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The roundtable Segregation in conversation dynamics. Massimo Mastrangeli , Lucas Lacasa and Martin Schmidt Complex Systems Summer School 2009. Prelude: modeling conversation dynamics. What drives social conversations? How do conversations evolve? A realistic model needs to include:
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The roundtableSegregation in conversationdynamics Massimo Mastrangeli, Lucas Lacasaand Martin Schmidt ComplexSystemsSummerSchool 2009
Prelude:modelingconversationdynamics What drives social conversations? How do conversations evolve? • A realistic model needs to include: • Aggregation vs. protagonism balance • Individuality • Topology • Memory • Abstract from conversation content • Simple, but not too simple CSSS09, Santa Fe Institute
Maintheme:baselinemodel – boundaryconds • Fellows (= FSA) sitting on a 1D torus, eachconnectedtotheir 2 first neighbours • Eachfellowhas a time-dependenthappinesslevel • Eachfellowaddresses the othersaccordingtohisownpreference (= probability density function) • Fixednumberoffellows • Uniforminitialpopulation (samehappiness and pdf forall) involved in the same (unique) conversation • One speaker at the time • Segregationleadstomultiple conversations CSSS09, Santa Fe Institute
Maintheme:baselinemodel – iterative dynamics • First speaker chosen randomly, other fellows silent • For each turn: • Speaker: 1) resets his happiness to the maximum, 2) chooses the new speaker among the fellows still in the conversation, 3) updates his own pdf accordingly • Silents decrease their happiness by 1 • Fellows who reach minimum happiness become latents, they look at their neighbours: • If no latents -> remain latent • If part of other conversation -> joint the conversation • If latent -> start new conversation In 2) and 3) they become in and reset their happiness CSSS09, Santa Fe Institute
Maintheme:first impressions • Propositions: • The model has a (degenerate) equilibrium state: • For N even: (N/2) 2-subchats • For N odd, (N/2 – 1) 2-subchats + (1) 3-subchat • The rate of approach to the equilibrium state depends on: • Maximum happiness / N ratio • Probability density distributions • Memory levels • Topology • Proofs: • Ehm… the margins of this page are too narrow CSSS09, Santa Fe Institute
Reprise:more investigations and insights • Analytical and computational investigations • Good old “pen and paper” • Fortran (fixed point analysis) • Netlogo (ABM) • Further: • Effects of: preferences, noise • Generalized topologies • Node degree > 2 • Cocktail party (agents moving and interacting in bounded space) CSSS09, Santa Fe Institute
Coda:Wait and see… The ComplexSocial SegregationSect wouldliketothankfortheirsupport: Tom Carter Daniel Rockmore … and all the wonderfulcrowdof CSSS09 CSSS09, Santa Fe Institute