Transcript Document

POWER LAWS
Bridges between microscopic and
macroscopic scales
Wolff's [1996] findings regarding the holdings of the top 1%, top
5%, and top 10% of the U.S. population in 1992 are reported in Table
Table 2
k
Pk
1%
37.2%
5%
60.0%
10%
71.8%
Pk denotes the percentage of total wealth
held by the top k percent
Davis [1941] No. 6
Commission
for
Economics, 1941.
of the Cowles
Research
in
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
LOGISTIC EQUATIONS
History, Applications
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
Verhulst way out of it: dX/dt = a X –
c X2
Solution: exponential ==========saturation at X= a / c
– 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.
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.
Montroll: Social dynamics and quantifying of social forces
“almost all the social phenomena, except in their relatively
brief abnormal times obey the logistic growth''.
- 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
dX/dt ~ X(N – X )
X = number of people that have already adopted the change and
N = the total population
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
Volterra:
d Xi = Xi (ai - ci F ( X1 … , X n ) )
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 =a1 X1 + ……+ an Xn
more rigorous  Kolmogorov.
MPeshel and W Mende The Predator-Prey Model;
Do we live in a Volterra World? Springer Verlag, Wien , NY 1986
Eigen
Darwinian selection and evolution in prebiotic environments.
Autocatalyticity = The fundamental property of:
life ; capital ; ideas ; institutions
each type i of DNA and RNA molecules
replicate in the presence of proteins at rate =
ai
Typos: instead of generating an identical molecule of type i
probability rate aij for a mutated molecule of type j
d Xi = Xi (ai - cii Xi - j cij Xj) +j aij Xj -j aij Xi
cij =
competition of the replicators for various resources
 saturation
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
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
Eigen: aij variability of the population
adaptability and survival
economic system: social security or
some form of mutual help
conglomerates with aij 
more stable in a stochastically changing environment
agents that are not fit now might become later the fittest
GLV and interpretations
wi (t+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 t
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 t
One can absorb the average ri (t) in c(w.,t) so
< ri (t) > =0
wi (t+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+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
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
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
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
POWER LAWS IN GLV
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-a
holds and its exponent a is independent on
c(w.,t)
This an important finding since the i-independence corresponds
to the famous market efficiency property in financial markets
take the average in both members of
wi (t+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+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)
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
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
P(w) 10-1
t=0
10-2
10-3
10-4
10-5
10-6
10-7
10-8
10-9
10-4
10-3
10-2
10-1
100
w
P(w) 10-1
10-2
10-3
10-4
10-5
t=10 000
10-6
10-7
10-8
10-9
10-4
10-3
10-2
10-1
100
w
P(w) 10-1
10-2
10-3
10-4
10-5
10-6
10-7
t=100 000
10-8
10-9
10-4
10-3
10-2
10-1
100
w
P(w) 10-1
10-2
10-3
10-4
10-5
10-6
10-7
10-8
t=1 000 000
10-9
10-4
10-3
10-2
10-1
100
w
P(w) 10-1
10-2
10-3
10-4
10-5
10-6
10
t=30 000 000
-7
10-8
10-9
10-4
10-3
10-2
10-1
100
w
P(w) 10-1
t=0
10-2
10-3
10-4
10-5
10-6
10
t=10 000
t=30 000 000
-7
t=100 000
10-8
t=1 000 000
10-9
10-4
10-3
10-2
10-1
100
w
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 a ~ 1/(1-1/L) ~ L/(L-1)
For L = 3 - 4 , a ~ 3/2 – 4/3; for internet L~ average nr of links/ site
In Statistical Mechanics,
if not detailed balance
 no Boltzmann
In Financial Markets,
if no efficient market
no Pareto
Further Analogies
Thermal Equilibrium
Boltzmann law
Efficient Market
Pareto Law
One cannot extract energy from
systems in thermal equilibrium
One cannot gain systematically
wealth from efficent markets
Except for “Maxwell Demons”
with microscopic information
Except if one has access to
detailed private information
By extracting energy from nonequilibrium systems , one brings
them closer to equilibrium
By exploiting arbitrage
opportunities, one eliminates
them (makes market efficient)
Irreversibility
Irreversibility
II Law of Theromdynamics
?
Entropy
?
Market Fluctuations Scaling
Feedback
Volatility  Returns
=> Long range Volatility correlations