Democratic Forecast: Small Groups Predict the Future Better Than Individuals and Crowds

Dezecache, Guillaume; Dockendorff, Martin; Ferreiro, Dardo N.; Deroy, Ophelia und Bahrami, Bahador (2022): Democratic Forecast: Small Groups Predict the Future Better Than Individuals and Crowds. In: Journal of Experimental Psychology: Applied, Bd. 28, Nr. 3: S. 525-537

Abstract

Public Significance Statement Governments and companies have started to pay attention to the demonstrated success of collective intelligence in the domain of forecasting. Here we highlight that the wisdom of crowds solutions pushed so far face at least two issues: they are not really about crowds and their wisdom remains expensive. Democratic deficit and large costs and numbers remain obstacles in the adoption of collective forecast. Here, we demonstrate how these obstacles could be lifted. In particular, we show that small crowds of untrained, unselected citizens make better predictions than individuals, if given the opportunity to deliberate and agree. Predictions pose unique problems. Experts regularly get them wrong, and collective solutions (such as prediction markets and super-forecaster schemes) do better but remain selective and costly. Contrary to the idea that face-to-face discussion hinders collective intelligence, social deliberation improves the resolution of general knowledge problems, with four consensually agreed answers outperforming the aggregate knowledge of 5,000 nondeliberating individuals. Could discussion help predict the future in an efficient, cheap, and inclusive way? We show that smaller groups of lay individuals, when organized, come up with better predictions than those they provide alone. Deliberation and consensus made individual predictions significantly more accurate. Aggregating as few as two consensual predictions did better than classical wisdom of crowds aggregation of 100 individual ones. Against the view that discussion can impair decision-making, our results demonstrate that collective intelligence of small groups and consensus-seeking improves accuracy about yet unknown facts, opening the avenue for efficient, inclusive, and inexpensive group forecasting solutions.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Biologie ; Philosophie, Wissenschaftstheorie und Religionswissenschaft
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie ; 100 Philosophie und Psychologie > 100 Philosophie
• ISSN: 1076-898X
• Sprache: Englisch