Ignoring Information in Binary Choice with Continuous Variables: When is Less 'More'?

Posted: 8 May 2004

See all articles by Robin M. Hogarth

Robin M. Hogarth

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences

Natalia Karelaia

INSEAD - Decision Sciences

Date Written: February 2004

Abstract

When can a single variable be more accurate in binary choice than multiple sources of information? We derive, analytically, the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when "one-reason" decision making can be more effective than analyses that use more information. We, thus, provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect "less to be more."

Keywords: Decision making, bounded rationality, lexicographic rules, choice theory

JEL Classification: D81, M10

Suggested Citation

Hogarth, Robin M. and Karelaia, Natalia, Ignoring Information in Binary Choice with Continuous Variables: When is Less 'More'? (February 2004). Available at SSRN: https://ssrn.com/abstract=501804

Robin M. Hogarth (Contact Author)

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain
34 93 542 2561 (Phone)
34 93 542 1746 (Fax)

Natalia Karelaia

INSEAD - Decision Sciences ( email )

United States

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