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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Large-scale community study reveals information sampling drives fairness decisions.

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People demonstrate fairness norms even outside the lab. In a museum Ultimatum Game (UG), participants rejected unfair offers and used proposer history to inform decisions, showing real-world inequality aversion.

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Area of Science:

  • Behavioral Economics
  • Social Psychology
  • Citizen Science

Background:

  • Empirical understanding of fairness often relies on lab studies with limited generalizability.
  • Ecological validity of fairness research is challenged by homogeneous samples.

Purpose of the Study:

  • To investigate fairness norms in a real-world, ecologically valid setting.
  • To examine decision-making in the Ultimatum Game (UG) using a citizen science approach.
  • To explore the impact of proposer history on fairness judgments.

Main Methods:

  • Embedded the Ultimatum Game (UG) in a museum setting over 13 months.
  • Collected >18,672 decisions from a heterogeneous public sample.
  • Included an option for participants to view proposer history before deciding.

Main Results:

  • Replicated classic UG findings: unfair offers were frequently rejected, confirming inequality aversion.
  • Majority of participants utilized proposer history information.
  • Information sampling heightened sensitivity to fairness violations, especially from previously generous proposers.

Conclusions:

  • Fairness norms and inequality aversion extend beyond laboratory settings.
  • Information seeking about past behavior significantly influences judgments of fairness.
  • The lab-in-the-field approach offers a scalable model for studying authentic decision-making.