This talk is motivated by, and relies on data from, recent large-scale geopolitical forecasting tournaments (Mellers et al., 2014).  Two key results of the tournaments are (1) the possibility to identify reliably expertise (Budescu & Chen, 2015; Chen et al, 2016) of individual forecasters and leverage it to improve the accuracy of the forecasts through efficient aggregation of relatively small crowds of “selected” forecasters (Mannes et al. 2014), and (2) the surprising success of small collaborative teams. This “teaming effect” is particularly intriguing because it seems to contradict the “wisdom of the crowd” hypothesis that emphasizes the importance of independence among forecasters. We discuss both results with special attention to the “teaming effect” which we attribute to the hybrid approach implemented, which allowed forecasters to share information electronically and asynchronously, but required them to provide forecasts individually, for a statistical aggregation procedure. This hybrid approach benefits from the strengths of Computer Mediated Communication (CMC) and Statistical Aggregation.

Ben is Professor for Empirical Business Research at WU Vienna (Vienna University of Economics and Business), Head of the WU Institute for Markets and Strategy, and Director of the WU Competence Center for Experimental Research. He obtained his PhD from the University of Cologne in 2006. From 2006 to 2008, Ben was the CLER Postdoctoral Research Fellow at Harvard Business School, and from 2008 to 2016 first Lecturer, then Senior Lecturer and Associate Professor at the School of Economics at the University of New South Wales in Sydney, Australia. Ben works in experimental and behavioral economics, with research topics in bargaining, trust, market design, group decision behavior, and political economy.

A growing body of evidence suggests that decision-makers fail to account for correlation in signals that they receive. We study the relevance of this mistake in students’ interactions with school- choice matching mechanisms. In a lab experiment presenting simple and incentivized school-choice scenarios, we find that subjects tend to follow optimal application strategies when schools’ admissions decisions are determined independently. However, when schools rely on a common priority — inducing correlation in their decisions — decision making suffers: application strategies become substantially more aggressive and fail to include attractive “safety” options. We document that this pattern holds even within-subject, with significant fractions of participants applying to different programs in mathematically equivalent situations that differ only by the presence of correlation. We provide a battery of tests suggesting that this phenomenon is at least partially driven by correlation neglect, and we discuss implications that arise for the design and deployment of student-to-school matching mechanisms.

We compare two equivalent variants of an experimental common value auction: In one more-standard auction format, uncertainty about the common-value item is defined over outcomes, while in another format–a so-called common-probability auction–uncertainty is defined over probabilities. In contrast to the substantial overbidding found in common-value auctions, bidding behavior in common-probability auctions is consistent with risk-neutral Nash equilibrium bids and even exhibits slight underbidding.