Strategic interactions, affective reactions, and fast adaptations

Area of Science:

• Behavioral Economics
• Decision Science
• Game Theory

Background:

• Understanding decision-making under uncertainty is crucial for behavioral economics.
• Previous models often focus on reward-based learning or simple affect-based accounts like disappointment.

Purpose of the Study:

• To investigate the psychological drivers of repeated choices when uncertainty stems from partner selection.
• To differentiate between reward-based, disappointment-based, and regret-based explanations for choice dynamics.

Main Methods:

• Two experiments involving repeated decisions in a mixed-motive game and a mixed-motive/coordination game with unannounced transitions.
• Analysis of choice dynamics (outcome-choice relationship) and aggregate choice probabilities.

Main Results:

• Choice dynamics were consistent across both games and experimental stages.
• The observed dynamics strongly supported a regret-based account over reward-based or disappointment-based accounts.
• Regret-based dynamics explained aggregate choice probabilities and rapid adaptation to game changes.

Conclusions:

• Regret is a primary driver of choices in repeated uncertain interactions, independent of the specific game context.
• Affect-based decision-making, specifically regret, provides a more robust explanation for observed choice patterns than reward-based models.