Browse 3 peer-reviewed papers from Boston University spanning Strategic decision-making, Machine learning (2022–2025). Research powered by Prolific's high-quality participant data.
This page lists 3 peer-reviewed papers from researchers at Boston University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: JQ Zhu, JC Peterson, B Enke, TL Griffiths
Year: 2025
Published in: Nature Human Behaviour, 2025 - nature.com
Institution: Princeton University, Boston University, Harvard University
Research Area: Strategic decision-making, Machine learning, Computational Cognitive Science
Discipline: Artificial Intelligence
This study used deep neural networks to analyze human strategic decision-making, predicting choices more accurately than existing theories and uncovering the context-dependent nature of reasoning and decision-making in complex games.
Methods: Deep neural networks trained on data from procedurally generated matrix games with over 2,400 variations; models were modified for interpretability.
Key Findings: Human choices and reasoning in initial play of two-player matrix games, focusing on strategic decision-making and response to game complexity.
DOI: https://doi.org/10.1038/s41562-025-02230-5
Citations: 16
Sample Size: 90000
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Authors: Y Gao, D Lee, G Burtch, S Fazelpour
Year: 2024
Published in: arXiv preprint arXiv:2410.19599, 2024 - arxiv.org
Institution: Boston University, Northeastern University
Research Area: LLMs as Human Surrogates, Social Science Research Methods, Human Behavior Simulation
Discipline: Economics, Artificial Intelligence, Social Science
LLMs fail to accurately replicate human behavior in the 11-20 money request game, cautioning against their use as surrogates for human cognition in social science research.
Methods: The study evaluates the reasoning depth of various advanced LLMs through their performance on the 11-20 money request game, analyzing failure points related to input language, roles, and safeguarding.
Key Findings: The ability of LLMs to replicate human-like behavior and reasoning distribution in the context of social science simulations.
Citations: 25
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Authors: SX Li, R Halabi, R Selvarajan, M Woerner
Year: 2022
Published in: JMIR Formative ..., 2022 - formative.jmir.org
Institution: Massachusetts General Hospital, Harvard Medical School, Boston University, University of Waterloo
Research Area: Digital Health, Remote Research Methods, Recruitment and Retention Studies
Discipline: Digital Health, Research Methodology
Citations: 19