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Advanced Political Economy

- New trends in Political Economy Econophysics

Why econophysics?

- MANY reasons but
- Two stand out
- Because weve solved all the big problems in

physics - Remark by physicist Cheng Zhang at 1st

Econophysics Conference, Bali 2002, in response

to question from economist Paul Ormerod - Because one dominant concept in modern physics is

highly applicable to finance uncertainty - Pre-Einsteinian physics based on uniformity

certainty - Newtonian Laws of Motion circa 1700s
- Supplemented by Maxwells equations for

electromagnetic phenomena circa 1800s - La Places conceit Give me the equations for

the universe I can predict not just the future

but also the past

A Quick Physics Primer

- By late 19th century, just two anomalies
- Speed of light
- Light seen as wave
- Waves presumed to move through medium
- E.g., sound waves are cyclic compression/expansion

of air molecules - Light thought to move through aether
- Unobserved substance thought to permeate all

space - Aether fixed, universe moves with respect to it
- IF aether exists Earth moving through it, THEN

speed of light in one direction (forward into

aether) should be slower than other (backwards

with aether) - Michelson-Morley experiment speed of light

constant in all directions

A Quick Physics Primer

- Black body radiation problem
- Atom known to exist
- Model of atom was positive nucleus orbited by

negative electrons - By Maxwells Newton equations, orbiting charge

should radiate energy - Electron should rapidly spiral into nucleus
- Black bodies (i.e. any object, not just heated

ones) should radiate energy - Fitted to experimental data, model predicted

EITHER infinite energy at low frequency OR

infinite energy at high energy - Actual energy profile was a hump
- Theory could fit one side or other but not both

A Quick Physics Primer

- Einstein/Planck solutions to dual problems
- light comes in small discrete packets called

quanta - Energy not continuous but discrete with minimum

unit Plancks constant - Probability uncertainty became essential

aspects of physics - Physics also accepted Boltzmanns Laws after

strong 19th century resistance - Progression of energy from highly ordered to

disordered state increase in entropy - All work involves generation of wasted energy

work (desired) necessitates heat undesired but

unavoidable) - Combination of ideas develops measure of

knowledge called Shannons entropy

A Quick Physics Primer

- Later refinements of Einstein-Planck physics
- Deterministic general theory of relativity
- Highly successful model of universe on large

scale - Speed of light, relativistic mass effects,

gravity bending of light - Probabilistic quantum theory of matter
- Bizarre experimental outcomes
- Double slit experiment
- Photons etc. interfere with each other even

when emitted singly - Dominant Copenhagen interpretationobserver

affects outcomebut many others - Essential some form of uncertain simultaneity

between quantum-entangled particles - Theorems/measurement derived from huge

experimental base

A Quick Physics Primer

- Experiments involve massive particle

accelerators - Electro-magnetic cylinders pushing particles in

near vacuum to near light speed - Into collision with other particles
- Massive sprays of fundamental particles

(leptons, muons, bosons, quarks) analysed by

sensitive detectors - Heavy-duty statistical apparatus developed to

cope with data (computer hardware software,

mathematical theorems) - Many other areas of analysis opened up with

computing (e.g., Josephson junction circuitry,

quantum tunnelling circuitry, quantum

computing) but no breakthroughs - Physicists also develop complexity theory as

explanation for large-scale phenomena (many

standard deviations events) regularly seen in

physical data (weather, earthquakes)

A Quick Physics Primer

- Todays unresolved boundaries
- Conflict between relativity quantum mechanics

on scale of very small very new - First microseconds of universe
- Behavior of matter at black holes, etc.
- Main theoretical development string theory
- Matter as multi-dimensional vibrating strings
- Standard models universe 10-11 dimensional
- Only 4 dimensions (space time) visible to us
- Tiny fraction of physicists now working on this

at highly abstract level (but still with

experimental-theoretical interplay - Experiments needed for string theory

controversies prohibitively expensive

Enter econophysics

- Some physicists (e.g., Cheng Zhang, Joe McCauley,

Tsallis) had innate curiousity about economics

social phenomena - Large numbers physics graduate students with

little possibility of experimental

apprenticeship - Huge body of pure financial data available for

experimental analysis - Clear (and, to physicists, strange but not

unfamiliar) signs of discord in

economic/financial theory - Planck on acceptance of his ideas in physics An

important scientific innovation rarely makes its

way by gradually winning over and converting its

opponents it rarely happens that Saul becomes

Paul. What does happen is that its opponents

gradually die out, and that the growing

generation is familiarised with the ideas from

the beginning. (M. Planck, in G. Holton (Ed.),

Thematic Origins of Scientific Thought, Harvard

University Press, Cambridge, MA, 1973 in

Scientific Autobiography, New York Philosophical

Library, New York, 1949.)

Enter econophysics

- A research paradigm develops
- Why not apply tools of theoretical physics to

large body of financial data see what we find? - Large number of regularities seen by physicists

with respect to advanced physics that, from

neoclassical economics point of view, were

anomalies - Distributions of financial data follow Power /

Zipf / Pareto Distributions - Standard characteristic of highly interacting

nonlinear nonequilibrium processes - Versus neoclassical belief data should follow

innately random distributions since markets

assumed to be rational, rational defined as

all knowing, system assumed stable - Huge baggage of a priori assumptions at conflict

with data

Enter econophysics

- Main areas of research
- Statistical patterns in finance
- Also income distribution, firm sizes, extinction

patterns - Initially chaos (Mandlebrot etc.) but

subsequently Power Laws, Zipf Laws, Pareto,

Exponential, Levy Gamma distributions now

Tsalliss q nonextensive statistical

mechanics - Parsimonious models of financial market

behaviour - El Farol model Minority Game
- Little work to date in alternative economic

foundations - May come with time, and will probably be

radically different to either neoclassical or

classical foundations

Statistical Patterns

- Perspective of econophysicists very different to

neoclassical economists (and other victims of

equilibrium thinking) - Statistical physicists, myself included, are

extremely interested in fluctuations. In the

field of economics, we find ourselves surrounded

by fluctuationswere it not for economic

fluctuations, economists would have no work to

do. Gene Stanley, (editor Physica A journal of

inter-disciplinary physics) 2000, Exotic

statistical physics Applications to biology,

medicine, and economics, Physica A 285 1-17. - Versus mechanisms that achieve equilibrium

focus of standard economic paradigm

Statistical Patterns

- What do we do when we carry out research on

economic fluctuations? Our approach has been to

use our experience in critical phenomena research

and assume that when we see fluctuations,

correlations may be present. (10) - A search for feedback effects between data rather

than assumption of independence - Main finding events that are rare by 8 orders

of magnitudeevents that occur once in every 100

million tradesfall on the same curve as everyday

events. (12) - Subsumes results with all distribution types
- Inspiration for main theoretical development

nonextensive statistical mechanics - Commenced with Mandlebrots work on fractals in

1960s

Mandelbrot, fractals, chaos

- Mandelbrot began research in economics into

income distribution - Vilfredo Pareto in late 1800s noticed Power Law

in income distribution - Number of people N earning more than x follows

formula - log N log A m log x (A, m constants)
- In 1961 by chance saw graph of cotton prices that

mirrored data on income distribution - Noticed scale invariance as a feature of

economic data argued fundamental feature of

financial data - BUT ignored in favour of The New Finance of

Sharpe CAPM! - Shifted into geography geometry now insights

re-emerging as foundation of new approach to

finance

Power (and other) Laws

- Power Laws, Zipf Laws, Pareto Laws all relate to

distributions in which elements in the system

affect each other very strongly and nonlinearly - Resulting patterns appear random but are not
- Compared to truly random data, have many more

extreme events - But random processes can be generated by strongly

chaotic processes! - Difference appears to lie in mixing random

processes achieve strong mixing of elements

chaotic processes lead to patterns of

self-similarity, not uniformity - Area still very speculative but clearly on track

to much more successful theory of finance than

CAPM

Non-extensive statistical mechanics

- The basics Entropy
- Boltzmann-Gibbs statistical mechanics
- Shannon information theory
- Advanced non-extensive statistical mechanics
- After the hairy stuff, a quick survey of major

trends in econophysics

Entropy

Warning!

Warning!

- Entropy is

Mind-bending material approaching!

- Best introduced by jokes
- The 3 laws of thermodynamics are
- 1. You can't win.
- 2. You can't even break even.
- 3. You can't get out of the game.
- A more informative version
- 1. You can't win, you can only break even.
- 2. You can only break even at absolute zero.
- 3. You can never reach absolute zero.

Entropy the 2nd law

- Starting at the beginning
- Key concept in science in general is conservation

law some key entity is conserved through a

series of transformations - In physics, its energy
- 1st law is Law of Conservation of Energy

Energy can change form but cannot be created or

destroyed - Hence You cant win
- Not a priori belief (like economics law of one

price or other empirically false propositions)

but expression of observed regularity - Form of energy can change but amount of energy in

a system remains constant - Define overall energy as U and two

transformations of it as Q (heat) W (work)

Entropy

- Then DUQW0
- Rule first developed in experiments with steam

engines where focus was on inputting heat

(burning coal) and getting out work (turning a

shaft), so expressed as - DUQ-W
- Just a convention reflecting heat in, work out
- Objective of engineers was to achieve maximum

conversion of heat (Q) into work (W), but found

waste heat always generated. - Puzzle became why?
- Solved by imagining ideal device that converted

all energy of system U into work W and then

extending analysis to non-ideal systems (compare

this to economics)

Entropy

- Basic model piston moving weight

- If weight removed from piston, then gas would

move piston to new location where pressure in gas

balanced weight of piston

Weightat height H

CylinderVolume V

- What if weight consisted of many fine grains of

sand one was removed at a time? - How far could piston itself raise the sand?
- How much work could the piston do on the sand?

PistonArea A

GasPressure P

Entropy

- Upwards force from compressed gas equals Pressure

P times area of piston A - At start of process, weight stationary gas at

pressure P - Forces must then be in balance
- Force of gas (P.A) just equals downwards force of

gravity on weight - If weight moves small dh distance then change in

volume dV equals A times dh - Work done is integral of force over distance it

operates

where

so that

Entropy

- In ideal cylinder (all energy converted into

work), work equals integral of pressure with

respect to volume - In 0 efficiency cylinder (all energy converted

into heat), heat equals integral of temperature

with respect to something well label S for

now. - In between, the rule applies that

- Change in energy equals heat plus work becomes
- Change in energy Temperature times change in

Entropy Pressure times change in Volume - (change in volume is workuseful expenditure of

energy) - So how efficient can a working engine be?

Entropy

- Basic cycle of internal combustion engine is
- Piston at top of cylinder pressure temperature

low - Call Volume V1, Pressure P1, Temperature T1
- Piston pushed by crankshaft pressure increased
- Temperature necessarily rises
- Volume V2, Pressure P2, Temperature T2
- Gas ignited
- Temperature rises dramatically
- Volume V3V2, Pressure P3P2, Temperature T3
- Piston pushed back to starting position
- Temperature falls, volume rises, pressure drops
- Volume V4V1, Pressure P4, Temperature T4
- Hot gases expelled
- Return to V1, P1, T1

Entropy

- Perfect efficiency engine now assumed no

friction losses etc., all processes involve only

changes in pressure or temperature, not both at

once - Change in energy of perfect gas given by heat

capacity times change in temperature e.g. heat

capacity 5 units - Example temperatures of
- T1300K (Kelvin or temperature above absolute

zero) - T2400
- T31600
- T4600
- Can now apply

- where at each stage either dS0 or dV0 (perfect

efficiency)

Entropy

- Stage 1 all compression, dS0. So

- (work in so negative work output)

- Stage 2 all temperature change, dV0. So

- Stage 3 all expansion, dS0. So

- Stage 4 all temperature change, dV0. So

- Work sum is -50050004500
- Energy input is 6000
- Ratio is efficiency of perfect engine

4500/600075

Entropy

- Actual engine has lower efficiency
- Conversion of some compression into temperature,

some rise in temperature into rise in volume - As well as the usual suspects friction, etc.
- Typically achieve only half ideal ratio.
- Whats the problem?
- Truly ideal engine design reveals part of cause
- Carnot (1824) imagined perfect heat-exchange

engine - Found engine had to discharge heat to perform

work - Efficiency function of discharge temperature

level - Only if discharge temperature was absolute zero

could engine be 100 efficient

Entropy Carnot engine

- During initial expansion phase, gas in cylinder

kept at constant temperature TH heat QH must be

added

- During work expansion phase, temperature drops

because volume expands W extracted

- During initial contraction phase, gas in cylinder

kept at constant temperature TC heat QC must be

extracted

- During final contraction phase, temperature rises

because volume contracts

Entropy Carnot engine

- Since engine repeats cycle, energy change over

whole cycle zero so Work extracted must equal

sum of heat input extraction

- Engine efficiency is ratio of work output to

energy input

- There is a simple relationship between Q and T

- So energy efficiency can only be 100 of TC0

Kelvin

- Also explains why high temperature engines are

more efficient

Entropy

- So something in nature means that no work

process can occur without generating waste heat. - That something is 2nd law of thermodynamics

entropy increases where entropy is the S in

- General statement For any process by which a

thermodynamic system is in interaction with the

environment, the total change of entropy of

system and environment can almost never be

negative. If only reversible processes occur, the

total change of entropy is zero if irreversible

processes occur as well, then it is positive. - S taken as measure of disorder of system since

related by Boltzmann Gibbs to the number of

distinguishable states W that a system can be in

by

Entropy

- Boltzmanns formula linked to structure of matter

by concept of microstates - Overall state (temperature, pressure, etc) of

given system reflects ensemble of states of

constituents (atoms, molecules, etc.) - State of constituents reflects
- How many ways constituents can be organised
- Number of constituents having each possible state
- E.g., consider placing colour squares on 4x4 grid

- Say 1st square is red can be placed in any of 16

locations

1

- Next e.g. blue placed in any of 15
- 16!20,920,000,000,000,000 possible combinations!

Entropy

- But say there is 1 red, 3 green, 5 blue, 7 orange

squares in ensemble. Then

- are different combinations but cant be

distinguished from each other

and

- Ditto for other arrangements of other colours

(numbers there just to show difference) - To compensate, have to divide 16! by product of

all possible ways of achieving identical

microstates - Divide by 1! x 3! x 5! x 7!3,628,800, leaving

5,765,760 distinct arrangements - General formula is

Entropy

- Can also be put as

- Where W is number of discrete microstates system

can be in and pi is probability of the ith such

state - Entropy as defined here applies to ergodic

systems - dynamics whose time averages coincide with

ensemble averages (Tsallis et al. 2003,

Nonextensive statistical mechanics and

economics, Physica A 324 89-100) - Colloquially, systems that converge to or orbit

long run equilibrium values that over time fill

the entire phase space

Entropy

- Consider our 4x4 grid
- Imagine these represent entities in a dynamic

system - E.g., gas molecules in a tiny container
- Odds of squares being in highly ordered initial

state (all similar colours next to each other)

very low

- Many more ways for squares to be in more

disordered arrangement than one where all

colours are mixed up - Over time, each square will spend 1/16th of its

time in each of 16 possible positions (time

averages coincide with ensemble averages)

- But far from all (physical or social) systems

have this characteristic

Entropy

- For example, Lorenzs model

- Complex dynamics means time average very

different to average over phase space because

system never goes near equilibria

Entropy

- Problem of failure of deep, established concept

like Boltzmann-Gibbs entropy to characterise many

real world systems troubled physicists,

statisticians - Ironically, CAPM analysis of derivatives

(Black-Scholes) related to this area - Many alternative characterisations proposed
- Power Laws
- Hurst exponents
- Best to date is revised version of

Boltzmann-Gibbs entropy suggested by Tsallis in

1985 - Sheer intuitionnot derived but guessed at.
- Interesting example of how scientific advance can

occur. In his words

Nonextensive Statistical Mechanics

- A MexicanFrenchBrazilian workshop entitled

First Workshop in Statistical Mechanics was

held in Mexico City, during 213 September 1985

That was the time of fashionable multifractals

and related matters. During one of the coffee

breaks, everybody went out from the lecture room,

excepting Brezin, a Mexican student , and

myself Brezin was explaining something to the

student. At a certain moment, he addressed some

point presumably related to multifractalsfrom my

seat I could not hear their conversation, but I

could see the equations Brezin was writing. He

was using pq, and it suddenly came to my

mindlike a flash and without further

intentionthat, with powers of probabilities, one

could generalize standard statistical mechanics,

by generalizing the BG entropy itself and then

following Gibbs path. Back to Rio de Janeiro, I

wrote on a single shot the expression for the

generalized entropy, namely

Tsallis 2004 727

Nonextensive Statistical Mechanics

- Why does it matter?
- q 1 returns standard distributions
- q gt 1 privileges common events
- Common (near mean events) occur more frequently

than for Gaussian/standard entropy distributions

and - rare events will lead to large fluctuations,

whereas more common events will result in more

moderate fluctuations. - A concrete consequence of this is that the BG

formalism yields exponential equilibrium

distributions (and time behavior of typical

relaxation functions), whereas nonextensive

statistics yields (asymptotic) power-law

distributions (Tsallis et al. 2003 91) - Tsalliss q may capture interactive instability

of finance markets. Tsallis distributions fit

finance data accurately with q1.4

Nonextensive Statistical Mechanics

- E.g., Stock market returns for top ten stocks on

NYSE

Dotted line is the Gaussian distribution 2-

and 3-min curves are moved vertically for display

purposes Far better fit to data than CAPM

models

- Many other areas where Tsalliss q enables

accurate fit to data whereas standard extensive

statistics models (Black-Scholes, CAPM, EMH etc.)

do not

Econophysics

- Tsalliss analysis may become foundation of all

other statistical analysis by econophysicists - In meantime, many other areas where skills

technologies of physicists are being applied.

E.g. - Sornettes analysis of asset bubbles and bursts
- Minority Game parsimonious model of finance

markets - Scarfettas analysis of income distribution
- Ponzis model of multi-sectoral dynamics
- Many others can be found at
- http//www.unifr.ch/econophysics/

Why Stock Markets Crash

- Sornette geophysicist
- Study of earths dynamics
- Developed theory of earthquakes as extension of

Per Baks theory of self-organised criticality - Classic model the sand pile
- Pour sand onto surface one grain at a time
- For a while, pyramidal shape forms
- Slope of pyramid gets steeper
- Slope then collapses in avalanche
- One grain of sand causes more than one to fall in

a chain reaction - Collapse of pyramid reduces slope below critical

level - Pyramid reforms process repeats

Why Stock Markets Crash

- Sornettes earthquake model similar with tectonic

plates as the grains of sand, motion of earths

core as pouring force - Movement of molten core causes plates to move on

surface - Increasing tension between plates causing

vibrations that increased over time - Release in large scale earthquake
- Decreasing tension between plates over time

process repeats - Pattern captured by log periodic function
- Applied to stock market where collective

interactions between agents leading to a cascade

of amplifications replace movement of plates

Why Stock Markets Crash

- Basic function for change of index is of the form

- Predicts increasing frequency of fluctuations as

critical time approaches - Problem is to identify critical time!

Why Stock Markets Crash

- On other side of crash, critical time known
- Curiosity now is does crash fit log-periodic

form? - Graph fits US SP500 to function

- Problems develop when extended further in time
- Tectonic plate dynamics dont change on human

time scale finance markets economies do - But clear relevance of log periodic form to

short-term market movements before/after crash

The Minority Game

- Minority Game development begun by economist

Brian Arthur - Model was El Farol Irish (yes, Irish!) pub in

Santa Fe - Popular after-hours venue but only pleasant when

neither empty nor full - Problem how to predict whether worth attending a

given night? - Arthurs model 100 Irish music fans in Santa Fe

bar only enjoyable when less than 40 attend a

night - Fans decide whether or not to attend based on

various strategies - A minority game you win by being in the

minority - Therefore no equilibrium any winning strategy

will break down as other agents adopt it

The Minority Game

- Extended by Yi-Cheng Zhang others to Minority

Game - Artificial stock market in which winning strategy

is to sell when majority is buying, buy when

majority selling - Realisation that MG isnt a complete model
- In financial trading, often it is convenient to

join the majority trend, not to fight against the

trend. During the Internet stock follies, it was

possible to reap considerable profits by going

along with the explosive boom, provided one got

off the trend in time. There are many other

situations where success is associated with

conforming with the majority. - But proposition that might still be on the

money because

The Minority Game

- majority situations may actually have minority

elements embedded in them. The real financial

trading probably requires a mixed

minority-majority strategy, in which timing is

essential. The minority situations seem to

prevail in the long run because speculators

cannot all be winners. Indeed no boom is without

end, being different from the crowd at the right

time is the key to success. In a booming trend,

it is the minority of those who get off first who

win, the others lose. (Damien Challet, Matteo

Marsili, Yi-cheng Zhang 2003, Minority Games,

Oxford University Press, Oxford, 12-13)

A two-part income distribution model

- Basic Econophysics model a Power Law
- Implies increasing concentration all the way up
- Actual empirics of US data suggested a tail (low

income) that didnt fit Power Law - An empirical distribution of wealth shows an

abrupt change between the lowmedium range, that

may be fitted by a non-monotonic function with an

exponential-like tail such as a gamma

distribution, and the high wealth range, that is

well fitted by a Pareto or inverse power-law

function. (Nicola Scafetta, Sergio Picozzi and

Bruce J West, 2004, An out-of-equilibrium model

of the distributions of wealth, Quantitative

Finance 4 353)

A two-part income distribution model

- Scarfetta et al suggest
- Top end (rich) due to investment
- Power Law wealth distribution generates matching

income one - Bottom end (poor) due to trade which is biased in

favour of poor - Hard to explain from neoclassical foundation
- Neoclassic economists do not expect trade to

involve a transfer of wealth, but rather an

increase in utility for both parties with a zero

net transfer of wealth.

A two-part income distribution model

- Scarfetta et al. propose
- in trades there may be a transfer of wealth from

one agent to the other because the price paid

fluctuates around an equilibrium price ( value)

and, therefore, the price may differ from the

value of the commodity transferred - (b) in a trade transaction the amount of wealth

that may move from one agent to the other is

bounded because the price and the value of a

commodity cannot (usually) exceed the wealth of

the poorer of the two traders - (c) the price is socially determined in such a

way that the trade is statistically biased in

favour of the poorer trader.

A two-part income distribution model

- In fact results validate my interpretation of

Marx on value - Marx spoke of the relationship between the wage

and the value of labour-power, he used the term

minimum wage, that is, a subsistence payment

1315, thus emphasizing that in practice he

expected the wage to exceed this minimum and

hence there to be a price-value divergence in

favour of the working class at the expense of

capitalists. These effects can be incorporated

into the social equality index f of equation (14)

that measures the statistical bias of the trade

in favour of the poor.

Multi-sectoral instability

- Physicists perception of economic cycles
- Economic dynamics is easily observed to be far

from equilibrium where periodic recessions,

unemployment and unstable prices occur

persistently. An understanding of the origins of

this behaviour from the viewpoint of complex

dynamical systems theory would be very valuable.

(Adam Ponzi, Ayumu Yasutomi, and Kunihiko Kaneko

2003, Multiple Timescale Dynamics in Economic

Production Networks, APS/123-QED - Starting point is standard von Neumann

equilibrium growth path

Multi-sectoral instability

- The VNM is defined as a static equilibrium model

describing relationships between the variables

which must hold at equilibrium. Equilibrium is a

state of balanced growth where prices are

constant. There are no dynamics defined by the

model which might describe out of equilibrium or

approach to equilibrium behaviour. (1) - However it is rarely the case that economic

processes are in equilibrium - Their approach actual output depends on the

quantity of the minimum of its input supplies and

not on the quantities of its other supplies. (1)

Multi-sectoral instability

- Basic picture of economy reflects physics

backgroundprocesses described as catalytic

play important role

- A production process is the operation which

converts one bundle of goods, including capital

equipment, into another bundle of goods,

including the capital equipment.

- Capital goods therefore function approximately

like catalysts in chemical reactions, reformed at

the end of the reaction in amounts conserved in

the reaction. (1)

Multi-sectoral instability

- Model implemented as computer simulation and

generates obvious cycles in output, prices, etc.

- Not necessarily correct model of market economy
- Constraint normally effective demand, not supply

(stocks provide buffer)

- But useful example of dynamic multisectoral

modelling

Conclusion?

- Econophysics still in infancy, but
- Unencumbered by equilibrium obsession
- Equipped with advanced mathematical computing

data analysis tools designed to cope with

uncertainty nonlinearity - For first time ever, a coherent group of rivals

to neoclassicism who totally outgun it in

technical terms - At time of great weakness of neoclassical

paradigm in finance - Best chance yet for break in neoclassical

hegemony - However, some problems

Worrying Trends in Econophysics

- Paper in Physica A by Mauro Gallegati, Steve

Keen, Thomas Lux, Paul Ormerod - First, a lack of awareness of work that has been

done within economics itself. - Second, resistance to more rigorous and robust

statistical methodology. - Third, the belief that universal empirical

regularities can be found in many areas of

economic activity. - Fourth, the theoretical models which are being

used to explain empirical phenomena. - My beef use of conservation laws where they

dont apply - Energy is conserved nothing in economics (like

utility, income, etc.) is conserved

Spurious conservation

- The criticism by some economists that the model

is not valid because money is conserved and other

levels of money such as credit are not accounted

for seems to these authors to be ill founded. It

is certainly possible to develop any model to

include, for example, debt. This could be simply

a matter of allowing the money held by an

individual to take negative values...20, p. 145 - No it cantremember Circuit model
- Money not conserved but endogenously created
- Debt not simply negative money

Conclusion

- But overall, empirical focus of econophysicists

vital - No ideological commitment to spurious analysis
- Ultimate desire to model the data, not turn

the model into the data - There may yet be an empirical economics
- BUT
- The math (and the computing) will be a bugger!