Why we should quit while we're ahead: When do averages matter more than sums?

Michael Brusovansky*, Yonatan Vanunu, Marius Usher

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

An enduring debate in decision-making and social cognition concerns the algorithm governing the formation of intuitive preferences and attitudes. Here we contrast 2 principles that are considered central to such judgments: averaging versus summation. Participants in 4 experiments were prompted to rely on their intuition when rating the Hall of Fame eligibility of basketball players, or their liking of athletes, lecturers or slot-machines, on the basis of rapid numerical sequences that represent performances, class feedback, or rewards. Experiment 1 showed that participants are sensitive to the sequences' averages, and prefer alternatives with high averages over those with high sums. Experiment 2 replicated these findings, and further showed that in a comparison between several models such as averaging, summation and the Peak-End heuristic, averaging type models account best for participants' preferences. Experiment 3 indicated that these evaluations are mediated by automatic/intuitive processes. Based on computational considerations we propose that the critical variable, determining whether preferences are more sensitive to sums or to averages, is the presentation and evaluation format: one by one versus grouped. This prediction is confirmed in Experiment 4.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalDecision
Volume6
Issue number1
DOIs
StatePublished - Jan 2019

Funding

FundersFunder number
NSF-BSF-NIH2014612
Israel Science Foundation743/12

    Keywords

    • Averaging
    • Evaluation format
    • Intuitive preferences
    • Peak-End heuristic
    • Summation

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