modeling the fat tails of size fluctuations in organizations
TRANSCRIPT
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
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
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
Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001.
Examples
Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001.
Examples
Growth rate US firmsBuldyrev & al., J Phys I France 7 (1997), 635–650.
Examples
Growth rate Italian firmsBottazzi, Secchi, Physica A 324 (2003), 213–219.
Examples
Examples
Growth rate Italian firmsBottazzi, Secchi, Physica A 324 (2003), 213–219.
MHR
Sta
nley
& a
l, 199
6 Na
ture
379
: 804
–806
.
Grow
th ra
te (s
ome
othe
r set
of)
US fi
rms
Examples
Universality
Previous models
Previous models
Economic models
Previous models
Economic modelsDoesn’t fit e.g.
voluntary organizations
Physics models
Previous models
Physics models Not without problems either…
Previous models
Stochastic models
Previous models
Stochastic models
Previous models
Stochastic modelsOriginal has log-
normal growth rate distribution
Previous models
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.
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.
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.
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.
The SAF modelSchwartzkopf, Axtell, Farmer, arxiv:1004.5397.
cf. threshold models (Prof. Kertesz’s talk)
The SAF modelSchwartzkopf, Axtell, Farmer, arxiv:1004.5397.
cf. threshold models (Prof. Kertesz’s talk)
Our extended SAF model
Additional assumptions-Trying different networks-Organization cannot die (if the last person leaves
a new person joins)-Attachment probability:
ResultsTent plot, ER model δ = 1.
ResultsTent plot, directed ER model δ = 1.
ResultsTent plot, scale-free networks, δ = 1.
ResultsTent plot, directed scale-free networks, δ = 1.
Results2D grid, δ = 0
Results2D grid, δ = 1
Results2D grid, δ = 10
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.