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Simulating the Social Processes of Science Leiden| 9 April 2014

www.ingenio.upv.es. Simulating the Social Processes of Science Leiden| 9 April 2014. DIFFUSION OF SCIENTIFIC KNOWLEDGE: A PERCOLATION MODEL Elena M. Tur With P Zeppini and K Frenken. INGENIO [CSIC-UPV] Ciudad Politécnica de la Innovación | Edif 8E 4º Camino de Vera s/n

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Simulating the Social Processes of Science Leiden| 9 April 2014

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  1. www.ingenio.upv.es Simulating the Social Processes of Science Leiden| 9April 2014 DIFFUSION OF SCIENTIFIC KNOWLEDGE: A PERCOLATION MODEL Elena M. Tur With P Zeppini and K Frenken INGENIO [CSIC-UPV] Ciudad Politécnica de la Innovación | Edif 8E 4º Camino de Vera s/n 46022 Valencia tel +34 963 877 048 fax +34 963 877 991

  2. Focus • How do social pressure and assortativityaffecttheprocess of diffusion? • How do theireffectinteractwiththe social networkstructure? • How do local effectsdictate global diffusionthroughtheoverallnetworkstructure? • Maincontribution: introducing social pressure in a model of diffusionwithdifferentnetworkstructures

  3. Diffusion of ideas • Spread of rumors(Zanette, 2002) • Online spreading of behavior(Centola, 2010) • Opiniondynamics(Shao et al., 2009) • Paradigmshift in science(Brock and Durlauf, 1999) • How to approachtheproblem(Zeppini and Frenken, 2013) • Heterogeneityof agents • Externalities • Social network • Percolation(Solomon et al., 2000)

  4. Percolation in a social network • : value of the idea • : minimumqualityrequirement of agent Agentadoptsthe idea at time if: • has notadoptedbefore • isinformed: a neighbor has adopted at time • iswilling to adopt:

  5. Percolation in a social network

  6. Percolation in a social network

  7. Social network • Small world(Watts and Strogatz, 1998)Startingwith a regular lattice, with 𝑛 nodes and 𝑘 edges per node, rewire each edge at random with probability 𝑝 • High clusteringcoefficient, lowaveragepathlength

  8. Percolationwithout social pressure • Upper bound to diffusion: 45º line (well-mixedpopulation) • Diffusionincreaseswith • Phasechanges: from a non-diffusion to a diffusionregime • Percolationthresholdsdecreasewith

  9. Social pressure

  10. Why social pressure? • Theunwilling to adopt can be persuaded • Network externalities: sharedlanguage, sharedexperiences, sharedbeliefs… • Increasingamount of evidence • Conformity(Asch,1958) • Weariness

  11. Modelling social Pressure • : # neighbors of agentthathaveadopted at time • : social pressureintensity • isdecreasing in thenumber of adoptingneighbors • isdecreasing in the social pressureintensity • if • if

  12. Parametersforthesimulations • agents • original neighbors in thesmallworldalgorithm • seedsor original adopters • time periods • runs of Monte Carlo simulations • iid • Social networks: lattice, smallworld, Poissonnetwork

  13. Percolation with social pressure

  14. Clusteringwith social pressure

  15. People tend to be friends with similar people Assortativity

  16. Modellingassortativity Most unwilling to adopt Most willing to adopt

  17. Percolationwithassortativity • No criticaltransitionfrom non-diffusion to diffusion • Diffusionsizescaleslinearlywithdiffusionsize • No effect of thenetworkstructure (well-mixedpopulation)

  18. Percolation with social pressure and assortativity

  19. Does assortativity help diffusion?

  20. Does assortativity help diffusion? (2)

  21. Conclusions

  22. Conclusions • Social pressurechangesthebehavior of thepercolationprocess: higherdiffusionsize, lowerpercolationthresholds • Theeffect of social pressureisdifferentfordifferentnetworkstructures: criticaltransition in smallworlds and lattices • With social pressure, clusteringbecomes beneficial fordiffusion: reduceddifferencesbetweennetworks • Assortativityremovestheeffect of thenetworkstructure: allbehave as a well-mixedpopulation • Assortativity can helporhamperdiffusiondependingonthesetting

  23. Futurework and limitations • Other social networkstructures: scale-free networks(Barábasi and Albert, 1999) • Evolvingendogenous social network • Re-thinkthe social pressureimplementation: alwayspossitiveefffect? • Differentminimumqualityrequirementdistributions,

  24. THANK YOU INGENIO [CSIC-UPV] Ciudad Politécnica de la Innovación | Edif 8E 4º Camino de Vera s/n 46022 Valencia tel +34 963 877 048 fax +34 963 877 991

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