Browse 5 peer-reviewed papers from Harvard University spanning Strategic decision-making, Machine learning (2023–2025). Research powered by Prolific's high-quality participant data.
This page lists 5 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, Reinforcement Learning from Human Feedback (RLHF), Large Language Models
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 Methodology, 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: 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: 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, Behavioral Science
