Incidental ostracism emerges from simple learning mechanisms

Area of Science:

• Social Psychology
• Behavioral Economics
• Computational Social Science

Background:

• Ostracism (social exclusion) is prevalent and linked to negative outcomes.
• Existing theories explain ostracism as instrumental punishment, but this is insufficient.
• The prevalence and features of ostracism require alternative explanations.

Purpose of the Study:

• To investigate the incidental emergence of ostracism through partner selection.
• To test the role of simple learning mechanisms in ostracism.
• To explore methods for reducing incidental ostracism.

Main Methods:

• Four experiments with 456 participants using economic games in dynamic social networks.
• Computational modeling to compare instrumental and reinforcement learning accounts.
• Experimental manipulation based on reinforcement learning to reduce ostracism.

Main Results:

• Ostracism targets are not primarily free-riders, contrary to instrumental theories.
• Initial variability in partner choice better predicts ostracism than instrumental factors.
• Reinforcement learning models explain incidental ostracism more effectively than instrumental models.

Conclusions:

• Ostracism emerges incidentally through basic learning mechanisms during partner selection.
• The prevalence of ostracism may stem from non-instrumental processes.
• Reinforcement learning principles can be applied to mitigate ostracism.