Complexity
Pier Paolo Saviotti
•ECIS (Eindhoven Technological University),
•INRA GAEL, GREDEG CNRS, Department of Economics, Hohenheim University
Prepared for the meeting: Advances in Economic Dynamics and Development: Economics and Complexity –
to be held in Curitiba on 11-13 November 2013
Complexity Development_Curitiba_11-13
November 2013
Vs evolutionary theories
Evolutionary
theories
related to
• many different research traditions/disciplines including
complexity
• probably undergoing increasing fragmentation (Robert,
Yoguel)
Here I will
opt for a
path which
• (a) will draw from several but not all research traditions
mentioned in RY
• (b) is related to my (and co-authors’) work on long run
economic development
• (c) will necessarily neglect some of the possible
contributing research traditions
Complexity Development_Curitiba_11-13
November 2013
Complexity economics vs equilibrium
economics (Arthur, 2013)
Complexity
Economics (a)
different from
Equilibrium
Economics (b)
• (b) cannot understand certain types of
phenomena that (a) can understand
• Are (a) and (b) complementary? Or can (a)
provide a different interpretation of the same
phenomena that (b) understood in particular
way?
Answer
(B.A.):Complexity
Economics (a)
more general
than Equilibrium
Economics (b)
• The economy is not necessarily in equilibrium
• Economic agents (firms, consumers, investors)
constantly change their actions and strategies in
response to the outcome they mutually create
• This further changes the outcome, which requires
them to adjust afresh.
Complexity Development_Curitiba_11-13
November 2013
Complexity Economics vs Equilibrium
Economics (2)
Equilibrium
economics
•
•
•
•
order
determinacy
deduction
stasis
Complexity
economics
•
•
•
•
contingency
indeterminacy
sense-making
openness to
change
Complexity Development_Curitiba_11-13
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Patterns
• …interacting elements
→ overall patterns &
overall patterns cause
the interacting
elements to change or
adapt (feedback loops,
positive or negative)
Interacting
elements
Complexity Development_Curitiba_11-13
November 2013
Patterns
Main concepts
•
•
•
•
•
•
•
•
•
Dynamics
Equilibrium
Interactions
Uncertainty
Emergence
Qualitative change
Discontinuities
Transformation
Creativity
Complexity Development_Curitiba_11-13
November 2013
General equilibrium
Equilibrium vs innovation
Innovation
Systems
destroying
continuously
(general)
in movement
equilibrium
(Arthur)
(Schumpeter)
Restless
capitalism
(Metcalfe)
Endogenous
General
innovations
equilibrium as
destroy
an attractor
general
(Nelson,
equilibrium
1995)
(Kaldor, 1972)
Complexity Development_Curitiba_11-13
November 2013
General equilibrium as an attractor
General
equilibrium
An innovation
The new
equilibrium
position
• only as an attractor
• present but not necessarily visited by the
economic system
• creates a new (potential) equilibrium position
• at a distance from the present state of the
system
• can drive the system towards it
• but at a finite speed
Complexity Development_Curitiba_11-13
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Two processes
• An equilibrium can be attained
only if
• Rs ˃ RI
• General equilibrium good
approximation only if as an
innovation creates a new
(potential) equilibrium
position the system moves
instantaneously towards it,
when:
• RS ˃˃ RI
• Most of the times system not
in equilibrium
Innovation (attractor)
Movement of the equilibrium
position (Ri)
movement of the system
towards the equilibrium position
(Rs)
Further innovations (growth)
Further movement of the
equilibrium position (Ri)
Further movement of the
system towards the equilibrium
position (Rs)
Maturity, Saturation
Slowing down of innovation
(d(dInn))/dt ˂ 0)
Complexity Development_Curitiba_11-13
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Is this state really stable?
System, equilibrium, innovation
S1 = state of
system at t
Movement of
System occurring at
RS
Movement of
Innovation occurring
at RI
When the innovatin emerges it creates an attractor which drives
the system towards itself at a rate Rs .
If Rs ˃˃ RI the system will eventually, at a finite rate, reach the
attractor and rest there (equilibrium)
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Examples
• Railways
•
• Cars + petroleum industry
• ITC + Internet etc
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Life cycles
Birth,
novelty
Growth
Maturity
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Saturation
Life cycles
Emergence of a radical • System driven at finite rate towards the new
attractor
innovation introduced
• Most of the times system out of equilibrium
by a Schumpeterian
entrepreneur → new • Imitation bandwagon, complementary industries,
infrastructures, institutions = co-evolution
sector (attractor)
Birth, growth,
maturity, saturation
• At saturation attractor attained? Equilibrium?
• But, what if saturation induces new innovation(s)
which can give rise to new sectors?
• One or more new attractors likely to be
established and so on
Complexity Development_Curitiba_11-13
November 2013
Order, change and chaos
Economic systems
are generally not
completely random
or chaotic
They have order
and structure
We need to explain
both the stability
Type of order and of some structures
structure changes
for considerably
in time (structural
long periods of
change,
time and their
transformations,
changes,
transitions)
sometimes
discontinuous and
disruptive
Complexity Development_Curitiba_11-13
November 2013
General equilibrium vs change
General equilibrium
• freezes the system and creates no incentives for any agents to
change
Change in these conditions
• can only occur if there are exogenous shocks and even then:
Only if
• the rate of movement of the system towards the equilibrium
position (Rb) is (much) greater than the rate of movement of the
equilibrium position (Ra)
Complexity Development_Curitiba_11-13
November 2013
Order & steady states
Disruption of
existing order
• Emergence of innovation
• some incumbent activities and institutions disappear
Growth,
maturity
• appropriate institutions created, subsequent evolution
occurs smoothly, with ‘good’ values
• Giving rise to steady states,
• characterized by constant set of institutions,
organizations and interactions
Saturation
• Increasing ‘rigidity’ of the economic system
• Inducement to innovate out of the system
• Innovation, fluctuations arise and invade the system
Complexity Development_Curitiba_11-13
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Structural change
Structure
• components and interactions
• system as network
Structural
change
• emergence of new components/nodes
or disappearance of older ones
• new interactions (links)
• new forms or levels of aggregation
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"weak emergence" and "strong
emergence".
Weak
emergence
• A type of emergence in which the emergent
property is reducible to its individual
constituents
• Example: phase transition
Strong
emergence
• The emergent property is irreducible to its
individual constituents
• Strong emergence → Qualitative change →
Creativity → (Radical) uncertainty (Knight)
Complexity Development_Curitiba_11-13
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Levels of aggregation
Micro (minimum) to
Macro (maximum)
Complex systems can
have many meso
levels of aggregation
Social systems with
the same micro and
macro states
• +, many possible intermediate (meso) ones
• But, only some intermediate levels of aggregation are meaningful
• 15 people are an aggregate but they are meaningful only if they
constitute some, organizational form (a firm, a cooperative etc)
• Macromolecules, cells, organs in a biological organism
• Firms, sectors, schools, hospitals, cities, regions, countries
governments etc
• can behave differently if
• they have different meso states
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Biological, social
Different ontology or different micro levels?
• Human beings are biological organisms but
• Do their biological properties have an impact on their
social or economic behaviour?
Biologically rooted necessities but
• Transition to imaginary worlds
Complexity Development_Curitiba_11-13
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Biological, social (2)
Biological analogies in evolutionary economics
Generalised Darwinism
• Variation, selection inheritance, replicators, interactors,
fitness etc (Hogdson, Knudsen)
both Biological organisms and social systems
• are complex systems, to be explained based on complexity
Complexity Development_Curitiba_11-13
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Differentiation
• Necessary condition for the long run
continuation of economic development
• Growing variety
• Complementary with growing efficiency
• Distribution of differentiation amongst
different countries
• See Imbs, Wacziarg, 2003; Hidalgo et al, 2007,
2010; Frenken et al 2007; Saviotti, Pyka, 2004,
2008, 2013; Saviotti, Frenken, 2008)
Complexity Development_Curitiba_11-13
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Differentiation
Differentiation
as
• Necessary condition for the long run continuation of
economic development
• Growing variety, complementary with growing efficiency
• Distribution of differentiation amongst different countries
Variety and
Complexity
• Variety (i): N° distinguishable components
• Complexity: N° distinguishable components +
Interactions/linkages (constraint, degrees of freedom)
Complexity
• Max N° linkages → rigid system (uninteresting)
• Min N° linkages (= 0) → trivial system, no interactions
• Intermediate degrees of freedom : interesting complex
systems with evolutionary potential
Complexity Development_Curitiba_11-13
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Variety vs complexity
When variety
increases
• more components but are there more
interactions?
• In a dynamical system the rate of creation of
new components/nodes is not necessarily
equal to the rate of creation of new links
it does not
necessarily
rise or fall all
the time
• See network density, connectivity
Complexity Development_Curitiba_11-13
November 2013
Variety vs complexity (2)
Countries
need to
differentiate
to grow
• The probability that a country starts to export a
new (to the country) product is proportional to
• the proximity (similarity) of the new to the
already exported products, or
• related variety more important in the short run
DCs vs LDCs
• Developed countries export more differentiated
and more complex products
• Less developed countries export less
differentiated and less complex products
Complexity Development_Curitiba_11-13
November 2013
Network density
35
16
30
14
12
Centralization Index
Centralization index
25
R2 = 0, 9198
20
15
10
R2 = 0, 8969
10
8
6
4
5
2
0
0
1988
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
Years
Years
16
14
Centralization Index
12
R2 = 0, 8969
10
8
6
4
2
0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Years
Evolution of network density for (a) 1st biotechnology generation
(top left); 2nd biotechnology generation (top right); the two
biotechnology generations combined (bottom)
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1999
.055
.05
.045
density
.06
.065
Knowledge networks density in
biotechnology
1980
1985
1990
1995
2000
Year
Dynamics of Network Density for Biotechnology
(Krafft, Quatraro, Saviotti)
Complexity Development_Curitiba_11-13
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Creation of increasing variety
•
•
•
•
Open and closed systems (Prigogine etc)
Equilibrium vs non-equilibrium
Dissipative systems
Auto-catalysis and the emergence of new
structures
• Co-evolution
Complexity Development_Curitiba_11-13
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Open vs closed systems
Closed
systems
Open systems
• No flow of energy, matter and
information across system
boundaries
• Equilibrium
• Maximum disorder
• Flow of energy, matter and
information across system
boundaries
• Steady states
• Emergence of structure
• changes of structure
• But near equilibrium linrear region
Complexity Development_Curitiba_11-13
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Distance from equilibrium
Distance from
equilibrium
• Measured by the rate of flow through the boundaries
of the system
• Equilibrium corresponds to zero rates of flow through
the boundaries of the system
• Near equilibrium behaviour similar to equilibrium
Emergence of
new structures
• beyond a given threshold distance the behaviour can change
radically and
• give rise to qualitative change, emergence of new structures
• Equilibrium structures (crystals, structure and order ) vs
Dissipative structures (waste, dissipation)
Carnot vs
Darwin
• ..life is incompatible with Boltzmann’s order principle
but not with the kind of behaviour that can occur in
far-from equilibrium conditions (Prigogine, Stengers)
Complexity Development_Curitiba_11-13
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Fluctuations
In an equilibrium system
• they tend to die out
In a dissipative system
• they can increase in amplitude and invade the whole system
leading to a transformation
Innovation
• as an endogenous fluctuation leading to the
transformation/emergence of new subsystems
Complexity Development_Curitiba_11-13
November 2013
Open systems, structure and
Differentiation
• Bifurcations, transitions
lead to the emergence of
new structures
subsystems
• They occur only out of
equilibrium
• The number of possible
states of the system can
increase
• Conditions leading to
bifurcations, transitions?
Complexity Development_Curitiba_11-13
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Co-evolution
Definition
Effect
Coevolution of
• Interactions and feedback between the components of the
system
• Can accelerate (positive feedback) or slow down (negative
feedback) the changes of the system
• Can facilitates the emergence of new subsystems
• Vs cumulative causation (Robert, Yoguel)
• Technologies and institutions
• Technologies and infrastructures
• Complementary industries, activities
Complexity Development_Curitiba_11-13
November 2013
Auto catalysis and Co-evolution
• Auto catalysis: occurs in
a process in which one
of the outputs is also
one of the inputs
A B  AC  D
• Co-evolution acts in a
similar way by
exploiting the positive
feedback between
different components of
the system
• Can accelerate
transitions towards new
states/Phases/compone
nts
Complexity Development_Curitiba_11-13
November 2013
Examples of co-evolution
Cars
Rules,
police
tc
Roads
Petroche
micals
Service
stations
Fuel, oil
refining
Complexity Development_Curitiba_11-13
November 2013
Transition LQ→ HQ
• (LQ  HQ)
transition can
occur through a
virtuous circle,
but not under all
circumstances.
See for example
effect of kqual
• Wages as costs vs
purchasing power
Falling rate of
population growth
Increasing
Quality & Diff
Increasing
education
Higher product
price
Disposable
income,
demand
Increasing
Competencies
Higher wages
(Complementary) Trajectories
Trajectory 1: The efficiency of
productive processes
increases during the course of
economic development.
• Efficiency = the ratio of the inputs used to the output
produced, when the type of output remains constant.
Trajectory 2: The output
variety of the economic
system increases in the course
of time.
• Here such variety is measured by the number of
distinguishable sectors
• A sector is defined as the set of firms producing a
common although highly differentiated output
• Arrow of time
Trajectory 3: The output
quality and internal
differentiation of existing
sectors increases in the course
of time after their creation.
• This means that if during the period of observation the
type of output changes what we will observe is a
combination of growing productive efficiency and of
quality change
Qualitative change, discontinuities
(Strong) emergence
• requires qualitative change and discontinuities
Variety can only increase
• if sectors are distinguishable = qualitatively different
Emergence of new sectors
• (Strong) emergence
Complexity Development_Curitiba_11-13
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Differentiation by the emergence of
new sectors
Dynamics and competition
• Sector dynamics: entry
(Entrepreneurs + bandwagon
of imitators) and exit (failure +
M&A) of firms
• Competition: from temporary
monopoly to bandwagon of
imitators (rising intensity of
competition) to the circular
flow
• Competition: both inter- and
intra-sector
Emergence of new sectors
development
•Entrepreneurs
•Temporary
monopoly
entry
•bandwagon of
imitators
•Increasing ICi
•failure
•M&A
Saturation
Inducement
new sector
•Creation of new
niche if
technology
available
Equilibrium and development
Closed
system
• moves towards equilibrium and rests there
• with fluctuations around the equilibrium
position
Open
system
• reaches a given configuration which may be
stable for a period of time but
• fluctuations can increase in intensity, spread
over the system and induce a transition
Complexity Development_Curitiba_11-13
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Stability
Closed
system
Open
system
• Equilibriums is stable unless
• an exogenous shock opens up the system
• Are steady states in an open system stable? Is the
Schumpeterian circular flow stable?
• Apparent stability : Constant number of distinguishable
components of the system, steady rates of growth, no
undesirable properties (unemployment, indebtedness etc)
• But their size can vary, undesirable properties can grow →
Fluctuations can induce transitions
Complexity Development_Curitiba_11-13
November 2013
Stability (2)
Modern (capitalist) economic
systems
The long run evolution of
economic systems
There can be a potentially
large) number of short run
situations in which qualitative
change is almost absent
• are open systems and
• most of the time they are out of equilibrium
• and the changes in composition that accompanied it
• can only be interpreted by considering them open
systems out of equilibrium
• and in which the assumptions of equilibrium
economics
• can be an adequate approximation
Complexity Development_Curitiba_11-13
November 2013
Stability and long run change
Capitalist
economic systems
Equilibrium
Open, dissipative,
out of equilibrium
systems
• have high creativity by innovation but
• they are intrinsically unstable and prone to crises
• Restless capitalism (Metcalfe)
• is not the appropriate conceptual framework
• to understand their long run dynamics
• are a more appropriate intellectual framework,
• of which equilibrium economics would be subset
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