ferromagnetic fluid as a model of social impact piotr fronczak, agatka fronczak and janusz a....
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Ferromagnetic fluid as a model of social impact
Piotr Fronczak, Agatka Fronczak and Janusz A. Hołyst
Faculty of Physics and Center of Excellence for Complex Systems Research, Warsaw University of Technology
… Individuals emerges only in society. Society emerges only due to individuals...
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Social impact theory (B. Latane, 1981)
• N - individuals holding one of two opposite opinions: yes - no , i = 1, i =1,2,3,...N (spins)
• Each individual is characterised by a strength parameter si and is located in a social space, every (i,j) is ascribed a „social distance” dij
• Individuals change their opinions according to i (t+1) = i (t) sign [-Ii(t)] where Ii(t) is the social impact (local field) acting on the individual i
click here for demonstration
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Condition for the cluster radius a(SL):
impact at the cluster border I(a)=0 (metastable state)
After some integration:
LShRhRa 32)2(2
16
1 2
where
R- radius of the social space,
h – external social impact
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What is the proper geometry for social networks ?
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From Euclidean geometry
to Newton interactions
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Geometry = interactions
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2 2
k=2
2
2
2
2
3
3
36
78
Ising interactions in BA model (Aleksiejuk, Holyst, Stauffer, 2002)
= 1=si
= -1=sij
jiissJH
***
,
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Fig. 1a: Mean magnetization versus temperature for 2 million nodes and various m
Fig. 1b: Effective Tc versus Nfor m =5
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What is the order parameter ?
k1=6 k2=2
s1+s2=0
s1k1+s2k2<0
s1 s2
no order ?
order !
local field created by the spin s1
local field created by the spin s2
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Fig. 3: Total magnetization versus time, summed over 100 networks ofN = 30; 000 when after every 50 iterations the most-connected free spin is forced down permanently. For higher temperatures the sign change of the magnetization happens sooner.
Effect of leader(s) in scale-free networks –nucleation of a new phase due to pinning of most connected spins
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Magnetic liquids
Solutions of single-domain magnetic particles (~10 nm) in liquids (water, oils)
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Main features In the presence of nonhomogenous magneti field B(r) magnetic moments are ordering along the field direction and nano-particles moving to higher field regions
H = 0 H 0
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Applications
• Dynamical sealing
S
N
High-pressure region
Low-pressure region
MF
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Applications
• Cooling and vibrations damping
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Applications• Magnetic drug targeting
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Modeling of ferrofluids
i
iji
ijexjiij BrrH )()(
Hamiltonian
)()(
),()(
,)(
rar
drrar
drr
mgd
ex
dattr
Interactions
rdr
rdrR
attr
ex
)(
)(
Characteristic parameter
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Ferrofluid-like model of social impact
jijiji
jijiji
tJtJ
tJH
1)()1(
)(
,,
,
system consists of N individuals (members of a social group)
each of them can share one of two opposite opinions on a certain subject, denoted as i = ±1, i = 1, 2, ...N
- grogariousness
- individuality
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• New kind of art
Applications
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The same is boring... different is attractive...
σi(t) = σj(t) σi(t) ≠ σj(t)
)()()( ,, tJttJ jijiji
Social meaning of the model
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System phase diagram
jijiji tJtJ 1)()1( ,,
1.
13.
J(t) J(t + 1) J(t + 2) = J(t)
J(t + 1) > J(t)
J(t+1) = J(t)(1 + + )
J(t+2) = J(t+1)(1 + - )
(1 + + ) (1 + - ) = 1
2.
11 2 c
J(t) J(t + 1) J(t + 2) … J(t + 2n) = J(t)
(1 + + )n (1 + - )n = 1
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Algorithm1. Dynamics of opinions in opinions in social group - Monte Carlo
algorithm for spin variables (Metropolis). Temperature T – stochasticity of individuals opinions
2. After updating all N-spins we modify matrix Ji,j(t).
jijiji tJtJ 1)()1( ,,
Ji,j(t) ii
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Time scales
- time scale ratio
jijiji tJtJ 1)()1( ,,
large faster changes of J(t) than opinions (spin) dynamics temporary ferro- and paramagnetism
m
t
c < , 1
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Results
Second order phase transition
No dependence on temperature !!!
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Why temperature does not play any role ?
< ij > ~ <m> ~ f ( exp(-Ji,j / T) )but
thus
in mean field
m is just a function of ( / ) It follows:
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Temperature dependence of Ji,j
jiJ ,
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Distributiuon of interactions strengths
• system is described by a full weighted graph• for high temperatures – scale free distribution with 0.85
)(, tJ ji
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Conclusions
• Ferromagnetic fluids offer interesting analogy for modeling of social dynamcis
• We observed a self-organized ordered state with a second order phase transition and power law distributions of interactions strengths
• Mean value of <Ji,j>/T is just a function of η/α
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Thank you for your attention
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