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Gossip Project

Next project meeting 6: 30 Thursday after dinner

Tuesday dinner meeting: Mark Laidre, Anna Packenkina, Megan Olsen, Daniel Jones, Erika, Kaisa, Dan MacKinlay, Susanne Shultz

Suggestions (SS): Could we have a stub with recommended papers either listed or uploaded? The following are my notes from the meeting, please comment/edit

Project concept

Evolutionary dynamics models of gossip networks and honesty:
  1. Social rules/norms and norms
    1. Use sociology gossip models
      1. Reliability may be a function of number of informants (the more confirmation of a piece of gossip, the more reliable the information)
      2. Help from sociology literature????
    2. How does bogus information/informants (false/distorted gossip) affect optimal rules
    3. Extensions: influence of positive versus negative information, in group versus outgroup impacts
  2. Future possibilities?
    1. How does network structure impact on gossip dissemination?
    2. Parameterise models based on realistic networks
    3. How does gossip impact on stability of cooperation?
    4. Gossip and the evolution of language?


  1. Structure
    1. Graph versus agent-based models
    2. If there is no movement of ‘agents’, then it is probably better to use graphs.
    3. Network based model (use realistic parameters)
    4. Start with netlogo?
  2. Parameters
    1. Stable network
    2. Subgroup that has information about a third party
    3. Can propagate (at different levels)
    4. Can have reliable information
    5. Extensions/additional parameters: Secondary rumours, memory, trust, Hebbian learning (mentioned but not sure in what context)???
  3. Fitness functions (per interaction/click?- absolutely critical to define these, since they are core of model)
    1. Target
    2. Honest signals
    3. Bogus gossipers
    4. Listeners

Related issues/models

  1. Byzantine generals and traitors (could someone add a few key references)
  2. Emperical tests?
    1. Mafia Game (more information please)
    2. Try to seed gossip statement and see how they travel through the network based on their 'importance'