Decision Science: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Decision Science (2024–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Decision Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Capturing the complexity of human strategic decision-making with machine learning

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
Overcoming algorithm aversion: A comparison between process and outcome control

Authors: L Cheng, A Chouldechova

Year: 2024

Published in: Proceedings of the 2023 CHI Conference ..., 2023 - dl.acm.org

Institution: Carnegie Mellon University

Research Area: Human-Computer Interaction (HCI), Algorithm Aversion, Decision Science

Discipline: Human-Computer Interaction (HCI)

Giving users process control by selecting the training algorithm mitigates algorithm aversion, but not by changing input factors, while combined outcome and process control is not more effective than each individually.

Methods: Replication study on outcome control and novel process control conditions tested on MTurk and Prolific platforms.

Key Findings: Impact of outcome control, process control, and combined controls on algorithm aversion mitigation.

DOI: https://doi.org/10.1145/3544548.3581253

Citations: 41