"The trouble with having an open mind, of course, is that people will insist on coming along and trying to put things in it"
Hi there, I'm Cem!
I'm an Economics PhD candidate at Tinbergen Institute, Erasmus University Rotterdam. My PhD supervisor is Aurélien Baillon.
My research develops Bayesian methods to elicit and aggregate preferences, beliefs and judgments effectively. I use insights and tools from Decision Sciences, Experimental Economics and Behavioral Economics.
Applications include truthful elicitation in surveys and experiments, identifying experts in forecasting, harnessing the wisdom of crowds etc.
I will be available for interviews during the EJME (Dec 15-18, 2021) and the AEA Annual meeting in Jan 2022.
Experts often disagree in their judgments on the likelihood of an event. How should we combine individual estimates for maximum accuracy? I develop a novel algorithm for judgment aggregation. Experts are asked to report a prediction and an estimate on the average of others' predictions. The algorithm uses the latter to produce a consistent estimator for the unknown probability. Evidence suggests that the algorithm performs well especially in difficult aggregation problems where individuals disagree greatly.
Simple average of forecasts is a common method to aggregate expert judgments. However, experts' shared information is over-represented in the average forecast. I propose a simple incentive-based solution: Experts are grouped with non-experts and rewarded for group accuracy. As a result, experts extremize their forecasts towards away from the shared information to boost crowd accuracy.
We propose a prediction market to incentivize effort in reporting unverifiable judgments, such as personal experiences or long-term forecasts. Incentives are aligned such that Bayesian agents exert mental effort and trade accordingly, indirectly revealing their true judgment in the process. Evidence from two experimental studies indicate that peer predicition markets elicit high-quality subjective data.
Robust recalibration of aggregate probability forecasts using meta-beliefs
(in progress, joint with T. Wilkening)
Average probabilistic forecast of a crowd is typically too conservative. However, blind extremization may introduce further miscalibration in some tasks. We develop a recalibration method, which uses forecasters' meta-beliefs to improve accuracy.
My teaching interests:
At Erasmus School of Economics, I teach Applied Behavioral Economics in our masters' programme. I supervise experimental projects that apply behavioral insights in practical problems.
I also regularly supervise bachelors' and masters' theses in Behavioral Economics.
In the past, I have been a TA in various courses in Tinbergen Institute and Bogazici University, Turkey.