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Published on: October 17, 2019
Direct and Indirect Influence of Altruistic Behavior in a Social Network.
Pei-Pei Liu1, Vasiliy Safin1, Barry Yang1
1Department of Psychology, Stony Brook University, Stony Brook, New York, United States of America.
Generosity and selfishness spread through social networks. Selfishness spreads more effectively, influenced by expectations of future interactions, but indirect social influence was not observed.
Area of Science:
- Social psychology
- Behavioral economics
- Network science
Background:
- Prior research suggests direct social influence (recipients of generosity act more generously).
- Evidence for indirect social influence (interacting with a generosity recipient increases one's generosity) is conflicting.
- The capacity for altruistic behavior to cascade through social networks remains uncertain.
Purpose of the Study:
- To investigate the transmission range of selfish and generous behaviors in social networks.
- To explore the cognitive mechanisms underlying behavioral transmission.
- To clarify the existence and extent of direct versus indirect social influence.
Main Methods:
- Participants engaged in sequential public goods games within a controlled chain network.
- The behavior of the initial participant (first link) was experimentally set as either generous or selfish.
- The chain network structure allowed for unambiguous tracking of influence pathways.
Main Results:
- Direct social influence was confirmed, with behavior transmitted from one link to the next.
- No evidence for indirect social influence was found, suggesting altruism does not cascade through intermediaries.
- Selfish behavior demonstrated a significantly stronger influence than generous behavior.
- Expectations regarding future partners' behavior were identified as a key mediator of social influence.
Conclusions:
- Social influence in networks is primarily direct, not indirect.
- Selfishness is more contagious than generosity within these networks.
- Behavioral transmission is adaptive, driven by expectations of future interactions.
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