Decision
Introduction
Decision, 11(1)
INTEREST CATEGORY: CONSUMER BEHAVIOR
POSTING TYPE: TOCs
Introduction to the special issue on judgment and decision research on the wisdom of the crowds
—Budescu, David V.; Grushka-Cockayne, Yael; Soll, Jack B. []
A hypothesis test algorithm for determining when weighting individual judgments reliably improves collective accuracy or just adds noise
—Huang, Shu; Broomell, Stephen B.; Golman, Russell []
Using cross-domain expertise to aggregate forecasts when within-domain expertise is unknown
—Howe, Piers D. L.; Martinie, Marcellin; Wilkening, Tom []
The wisdom of the coherent: Improving correspondence with coherence-weighted aggregation
—Collins, Robert N.; Mandel, David R.; Karvetski, Christopher W.; Wu, Charley M.; Nelson, Jonathan D. []
Using selected peers to improve the accuracy of crowd sourced forecasts
—Feng, Ye; Budescu, David V. []
Automated update tools to augment the wisdom of crowds in geopolitical forecasting
—Summerville, Amy; Widmer, Cara L.; Minnery, Brandon; Juvina, Ion; Ganapathy, Subhashini []
Harnessing the wisdom of the confident crowd in medical image decision-making
—Hasan, Eeshan; Eichbaum, Quentin; Seegmiller, Adam C.; Stratton, Charles; Trueblood, Jennifer S. []
Incentives for self-extremized expert judgments to alleviate the shared-information problem
—Peker, Cem []
Skew-adjusted extremized-mean: A simple method for identifying and learning from contrarian minorities in groups of forecasters
—Powell, Ben; Satopää, Ville A.; MacKay, Niall; Tetlock, Philip E. []
How expertise mediates the effects of numerical and textual communication on individual and collective accuracy
—Beauchamp, Nicholas; Shugars, Sarah; Swire-Thompson, Briony; Lazer, David []
Sequential collaboration: The accuracy of dependent, incremental judgments
—Mayer, Maren; Heck, Daniel W. []
How people use information about the number and distribution of judgments when tapping into the wisdom of the crowds
—Schultze, Thomas; Treffenstädt, Christian; Schulz-Hardt, Stefan []