Political Economy: Evolutionary Economics
Power Laws & Evolutionary Modelling
Steve Keen
Recap
• Modern evolutionary economics Veblen/Schumpeter
hybrid
– Necessity of non-equilibrium, dynamic modelling
– Chaos/complexity
• Complex patterns from simple models
• Evolution to “edge of chaos”
– Simple tools for dynamic analysis
– Difficult tools for evolutionary modelling…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
2
Chaos, Complexity & Evolution
• Logistic difference equation (last week) typical chaotic
system
– Low value of parameter: Convergence to equilibrium
– Medium value: regular cycles (2,4,8 cycles, etc.)
– Higher value: chaos (aperiodic cycles)
• Chaos: measures of systemic instability > 1 (Lyapunov
exponent…)
• Evolutionary data: systemic instability measure  1…
– “the edge of chaos”
– Why?
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
3
Chaos, Complexity & Evolution
• Chaotic models remain chaotic because parameters
are fixed:
– Tiny change in parameter, huge change in system
– But same value, same outcome every time…
t  0  100 a  2.81 b  0.001
t  0  100 a  2.8
 2
Nt
a
b
Chaotic Population with a=2.81
Nt
a
2000
b
0
a
b
0
20
3000
2000
1000
a
 2810
40
60
t
©Steve Keen 2005
Chaotic Population with a=2.8
4000
3000
1000
80
 2
N0  100 Nt1  ( 1  a)  Nt  b  Nt
N0  100 Nt1  ( 1  a)  Nt  b  Nt
4000
b  0.001
100
0
b
0
20
 2800
40
60
80
100
t
Advanced Political Economy, Economics & Finance, University of Western Sydney
4
Chaos, Complexity & Evolution
• In evolutionary system, parameters change
– Change seems to evolve systems to “edge of chaos”
• Then system fluctuates either side of edge over
time
– Therefore, patterns of “edge of chaos” appear in
all evolutionary systems
• Self-similarity
• Scale invariance
• Power Laws
• Self-organised criticality
– Concepts from “self-organised criticality” may be
used to interpret evolutionary systems that are
currently too difficult to model…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
5
Power Laws: an empirical regularity
• Empirical research shows many phenomena follow
“power law” distribution:
– Number of size X events  X raised to some power
1

N X   X  
X
• Result of statistical relation: a “straight line”
between size of event and event frequency when
graphed on log-log plot:
• “Log of number of events of

log N  X   log  X 
size X = - times log(X)”
  log  X  • Rule applies to huge range
of phenomena…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
6
Power Laws: an empirical regularity
• “Log of number of earthquakes of size X is  times
log of X”
• “Log of number of species extinctions of size X is 
times log of X”
• “Log of number of meteor impacts of size X is 
times log of X”
• And, if economic systems are evolutionary,
– “Log of number of stock market movements of size
X is  times log of X”
– “Log of number of recessions of size X is  times
log of X”
– “Log of number of firms of size X in an industry is
 times log of X”…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
7
Power Laws: an empirical regularity
• Example: Earthquakes in S.E. USA:
1000
10
1
Richter scale
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
8
Applied to Economics?
• Power law
fit Dow
Jones:
N  X   X 3.96
Power law
predicts
6 10% daily
movements
per century
1 means 101=10
events per century
logN  X   3.96  log  X 
Power Law Plot Log Number Events versus Per Cent Daily Change
Log Number of Events
Power Law Linear Fit
4
Slope Coefficient = -3.96
2
1
• Does this tell 0
us anything the
EMH doesn’t?
©Steve Keen 2005
Actual
number
was 8
3
-1 means 10-1=10% daily change
-2.0
-1.8
-1.6
-1.4
Log Per Cent
-1.2
-1.0
Advanced Political Economy, Economics & Finance, University of Western Sydney
-0.8
9
Applied to Economics?
• You betcha!
• “Random walk”
Power Law versus Gaussian Prediction Per Cent Daily Change
prediction OK
for small
Log Number of Events
Pow er Law Linear Fit
Gaussian Prediction
movements
2
-2 means 10-2: one
• +/-3% 780
such event predicted
reality v 718
every century
11 last
random prob. -2
century
-6: 1 event predicted
10
• Hopeless for
Actual
every 1 million centuries
large
number
-1.1
-6
10
:
57
• +/-6%: 57 v 1
8% change
• +/- 8%: 11 v -1.2 means 10-1.2=6% daily change
1 in a million -10
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
-0.8
Log Per Cent
chance…
4
-0
-4
-8
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
10
Applied to Economics?
Magnitude +/Events
Gaussian
Ratio Actual/Random
• Belief system is
1%
17813
18648
0.96
2%
3818
3447
1.11
– in equilibrium
3%
780
719
1.09
4%
257
67
3.83
– changes due
5%
106
2.79
38
to random
6%
57
0.0511
1,114
7%
22
0.000411
53,464
shocks
8%
11
0.00000144
7,613,560
9%
3 0.0000000022
1,363,030,944
• Results in
10%
8 0.00000000000
Gaussian prediction
prediction that
11%
1 0.00000000000
is zero to 20+
12%
2
0.00000000000
huge events
decimal places
13%
2 0.00000000000
vanishingly rare
• Applies to all economics; e.g. firms:
• Actual data
– Rather than “monopoly” & “perfect
manifestly
competition”, real industries have
different:
power law distribution of firms…
some very big, many small…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
11
Applied to Economics?
• Key insights w.r.t. neoclassical economics
– Scale-invariance: no “typical size” of anything
• Earthquakes of all scales occur
– Many more small ones than large ones
– But large ones aren’t “vanishingly rare”
• Bubbles in bubble bath (try it!)
• Daily movements in stock exchange
– Any size crash feasible
– Likelihood far higher than predicted by
random/equilibrium model
– “Crashes” not aberrations but normal behaviour
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
12
Irrelevance of Equilibrium…
• ‘General equilibrium theory, assumes that perfect
markets, perfect rationality, and so on bring economic
systems into stable Nash equilibria in which no agent
can improve his situation by any action. In the
equilibrium state, small perturbations or shocks will
cause only small disturbances, modifying the
equilibrium state only slightly. The system’s response
is proportional to the size of the impact… Small freak
events can never have dramatic consequences. Large
fluctuations in equilibrium systems can occur only if
many random events accidentally pull in the same
direction, which is prohibitively unlikely. Therefore,
equilibrium theory does not explain much of what is
actually going on, such as why stock prices fluctuate
the way they do…’ (Per Bak 1996: 18)
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
13
Normality of cycles/catastrophic change…
• ‘We must accept instability and catastrophes as
inevitable in biology, history, and economics. Because
the outcome is contingent upon specific minor events
in the past, we must also abandon any idea of
detailed long-term determinism or predictability…
Large catastrophic events occur as a consequence of
the same dynamics that produces small ordinary
everyday events… [unlike] the usual way of thinking
about large events, which … looks for specific reasons
… to explain large cataclysmic events. Even though
there are many more small events than large ones,
most of the changes of the system are associated
with the large, catastrophic events. Self-organized
criticality can be viewed as the theoretical
justification for catastrophism.’ (Per Bak 1996: 32)
– “Self-organized criticality”?
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
14
Self-organized criticality
• One of several attempts to explain power law
phenomena
– Evolutionary change leads to system reaching point
at which small changes can have dramatic
consequences
• “Straw that broke the camel’s back”
– May be possible to model evolutionary systems by
• Using dynamic modelling techniques
– Where dynamic model displays same
statistical characteristics
• Unstable equilibria
• Instability “critical” rather than
overwhelming
• On border between order and chaos
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
15
Modelling evolutionary systems
• Dynamic models may simulate evolved systems
• Relatively easy to do
– Characterise macro, tops-down behaviour
– Develop dynamic model to suit
• Can use dynamic insights of great economists
– Reproduce qualitative behaviour of data
• Model using well-developed, easy to use technology…
Initial productivity
Productivity
*
0.03
1
1/S
+
+
1
d
    w      1      w        1  
a
dt


Productivity
d
 k        
   

dt
 v

Plot
3.0
2.5
d
 1
d  d   r     1      w        1 
dt
 a
2.0
 k       k   Pk  

 
Pc
 v
 
1.5
1.0
0
5
10
15
Time (years)
©Steve Keen 2005
20
25
Pc
d
Pc 
  1      w        1
a
dt
Advanced Political Economy, Economics & Finance, University of Western Sydney
16
Modelling evolutionary systems
• To model actual evolution
– Must be “bottoms up” model
• Define behaviour of each organism
• Define relationships between them
• Define adaptive behaviour
– Simple: adaptive parameter change
– Advanced:
• Reproduction/Birth/Death
• Crossover/imitation
• Mutation/innovation
• Spontaneous development of new species…
– Model as computer program…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
17
Programming and Evolution…
• Essence of Standard Programming
– Take a problem
• E.g., doing inventory of warehouse
– Describe process in minute detail
• Enter warehouse
• Start in 1st aisle
– Record product code of first product
– While code remains same
• Add one to count of products
– Move to next product & repeat
• Move to next aisle and repeat
• Leave warehouse
• End program
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
18
Programming and Evolution…
• Essence of procedure is
– Develop successful algorithm
• Set of minute procedures that achieves desired
outcome
– Code algorithm into computer language
• For success, programmer must exactly specify
algorithm that achieves overall objective
• Example: algorithm to allocate students to tutorials…
– Allocate students on basis of preferences
– If get to point where one student can’t get any of
her preferences
– See if she can be swapped with someone who’s
already been allocated
– Sounds easy?
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
19
Procedural Programming
• While not at end of list of students
– While not at end of student’s preferences
• Select all tutorials preference in student’s list
• While not at end of list of tutorials
– Check capacity of tutorial
– If tutorial has room, place student in tutorial
– Else check next tutorial
• End while (tutorials)
– End while (student’s preferences)
– If student not allocated
• Select all students in all tutorials in student’s list
• While not at end of list of students
– Check preferences of student
– If has another preference that can be fulfilled, swap…
– … and on it goes
• Sounds a bit less easy? Here’s the computer code:
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
20
Procedural Programming
LOOP
| TUT.CHOICE = ""
| READNEXT ID USING CURSOR ELSE EOF = TRUE$
UNTIL EOF = TRUE$ DO
| READ STUDENT.REC FROM STUDENT.FILE,ID THEN
| | NUMPREFS = COUNT(STUDENT.REC<TIME.PREF$>,VM$) + LEN(STUDENT.REC<TIME.PREF$>[1,1])
| | * WHILE statement stops a student being entered into additional tutorials once he/she has already been allocated to one.
| | FOR CURRENTPREF = 1 TO NUMPREFS WHILE TUT.CHOICE = ""
| | | READ TIMES.REC FROM TIMES.FILE,STUDENT.REC<TIME.PREF$,CURRENTPREF> THEN
| | | | NUMTUTS = COUNT(TIMES.REC<TUT.TIMES.TUT.NO$>,VM$) + LEN(TIMES.REC<TUT.TIMES.TUT.NO$>[1,1])
| | | | * While condition repeated to stop students being allocated to several tutorials at the same time.
| | | | FOR CURRENTTUT = 1 TO NUMTUTS
| | | | | READ TUTORIAL.REC FROM TUTORIAL.FILE,TIMES.REC<TUT.TIMES.TUT.NO$,CURRENTTUT> THEN
| | | | | | SIZE = COUNT(TUTORIAL.REC<TUT.SN$>,VM$) + LEN(TUTORIAL.REC<TUT.SN$>[1,1])
| | | | | | IF SIZE < MAX.SIZE THEN
| | | | | | | IF SIZE < TUT.CHOICE<TUT.SIZE$> OR TUT.CHOICE = '' THEN
| | | | | | | | TUT.CHOICE<TUT.NO$>
= TIMES.REC<TUT.TIMES.TUT.NO$,CURRENTTUT>
| | | | | | | | TUT.CHOICE<TUT.SIZE$> = SIZE
| | | | | | | END
| | | | | | END
| | | | | END ELSE
| | | | | | MSG('ST102','UB','',TIMES.REC<TUT.TIMES.TUT.NO$,CURRENTTUT>)
| | | | | END
| | | | NEXT CURRENTTUT
| | | END ELSE
| | | | MSG('ST101','UB','',STUDENT.REC<TIME.PREF$,CURRENTPREF>)
| | | END
| | NEXT CURRENTPREF
| END ELSE
| | MSG('ST100','UB','',ID)
| END
| IF TUT.CHOICE THEN
| | GOSUB UPDATE
| END ELSE
| | GOSUB REALLOCATION
| END
| STU.COUNT += 1
| GOSUB STATUS.LINE
REPEAT
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
21
Programming and Evolution…
• Compared to evolution?
– Input energy & matter…
– Life appears (don’t know how—& may never)
– Lifeforms adapt (crossover/mutation/selection…)
– life/matter/energy feedbacks change environment…
– No end goal, no tops-down design…
• At basic level, programming & evolution incompatible
• But recent (1990+) developments in programming
emulate of evolution/adaptation/learning
– Neural networks (brain analogy: adapt by learning)
– Genetic programming (evolution analogy)…
– Multi-agent simulations (species interaction analogy)
• Used to model “simple” evolutionary problems…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
22
Neural networks
• Brain regarded as most complex product of evolution
• Neural networks “mimic” brain by mimicking structure
– Human brain complex network of neural structures
• About 100,000,000,000 neurones
• Each neurone has about 1000 connections with
other neurones
• Inputs from 1000 input neurones determine
whether and how much a neurone will “fire”
– Some connections inhibit, others enhance
firing
• Learning seems to involve changing significance
attached to connections between neurones
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
23
Neural networks
• Structure mimicked by neural network (NN) software
– 3 or more interconnected layers of “neurones”
• Input level simulates sensory processing
• “Hidden layer(s)” simulates brain reasoning
• Output layer simulates brain response
– Initial random relationships between neurones
– NN trained on test data
• Initial answers wrong
• Difference between actual & desired answers
used to alter weights
• NN gradually converges to correct answer…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
24
Neural networks
• Training: input neurones receive values from data set
• Fruit recognition
.1
Wrong: weights
a
.1
program
adjusted
.1
1  e .9
– Colour
(a)
.3
.1
• Red: .1 …
…
Yellow .9
• Fruit given
.7?
– Shape
arbitrary
• Sphere .1 … numerical
Cylinder .9
value
– Texture
• Apple .1
• Smooth .1 • Pineapple .9
… Spiky .9
• Process repeated till NN
•…
returns correct answers…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
25
Neural networks
• Sample Neural 
Network (written
in Mathcad)
• See Advanced
Finance lectures
for explanation
(if interested…)

bpn t il h l  o l t d  lr  i 


o w  mrand  h l o l .2
l  rows  td   1
h w  mrand il h l  .2
Random hidden weights
Random output weights
Length of data vector
j0
for counter  0  i
 

 

col  ceil rnd cols tdata  1  .5
 col
data  td
out d  data
l
in h  h w in

out  sigmoid v o w out h
Outputs of hidden layer (one per node) are sigmoid
function of input. These become input to output layer.

Error is diff between desired and actual
out e  out d  out
out   out e [ out ( 1  out) ]

h   outh  1  out h   o w  out  
T


h w  h w  lr outer h   in 
o w  o w  lr outer out   out h
counter
 out e
counter  counter  1
X  ow
0
X  hw
1
X j
2
X
Desired output is last entry
Input to hidden layer is hidden
weights times data input
 
out h  sigmoid v in h
j
Training data on this iteration is
this column
Input data is all but last entry
in  submatrix( data  0  l  1  0  0)

For iterations
Randomly choose a column
Change amount using slope of sigmoid for output and
hidden layer, with output error as key argument

Change amount used to modify weight functions at
output and hidden layers
Increment counter and continue loop
Keep track of the error term at each step
The multidimensional array X stores the results: the
output weights, hidden layer weights, and the
output error. If this last value is small, the network
has been successfully trained. If it is large, then the
network has not converged to a successful set of
weights and will need to be redesigned.
iterations  2000
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
26
Neural networks
• “Evolutionary” aspects: origin of metaphor (brain) via
evolution; adaptive nature of program learning
• Most developed software approach
– Commercial NN programs available
– Basic structure of NNs easily programmed
– Used extensively in game software, optical
character recognition, medical diagnosis…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
27
Genetic Programming
• More truly evolutionary approach is “Genetic
Programming”
– Simulates (neo-Darwinian) evolution
• Environment (desired outcomes/data to explore)
• Variation of population of randomly generated
programs
• Survivors from one generation selected via
fitness function related to desired outcome
• Reproduction via crossover/Mutation
• New generation produced and evaluated against
criteria; process repeated…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
28
Genetic Programming
• Standard programming
– Carefully detail problem to solve
– Write exact algorithm to solve problem
• Genetic programming (GP)
– Carefully detail problem to solve
– Evolve population of random programs towards
solution
• Huh?…
– Programs can be considered as organisms
– Sub-units of programs can be
• Swapped (cross-over/sexual reproduction)
• Altered (mutation)
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
29
Genetic Programming
• Example program fragment:
– (* 1 2 (IF (> Time 10) 3 4))
Before 10
After 10
True
False
(IF T 3 4)
(IF F 3 4)
3
4
(* 1 2 3)
(* 1 2 4)
6
8
©Steve Keen 2005
• Computer
executes
program
from inside
parenthesis
outwards
• Program
could be
altering pay
rates based
on hour of
day…
Advanced Political Economy, Economics & Finance, University of Western Sydney
30
Genetic Programming
• Program can be represented as
tree (read bottom up)
– (+ 1 2 (IF (> Time 10) 3 4))
• Fragment could be one of
many such fragments
• Environment specifies
desired outcome, e.g.:
• Payrates after 6pm 1.5
times payrates before
6pm
6 or 8
*
1
IF
3
>
Time
©Steve Keen 2005
2
4
10
Advanced Political Economy, Economics & Finance, University of Western Sydney
31
Genetic Programming
• Two such fragments could be:
11 or 11.5
6 or 8
+
*
10
IF
1
>
Time
10
1
1.5
• Neither very
“fit” for purpose Time
2
IF
3
>
4
10
• But “crossover” of highlighted segments could
produce “descendants” that were more fit…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
32
Genetic Programming
2.5 or 3.5
20 or 40
30 or 40
+
*
1
2
1
>
Time
IF
10
10
IF
3
2
>
1.5
Time
4
10
• LHS program less fit; will “die”
• RHS fit enough to survive; will reproduce/mutate…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
33
Genetic Programming
• Process continues for many generations until average
level of “fitness”
– Conformity to condition:
• “Payrates after 6pm 1.5 times payrates before
6pm”
• Reaches acceptable levels
• “Best of generation” programs then implemented
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
34
Multi-agent modelling
• “In the beginning” programs were “procedure oriented”
– Define the operations to be done
• “Pay payroll”
– Apply them to data
• “Full-time employees”, “Part-time”,
“Contractors”, “Consultants”
• Modern programming is “object oriented”
– Define the objects/entities
• Person-employee-full-time, Person-employeepart-time, Person-contractor,…
– Define the operations relevant to each one
• Technical computing reasons for shift
– Side-effect: modelling “artificial worlds”
– Multi-agent modelling (MAM)
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
35
Multi-agent modelling
• Define agents, behaviour
– E.g., Insects
• Move, Forage, Eat
• Define environment
– Landscape
– Food items
– Poison items
• Evolve..
– Insects that eat poison die
– “Avoid poison” behaviour evolves…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
36
NN, GP, MAM, and Economics
• Several possible ways
– “Data mining”
• NNs and GPs used extensively (and often
secretly) to find patterns in economic data
• Pattern then exploited by trained NN/GP
– See, e.g., Colin (2000): GP used to profit
from foreign exchange volatility
– Pricing strategy
• Analyse past pricing behaviour
• Derive pricing strategy that “beats” past
competitors
– See, e.g., Marks (2000) & references
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
37
NN, GP, MAM, and Economics
– GP alone
• Standard GP works by having a target to evolve
towards: “environment” is conformity to predetermined objective
• In economics? No such thing…
– MAMs with GPs & NNs
• Build “artificial economy”
– Workers, capitalists, firms, factories
• NN or GP can be used to simulate decision
processes of agents
• Agents in evolutionary model of economy
can’t be assumed to be optimisers
• Run system to see behaviour
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
38
NN, GP, MAM, and Economics
• Problems:
– Extremely difficult to build such models
– Technical
• Need knowledge of dynamics, computer
programming, etc.
– Systemic
• Properties of system known at “emergent” level
– Aggregate behaviour of stock market: Bear &
Bull markets
– Aggregate behaviour of macro-economy:
booms and busts
• Difficult to know what (unknown) agent level
behaviour will generate this (known) macro
behaviour
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
39
NN, GP, MAM, and Economics
• As a result, existing evolutionary models
– Capture only very limited range of phenomena of
real economy
– Often fail due to agent initial parameter values
that turn out to be inappropriate
– Very difficult to design
• Models written in programming languages like
Swarm (specialist MAM extension to C++), Lisp
(object-oriented list processing language [source
of previous examples])
• Programs as difficult to construct as
conventional programs
• So that ‘Twenty years after the publication of
these pioneering contributions [Nelson &
Winter]…’
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
40
Whither Evolutionary Economics?…
• ‘it is fair to say that the great expectations of some
observers have only become substantiated slowly and
partially. There are, of course, many reasons for this,
but two related reasons seem to have special
importance. First, it is obvious that there are
significant barriers to entry for students and
researchers who want to explore and extend
evolutionary models by simulation; you simply have to
master a great many skills to be able to combine
evolutionary theorising and simulation in a fruitful way.
Second, there is a lack of cumulativeness of the
efforts of developing evolutionary analysis with the help
of computer simulation; instead many entrants to the
field seem to build their efforts from scratch.’
(Anderson & Valente: 44)
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
41
Whither Evolutionary Economics?…
• Feasible future directions
– Persist with MAM modelling?
• Improve tools (see e.g. Andersen & Valente
2002)
• Develop Simulink/Vissim-like Graphical User
Interface?
– Take GP approach?
• Design agents with GP/NN behaviours
• Make fitness function past economic data (e.g.,
US 19th century trade cycle, inflation, interest
rates)
• Evolve agents till systemic output resembles
“fitness function”
– (No-one has tried this yet)
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
42
Whither Evolutionary Economics?…
• Use insights and tailor dynamic models?
– The KISS approach
• Einstein’s “Keep it as simple as possible, but no
simpler”
– Statics too “simple”
• Will lead to erroneous answers to the
wrong questions
– Dynamics with “self-organised criticality”,
“highly optimised tolerance” etc. might be
both simple and not too simple
• Key insight of EE: Economic theory should explain why
the economy keeps changing, not model a system in
which change must end.
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
43
Conclusion
• The economy is dynamic, and our modelling of it must
be (cannot be static)
• The economy is evolutionary, and our modelling of it…
– Has to be aware of evolutionary process
– Has to capture nature of evolutionary data
• Power laws, etc.
– May not be able to be truly evolutionary until
techniques (GPs, MAM, etc.) develop in 21st century…
• Key insights of founders
– Dynamic instability (Veblen)
– Creative destruction (Schumpeter)
– Remain true as “vision” of economic process
– Can be melded with “vision” of Marx, Post
Keynesians, etc.…
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
44
References
• Andersen, E.S., & Valente, M., (2002). “Model Exploration and
Extension in the Laboratory for Simulation Development”,
Artificial Economic Evolution.
• Bak, P. (1996). How Nature Works, Copernicus, New York
• Barnett, W., Chiarella, C., Keen, S., Marks R., & Schnabl, H.,
(eds.), Commerce, Complexity & Evolution, Cambridge University
Press, New York.
• Colin, A., (2000). “A genetic programming-based approach to
the generation of foreign exchange trading models”, in Barnett
et al. (2000).
• Koza, J.R., (1992). Genetic Programming, MIT Press, Cambridge
MA.
• Marks, R., (2000). “Evolved Perception and the Validation of
Simulation Models”, in Barnett et al. (2000).
©Steve Keen 2005
Advanced Political Economy, Economics & Finance, University of Western Sydney
45