1 / 34

Tipping Points, Statistics of opinion dynamics

Tipping Points, Statistics of opinion dynamics. Chjan Lim, Mathematical Sciences, RPI Collaboration with B. Szymanski, W Zhang, Y. Treitman, G. Korniss. Funding. Main: ARO grant 2009 – 2013 Prog Officer C. Arney, R. Zachary; ARO grant 2012 – 2015 Prog Officer P. Iyer

onella
Download Presentation

Tipping Points, Statistics of opinion dynamics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tipping Points, Statistics of opinion dynamics Chjan Lim, Mathematical Sciences, RPI Collaboration with B. Szymanski, W Zhang, Y. Treitman, G. Korniss

  2. Funding • Main: ARO grant 2009 – 2013 Prog Officer C. Arney, R. Zachary; ARO grant 2012 – 2015 Prog Officer P. Iyer • Secondary: ARL grants 2009 – 2012, ONR

  3. Applications • Predict Average Outcomes, Properties in Networks of Semi-Autonomous sensor-bots / drones. • Less direct and more qualitative predictions of social-political-cultural-economic networks

  4. NG NG NG and more • Background of Naming Games (NG) • Other variants of signaling games • NG on Different networks • On Complete graphs – simple mean field • NG on ER graph • SDE model of NG

  5. NG in detection community structure Q. Lu, G. Korniss, and B.K. Szymanski, J. Economic Interaction and Coordination,4, 221-235 (2009).

  6. A B AB ABC A B AC BC C C Two Names NG are End-Games for 3 Names case

  7. Tipping Point of NG • A minority of committed agents can persuade the whole network to a global consensus. • The critical value for phase transition is called the “tipping point”. J. Xie, S. Sreenivasan, G. Korniss, W. Zhang, C. Lim and B. K. Szymanski PHYSICAL REVIEW E (2011)

  8. Node Saddle Node unstable Node Saddle node bifurcation Below Critical Above Critical

  9. Meanfield Assumption and Complete Network • The network structure is ignored. Every node is only affected by the meanfield. • The meanfield depends only on the fractions(or numbers) of all types of nodes. • Describe the dynamics by an equation of the meanfield (macrostate).

  10. Scale of consensus time on complet graph

  11. Expected Time Spend on Each Macrostate before Consensus (without committed agents)

  12. NG with Committed Agents q=0.12>qc q=0.06<qc q is the fraction of agents committed in A. When q is below a critical value qc, the process may stuck in a meta-stable state for a very long time.

  13. Higher stubbornness – same qualitative, robust result

  14. A B AB ABC A B AC BC C C Two Names NG are End-Games for 3 Names case

  15. n_B Transient State Absorbing State P(B+) P(A-) P(A+) P(B-) n_A 2 Word Naming Game as a 2D random walk

  16. Linear Solver for 2-Name NG Have equations: Then we assign an order to the coordinates, make , into vectors, and finally write equations in the linear system form:

  17. SDE models for NG, NG and NG

  18. Higher stubbornness – same qualitative, robust result

  19. Diffusion vs Drift • Diffusion scales are clear from broadening of trajectories bundles • Drift governed by mean field nonlinear ODEs can be seen from the average / midlines of bundles

  20. Other NG variants – same 1D manifold

  21. 3D plot of trajectory bundles – stubbornness K = 10 as example of variant (Y. Treitman and C. Lim 2012)

  22. Consensus time distribution • Recursive relationship of P(X, T), the probability for consensus at T starting from X, Q is the transition matrix. Take each column for the same T as a vector: Calculate the whole table P(X,T) iteratively. Take each row for the same X as a vector:

  23. Consensus time distribution Red lines are calculated through the recursive equation. Blue lines are statistics of consensus times from numerical simulation(very expensive), (done by Jerry Xie)

  24. Consensus Time distribution Below critical, consensus time distribution tends to exponential. Above critical, consensus time distribution tends to Gaussian. For large enough system, only the mean and the variance of the consensus time is needed.

  25. Variance of Consensus time Theorem: the variance of total consensus time equals to the sum of variances introduced by every macrostate: is the expected total number of steps spend on the given macrostate before consensus. is the variance introduced by one step stay in the given macrostate.

  26. Naming Game on other networks • Mean field assumption • Local meanfield assumption • Homogeneous Pairwise assumption • Heterogeneous Pairwise assumption

  27. P(·|A) A Mean field P(·|B) B P(·|A) AB Homogeneous Pairwis Assumption The mean field is not uniform but varies for the nodes with different opinion.

  28. Numerical comparison

  29. Trajectories mapped to 2D macrostate space

  30. Concentration of the consensus time

  31. C C A B Related×(<k>-1) C Direct Analyze the dynamics 1.Choosing one type of links, say A-B, and A is the listener. 2.Direct change: A-B changes into AB-B. 3.Related changes: since A changes into AB, <k>-1 related links C-A change into C-AB. The probability distribution of C is the local mean field P(·|A).

  32. Merits of SDE model • Include all types of NG and other communication models in one framework and distinguish them by two parameters. • Present the effect of system size explicitly. • Collapse complicated dynamics into 1-d SDE equation on the center manifold.

  33. Thanks

More Related