prompting technique on large language models (LLMs) by asking LLMs to g…

Optimizing Shanghai's Household Waste Recycling Collection Program by Decision-Making based on Mathematical Modeling
Jiaxuan Chen, Ling Zhou Shen, Jinchen Liu
https://arxiv.org/abs/2507.03844

Optimizing Shanghai's Household Waste Recycling Collection Program by Decision-Making based on Mathematical Modeling
In this article, we will discuss the optimization of Shanghai's recycling collection program, with the core of the task as making a decision among the choice of the alternatives. We will be showing a vivid and comprehensive application of the classical mathematical multi-criteria decision model: Analytical Hierarchy Process (AHP), using the eigenvector method. We will also seek the key criteria for the sustainability development of human society, by assessing the important elements of waste rec…

Spontaneous Symmetry Breaking, Group Decision Making and Beyond 2. Distorted Polarization and Vulnerability
Serge Galam
https://arxiv.org/abs/2509.06649 https://

Spontaneous Symmetry Breaking, Group Decision Making and Beyond 2. Distorted Polarization and Vulnerability
This paper extends previous work on echo chambers modeled by an Ising-like system at zero temperature (1. Echo Chambers and random Polarization, Symmetry 2024, 16(12), 1566). There, polarization emerged as a spontaneous symmetry-breaking process with a randomly selected direction. Here using a mean-field analysis and Monte Carlo simulations I show that this mechanism is highly vulnerable to minimal distortions. An external symmetry-breaking field, even vanishingly small, suffices to impose a gl…

The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management
Somtochukwu Azie, Yiping Meng
https://arxiv.org/abs/2509.04505

The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management
The integration of Artificial Intelligence (AI) into construction project management (CPM) is accelerating, with Large Language Models (LLMs) emerging as accessible decision-support tools. This study aims to critically evaluate the ethical viability and reliability of LLMs when applied to the ethically sensitive, high-risk decision-making contexts inherent in CPM. A mixed-methods research design was employed, involving the quantitative performance testing of two leading LLMs against twelve real…

Bio-inspired decision making in swarms under biases from stubborn robots, corrupted communication, and independent discovery
Raina Zakir, Timoteo Carletti, Marco Dorigo, Andreagiovanni Reina
https://arxiv.org/abs/2509.07561

Bio-inspired decision making in swarms under biases from stubborn robots, corrupted communication, and independent discovery
Minimalistic robot swarms offer a scalable, robust, and cost-effective approach to performing complex tasks with the potential to transform applications in healthcare, disaster response, and environmental monitoring. However, coordinating such decentralised systems remains a fundamental challenge, particularly when robots are constrained in communication, computation, and memory. In our study, individual robots frequently make errors when sensing the environment, yet the swarm can rapidly and r…

Large Language Models Predict Human Well-being -- But Not Equally Everywhere
Pat Pataranutaporn, Nattavudh Powdthavee, Chayapatr Archiwaranguprok, Pattie Maes
https://arxiv.org/abs/2507.06141

Large Language Models Predict Human Well-being -- But Not Equally Everywhere
Subjective well-being is a key metric in economic, medical, and policy decision-making. As artificial intelligence provides scalable tools for modelling human outcomes, it is crucial to evaluate whether large language models (LLMs) can accurately predict well-being across diverse global populations. We evaluate four leading LLMs using data from 64,000 individuals in 64 countries. While LLMs capture broad correlates such as income and health, their predictive accuracy decreases in countries unde…

Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Zilong Wang, Turgay Ayer, Shihao Yang
https://arxiv.org/abs/2509.05775 https://

Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying subpopulations that respond differently to interventions, thereby enabling more targeted and effective decision-making. While clustering methods are well-studied in unsupervised learning, their integration with causal inference remains limited. We propose a novel framework that clusters individuals based on estimated…

Application Space and the Rate-Distortion-Complexity Analysis of Neural Video CODECs
Ricardo L. de Queiroz, Diogo C. Garcia, Yi-Hsin Chen, Ruhan Concei\c{c}\~ao, Wen-Hsiao Peng, Luciano V. Agostini
https://arxiv.org/abs/2509.05929

Application Space and the Rate-Distortion-Complexity Analysis of Neural Video CODECs
We study the decision-making process for choosing video compression systems through a rate-distortion-complexity (RDC) analysis. We discuss the 2D Bjontegaard delta (BD) metric and formulate generalizations in an attempt to extend its notions to the 3D RDC volume. We follow that discussion with another one on the computation of metrics in the RDC volume, and on how to define and measure the cost of a coder-decoder (codec) pair, where the codec is characterized by a cloud of points in the RDC sp…

Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2509.05498 https://arxiv.org/pd…

Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Cognitive vulnerabilities shape human decision-making and arise primarily from two sources: (1) cognitive capabilities, which include disparities in knowledge, education, expertise, or access to information, and (2) cognitive biases, such as rational inattention, confirmation bias, and base rate neglect, which influence how individuals perceive and process information. Exploiting these vulnerabilities allows an entity with superior cognitive awareness to gain a strategic advantage, a concept re…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2507.03021 htt…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Cyber warfare has become a critical dimension of modern conflict, driven by society's increasing dependence on interconnected digital and physical infrastructure. Effective cyber defense often requires decision-making at different echelons, where the tactical layer focuses on detailed actions such as techniques, tactics, and procedures, while the strategic layer addresses long-term objectives and coordinated planning. Modeling these interactions at different echelons remains challenging due to …

Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu, Michael Wooldridge, Fabio Massimo Zennaro
https://arxiv.org/abs/2509.04296

Using causal abstractions to accelerate decision-making in complex bandit problems
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the information and computational advantages of each abstraction level is missing. In this paper, we propose AT-UCB, an algorithm which efficiently exploits shared information between CMAB problem instances defined at different levels of abstraction. More specifically, AT-UCB leverages causal abstraction (CA) theory to explo…

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…

Temporal Counterfactual Explanations of Behaviour Tree Decisions
Tamlin Love, Antonio Andriella, Guillem Aleny\`a
https://arxiv.org/abs/2509.07674 https://…

Temporal Counterfactual Explanations of Behaviour Tree Decisions
Explainability is a critical tool in helping stakeholders understand robots. In particular, the ability for robots to explain why they have made a particular decision or behaved in a certain way is useful in this regard. Behaviour trees are a popular framework for controlling the decision-making of robots and other software systems, and thus a natural question to ask is whether or not a system driven by a behaviour tree is capable of answering "why" questions. While explainability for behaviour…

Sensor Management in Multi-Stage Stochastic Control Problems with Imperfect State Information
Patrick Kreidl
https://arxiv.org/abs/2509.07840 https://arxiv…

Sensor Management in Multi-Stage Stochastic Control Problems with Imperfect State Information
Technological advancements in miniaturization and wireless communications are yielding more affordable and versatile sensors and, in turn, more applications in which a network of sensors can be actively managed to best support overall decision-making objectives. We propose modeling the opportunity for sensor management within multi-stage stochastic control problems with imperfect state information. Such formulations inherently assume the state of the modeled environment cannot be accessed direc…

Collective decision-making dynamics in hypernetworks
Angela Fontan, Silun Zhang
https://arxiv.org/abs/2509.05182 https://arxiv.org/pdf/2509.05182

Collective decision-making dynamics in hypernetworks
This work describes a collective decision-making dynamical process in a multiagent system under the assumption of cooperative higher-order interactions within the community, modeled as a hypernetwork. The nonlinear interconnected system is characterized by saturated nonlinearities that describe how agents transmit their opinion state to their neighbors in the hypernetwork, and by a bifurcation parameter representing the community's social effort. We show that the presence of higher-order intera…

STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
Moshe Unger, Alexander Tuzhilin, Michel Wedel
https://arxiv.org/abs/2508.04148 https://

STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decision environment, for which currently no foundational models are available. The architecture, called STARE (Spatio-Temporal Attention Representation for Eye Tracking), uses a new tokenization strategy, which involves mapping the x- and y- pixel coordinates of eye-movement time series on predefined, contiguous Regions of…

Computational Concept of the Psyche
Anton Kolonin, Vladimir Kryukov
https://arxiv.org/abs/2509.07009 https://arxiv.org/pdf/2509.07009

Computational Concept of the Psyche
The article provides an overview of approaches to modeling the human psyche in the perspective of building an artificial one. Based on the review, a concept of cognitive architecture is proposed, where the psyche is considered as an operating system of a living or artificial subject, including a space of needs that determines its life meanings in connection with stimuli from the external world, and intelligence as a decision-making system for actions in relation to this world in order to satisf…

Utilitarian or Quantile-Welfare Evaluation of Health Policy?
Charles F. Manski, John Mullahy
https://arxiv.org/abs/2509.05529 https://arxiv.org/pdf/2509.05…

Utilitarian or Quantile-Welfare Evaluation of Health Policy?
This paper considers quantile-welfare evaluation of health policy as an alternative to utilitarian evaluation. Manski (1988) originally proposed and studied maximization of quantile utility as a model of individual decision making under uncertainty, juxtaposing it with maximization of expected utility. That paper's primary motivation was to exploit the fact that maximization of quantile utility requires only an ordinal formalization of utility, not a cardinal one. This paper transfers these ide…

Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers
Guoyue Xu, Renzheng Zhang, Tengfei Luo
https://arxiv.org/abs/2509.05351 ht…

Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers
To overcome the inherent inefficiencies of traditional trial-and-error materials discovery, the scientific community is increasingly developing autonomous laboratories that integrate data-driven decision-making into closed-loop experimental workflows. In this work, we realize this concept for thermoresponsive polymers by developing a low-cost, "frugal twin" platform for the optimization of the lower critical solution temperature (LCST) of poly(N-isopropylacrylamide) (PNIPAM). Our system integra…

«I don’t know how to run a community forum in this future. I do not have the time or emotional energy to screen out regular attacks by Large Language Models, with the knowledge that making the wrong decision costs a real human being their connection to a niche community. I do not know how to determine whether someone’s post about their new bicycle is genuine enthusiasm or automated astroturf.»
I hate everything about this future and how it isolates us.
https://aphyr.com/posts/389-the-future-of-forums-is-lies-i-guess

A few thoughts I had after a conversation with a friend. What are you - as a professional - even saying when openly using ChatGPT for your decision-making?
https://tante.cc/2025/07/06/so-what-are-you-saying/

So what are you saying?
I had a conversation with a friend who described sitting in a workshop where clients and the people they hired were working on problems. As you do. Happens in every consultingy kind of job. But what has totally become normalized is for the people hired, the “experts” to use ChatGPT and similar services to come […]

The Actuary's Final Word on Algorithmic Decision Making
Benjamin Recht
https://arxiv.org/abs/2509.04546 https://arxiv.org/pdf/2509.04546

The Actuary's Final Word on Algorithmic Decision Making
Paul Meehl's foundational work "Clinical versus Statistical Prediction," provided early theoretical justification and empirical evidence of the superiority of statistical methods over clinical judgment. Despite a century of empirical evidence supporting Meehl's central thesis, from early parole prediction studies in the 1920s to modern meta-analyses, confusion persists regarding when and why his troubling finding applies. This paper provides a contemporary theoretical justification for Meehl's …

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/4]:
- No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Huma...
Kyra Wilson, Mattea Sim, Anna-Maria Gueorguieva, Aylin Caliskan

"""
Customarily, the honour of having liberated hysteria from the ancient myths about a displacement of the uterus goes to Le Pois and Willis. Jean Liebaud, translating or rather adapting Marinello’s work for the seventeenth century, still accepted (with a small number of caveats) the idea of a spontaneous movement of the womb. If it moved, it was “to be more at ease; not that this came about through prudence, nor was it a conscious decision or an animal stimulus, but by a natural instinct, to safeguard health and to have the pleasure of something delectable.” The idea that it could change its place and move around the body, bringing convulsions and spasms everywhere it travelled, had been abandoned, for it was now taken to be ‘tightly held in place’ by the cervix, ligaments, vessels and the sheath of the peritoneum; yet in some senses it could change its location. “The womb therefore, even though it is tightly fixed to the parts that we have described and cannot easily change its place, still manages to roam, making strange, petulant movements around the woman’s body. These diverse movements include ascensions and descents, convulsions, wanderings and prolapses. It can wander up to the liver, spleen, diaphragm, stomach, chest, heart, lung, throat and head.” Physicians of the classical age are more or less unanimous in refusing this explanation.
[…] Yet these analyses were not sufficient to break the theme of an essential link between hysteria and the womb. But the link is now conceived in different terms. It is no longer considered to be the trajectory of a real displacement through the body, but rather a sort of mute propagation through the paths of the organism and its functional proximities. It cannot be said that the seat of the malady has become the brain, nor that thanks to Willis a psychological explanation of hysteria was now possible. But the brain does take on the role of a relay that distributes a malady whose origins are visceral, and the womb brings it on just as the other viscera do. Up until the end of the eighteenth century, and Pinel, the uterus and the womb are still present in the pathology of hysteria, but thanks to a privileged diffusion by the humours and nerves, not because of any particular prestige of their nature.
"""
(Michel Foucault, History of Madness)

ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva, Shalmali Joshi, No\'emie Elhadad
https://arxiv.org/abs/2509.07108

ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictive performance. However, most of these models do not provide interpretable insights into the association between exposures and the modeled outcomes, a critical requirement for decision-making in clinical practice. To address this limitation, we propose Additive Deep Hazard Analysis Mixtures (ADHAM), an interpretable ad…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/2]:
- Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude
Yile Yan, Yuqi Zhu, Wentao Xu

I’m #ActuallyAutistic, and right now, I’m calculating whether I should watch this Twitch VOD or keep listeing to my special interest.
This decision-making process is overwhelming and making me feel physically uncomfortable and anxious.
#Autism is fun!

Structured AI Decision-Making in Disaster Management
Julian Gerald Dcruz, Argyrios Zolotas, Niall Ross Greenwood, Miguel Arana-Catania
https://arxiv.org/abs/2509.01576 https://

Structured AI Decision-Making in Disaster Management
With artificial intelligence (AI) being applied to bring autonomy to decision-making in safety-critical domains such as the ones typified in the aerospace and emergency-response services, there has been a call to address the ethical implications of structuring those decisions, so they remain reliable and justifiable when human lives are at stake. This paper contributes to addressing the challenge of decision-making by proposing a structured decision-making framework as a foundational step towar…

On Saturday, the Oakland Ballers manager Aaron Miles will cede his decision-making duties to an AI.
It is believed to be the first time a professional sports team will be managed by an AI in a regular-season game.
The AI will determine most of the regular in-game decisions a typical manager would make.
It isn’t choosing the starting pitcher since the Ballers are on a set rotation,
but the AI will create the starting lineup, decide when pitchers need to be replaced and …

The Keychain Problem: On Minimizing the Opportunity Cost of Uncertainty
Ramiro N. Deo-Campo Vuong, Robert Kleinberg, Aditya Prasad, Eric Xiao, Haifeng Xu
https://arxiv.org/abs/2509.06187

The Keychain Problem: On Minimizing the Opportunity Cost of Uncertainty
In this paper, we introduce a family of sequential decision-making problems, collectively called the Keychain Problem, that involve exploring a set of actions to maximize expected payoff when only a subset of actions are available in each stage. In an instance of the Keychain Problem, a locksmith faces a sequence of choices, each of which involves selecting one key from a specified subset (a keychain) to attempt to open a lock. Given a Bayesian prior on the effectiveness of keys, the locksmith'…

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng, Mingyang Liu, Peijun Zheng, Jie Liu, Dapeng Wu, Yinqiao Li, Linqi Song
https://arxiv.org/abs/2509.07860

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly adva…

Semi-supervised inference for treatment heterogeneity
Yilizhati Anniwaer, Yuqian Zhang
https://arxiv.org/abs/2509.05048 https://arxiv.org/pdf/2509.05048

Semi-supervised inference for treatment heterogeneity
In causal inference, measuring treatment heterogeneity is crucial as it provides scientific insights into how treatments influence outcomes and guides personalized decision-making. In this work, we study semi-supervised settings where a labeled dataset is accompanied by a large unlabeled dataset, and develop semi-supervised estimators for two measures of treatment heterogeneity: the total treatment heterogeneity (TTH) and the explained treatment heterogeneity (ETH) of a simplified working model…

Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment
Cyrus Cousins, Vijay Keswani, Vincent Conitzer, Hoda Heidari, Jana Schaich Borg, Walter Sinnott-Armstrong
https://arxiv.org/abs/2509.04445

Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment
Recent AI work trends towards incorporating human-centric objectives, with the explicit goal of aligning AI models to personal preferences and societal values. Using standard preference elicitation methods, researchers and practitioners build models of human decisions and judgments, which are then used to align AI behavior with that of humans. However, models commonly used in such elicitation processes often do not capture the true cognitive processes of human decision making, such as when peop…

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Luisa Gall\'ee, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael G\"otz
https://arxiv.org/abs/2508.00639

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual attributes used by radiologists alongside the diagnosis, aligning AI decision-making with clinical reasoning. Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making both enhances transparency and enables expli…

Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories
Francisco Munguia-Galeano, Zhengxue Zhou, Satheeshkumar Veeramani, Hatem Fakhruldeen, Louis Longley, Rob Clowes, Andrew I. Cooper
https://arxiv.org/abs/2508.05148

Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories
The integration of robotics and automation into self-driving laboratories (SDLs) can introduce additional safety complexities, in addition to those that already apply to conventional research laboratories. Personal protective equipment (PPE) is an essential requirement for ensuring the safety and well-being of workers in laboratories, self-driving or otherwise. Fires are another important risk factor in chemical laboratories. In SDLs, fires that occur close to mobile robots, which use flammable…

Cardiac mechanics modeling: recent developments and current challenges
Aaron L. Brown, Ju Liu, Daniel B. Ennis, Alison L. Marsden
https://arxiv.org/abs/2509.07971 https://

Cardiac mechanics modeling: recent developments and current challenges
Patient-specific computational models of the heart are powerful tools for cardiovascular research and medicine, with demonstrated applications in treatment planning, device evaluation, and surgical decision-making. Yet constructing such models is inherently difficult, reflecting the extraordinary complexity of the heart itself. Numerous considerations are required, including reconstructing the anatomy from medical images, representing myocardial mesostructure, capturing material behavior, defin…

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…

Fractional order entropy-based decision-making models under risk
Poulami Paul, Chanchal Kundu
https://arxiv.org/abs/2507.02683 https://

Fractional order entropy-based decision-making models under risk
The construction of an efficient portfolio with a good level of return and minimal risk depends on selecting the optimal combination of stocks. This paper introduces a novel decision-making framework for stock selection based on fractional order entropy due to Ubriaco. By tuning the fractional parameter, the model captures varying attitudes of individuals toward risk. Values of fractional parameter near one indicate high risk tolerance (adventurous attitude), while those near zero reflect risk …

When Heterogeneity Drives Hysteresis: Anticonformity in the Multistate q-Voter Model on Networks
Arkadiusz Lipiecki, Katarzyna Sznajd-Weron
https://arxiv.org/abs/2509.07944 http…

When Heterogeneity Drives Hysteresis: Anticonformity in the Multistate q-Voter Model on Networks
Discontinuous phase transitions are closely linked to tipping points, critical mass effects, and hysteresis, phenomena that have been confirmed empirically and recognized as highly important in social systems. The multistate $q$-voter model, an agent-based approach to simulate discrete decision-making and opinion dynamics, is particularly relevant in this context. Previous studies of the $q$-voter model with anticonformity on complete graphs uncovered a counterintuitive result. Changing the mod…

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Phuc Hao Do, Tran Duc Le
https://arxiv.org/abs/2508.04288 https://

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free sim…

Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata
Giovanni Cerulli
https://arxiv.org/abs/2509.06851 https://arxiv.org/pdf…

Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata
This paper presents the Stata community-distributed command "opl_ma_fb" (and the companion command "opl_ma_vf"), for implementing the first-best Optimal Policy Learning (OPL) algorithm to estimate the best treatment assignment given the observation of an outcome, a multi-action (or multi-arm) treatment, and a set of observed covariates (features). It allows for different risk preferences in decision-making (i.e., risk-neutral, linear risk-averse, and quadratic risk-averse), and provides a graph…

Every time we make a decision, we build a bit of confidence for the next one https://www.overcomecompulsivehoarding.co.uk/podcast-ep-188-decision-making-hoarding-disorder/

Influence of Stakeholder Involvement in M&E on the Performance of Donor-Funded Projects in Informal Settlements in Kisumu Central Sub-County, Kisumu County, Kenya
Brighton Savayi Amuni, Stella Silas Karimi, Felistus Mwikali Patrick
https://arxiv.org/abs/2509.05369

Influence of Stakeholder Involvement in M&E on the Performance of Donor-Funded Projects in Informal Settlements in Kisumu Central Sub-County, Kisumu County, Kenya
Stakeholder engagement and participatory approaches influence the effectiveness of donor-funded projects. Participatory Monitoring and Evaluation (PM&E) methodologies ensure that local communities play an active role in decision-making, leading to more sustainable outcomes. Given the complex socio-political landscape of Kisumu Central Sub-County, there is a critical need for inclusive and context-responsive project monitoring strategies. Initiatives that have integrated local leaders, youth gro…

Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization
Philippe J. Giabbanelli, Ameeta Agrawal
https://arxiv.org/abs/2509.04646

Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization
Modeling & Simulation (M&S) approaches such as agent-based models hold significant potential to support decision-making activities in health, with recent examples including the adoption of vaccines, and a vast literature on healthy eating behaviors and physical activity behaviors. These models are potentially usable by different stakeholder groups, as they support policy-makers to estimate the consequences of potential interventions and they can guide individuals in making healthy choices in co…

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
N. Jean, G. Le Pera
https://arxiv.org/abs/2509.02388 https://arxiv.org…

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is often unmet in practice. This paper proposes a general purpose framework that bridges state of the art explainability techniques with Malle's five category model of behavior explanation: Knowledge Structures, Simulation/Projection, Covariation, Direct Recall, and Rationalization. The framework is designed to be applicabl…

A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness
Ahmed Abdal Shafi Rasel, Ahmed Mustafa Amlan, Tasmim Shajahan Mim, Tanvir Hasan
https://arxiv.org/abs/2508.05281

A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness
This study explores perceptions of fairness in algorithmic decision-making among users in Bangladesh through a comprehensive mixed-methods approach. By integrating quantitative survey data with qualitative interview insights, we examine how cultural, social, and contextual factors influence users' understanding of fairness, transparency, and accountability in AI systems. Our findings reveal nuanced attitudes toward human oversight, explanation mechanisms, and contestability, highlighting the im…

F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Qi Lv, Weijie Kong, Hao Li, Jia Zeng, Zherui Qiu, Delin Qu, Haoming Song, Qizhi Chen, Xiang Deng, Jiangmiao Pang
https://arxiv.org/abs/2509.06951

F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to short-sighted behaviors and poor robustness in dynamic scenes. In this paper, we introduce F1, a pretrained VLA framework which integrates the visual foresight generation into decision-making pipeline. F1 adopts a Mixture-of-Transformer architecture with dedicated modul…

Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network
Jinqi Zhang, Fangzhou Han, Di Zhuang, Lamei Zhang, Bin Zou, Li Yuan
https://arxiv.org/abs/2507.03315

Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network
In recent years, Deep Learning (DL) based methods have received extensive and sufficient attention in the field of PolSAR image classification, which show excellent performance. However, due to the ``black-box" nature of DL methods, the interpretation of the high-dimensional features extracted and the backtracking of the decision-making process based on the features are still unresolved problems. In this study, we first highlight this issue and attempt to achieve the interpretability analysis o…

Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
Yuki Takemoto
https://arxiv.org/abs/2509.04921 https://

Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
Time series forecasting plays a critical role in decision-making processes across diverse fields including meteorology, traffic, electricity, economics, finance, and so on. Especially, predicting returns on financial instruments is a challenging problem. Some researchers have proposed time series foundation models applicable to various forecasting tasks. Simultaneously, based on the recognition that real-world time series exhibit chaotic properties, methods have been developed to artificially g…

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…

Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach
Haosheng Zhou, Daniel Ralston, Xu Yang, Ruimeng Hu
https://arxiv.org/abs/2509.03727

Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach
Adversarial decision-making in partially observable multi-agent systems requires sophisticated strategies for both deception and counter-deception. This paper presents a sequential hypothesis testing (SHT)-driven framework that captures the interplay between strategic misdirection and inference in adversarial environments. We formulate this interaction as a partially observable Stackelberg game, where a follower agent (blue team) seeks to fulfill its primary task while actively misleading an ad…

LLMs as Architects and Critics for Multi-Source Opinion Summarization
Anuj Attri, Arnav Attri, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Nikesh Garera
https://arxiv.org/abs/2507.04751

LLMs as Architects and Critics for Multi-Source Opinion Summarization
Multi-source Opinion Summarization (M-OS) extends beyond traditional opinion summarization by incorporating additional sources of product metadata such as descriptions, key features, specifications, and ratings, alongside reviews. This integration results in comprehensive summaries that capture both subjective opinions and objective product attributes essential for informed decision-making. While Large Language Models (LLMs) have shown significant success in various Natural Language Processing …

Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
https://arxiv.org/abs/2509.02025

Curiosity-Driven Testing for Sequential Decision-Making Process
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios rema…

Enabling decision support over confidential data
Edoardo Marangone, Eugenio Nerio Nemmi, Daniele Friolo, Giuseppe Ateniese, Ingo Weber, Claudio Di Ciccio
https://arxiv.org/abs/2509.02413

Enabling decision support over confidential data
Enabling automated decision-making processes by leveraging data-driven analysis is a core goal of Decision Support Systems (DSSs). In multi-party scenarios where decisions rely on distributed and sensitive data, though, ensuring confidentiality, verifiability, transparency, integrity, and consistency at once remains an open challenge for DSSs. To tackle this multi-faceted problem, we propose the Secure Platform for Automated decision Rules via Trusted Applications (SPARTA) approach. By leveragi…

Meta-learning ecological priors from large language models explains human learning and decision making
Akshay K. Jagadish, Mirko Thalmann, Julian Coda-Forno, Marcel Binz, Eric Schulz
https://arxiv.org/abs/2509.00116

Meta-learning ecological priors from large language models explains human learning and decision making
Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled adaptation to the statistical structure of real-world tasks. We introduce ecologically rational analysis, a computational framework that unifies the normative foundations of rational analysis with ecological grounding. Leveraging large language models to generate ecologically valid cognitive tasks at scale, and using…

Implementation and Application of Multi-Format 3D Data Integration in a Cross-Device Commercial Metaverse Platform
Masanori Ibara, Yuichi Hiroi, Takushi Kamegai, Takefumi Hiraki
https://arxiv.org/abs/2508.05332

Implementation and Application of Multi-Format 3D Data Integration in a Cross-Device Commercial Metaverse Platform
Traditionally, specialized 3D design data, such as BIM and CAD, have been accessible only to a select group of experts, creating significant barriers that prevent general users from participating in decision-making processes. This paper provides a systematic overview of practical insights for utilizing 3D data in industrial and architectural domains by presenting implementation cases of the industrial metaverse on Cluster, a commercial cross-device metaverse platform. This paper analyzes the ch…

Quantum game models for interaction-aware decision-making in automated driving
Karim Essalmi, Fernando Garrido, Fawzi Nashashibi
https://arxiv.org/abs/2509.01582 https://…

Quantum game models for interaction-aware decision-making in automated driving
Decision-making in automated driving must consider interactions with surrounding agents to be effective. However, traditional methods often neglect or oversimplify these interactions because they are difficult to model and solve, which can lead to overly conservative behavior of the ego vehicle. To address this gap, we propose two quantum game models, QG-U1 (Quantum Game - Unitary 1) and QG-G4 (Quantum Game - Gates 4), for interaction-aware decision-making. These models extend classical game th…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/6]:
- Unified Bayesian Frameworks for Multi-criteria Decision-making Problems
Majid Mohammadi

Interpretable Decision-Making for End-to-End Autonomous Driving
Mona Mirzaie, Bodo Rosenhahn
https://arxiv.org/abs/2508.18898 https://arxiv.org/pdf/2508.18…

Interpretable Decision-Making for End-to-End Autonomous Driving
Trustworthy AI is mandatory for the broad deployment of autonomous vehicles. Although end-to-end approaches derive control commands directly from raw data, interpreting these decisions remains challenging, especially in complex urban scenarios. This is mainly attributed to very deep neural networks with non-linear decision boundaries, making it challenging to grasp the logic behind AI-driven decisions. This paper presents a method to enhance interpretability while optimizing control commands in…

Decision-oriented Text Evaluation
Yu-Shiang Huang, Chuan-Ju Wang, Chung-Chi Chen
https://arxiv.org/abs/2507.01923 https://arxiv.org/p…

Decision-oriented Text Evaluation
Natural language generation (NLG) is increasingly deployed in high-stakes domains, yet common intrinsic evaluation methods, such as n-gram overlap or sentence plausibility, weakly correlate with actual decision-making efficacy. We propose a decision-oriented framework for evaluating generated text by directly measuring its influence on human and large language model (LLM) decision outcomes. Using market digest texts--including objective morning summaries and subjective closing-bell analyses--as…

Accounting for Subsystem Aging Variability in Battery Energy Storage System Optimization
Melina Grane, Martin Cornejo, Holger Hesse, Andreas Jossen
https://arxiv.org/abs/2507.04813

Accounting for Subsystem Aging Variability in Battery Energy Storage System Optimization
This paper presents a degradation-cost-aware optimization framework for multi-string battery energy storage systems, emphasizing the impact of inhomogeneous subsystem-level aging in operational decision-making. We evaluate four scenarios for an energy arbitrage scenario, that vary in model precision and treatment of aging costs. Key performance metrics include operational revenue, power schedule mismatch, missed revenues, capacity losses, and revenue generated per unit of capacity loss. Our ana…

Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Gabriella Waters
https://arxiv.org/abs/2507.04555 https…

Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper pr…

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Simon Baur, Wojciech Samek, Jackie Ma
https://arxiv.org/abs/2508.04457 https://…

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains underexplored. In this study, we provide an extensive uncerta…

Nonparametric regression for cost-effectiveness analyses with observational data -- a tutorial
Jonas Esser, Mateus Maia, Judith Bosmans, Johanna van Dongen
https://arxiv.org/abs/2507.03511

Nonparametric regression for cost-effectiveness analyses with observational data -- a tutorial
Healthcare decision-making often requires selecting among treatment options under budget constraints, particularly when one option is more effective but also more costly. Cost-effectiveness analysis (CEA) provides a framework for evaluating whether the health benefits of a treatment justify its additional costs. A key component of CEA is the estimation of treatment effects on both health outcomes and costs, which becomes challenging when using observational data, due to potential confounding. W…

Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
Mukul Singh, Arjun Radhakrishna, Sumit Gulwani
https://arxiv.org/abs/2509.04847 htt…

Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
Language models are increasingly deployed in interactive online environments, from personal chat assistants to domain-specific agents, raising questions about their cooperative and competitive behavior in multi-party settings. While prior work has examined language model decision-making in isolated or short-term game-theoretic contexts, these studies often neglect long-horizon interactions, human-model collaboration, and the evolution of behavioral patterns over time. In this paper, we investig…

Introduction to the Analysis of Probabilistic Decision-Making Algorithms
Agustinus Kristiadi
https://arxiv.org/abs/2508.21620 https://arxiv.org/pdf/2508.21…

Introduction to the Analysis of Probabilistic Decision-Making Algorithms
Decision theories offer principled methods for making choices under various types of uncertainty. Algorithms that implement these theories have been successfully applied to a wide range of real-world problems, including materials and drug discovery. Indeed, they are desirable since they can adaptively gather information to make better decisions in the future, resulting in data-efficient workflows. In scientific discovery, where experiments are costly, these algorithms can thus significantly red…

On characterization and existence of a constrained correlated equilibria in Markov games
Tingting Ni, Anna Maddux, Maryam Kamgarpour
https://arxiv.org/abs/2507.03502

On characterization and existence of a constrained correlated equilibria in Markov games
Markov games with coupling constraints provide a natural framework to study constrained decision-making involving self-interested agents, where the feasibility of an individual agent's strategy depends on the joint strategies of the others. Such games arise in numerous real-world applications involving safety requirements and budget caps, for example, in environmental management, electricity markets, and transportation systems. While correlated equilibria have emerged as an important solution c…

TRAIL: Joint Inference and Refinement of Knowledge Graphs with Large Language Models
Xinkui Zhao, Haode Li, Yifan Zhang, Guanjie Cheng, Yueshen Xu
https://arxiv.org/abs/2508.04474

TRAIL: Joint Inference and Refinement of Knowledge Graphs with Large Language Models
Recent advances in large language models (LLMs) have unlocked powerful reasoning and decision-making capabilities. However, their inherent dependence on static parametric memory fundamentally limits their adaptability, factual accuracy, and interpretability in knowledge-intensive scenarios. Knowledge graphs (KGs), as structured repositories of explicit relational knowledge, offer a promising approach for augmenting LLMs with external, interpretable memory. Nevertheless, most existing methods th…

DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model
Rui Yu, Xianghang Zhang, Runkai Zhao, Huaicheng Yan, Meng Wang
https://arxiv.org/abs/2508.05402

DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model
End-to-end autonomous driving has been recently seen rapid development, exerting a profound influence on both industry and academia. However, the existing work places excessive focus on ego-vehicle status as their sole learning objectives and lacks of planning-oriented understanding, which limits the robustness of the overall decision-making prcocess. In this work, we introduce DistillDrive, an end-to-end knowledge distillation-based autonomous driving model that leverages diversified instance …

Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process
Youssef Ait El Mahjoub, Jean-Michel Fourneau, Salma Alouah
https://arxiv.org/abs/2508.00816

Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process
Solving Markov Decision Processes (MDPs) remains a central challenge in sequential decision-making, especially when dealing with large state spaces and long-term optimization criteria. A key step in Bellman dynamic programming algorithms is the policy evaluation, which becomes computationally demanding in infinite-horizon settings such as average-reward or discounted-reward formulations. In the context of Markov chains, aggregation and disaggregation techniques have for a long time been used to…

PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography
Yichi Zhang, Wenbo Zhang, Zehui Ling, Gang Feng, Sisi Peng, Deshu Chen, Yuchen Liu, Hongwei Zhang, Shuqi Wang, Lanlan Li, Limei Han, Yuan Cheng, Zixin Hu, Yuan Qi, Le Xue
https://arxiv.org/abs/2508.04062

PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography
Positron emission tomography (PET) is a cornerstone of modern oncologic and neurologic imaging, distinguished by its unique ability to illuminate dynamic metabolic processes that transcend the anatomical focus of traditional imaging technologies. Radiology reports are essential for clinical decision making, yet their manual creation is labor-intensive and time-consuming. Recent advancements of vision-language models (VLMs) have shown strong potential in medical applications, presenting a promis…

Beyond Accuracy: How AI Metacognitive Sensitivity improves AI-assisted Decision Making
ZhaoBin Li, Mark Steyvers
https://arxiv.org/abs/2507.22365 https://a…

Beyond Accuracy: How AI Metacognitive Sensitivity improves AI-assisted Decision Making
In settings where human decision-making relies on AI input, both the predictive accuracy of the AI system and the reliability of its confidence estimates influence decision quality. We highlight the role of AI metacognitive sensitivity -- its ability to assign confidence scores that accurately distinguish correct from incorrect predictions -- and introduce a theoretical framework for assessing the joint impact of AI's predictive accuracy and metacognitive sensitivity in hybrid decision-making s…

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Ziteng Gao, Jiaqi Qu, Chaoyu Chen
https://arxiv.org/abs/2509.01611 https:/…

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the rein…

Towards Understanding Decision Problems As a Goal of Visualization Design
Lena Cibulski, Stefan Bruckner
https://arxiv.org/abs/2507.18428 https://arxiv.org…

Towards Understanding Decision Problems As a Goal of Visualization Design
Decision-making is a central yet under-defined goal in visualization research. While existing task models address decision processes, they often neglect the conditions framing a decision. To better support decision-making tasks, we propose a characterization scheme that describes decision problems through key properties of the data, users, and task context. This scheme helps visualization researchers specify decision-support claims more precisely and informs the design of appropriate visual enc…

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Yuan Sui, Yanming Zhang, Yi Liao, Yu Gu, Guohua Tang, Zhongqian Sun, Wei Yang, Bryan Hooi
https://arxiv.org/abs/2509.04791

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Large language models (LLMs) excel at processing information reactively but lack the ability to systemically explore hypothetical futures. They cannot ask, "what if we take this action? how will it affect the final outcome" and forecast its potential consequences before acting. This critical gap limits their utility in dynamic, high-stakes scenarios like strategic planning, risk assessment, and real-time decision making. To bridge this gap, we propose WiA-LLM, a new paradigm that equips LLMs wi…

A Digital Twin Based Decision Support System for the Management of an Operating Room
Leah Rifi (CGI), Canan Pehlivan (CGI), Cl\'ea Martinez (CGI), Maria Di Mascolo (G-SCOP, G-SCOP\_DOME2S), Franck Fontanili (CGI)
https://arxiv.org/abs/2509.03094

A Digital Twin Based Decision Support System for the Management of an Operating Room
With healthcare demand rising worldwide, hospital services are increasingly needed. Hospitals' performance is tightly linked to their surgical suite performance, which makes it necessary for surgical suites to be efficient. In this paper, we focus on the operating room schedule execution and related decision-making. We thus propose a digital twin-based decision support system for the prospective and retrospective simulation and analysis of the operating room schedule execution. We describe the …

Who Pays for Fairness? Rethinking Recourse under Social Burden
Ainhize Barrainkua, Giovanni De Toni, Jose Antonio Lozano, Novi Quadrianto
https://arxiv.org/abs/2509.04128 https:…

Who Pays for Fairness? Rethinking Recourse under Social Burden
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring the fairness of classifiers. Beyond just fair classification, emerging legislation now mandates that when a classifier delivers a negative decision, it must also offer actionable steps an individual can take to reverse that outcome. This concept is known as algorithmic recourse. Nevertheless, many researchers have exp…

Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program
Xin Li, Li Ding, Qiao Lin, Zhen-Wei Yu
https://arxiv.org/abs/2509.02946

Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program
Demand response providers (DRPs) are intermediaries between the upper-level distribution system operator and the lower-level participants in demand response (DR) programs. Usually, DRPs act as leaders and determine electricity pricing strategies to maximize their economic revenue, while end-users adjust their power consumption following the pricing signals. However, this profit-seeking bi-level optimization model often neglects the satisfaction of end-users participating in DR programs. In addi…

A New Three-Players Auction Bridge with Dynamic Opponents and Team Members
Sourish Sarkar, Aritrabha Majumdar, Moutushi Chatterjee
https://arxiv.org/abs/2508.05582 https://

A New Three-Players Auction Bridge with Dynamic Opponents and Team Members
This article presents a new three-player version of the bridge playing card game for the purpose of ending fixed partnerships so that the play can be more dynamic and flexible. By dynamically redefining team makeup in real time, this game design increases unpredictability and forces players to repeatedly update strategy. A novel scoring system is introduced to reduce biases present in conventional rule-based games by favoring fairness via reward systems that enforce tactical decision making and…

Manifestations of Empathy in Software Engineering: How, Why, and When It Matters
Hashini Gunatilake, John Grundy, Rashina Hoda, Ingo Mueller
https://arxiv.org/abs/2508.04479 htt…

Manifestations of Empathy in Software Engineering: How, Why, and When It Matters
Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings pr…

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Vishal Pandey, Debasmita Biswas
https://arxiv.org/abs/2508.21505

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Reinforcement learning agents based on Transformer architectures have achieved impressive performance on sequential decision-making tasks, but their reliance on dense matrix operations makes them ill-suited for energy-constrained, edge-oriented platforms. Spiking neural networks promise ultra-low-power, event-driven inference, yet no prior work has seamlessly merged spiking dynamics with return-conditioned sequence modeling. We present the Spiking Decision Transformer (SNN-DT), which embeds Lea…

FairLangProc: A Python package for fairness in NLP
Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Rosa E. Lillo
https://arxiv.org/abs/2508.03677 https://

FairLangProc: A Python package for fairness in NLP
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizational justice or healthcare. This, in turn, poses questions about the fairness of these models in critical settings, which leads to the developement of different procedures to address bias in Natural Language Processing. Although many datasets, metrics and algorithms have been proposed to measure and mitigate harmful …

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
Stefan Buijsman, Sarah Carter, Juan Pablo Berm\'udez
https://arxiv.org/abs/2506.23952

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific autonomy -- the capacity for self-governed action within defined realms of skill or expertise -- remain understudied. We analyze how AI decision-support systems affect two key components of domain-specific autonomy: skilled competence (the ability to make inform…

Empathy Guidelines for Improving Practitioner Well-being & Software Engineering Practices
Hashini Gunatilake, John Grundy, Rashina Hoda, Ingo Mueller
https://arxiv.org/abs/2508.03846

Empathy Guidelines for Improving Practitioner Well-being & Software Engineering Practices
Empathy is a powerful yet often overlooked element in software engineering (SE), supporting better teamwork, smoother communication, and effective decision-making. In our previous study, we identified a range of practitioner strategies for fostering empathy in SE contexts. Building on these insights, this paper introduces 17 actionable empathy guidelines designed to support practitioners, teams, and organisations. We also explore how these guidelines can be implemented in practice by examining …

An Appraisal-Based Approach to Human-Centred Explanations
Rukshani Somarathna, Madhawa Perera, Tom Gedeon, Matt Adcock
https://arxiv.org/abs/2508.01388 https://

An Appraisal-Based Approach to Human-Centred Explanations
Explainability remains a critical challenge in artificial intelligence (AI) systems, particularly in high stakes domains such as healthcare, finance, and decision support, where users must understand and trust automated reasoning. Traditional explainability methods such as feature importance and post-hoc justifications often fail to capture the cognitive processes that underlie human decision making, leading to either too technical or insufficiently meaningful explanations. We propose a novel a…

A Correspondence-Driven Approach for Bilevel Decision-making with Nonconvex Lower-Level Problems
Xiaotian Jiang, Jiaxiang Li, Mingyi Hong, Shuzhong Zhang
https://arxiv.org/abs/2509.01148

A Correspondence-Driven Approach for Bilevel Decision-making with Nonconvex Lower-Level Problems
We consider bilevel optimization problems with general nonconvex lower-level objectives and show that the classical hyperfunction-based formulation is unsettled, since the global minimizer of the lower-level problem is generally unattainable. To address this issue, we propose a correspondence-driven hyperfunction $ϕ^{\text{cd}}$. In this formulation, the follower is modeled not as a rational agent always attaining a global minimizer, but as an algorithm-based bounded rational agent whose decis…

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Bing Song, Jianing Liu, Sisi Jian, Chenyang Wu, Vinayak Dixit
https://arxiv.org/abs/2506.23107

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Large language models (LLMs) have made significant strides, extending their applications to dialogue systems, automated content creation, and domain-specific advisory tasks. However, as their use grows, concerns have emerged regarding their reliability in simulating complex decision-making behavior, such as risky decision-making, where a single choice can lead to multiple outcomes. This study investigates the ability of LLMs to simulate risky decision-making scenarios. We compare model-generate…

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Qingfu Zhang
https://arxiv.org/abs/2509.03244 https://

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve rebuilding Gaussian process surrogates from scratch for each objective in each new problem encountered, or rely on extensive past domain experiments for pre-training deep learning models, making them hard to generalize and impractical to cope with…

"Who Should I Believe?": User Interpretation and Decision-Making When a Family Healthcare Robot Contradicts Human Memory
Hong Wang, Natalia Calvo-Barajas, Katie Winkle, Ginevra Castellano
https://arxiv.org/abs/2506.21322

"Who Should I Believe?": User Interpretation and Decision-Making When a Family Healthcare Robot Contradicts Human Memory
Advancements in robotic capabilities for providing physical assistance, psychological support, and daily health management are making the deployment of intelligent healthcare robots in home environments increasingly feasible in the near future. However, challenges arise when the information provided by these robots contradicts users' memory, raising concerns about user trust and decision-making. This paper presents a study that examines how varying a robot's level of transparency and sociabilit…

Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
Tonghe Li, Jixin Liu, Weili Zeng, Hao Jiang
https://arxiv.org/abs/2509.03550 https://

Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
In the context of continuously rising global air traffic, efficient and safe Conflict Detection and Resolution (CD&R) is paramount for air traffic management. Although Deep Reinforcement Learning (DRL) offers a promising pathway for CD&R automation, existing approaches commonly suffer from a "unimodal bias" in their policies. This leads to a critical lack of decision-making flexibility when confronted with complex and dynamic constraints, often resulting in "decision deadlocks." To overcome thi…

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhongzhi Li, Xiangyuan Xue, Yijiang Li, Yifan Zhou, Yang Chen, Chen Zhang, Yutao Fan, Zihu Wang, Songtao Huang, Yue Liao, Hongru Wang, Mengyue Yang, Heng Ji, Michael Littman, Jun Wang, Shuicheng Yan, Philip Torr, Lei Bai

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LLMs from passive sequence generators into autonomous, decision-making agents embedded in complex, dynamic worlds. This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes…

Modeling Wise Decision Making: A Z-Number Fuzzy Framework Inspired by Phronesis
Sweta Kaman, Ankita Sharma, Romi Banerjee
https://arxiv.org/abs/2508.21517 https://

Modeling Wise Decision Making: A Z-Number Fuzzy Framework Inspired by Phronesis
Background: Wisdom is a superordinate construct that embraces perspective taking, reflectiveness, prosocial orientation, reflective empathetic action, and intellectual humility. Unlike conventional models of reasoning that are rigidly bound by binary thinking, wisdom unfolds in shades of ambiguity, requiring both graded evaluation and self-reflective humility. Current measures depend on self-reports and seldom reflect the humility and uncertainty inherent in wise reasoning. A computational fram…

Board Game Arena: A Framework and Benchmark for Assessing Large Language Models via Strategic Play
Lucia Cipolina-Kun, Marianna Nezhurina, Jenia Jitsev
https://arxiv.org/abs/2508.03368

Board Game Arena: A Framework and Benchmark for Assessing Large Language Models via Strategic Play
The Board Game Arena library provides a framework for evaluating the decision making abilities of large language models (LLMs) through strategic board games implemented in Google OpenSpiel library. The framework enables systematic comparisons between LLM based agents and other agents (random, human, reinforcement learning agents, etc.) in various game scenarios by wrapping multiple board and matrix games and supporting different agent types. It integrates API access to models via LiteLLM, local…

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
Junping Wang, Bicheng Wang, Yibo Xuea, Yuan Xie
https://arxiv.org/abs/2506.23926

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
Resilience non-equilibrium measurement, the ability to maintain fundamental functionality amidst failures and errors, is crucial for scientific management and engineering applications of industrial chain. The problem is particularly challenging when the number or types of multiple co-evolution of resilience (for example, randomly placed) are extremely chaos. Existing end-to-end deep learning ordinarily do not generalize well to unseen full-feld reconstruction of spatiotemporal co-evolution stru…