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Better-than-chance Prediction of Cooperative Behaviour from First and Second Impressions

Eric Schniter and Timothy Shields

Working Papers from Chapman University, Economic Science Institute

Abstract: Could cooperation among strangers be facilitated by adaptations that use sparse information to accurately predict cooperative behaviour? We hypothesize that predictions are influenced by beliefs, descriptions, appearance, and behavioural history available for first and second impressions. We also hypothesize that predictions improve when more information is available. We conducted a two-part study. First, we recorded thin-slice videos of university students just before their choices in a repeated Prisoner’s Dilemma with matched partners. Second, a worldwide sample of raters evaluated each player using either videos, photos, only gender labels, or neither images nor labels. Raters guessed players’ first-round Prisoner’s Dilemma choices and then their second-round choices after reviewing first-round behavioural histories. Our design allows us to investigate incremental effects of gender, appearance, and behavioural history gleaned during first and second impressions. Predictions become more accurate and better-than-chance when either gender, appearance, or behavioural history are added. However, these effects were not incrementally cumulative. Predictions from treatments showing player appearance were no more accurate than from treatments revealing gender labels and predictions from videos were no more accurate than from photos. These results demonstrate how people accurately predict cooperation under sparse information conditions, helping explain why conditional cooperation is common among strangers.

Keywords: Cheater detection; Cooperation prediction; Prisoner’s dilemma; Photographs; Thin-slice video (search for similar items in EconPapers)
JEL-codes: B52 C72 C73 D63 D64 D83 D84 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-cbe, nep-exp, nep-gth and nep-neu
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