Cem Peker's Resarch Homepage

I'm a postdoctoral researcher at New York University Abu Dhabi. My research uses quantitative methods to study judgments and decision making, with applications in Decision Theory, Behavioral Economics and Information Markets. 

I'm on the job market for 2025-2026. My job market paper investigates the efficiency of information aggregation in markets. I analyze data from an online betting community, and show that self-reported picks can predict outcomes beyond market odds. Furthermore, I find that LLMs can simulate such community picks!

Job market paper: click here

CV: click here

Email: cem.peker@nyu.edu 

Publications

• Peker, C., & Wilkening, T. (2025). Robust recalibration of aggregate probability forecasts using meta-beliefs. International Journal of Forecasting, 41 (2), 613-630. https://doi.org/10.1016/j.ijforecast.2024.09.005

• Peker, C. (2024). Incentives for self-extremized expert judgments to alleviate the shared-information problem. Decision, 11 (1), 150172. https://doi.org/10.1037/dec0000198

• Peker, C. (2023). Extracting the collective wisdom in probabilistic judgments. Theory and Decision, 94 (3), 467-501. https://doi.org/10.1007/s11238-022-09899-4

Work in Progress

• Can we improve market forecasts with community predictions? Evidence from betting markets (Working paper)

• Peer betting to elicit unverifiable information (joint with Aurélien Baillon and Sophie van der Zee) (Working paper)

• Optimal linear aggregation of correlated expert judgments (Under review, Working paper)

• Expert decisions under pressure: Evidence from professional tennis (joint with John Wooders) (Abstract)

• Using prediction interval skewness to improve forecast accuracy (joint with Yael Grushka-Cockayne, Victor R.R. Jose, Jacob Rittich and Jack Soll) (Abstract)