Modeling the fat tails of size fluctuations in organizations
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Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527
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Modeling the fat tails of size fluctuations in organizations
- 1. Mondani H, Holme P, Liljeros F (2014) Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. PLoS ONE 9(7): e100527. Modeling the fat tails of size fluctuations in organizations Petter Holme
- 2. Mondani H, Holme P, Liljeros F (2014) Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. PLoS ONE 9(7): e100527. Modeling the fat tails of size fluctuations in organizations Petter Holme
- 3. Local trade unions in Sweden, 1880–1939 -Long quiet periods -Large jumps F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Typical data: time series of sizes (not join / quit numbers) Examples
- 4. Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Examples
- 5. Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Examples
- 6. Growth rate US firms Buldyrev & al., J Phys I France 7 (1997), 635–650. Examples
- 7. Growth rate Italian firms Bottazzi, Secchi, Physica A 324 (2003), 213–219. Examples
- 8. Examples Growth rate Italian firms Bottazzi, Secchi, Physica A 324 (2003), 213–219.
- 10. Universality
- 11. Previous models
- 13. Previous models Economic models Doesn’t fit e.g. voluntary organizations
- 15. Physics models Not without problems either… Previous models
- 18. Stochastic models Original has log- normal growth rate distribution Previous models
- 19. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
- 20. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Claims the network is the key (still trying just one topology)... Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
- 21. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Claims the network is the key (still trying just one topology)... Non-equilibrium... Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
- 22. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Claims the network is the key (still trying just one topology)... Non-equilibrium... Hidden parameters... Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
- 23. The SAF model Schwartzkopf, Axtell, Farmer, arxiv:1004.5397. cf. threshold models (Prof. Kertesz’s talk)
- 24. The SAF model Schwartzkopf, Axtell, Farmer, arxiv:1004.5397. cf. threshold models (Prof. Kertesz’s talk)
- 25. Our extended SAF model Additional assumptions -Trying different networks -Organization cannot die (if the last person leaves a new person joins) -Attachment probability:
- 26. Results Tent plot, ER model δ = 1.
- 27. Results Tent plot, directed ER model δ = 1.
- 28. Results Tent plot, scale-free networks, δ = 1.
- 29. Results Tent plot, directed scale-free networks, δ = 1.
- 30. Results 2D grid, δ = 0
- 31. Results 2D grid, δ = 1
- 32. Results 2D grid, δ = 10
- 33. Conclusions -The SAF model works and it is independent of the network topology (it just needs a (strongly connected giant component). -The contextual influence parameter makes a difference and can cause the loss of tentity.