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Modelling Economic Evolution
Eric Beinhocker
McKinsey Global Institute
EC Workshop on the Development of Agent Based Models for the Global Economy and Its
MarketsBrussels, 1 October, 2010
Copyright © 2010 McKinsey & Company, Inc.
2
Today’s discussion
• Facts – five empirical observations to be explained
• Proposal – economic change as evolutionary search through physical, social, and economic design spaces
• Implications for agent-based modelling
3
Today’s discussion
• Facts – five empirical observations to be explained
• Proposal – economic change as evolutionary search through physical, social, and economic design spaces
• Implications for agent-based modelling
4
Fact no. 1 – discontinuous economic growth
World GDP per capita, constant 1992 US$
Source:J. Bradford DeLong, U. Cal. Berkeley
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1000
2000
3000
4000
5000
6000
7000
-2500000 -1500000 -500000
2.5m BC to 2000 AD 15,000 BC to 2000 AD
0
1000
2000
3000
4000
5000
6000
7000
-15000 -10000 -5000 0 5000
1750 to 2000
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Fact no. 2 – increased order and complexity
102 SKU economy
From . . .
1010 SKU economy
To . . .
• Wal-Mart 100,000 SKUs• Cable TV 200+ channels• 275 breakfast cereals
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Fact no. 3: evolutionary patterns in technology
“Add successfully as many mail coaches as you please, you will never get a railway thereby”
Joseph Schumpeter
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Fact no. 4: economies are physical systems subject to the laws of thermodynamics
Economic activity is fundamentally an order creating process
(Georgescu-Roegen)
Interacting agentsLow order inputs
• Food calories
• Fossil fuels
• Raw materials
• Information
Ordered outputs – goods and services(entropy locally decreased)
Disordered outputs – waste products, heat, gases(entropy exported – universally increasing)
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Fact no. 5 – no one is in charge
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Today’s discussion
• Facts – five empirical observations to be explained
• Proposal – economic change as evolutionary search through physical, social, and economic design spaces
• Implications for agent-based modelling
10
A paradigm shiftNeoclassical economics Complexity economics
DynamicsEconomies are closed, static, linear systems in equilibrium
Economies are open, dynamic, non-linear systems far from equilibrium
AgentsHomogeneous agents• Only use rational deduction• Make no mistakes/no biases• Already perfect, so why learn?
Heterogeneous agents• Mix deductive/inductive
decision-making• Subject to errors and biases• Learn and adapt over time
EmergenceTreats micro and macroeconomics as separate disciplines
Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions
EvolutionEvolutionary process creates novelty and growing order and complexity over time
Contains no endogenous mechanism for creating novelty or growth in order and complexity
NetworksExplicitly account for agent-to-agent interactions and relationships
Assume agents only interact indirectly through market mechanisms
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Do we need evolution in agent-based models?Complexity economics
DynamicsEconomies are open, dynamic, non-linear systems far from equilibrium
AgentsHeterogeneous agents• Mix deductive/inductive
decision-making• Subject to errors and biases• Learn and adapt over time
EmergenceSees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions
EvolutionEvolutionary process creates novelty and growing order and complexity over time
NetworksExplicitly account for agent-to-agent interactions and relationships
Agent-based models typically good at this
Do we also need this?
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Evolution as a form of computation
Search algorithms
Evolutionary search algorithms
Algorithms
Other types of algorithms
Non-evolutionary search algorithms
Biological evolution
Human social evolution
Physical technologies
Social technologies
Business Plans
Culture?
Other evolution
Other?
Co-evolution
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Evolution is a search algorithm for ‘fit designs’
Repeat
Create a variety of experiments
Variation
Select designs that are ‘fit’
Selection
Amplify fit designs, de-amplify unfit designs
Amplification
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A generic model of evolution
Design space Schema
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EnvironmentSchema
Reader – Builder
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0Interactor
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Evolution creates complexity from simplicity
Information World
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PhysicalWorld
Design encoded in a schema Interactor in an environment
Rendering of design
Feedback on fitness
Variation, selection,
amplification
Order,complexity
Energy
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Applying a computational view to social systems
Schema Reader – BuilderSchema
BUSINESS PLAN
MegaCorp
Design space
Design A Design BDesig
n
E
Design D Design
C
Physical artefacts
Social structuresEconomic designs
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Who designed the modern bicycle?
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The reality – evolution through ‘deductive-tinkering’
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Technologies evolve
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Economic evolution occurs in three ‘design spaces’
Physical technologies
Social technologies
Business plans
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Business plan evolution works at three levels
Individual minds Organizations Markets
Independent booksellers
A?E?D?
6?
A+C?
B+D+E?A?
D?C? E?
B?
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What would economic evolution predict?
•Periods of stasis/bursts of innovation
•Spontaneous self organization
•Increasing economic order (non-monotonic), increasing pollution
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Today’s discussion
• Facts – five empirical observations to be explained
• Proposal – economic change as evolutionary search through physical, social, and economic design spaces
• Implications for agent-based modelling
24
Should we include innovation processes in agent-based models?
• Stock market model testing options for institutional structure – PROBABLY NO
• Macro model exploring short-term options for monetary and fiscal policy – PROBABLY NO
• Model of the financial crisis – MAYBE
• Micro model of industry dynamics – YES
• Multi decade model of climate change mitigation – YES
• Macro model of long-term growth – YES
It depends…
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Options for modelling innovation• Exogenous, stochastic process
–What kind of stochastic process?
–No feedback from economy to innovation process
• Endogenous, increasing returns to R&D (Romer)
–Does not account for variety, complexity
–No networks, inter-relationships between innovations
–No “bursts” of innovation
• Endogenous, evolutionary
–Genetic algorithms
–Grammar models? Other?
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Can we incorporate economic evolution in agent-based modelling?
• Imagine agents searching a ‘design space’ (physical technology, social technology, or business plans) for ‘fit designs’
–Finite set of primitives, coded in a schema
–‘Grammar’ for re-combination of primitives into modules and architectures
• How to model the fitness function, how does it endogenously evolve?
• Who are the schema-reader/builders? (individuals, firms?)
• How to model processes for turning schema into interactors (new products and services, new firms)?
• How can evolution in social technologies change the structure of the model itself?
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Remember . . .
“Evolution is cleverer than we are”
Orgels’s second rule
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