The lecture by Henrik Olsson from Santa Fe Institute will take place at the Complexity Science Hub Vienna in Room 201.
If you are interested in participating, please email to email@example.com
Decisions and forecasts are often done in groups rather than by isolated individuals. Examples are teams of health care professionals assessing patients’ chances to recover from cancer, Federal Open Market Committee deciding upon the federal funds rate, and academic committees making hiring and funding decisions. It is typically assumed that larger groups make better decisions, and that the same strategies work well when individuals decide alone and when they decide in groups. In this talk I will present three studies that examine how group size, algorithms for selecting group members, and individual decision strategies affect the accuracy of decisions and forecasts. I will show that small groups can perform better than randomly selected individuals and larger groups. I will also show that a new algorithm that selects small groups of forecasting experts from past performance can outperform the whole group of experts. Finally, I will show that strategies that work well for individual forecasts do not necessarily work well when the forecasts are aggregated. I will discuss the implications of the results in the context of collective intelligence and how decision environments should be structured to maximize group performance.
Henrik Olsson is an External Professor at the Santa Fe Institute. He is a cognitive scientist studying decision making, judgment under uncertainty, social cognition, categorization, and visual perception. A recurrent theme in his research is the development of new psychological theories and the use of formal mathematical models to try to understand the underlying psychological processes. Another theme is the ecological perspective on adaptive behavior, that is how the social and physical environments are structured and how psychological processes exploit, or fail to exploit, these structures. Henrik’s current work focuses on understanding how properties of individual decision strategies and social network structures affect group performance by connecting research in social cognition and decision making with insights from statistics and machine learning.