social networks and gossip jeroen bruggeman 1. cooperation? 2
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Social networks and gossip
Jeroen Bruggeman
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Cooperation?
2
Cooperation• Reputation: infomation [knowledge] that
one individual has about another
• Reputation: through observation and for humans - with language - also through gossip
• Gossip requires network
• Reciprocity as special kind of reputation-based cooperation
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Reputations• Different reputations for different kinds
of actions and skills
• Let’s distinguish reputations for cooperation - as in game theory - from prestige for remainder actions and skills (gossip for both)
• Meta-reputations for contributing to transmission of reputations
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Gossip in theory• Who are trustworthy candidates to
cooperate with? Whom to avoid?
• Gossip as (evaluative) statements about non-present persons
• Gossip-based reputations can foster cooperation, even for public goods (Panchanathan & Boyd 2004)
• Boundary condition: actions observable
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Gossip in actuality• Emotional aspects: irresistable to hear,
gratifying to do• (Tacit) references to norms• Context dependent who can say what
about whom• Diffusion in network, but locally sticky
around gossipee• Conformist bias: (nearly) consensus
within groups; not necessarily across6
Gossip in actuality
• Negative gossip does not necessarily create trust, but requires it: risk of retaliation when gossiper is exposed
• Signaling group loyalty is self-serving without risk of sanctions: speak positively about group members who live up to group norms and negatively about norm violators
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Gossip in actuality• Combined with self-presentation • Not always honest: strategic actions to
make oneself look favorable compared with others (social comparison theory)
• Meta-gossip about reliability and goals gossiper, e.g. excessive or unreliable gossip mongers are less trusted
• Tactics: “the art of gossiping while not appearing to” N.Besnier
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Can network topology reduce noise (error & manipulation) and increase trust?
Appropriate definition of social cohesion to distinguish ‘better’ parts from ‘worse’ parts 9
Social cohesion as k-connectivity(Douglas White, Frank Harary 2001)
• (sub)group bonding as strong as minimal number of (sub)group members, k, who hold group together
• redundancy of information channels helps to reduce noise: minimal number of independent paths, m, that connect any pair of members
• Theorem (Menger 1927): m = k10
To be done: public goods experiment with imputed noise
3-connected 1-connected, with same size, density, degree distribution, degree centralization, path distance
Gossip tuples(X,Y,) 11
Complications
• Through meta-reputations, people will trust some info-sources more than others - no adoption of average gossip but weighted average
• In large groups, k-connectivity expected to have non-monotonic effect on cooperation.
• Acutal networks change, probably also with non-monotonic effect
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