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In this article, we study the effect of vector-valued interventions in votes under a binary voter model, where each voter expresses their vote as a $0-1$ valued random variable to choose between two candidates. We assume that the outcome is…

Applications · Statistics 2022-10-17 Manit Paul , Rishideep Roy , Soudeep Deb

We examine hypothesis testing within a principal-agent framework, where a strategic agent, holding private beliefs about the effectiveness of a product, submits data to a principal who decides on approval. The principal employs a hypothesis…

Machine Learning · Computer Science 2025-08-06 Safwan Hossain , Yatong Chen , Yiling Chen

Probabilistic models can learn users' preferences from the history of their item adoptions on a social media site, and in turn, recommend new items to users based on learned preferences. However, current models ignore psychological factors…

Information Retrieval · Computer Science 2013-11-07 Jeon-Hyung Kang , Kristina Lerman

Since Downs proposed that the act of voting is irrational in 1957, myriad models have been proposed to explain voting and account for observed turnout patterns. We propose a model in which partisans consider both the instrumental and…

Physics and Society · Physics 2020-03-24 Cameron Guage , Feng Fu

Content moderation is often performed by a collaboration between humans and machine learning models. However, it is not well understood how to design the collaborative process so as to maximize the combined moderator-model system…

Machine Learning · Computer Science 2021-07-12 Ian D. Kivlichan , Zi Lin , Jeremiah Liu , Lucy Vasserman

Iterative voting is a natural model of repeated strategic decision-making in social choice theory when agents have the opportunity to update their votes prior to finalizing the group decision. Prior work has analyzed the efficacy of…

Computer Science and Game Theory · Computer Science 2025-06-12 Joshua Kavner , Lirong Xia

Coalitional manipulation in voting is considered to be any scenario in which a group of voters decide to misrepresent their vote in order to secure an outcome they all prefer to the first outcome of the election when they vote honestly. The…

Theoretical Economics · Economics 2020-09-28 Mostapha Diss , Boris Tsvelikhovskiy

In the process of enacting or introducing a new policy, policymakers frequently consider the population's responses. These considerations are critical for effective governance. There are numerous methods to gauge the ground sentiment from a…

Social and Information Networks · Computer Science 2025-11-11 Eugene T. Y. Ang , Yong Sheng Soh

Elections involving a very large voter population often lead to outcomes that surprise many. This is particularly important for the elections in which results affect the economy of a sizable population. A better prediction of the true…

Computer Science and Game Theory · Computer Science 2018-01-31 Palash Dey , Pravesh K. Kothari , Swaprava Nath

Observation of other people's choices can provide useful information in many circumstances. However, individuals may not utilize this information efficiently, i.e., they may make decision-making errors in social interactions. In this paper,…

General Economics · Economics 2021-08-10 Mohsen Foroughifar

We consider a sequential decision making process, such as renewable energy trading or electrical production scheduling, whose outcome depends on the future realization of a random factor, such as a meteorological variable. We assume that…

Trading and Market Microstructure · Quantitative Finance 2021-07-01 Peter Tankov , Laura Tinsi

We investigate how individuals form expectations about population behavior using statistical inference based on observations of their social relations. Misperceptions about others' connectedness and behavior arise from sampling bias…

Theoretical Economics · Economics 2022-05-27 Andreas Bjerre-Nielsen , Martin Benedikt Busch

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our…

Artificial Intelligence · Computer Science 2015-12-21 Owain Evans , Andreas Stuhlmueller , Noah D. Goodman

We study the problem of disseminating a piece of information through all the nodes of a network, given that it is known originally only to a single node. In the absence of any structural knowledge on the network other than the nodes'…

Networking and Internet Architecture · Computer Science 2007-06-13 A. O. Stauffer , V. C. Barbosa

Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty.…

Machine Learning · Computer Science 2024-10-29 Anna Sokol , Nuno Moniz , Nitesh Chawla

When predictions support decisions they may influence the outcome they aim to predict. We call such predictions performative; the prediction influences the target. Performativity is a well-studied phenomenon in policy-making that has so far…

Machine Learning · Computer Science 2021-03-02 Juan C. Perdomo , Tijana Zrnic , Celestine Mendler-Dünner , Moritz Hardt

Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the…

Machine Learning · Statistics 2016-11-08 Zhen Xu , Wen Dong , Sargur Srihari

This paper models voters who invest effort to determine whether a particular claim relevant to their voting choices is correct. If a voter succeeds in determining whether the claim is correct, this information is shared via a social…

Theoretical Economics · Economics 2025-09-22 Boris Ginzburg

The availability heuristic is a strategy that people use to make quick decisions but often lead to systematic errors. We propose three ways that visualization could facilitate unbiased decision-making. First, visualizations can alter the…

Human-Computer Interaction · Computer Science 2016-10-11 Evanthia Dimara , Pierre Dragicevic , Anastasia Bezerianos

We present a new approach to the problems of evaluating and learning personalized decision policies from observational data of past contexts, decisions, and outcomes. Only the outcome of the enacted decision is available and the historical…

Machine Learning · Statistics 2019-06-04 Nathan Kallus
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