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Predicting persons' versus a person's goodness: behavioral forecasts diverge for individuals versus populations
Clayton R Critcher1, David Dunning
1Haas School of Business, University of California, Berkeley, CA 94720, USA. claytoncritcher@haas.berkeley.edu
Journal of Personality and Social Psychology
|November 21, 2012
Summary
Behavioral forecasts for individuals and populations differ. People predict individuals will act more morally and with more personal will than groups, due to differing sensitivity to social norms versus personal conscience.
Area of Science:
- Social Psychology
- Behavioral Economics
- Decision Science
Background:
- Behavioral forecasts for individuals and populations are frequently conflated.
- Existing research indicates systematic discrepancies between these two types of forecasts.
Purpose of the Study:
- To investigate the systematic differences between individual and population behavioral forecasts.
- To identify the underlying psychological mechanisms driving these forecast discrepancies.
Main Methods:
- Six experimental studies were conducted to compare individual and population behavioral forecasts.
- Forecasting tasks involved both morally relevant and non-morally relevant behaviors.
- The differential sensitivity to constraints hypothesis was tested as an explanation.
Main Results:
- Forecasts for individuals and populations systematically differed across studies.
- Individuals were predicted to act more selflessly and with greater personal will than populations.
- The differential sensitivity to constraints hypothesis, emphasizing conscience for individuals and social norms for populations, was supported.
Conclusions:
- Forecasting individual behavior involves weighting personal conscience, while population forecasts emphasize social norms and pressures.
- This "differential sensitivity to constraints" explains why individuals are predicted to be more moral and willful than groups.
- The findings extend to non-moral behaviors influenced by personal will versus social encouragement.
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