Public Prediction Bulletin

Abstract

We conducted a field experiment that examined the effectiveness of various messaging strategies for promoting the completion of voter registration checks in an online setting in the United States. We will ask you to predict the conversion rates of different prompts. Survey is not available on mobile devices and takes ~12 minutes.

Study ID sspp-2026-0016-v1
Authors Craig Fox, Megan Weber, Craig Brimhall
Discipline Psychology
Field Behavioral Science
Completion Time 10 Minutes
Close Date (UTC) July 10, 2026


Citation
Fox, Craig, Weber, Megan, and Craig Brimhall. 2026. "Field Experiment Promoting Voter Registration Checks." Social Science Prediction Platform. June 4. https://socialscienceprediction.org/s/bdz644

Abstract

This study has groups of four participants working together to solve a series of puzzles under time pressure. The key measures will be your forecasts about how well two different predictive models explain variation in group performance: one model based on psychological and demographic characteristics of group members, and one that adds machine learning-derived features from transcripts of the groups' deliberations. No special expertise in machine learning is required.

Study ID sspp-2026-0013-v1
Authors Diag Davenport, Yuji Winet
Discipline Other
Field Behavioral Science
Completion Time 10 Minutes
Close Date (UTC) July 7, 2026


Citation
Davenport, Diag, and Yuji Winet. 2026. "Group Decision Making and Machine Learning." Social Science Prediction Platform. May 23. https://socialscienceprediction.org/s/88yaee



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Abstract

A review of evidence about complementarity, defined as happening when multiple interventions have more-than-additive effects on outcomes.

Study ID sspp-2026-0011-v1
Authors Aaditya Mattoo, Anna Tompsett, Eddy (Weijian) Zou
Discipline Economics
Field Development Economics
Completion Time 10 Minutes
Close Date (UTC) June 30, 2026


Citation
Mattoo, Aaditya, Tompsett, Anna, and Eddy (Weijian) Zou. 2026. "Complementarity." Social Science Prediction Platform. May 21. https://socialscienceprediction.org/s/fee0e0



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Abstract

How do employers think about hybrid work? How do those views shape pay, productivity expectations, and hiring decisions? This study is about workplace arrangements and evaluates brief, realistic scenarios about recruitment and compensation.

Study ID sspp-2026-0005-v1
Authors Yaming Cao, Sarra Ben Yahmed, Daniel Erdsiek, Melanie Arntz, Davud Rostam-Afschar
Discipline Economics, Sociology, Management
Field Labor Economics
Completion Time 15 Minutes
Close Date (UTC) June 30, 2026


Citation
Cao, Yaming, Yahmed, Sarra Ben, Erdsiek, Daniel, Arntz, Melanie, and Davud Rostam-Afschar. 2026. "Employers’ beliefs about hybrid work." Social Science Prediction Platform. March 25. https://socialscienceprediction.org/s/c8bbbx



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Abstract

This study will compare overconfidence in humans and large language models. The key measure will elicit certainty in the form of a belief distribution. A distribution with a smaller mean absolute deviation (MAD) implies greater certainty. A reported distribution with smaller MAD than the true distribution implies overprecision: excessive certainty.

Study ID sspp-2026-0010-v1
Authors Don Moore
Discipline Psychology
Field Cognitive Psychology
Completion Time 5 Minutes
Close Date (UTC) June 5, 2026


Citation
Don Moore. 2026. "Overconfidence in people and LLMs." Social Science Prediction Platform. April 30. https://socialscienceprediction.org/s/bazbaa



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