Does counting change what counts? Quantification fixation biases decision-making

People often rely on numeric metrics to make decisions and form judgments. Numbers can be difficult to process, leading to their underutilization, but they are also uniquely suited to making comparisons. Do people decide differently when some dimensions of a choice are quantified and others are not? We explore this question across 21 preregistered experiments (8 in the main text, N = 9,303; 13 in supplement, N = 13,936) involving managerial, policy, and consumer decisions. Participants face choices that involve tradeoffs (e.g., choosing between employees, one of whom has a higher likelihood of advancement but lower likelihood of retention), and we randomize which dimension of each tradeoff is presented numerically and which is presented qualitatively (using verbal estimates, discrete visualizations, or continuous visualizations). We show that people systematically shift their preferences toward options that dominate on tradeoff dimensions conveyed numerically-a pattern we dub "quantification fixation." Further, we show that quantification fixation has financial consequences-it emerges in incentive-compatible hiring tasks and in charitable donation decisions. We identify one key mechanism that underlies quantification fixation and moderates its strength: When making comparative judgments, which are essential to tradeoff decisions, numeric information is more fluent than non-numeric information. Our findings suggest that when we count, we change what counts.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2024-10-28
DOI
https://doi.org/10.1073/pnas.2400215121
Citations
9
Primary Topic
Decision-Making and Behavioral Economics
Type
article
Field-Weighted Citation Impact
4.82

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article

Does counting change what counts? Quantification fixation biases decision-making

Erika Kirgios, Linda W. Chang, Katherine L. Milkman, Sendhil Mullainathan
9 citations
Proceedings of the National Academy of Sciences
Decision-Making and Behavioral Economics
4.82
article

Does counting change what counts? Quantification fixation biases decision-making

Erika Kirgios, Linda W. Chang, Katherine L. Milkman, Sendhil Mullainathan
article en
9 citations

Abstract

People often rely on numeric metrics to make decisions and form judgments. Numbers can be difficult to process, leading to their underutilization, but they are also uniquely suited to making comparisons. Do people decide differently when some dimensions of a choice are quantified and others are not? We explore this question across 21 preregistered experiments (8 in the main text, N = 9,303; 13 in supplement, N = 13,936) involving managerial, policy, and consumer decisions. Participants face choices that involve tradeoffs (e.g., choosing between employees, one of whom has a higher likelihood of advancement but lower likelihood of retention), and we randomize which dimension of each tradeoff is presented numerically and which is presented qualitatively (using verbal estimates, discrete visualizations, or continuous visualizations). We show that people systematically shift their preferences toward options that dominate on tradeoff dimensions conveyed numerically-a pattern we dub "quantification fixation." Further, we show that quantification fixation has financial consequences-it emerges in incentive-compatible hiring tasks and in charitable donation decisions. We identify one key mechanism that underlies quantification fixation and moderates its strength: When making comparative judgments, which are essential to tradeoff decisions, numeric information is more fluent than non-numeric information. Our findings suggest that when we count, we change what counts.

Proceedings of the National Academy of SciencesVol. 121(46)
University of Chicago (US), Massachusetts Institute of Technology (US), University of Pennsylvania (US)
University of Pennsylvania, University of Chicago
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Decision-Making and Behavioral Economics
4.82
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