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.

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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

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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

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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

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6000

7000

-2500000 -1500000 -500000

2.5m BC to 2000 AD 15,000 BC to 2000 AD

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1000

2000

3000

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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

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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

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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