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PARETO
POWER LAWS
Bridges between microscopic and macroscopic scales
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Davis [1941] No. 6 of the Cowles Commission for Research in Economics, 1941.
No one however, has yet exhibited a stable social order, ancient or modern, which has not followed the Pareto pattern at least approximately. (p. 395)
Snyder [1939]:
Pareto’s curve is destined to take its place as one of the great generalizations of human knowledge
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Alfred Lotka
the number of authors with n publications in a bibliography is a power law of the form
C/n
The exponent is often close to 1.
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LOTKA-VOLTERRA
LOGISTIC EQUATIONS
History, Applications
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Franco Scudo, "The 'Golden Age' of theoretical ecology. A conceptual appraisal", Rev.Europ.Etud.Social., 22, 11-64 (1984)
Franco Scudo e J.R. Ziegler, "The Golden Age of theoretical ecology: 1923-1949", Berlin, Springer, 1978
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La ricerca ecologica ha avuto, nel periodo 1920-1940, alcuni "anni d'oro", come li ha definiti Franco Scudo (1), con importanti contributi anche italiani (per esempio di V. Volterra e U. D'Ancona).
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Malthus : autocatalitic proliferation:
dX/dt = a X with a =birth rate - death rate
exponential solution: X(t) = X(0)ea t
contemporary estimations= doubling of the population every 30yrs
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Verhulst way out of it: dX/dt = a X – c X2
Solution: exponential ==========saturation at X= a / c
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– c X2 = competition for resources and other the adverse feedback effects
saturation of the population to the value X= a / c
For humans data at the time could not discriminate between
exponential growth of Malthus and
logistic growth of Verhulst
But data fit on animal population:
sheep in Tasmania:
exponential in the first 20 years after their introduction and
saturated completely after about half a century.
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Confirmations of Logistic Dynamics
pheasants
turtle dove
humans world population for the last 2000 yrs and
US population for the last 200 yrs,
bees colony growth
escheria coli cultures,
drossofilla in bottles,
water flea at various temperatures,
lemmings etc.
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Elliot W Montroll: Social dynamics and quantifying of social forces
“almost all the social phenomena, except in their relatively brief abnormal times
obey the logistic growth''.
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- default universal logistic behavior generic to all social systems
- concept of sociological force which induces deviations from it
Social Applications of the Logistic curve:
technological change; innovations diffusion (Rogers)
new product diffusion / market penetration (Bass)
social change diffusion
X = number of people that have already adopted the change and
N = the total population
dX/dt ~ X(N – X )
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Sir Ronald Ross Lotka: generalized the logistic equation
to a system of coupled differential equations
for malaria in humans and mosquitoes
a11 = spread of the disease from humans to humans minus the
percentages of deceased and healed humans
a12 = rate of humans infected by mosquitoes
a112 = saturation (number of humans already infected becomes
large one cannot count them among the new infected).
The second equation = same effects for the mosquitoes infection
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Xi = the population of species i
ai = growth rate of population i in the
absence of competition and other species
F = interaction with other species: predation competition symbiosis
Volterra assumed F =1 X1 + ……+ n Xn more rigorous Kolmogorov.
Volterra:
MPeshel and W Mende The Predator-Prey Model;
Do we live in a Volterra World? Springer Verlag, Wien , NY 1986
d Xi = Xi (ai - ci F ( X1 … , X n ) )
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Mikhailov
Eigen equations relevant to market economics
i = agents that produce a certain kind of commodity
Xi = amount of commodity the agent i produces per unit time
The net cost to an individual agent of the produced commodity is
Vi = ai Xi
ai = specific cost which includes expenditures for raw materials
machine depreciation labor payments research etc
Price of the commodity on the market is c
c (X.,t) = i ai Xi / i Xi
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The profits of the various agents will then be
ri = c (X.,t) Xi - ai Xi
Fraction k of it is invested to expand production at rate
d Xi = k (c (X.,t) Xi - ai Xi )
These equations describe the competition between agents in the free market
This ecology market analogy was postulated already in Schumpeter and Alchian See also Nelson and Winter Jimenez and Ebeling Silverberg Ebeling and Feistel Jerne Aoki etc
account for cooperation: exchange between the agents
d Xi = k (c (X.,t) Xi - ai Xi ) +j aij Xj -j aij Xi
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GLV and interpretations
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wi (t+) – wi (t) = ri (t) wi (t) + a w(t) – c(w.,t) wi (t)
w(t) is the average of wi (t) over all i ’s at time t
a and c(w.,t) are of order
c(w.,t) means c(w1,. . ., wN,t)
ri (t) = random numbers distributed with the same probability
distribution independent of i with a square standard deviation
< ri (t) 2> =D of order
One can absorb the average ri (t) in c(w.,t) so
< ri (t) > =0
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wi (t+) – wi (t) = ri (t) wi (t) + a w(t) – c(w.,t) wi (t)
admits a few practical interpretations
wi (t) = the individual wealth of the agent i then
ri (t) = the random part of the returns that its capital wi (t) produces during the time between t and t+
a = the autocatalytic property of wealth at the social level
= the wealth that individuals receive as members of the society in subsidies, services and social benefits. This is the reason it is proportional to the average wealth This term prevents the individual wealth falling below a certain minimum fraction of the average.
c(w.,t) parametrizes the general state of the economy:large and positive correspond = boom periods negative =recessions
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c(w.,t) limits the growth of w(t) to values sustainable for the current conditions and resources
external limiting factors:
finite amount of resources and money in the economy
technological inventions
wars , disasters etc
internal market effects:
competition between investors
adverse influence of self bids on prices
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A different interpretation:
a set of companies i = 1, … , N
wi (t)= shares prices ~ capitalization of the company i ~ total wealth of all the market shares of the company
ri (t) = fluctuations in the market worth of the company
~ relative changes in individual share prices (typically fractions of the nominal share price)
aw = correlation between wi and the market index w
c(w.,t) usually of the form c w represents competition
Time variations in global resources may lead to lower or higher values of c increases or decreases in the total w
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Yet another interpretation: investors herding behavior
wi (t)= number of traders adopting a similar investment policy or
position. they comprise herd i
one assumes that the sizes of these sets vary autocatalytically according to the random factor ri (t)
This can be justied by the fact that the visibility and social connections of a herd are proportional to its size
aw represents the diffusion of traders between the herds
c(w.,t) = popularity of the stock market as a whole
competition between various herds in attracting individuals
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BOLTZMANN
POWER LAWS IN GLV
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Crucial surprising fact
as long as
the term c(w.,t) and the distribution of the ri (t) ‘s
are common for all the i ‘s
the Pareto power law
P(wi) ~ wi –1-
holds and its exponent is independent on c(w.,t)
This an important finding since the i-independence corresponds
to the famous market efficiency property in financial markets
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take the average in both members of
wi (t+) – wi (t) = ri (t) wi (t) + a w(t) – c(w.,t) wi (t)
assuming that in the N = limit the random fluctuations cancel:
w(t+) – w(t) = a w(t) – c(w.,t) w (t)
It is of a generalized Lotka-Volterra type with quite chaotic behavior
x i (t) = w i (t) / w(t)
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and applying the chain rule for differentials d xi (t):
dxi (t) =dwi (t) / w(t) - w i (t) d (1/w)
=dwi (t) / w(t) - w i (t) d w(t)/w2
=[ri (t) wi (t) + a w(t) – c(w.,t) wi (t)]/ w(t)
-w i (t)/w [a w(t) – c(w.,t) w (t)]/w
= ri (t) xi (t) + a – c(w.,t) xi (t)
-x i (t) [a – c(w.,t) ]= crucial cancellation : the system splits into a set of independent linear stochastic differential equations with constant coefficients
= (ri (t) –a ) xi (t) + a
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dxi (t) = (ri (t) –a ) xi (t) + a
Rescaling in t means rescaling by the same factor in < ri (t) 2> =D and
a therefore the stationary asymptotic time distribution P(xi ) depends
only on the ratio a/D
Moreover, for large enough xi the additive term + a is negligible and
the equation reduces formally to the Langevin equation for ln xi (t)
d ln xi (t) = (ri (t) – a )
Where temperature = D/2 and force = -a => Boltzmann distribution
P(ln xi ) d ln xi ~ exp(-2 a/D ln xi ) d ln xi
~ xi -1-2 a/D d xi
In fact, the exact solution is P(xi ) = exp[-2 a/(D xi )] xi -1-2 a/D
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10010-3 10-2 10-110-4
10-9
10-4
10-5
10-6
10-7
10-8
10-1
10-2
10-3
t=0P(w)
w
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10010-3 10-2 10-110-4
10-9
10-4
10-5
10-6
10-7
10-8
10-1
10-2
10-3
t=10 000
P(w)
w
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10010-3 10-2 10-110-4
10-9
10-4
10-5
10-6
10-7
10-8
10-1
10-2
10-3
t=100 000
P(w)
w
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10010-3 10-2 10-110-4
10-9
10-4
10-5
10-6
10-7
10-8
10-1
10-2
10-3
t=1 000 000
P(w)
w
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10010-3 10-2 10-110-4
10-9
10-4
10-5
10-6
10-7
10-8
10-1
10-2
10-3
t=30 000 000
P(w)
w
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10010-3 10-2 10-110-4
10-9
10-4
10-5
10-6
10-7
10-8
10-1
10-2
10-3
t=0
t=10 000
t=100 000
t=1 000 000
t=30 000 000
P(w)
w
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K= amount of wealth necessary to keep 1 alive
If wmin < K => revolts
L = average number of dependents per average income
Their consuming drive the food, lodging, transportation and services prices to values that insure that at each time wmean > KL
Yet if wmean < KL they strike and overthrow governments.
So c=x min = 1/L
Therefore ~ 1/(1-1/L) ~ L/(L-1)
For L = 3 - 4 , ~ 3/2 – 4/3; for internet L average nr of links/ site
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In Statistical Mechanics,
if not detailed balance no BoltzmannIn Financial Markets,
if no efficient market no Pareto
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Thermal Equilibrium Efficient Market
Further Analogies
Boltzmann law
One cannot extract energy from systems in thermal equilibrium
Except for “Maxwell Demons” with microscopic information
By extracting energy from non-equilibrium systems , one brings them closer to equilibrium
Irreversibility
II Law of Theromdynamics
Entropy
Pareto Law
One cannot gain systematically wealth from efficent markets
Except if one has access to detailed private information
By exploiting arbitrage opportunities, one eliminates them (makes market efficient)
Irreversibility
?
?
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Paul Lévy
Drawing by Mendes-France
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Market Fluctuations Scaling
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Feedback
Volatility Returns
=> Long range Volatility correlations
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