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An increasingly common socio-technical problem is people being taken in by offers that sound ``too good to be true'', where persuasion and trust shape decision-making. This paper investigates how \abr{ai} can help detect these deceptive…

Computation and Language · Computer Science 2025-06-06 Wichayaporn Wongkamjan , Yanze Wang , Feng Gu , Denis Peskoff , Jonathan K. Kummerfeld , Jonathan May , Jordan Lee Boyd-Graber

This paper presents a plausible reasoning system to illustrate some broad issues in knowledge representation: dualities between different reasoning forms, the difficulty of unifying complementary reasoning styles, and the approximate nature…

Artificial Intelligence · Computer Science 2013-03-26 Wray L. Buntine

Trust and reliance are often treated as coupled constructs in human-AI interaction research, with the assumption that calibrating trust will lead to appropriate reliance. We challenge this assumption in educational contexts, where students…

Human-Computer Interaction · Computer Science 2026-01-26 Olivia Pal , Veda Duddu , Agam Goyal , Drishti Goel , Koustuv Saha

In the canonical examples underlying Shafer-Dempster theory, beliefs over the hypotheses of interest are derived from a probability model for a set of auxiliary hypotheses. Beliefs are derived via a compatibility relation connecting the…

Artificial Intelligence · Computer Science 2013-04-11 Kathryn Blackmond Laskey

Game theory has been applied in many fields of study, especially economics and political science. Arce M. and Sandler (2005) analyzed counter-terrorism using non-cooperative game theory (the players are, for example, the US and the EU),…

Optimization and Control · Mathematics 2021-01-05 Sung Chan Choi

The Shapley value is a conventional and well-studied function for determining the contribution of a player to the coalition in a cooperative game. Among its applications in a plethora of domains, it has recently been proposed to use the…

Databases · Computer Science 2020-01-01 Alon Reshef , Benny Kimelfeld , Ester Livshits

This paper contributes to an emerging literature that models votes and text in tandem to better understand polarization of expressed preferences. It introduces a new approach to estimate preference polarization in multidimensional settings,…

Computation and Language · Computer Science 2019-10-29 Caleb Pomeroy , Niheer Dasandi , Slava J. Mikhaylov

Incidence Calculus and Dempster-Shafer Theory of Evidence are both theories to describe agents' degrees of belief in propositions, thus being appropriate to represent uncertainty in reasoning systems. This paper presents a straightforward…

Artificial Intelligence · Computer Science 2013-04-05 F. Correa da Silva , Alan Bundy

With the increasing scale, complexity, and heterogeneity of the next generation networked systems, seamless control, management, and security of such systems becomes increasingly challenging. Many diverse applications have driven interest…

Systems and Control · Electrical Eng. & Systems 2021-04-19 Anousheh Gholami , Nariman Torkzaban , John S. Baras

We propose, develop, and analyze a mathematical model of intergroup attitudes in a community that is divided between two distinct social groups (which may be distinguished by religion, ethnicity, or some other socially distinguishing…

Physics and Society · Physics 2015-12-07 Christopher Thron , Rachel McCoy

Decentralized trust management is used as a referral benchmark for assisting decision making by human or intelligence machines in open collaborative systems. During any given period of time, each participant may only interact with a few of…

Social and Information Networks · Computer Science 2019-09-26 Xinxin Fan , Ling Liu , Rui Zhang , Quanliang Jing , Jingping Bi

Indirect reciprocity is one of the main mechanisms to explain the emergence and sustainment of altruism in societies. The standard approach to indirect reciprocity are reputation models. These are games in which players base their decisions…

Physics and Society · Physics 2013-05-29 Luis A. Martinez-Vaquero , José A. Cuesta

We explore Bayesian reasoning as a means to quantify uncertainty in neural networks for question answering. Starting with a multilayer perceptron on the Iris dataset, we show how posterior inference conveys confidence in predictions. We…

Computation and Language · Computer Science 2025-12-22 Riccardo Di Sipio

Probabilistic Graphical Bayesian models of causation have continued to impact on strategic analyses designed to help evaluate the efficacy of different interventions on systems. However, the standard causal algebras upon which these…

Dempster-Shafer theory of imprecise probabilities has proved useful to incorporate both nonspecificity and conflict uncertainties in an inference mechanism. The traditional Bayesian approach cannot differentiate between the two, and is…

Cryptography and Security · Computer Science 2015-03-20 Sari Haj Hussein

Currently the Dempster-Shafer based algorithm and Uniform Random Probability based algorithm are the preferred method of resolving security games, in which defenders are able to identify attackers and only strategy remained ambiguous.…

Artificial Intelligence · Computer Science 2015-08-11 Hossein Khani , Mohsen Afsharchi

Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing…

Artificial Intelligence · Computer Science 2019-12-23 Zhiming Huang , Lin Yang , Wen Jiang

This paper proposes a new approach to power in Game Theory. Cooperation and conflict are simulated with a mechanism of payoff alteration, called F-game. Using convex combinations of preferences, an F-game can measure players' attitude to…

Theoretical Economics · Economics 2024-01-30 Daniele De Luca

Dempster/Shafer (D/S) theory has been advocated as a way of representing incompleteness of evidence in a system's knowledge base. Methods now exist for propagating beliefs through chains of inference. This paper discusses how rules with…

Artificial Intelligence · Computer Science 2013-04-10 Paul K. Black , Kathryn Blackmond Laskey

Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance,…

Machine Learning · Statistics 2018-10-30 Heinrich Jiang , Been Kim , Melody Y. Guan , Maya Gupta