Browse 9 peer-reviewed papers from Harvard University spanning Strategic decision-making, Machine learning (2020–2025). Research powered by Prolific's high-quality participant data.
This page lists 9 peer-reviewed papers from researchers at Harvard 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: A Warrier, D Nguyen, M Naim, M Jain, Y Liang, K Schroeder, C Yang, JB Tenenbaum, S Vollmer, K Ellis, Z Tavares
Year: 2025
Published in: 2025 - arXiv preprint arXiv …, 2025 - arxiv.org
Institution: Basis Research Institute, DFKI GmbH, Harvard University, Quebec AI Institute, University of Cambridge, Massachusetts Institute of Technology, Cornell University
Research Area: Agent learning, World Models, Benchmarking, Evaluation protocols, RLHF, LLM
Discipline: Computer Science, Artificial Intelligence, Machine Learning
The paper introduces WorldTest, a novel protocol for evaluating model-learning agents using reward-free exploration and behavior-based scoring, and demonstrates that humans outperform models on the AutumnBench suite of tasks, revealing significant gaps in world-model learning.
Methods: The authors proposed WorldTest, a protocol separating reward-free interaction from scored tests in related environments, with evaluations done using AutumnBench—a dataset of 43 grid-world environments and 129 tasks across prediction, planning, and causal dynamics.
Key Findings: Performance of model-learning agents and humans in acquiring world models for masked-frame prediction, planning, and understanding causal dynamics.
Citations: 1
Sample Size: 517
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Authors: L Woodley, X Roberts-Gaal, R Calcott, F Cushman
Year: 2025
Published in: files.osf.io
Institution: Harvard University
Research Area: Experimental Psychology, Research Methods, Replication Studies
Discipline: Psychology, Social Science
Explicit demand cues do not alter participant behavior, judgments, or attitudes in online psychology experiments, despite participants adjusting their beliefs about study hypotheses.
Methods: Three preregistered experiments on Prolific tested the impact of explicit demand cues on participant behavior using a dictator game, a moral dilemma vignette, and a group attitude intervention. Participants were randomly assigned to receive information about the study hypothesis or no information.
Key Findings: Whether explicit demand cues influence behavior, judgments, or attitudes in online psychology studies.
Sample Size: 2254
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Authors: A Berke, R Mahari, A Pentland, K Larson
Year: 2024
Published in: Proceedings of the ACM ..., 2024 - dl.acm.org
Institution: Stanford's CodeX Center, Harvard Law School, MIT Media Lab, Stanford Institute for Human-Centered AI, The Larson Institute, Massachusetts Institute of Technology, Stanford University
Research Area: Crowdsourcing, Transparency, Human-Computer Interaction (HCI) in Social Science Research
Discipline: Computational Social Science, Human-Computer Interaction (HCI)
DOI: https://dl.acm.org/doi/abs/10.1145/3687005
Citations: 9
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Authors: T Sühr, S Samadi, C Farronato
Year: 2024
Published in: ArXiv
Institution: Harvard Business School, Harvard University, Tübingen AI Center
Research Area: Human-ML Collaboration, Performative Prediction
Discipline: Artificial Intelligence
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Authors: Nina Vestergaard Simonsen, Anne F. Klassen, Charlene Rae, Farima Dalaei, Stefan Cano, Lotte Poulsen, Andrea L. Pusic, Jens Ahm Sørensen
Year: 2023
Published in: Wiley
Institution: Harvard Medical School, McMaster University, Modus Outcomes, Odense University Hospital, University of Southern Denmark
Research Area: Patient-reported outcomes (PROMs), Psychometrics, Chronic Wound Care
Discipline: Health Sciences, Psychometrics
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Authors: H Kumar, J Chahal, Y Zhao, Z Zhang, A Wei
Year: 2023
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: University of Toronto, Harvard University
Research Area: AI and Well-Being, Human-AI Interaction, Online Advice-Seeking
Discipline: Artificial Intelligence, Human-Computer Interaction (HCI), Behavioral Science
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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
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Authors: B Cowgill, F Dell'Acqua, S Matz
Year: 2020
Published in: AEA Papers and Proceedings, 2020 - aeaweb.org
Institution: Columbia University, Harvard Business School
Research Area: Algorithmic Fairness in Management, Economics
Discipline: Economics
Citations: 34