Claudio castellano cnr infm statistical mechanics and complexity and universita di roma la sapienza
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Claudio Castellano CNR-INFM Statistical Mechanics and Complexity and Universita’ di Roma “La Sapienza”. The Axelrod model for cultural dissemination: role of noise, peer pressure and homophily. Outline: Phenomenology of the Axelrod model Variations of the original model

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Claudio castellano cnr infm statistical mechanics and complexity and universita di roma la sapienza

Claudio Castellano

CNR-INFM Statistical Mechanics and Complexity

and

Universita’ di Roma “La Sapienza”

The Axelrod model for cultural dissemination: role of noise, peer pressure and homophily

  • Outline:

  • Phenomenology of the Axelrod model

  • Variations of the original model

  • The relevance of noise

  • In search of robustness


Opinion dynamics
Opinion dynamics

  • How do opinions spread?

  • What affects the way consensus is reached?

  • What mechanisms are at work?

Interactions between individuals tend to

favor homogeneization

Other mechanisms favor fragmentation

  • Do regularities exist? Universal laws?

  • What is the effect of the structure of social networks and

    of mass-media?


A model for cultural dissemination
A model for cultural dissemination

  • “If people tend to become more alike in their beliefs, attitudes and behaviors when they interact, why do not all differences disappear?”

    [R. Axelrod, J. of Conflict Resolution, 41, 203 (1997)]

  • Culture is more complicated than opinions:

    several coupled features

  • Two basic ingredients

  • Social influence: Interactions make individuals more similar.

  • Homophily: Likeliness of interaction grows with similarity.


Definition of axelrod model
Definition of Axelrod model

Each individual is characterized by F integer variables, si,f , assuming q possible traits

1 <= si,f <= q

Dynamics:

  • Pick at random a site (i), a neighbor (j)

  • Compute

    overlap = # of common features/F

  • With prob. proportional to overlap:

    pick f’, such that si,f’ <> sj,f’ and set

    si,f’ = sj,f’

  • Iterate


Fragmentation consensus transition
Fragmentation-consensus transition

The evolution depends on the number q of traits in the initial state

High initial variability

Low initial variability


Phenomenology of axelrod model statics
Phenomenology of Axelrod model: statics

C. Castellano et al., Phys. Rev. Lett, 85, 3536 (2000)

Control parameter:

q = number of possible traits for each feature

Order parameter: smax/N fraction of system occupied by the largest domain

smax/N @ N -1 disorder

smax/N = O(1)  order

Low q: consensus

High q: fragmentation (polarization)

Consensus-fragmentation transition

depending on the initial condition


Phenomenology of axelrod model dynamics
Phenomenology of Axelrod model: dynamics

Observable:

density of active links n(t)

Active links: pairs of neighbors that are neither

completely different (overlap=0) nor equal

(overlap=1)

Close to the transition (q<q_c) the density of active links almost goes to zero and then grows up to very large values before going to zero.

Nontrivial interplay of different

temporal scales


Mean field approach
Mean-field approach

C. Castellano et al., Phys. Rev. Lett, 85, 3536 (2000)

F. Vazquez and S. Redner, EPL, 78, 18002 (2007)

Dynamics is mapped into

a dynamics for links

A transition between a state

with active links and a state

without them is found.

The divergence of the

temporal scales is computed:

  • ~ |q-qc|-1/2

    both above and below qc


On complex topologies
On complex topologies

K. Klemm et al., Phys. Rev. E, 67, 026120 (2003)

Small world (WS) networks: interpolations between regular

lattices and random networks.

  • Randomness in topology increases order

  • Fragmented phase survives for any p


On complex topologies1
On complex topologies

Scale-free networks: P(k)~k-g

  • Scale-free topology increases order

  • Fragmented phase disappears for infinite systems


The effect of mass media
The effect of mass media

Y. Shibanai et al., J. Conflict Resol., 45, 80 (2001)

J. C. Gonzalez-Avella et al, Phys. Rev. E, 72, 065102 (2005)

M = (m1,……, mF) is a

fixed external field

Also valid for

global or local coupling

Mass media tend to reduce consensus


The role of noise
The role of noise

K. Klemm et al., Phys. Rev. E, 67, 045101 (2003)

Cultural drift:

Each feature of each individual can spontaneously change at rate r


The role of noise1
The role of noise

What happens

as N changes?

Competition between temporal scale for noise (1/r) and for the relaxation of perturbation T(N).

T(N) << 1/r consensus

T(N) >> 1/r fragmentation

Noise destroys the q-dependent transition

For large N the system is always disordered, for any q and r


Another dis order parameter
Another (dis)order parameter

Ng = number of domains

g = <Ng>/N

r=0:

Consensus g ~ N-1

Fragmentation g ~ const

r>0:

Consensus g ~ r

Fragmentation g >> r


In search of robustness
In search of robustness

Are there simple modifications

of Axelrod dynamics that preserve

under noise the existence of

a transition depending on q?

Flache and Macy (preprint, 2007):

a threshold on prob. of interaction

Fluctuations simply accumulate until the threshold is overcome.


Old relatives of Axelrod model

Voter model

Agent becomes equal to

a randomly chosen neighbor

Glauber-Ising dynamics

Agent becomes equal to

the majority of neighbors

Voter gives disorder for any noise rate.

Glauber-Ising dynamics gives order for small noise.


Axelrod model with peer pressure
Axelrod model with peer pressure

Introduced by Kuperman (Phys. Rev. E, 73, 046139, 2006).

Usual prescription for the interaction of agents + additional step:

If the trait to be adopted, si,f’ , is shared by the majority of

neighbors then accept. Otherwise reject it.

This introduces peer pressure (surface tension)

For r=0 striped configurations

are reached.

A well known problem for

Glauber-Ising dynamics

[Spirin et al.,

Phys. Rev. E, 63, 036118 (2001)]

For r>0 there are long lived metastable striped configurations

that make the analysis difficult


Axelrod s model with peer pressure
Axelrod’s model with peer pressure

We can start from fully ordered configurations

For any q, discontinuity in the asymptotic value of g:

transition between consensus and fragmentation


Axelrod s model with peer pressure1
Axelrod’s model with peer pressure

The order parameter smax/N

Phase-diagram

Consensus-fragmentation transition for qc(r).

Limit r0 does not coincide with case r=0.


Summary and outlook
Summary and outlook

  • Axelrod model has rich and nontrivial phenomenology with

    a transition between consensus and fragmentation.

  • Noise strongly perturbs the model behavior .

  • If peer pressure is included the original Axelrod phenomenology is rather robust with respect to noise.

  • Coevolution

  • Theoretical understanding

  • Empirical validation

    Thanks to: Matteo Marsili, Alessandro Vespignani, Daniele Vilone.


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