0. Modelling Economic Evolution
LOX-ZYJ SGEB-P1
Modelling Economic Evolution
Eric Beinhocker
McKinsey Global Institute
EC Workshop on the Development of Agent Based Models for the Global Economy and Its Markets
Brussels, 1 October, 2010
Copyright © 2010 McKinsey & Company, Inc.

1. Today’s discussion Facts – five empirical observations to be explained
LOX-ZYJ SGEB-P1
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

2. Today’s discussion Facts – five empirical observations to be explained
LOX-ZYJ SGEB-P1
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. Fact no. 1 – discontinuous economic growth
LOX-ZYJ SGEB-P1
Fact no. 1 – discontinuous economic growth
World GDP per capita, constant 1992 US$
2.5m BC to 2000 AD
15,000 BC to 2000 AD
1750 to 2000
Source: J. Bradford DeLong, U. Cal. Berkeley

4. Fact no. 2 – increased order and complexity
LOX-ZYJ SGEB-P1
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

5. Fact no. 3: evolutionary patterns in technology
LOX-ZYJ SGEB-P1
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

6. LOX-ZYJ SGEB-P1
Fact no. 4: economies are physical systems subject to the laws of thermodynamics
Ordered outputs – goods and services
(entropy locally decreased)
Disordered outputs – waste products, heat, gases
(entropy exported – universally increasing)
Low order inputs
Food calories
Fossil fuels
Raw materials
Information
Interacting agents
Economic activity is fundamentally an order creating process (Georgescu-Roegen)

7. Fact no. 5 – no one is in charge
LOX-ZYJ SGEB-P1
Fact no. 5 – no one is in charge

8. Today’s discussion Facts – five empirical observations to be explained
LOX-ZYJ SGEB-P1
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

9. A paradigm shift Neoclassical economics Complexity economics Dynamics
LOX-ZYJ SGEB-P1
A paradigm shift
Neoclassical economics
Complexity economics
Dynamics
Economies are closed, static, linear systems in equilibrium
Economies are open, dynamic, non-linear systems far from equilibrium
Agents
Homogeneous 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
Networks
Explicitly account for agent-to-agent interactions and relationships
Assume agents only interact indirectly through market mechanisms
Emergence
Treats micro and macroeconomics as separate disciplines
Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions
Evolution
Evolutionary process creates novelty and growing order and complexity over time
Contains no endogenous mechanism for creating novelty or growth in order and complexity

10. Do we need evolution in agent-based models?
LOX-ZYJ SGEB-P1
Do we need evolution in agent-based models?
Complexity economics
Dynamics
Economies are open, dynamic, non-linear systems far from equilibrium
Agents
Heterogeneous agents
Mix deductive/inductive decision-making
Subject to errors and biases
Learn and adapt over time
Agent-based models typically good at this
Networks
Explicitly account for agent-to-agent interactions and relationships
Emergence
Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions
Evolution
Evolutionary process creates novelty and growing order and complexity over time
Do we also need this?

11. Evolution as a form of computation
LOX-ZYJ SGEB-P1
Evolution as a form of computation
Algorithms
Search algorithms
Other types of algorithms
Evolutionary search algorithms
Non-evolutionary search algorithms
Biological evolution
Human social evolution
Other evolution
Physical technologies
Social technologies
Business Plans
Culture?
Other?
Co-evolution

12. Evolution is a search algorithm for ‘fit designs’
LOX-ZYJ SGEB-P1
Evolution is a search algorithm for ‘fit designs’
Amplify fit designs, de-amplify unfit designs
Create a variety of experiments
Select designs that are ‘fit’
Variation
Selection
Amplification
Repeat

13. A generic model of evolution
LOX-ZYJ SGEB-P1
A generic model of evolution
Schema
Reader – Builder 1
Interactor
Design space
Schema 1
Environment

14. Evolution creates complexity from simplicity
LOX-ZYJ SGEB-P1
Evolution creates complexity from simplicity
Information World
Physical
World
Rendering of design
Order,
complexity 1
Energy
Variation, selection, amplification
Feedback on fitness
Design encoded in a schema
Interactor in an environment

15. Applying a computational view to social systems
LOX-ZYJ SGEB-P1
Applying a computational view to social systems
Design space
Design A
Design B
Design E
Design D
Design C
Physical artefacts
Social structures
Economic designs
Schema
BUSINESS PLAN
MegaCorp
Schema Reader – Builder

16. Who designed the modern bicycle?
LOX-ZYJ SGEB-P1
Who designed the modern bicycle?

17. The reality – evolution through ‘deductive-tinkering’
LOX-ZYJ SGEB-P1
The reality – evolution through ‘deductive-tinkering’

18. LOX-ZYJ SGEB-P1
Technologies evolve

19. Economic evolution occurs in three ‘design spaces’
LOX-ZYJ SGEB-P1
Economic evolution occurs in three ‘design spaces’
Physical technologies
Business plans
Social technologies

20. Business plan evolution works at three levels
LOX-ZYJ SGEB-P1
Business plan evolution works at three levels
Individual minds
Organizations
Markets
A+C? A? D? E? A? 6? B?
B+D+E? D? C? E?
Independent booksellers

21. What would economic evolution predict?
LOX-ZYJ SGEB-P1
What would economic evolution predict?
Periods of stasis/bursts of innovation
Spontaneous self organization
Increasing economic order (non-monotonic), increasing pollution

22. Today’s discussion Facts – five empirical observations to be explained
LOX-ZYJ SGEB-P1
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

23. Should we include innovation processes in agent-based models?
LOX-ZYJ SGEB-P1
Should we include innovation processes in agent-based models?
It depends…
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

24. Options for modelling innovation
LOX-ZYJ SGEB-P1
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?

25. Can we incorporate economic evolution in agent-based modelling?
LOX-ZYJ SGEB-P1
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?

26. “Evolution is cleverer than we are” Orgels’s second rule
LOX-ZYJ SGEB-P1
Remember . . .
“Evolution is cleverer than we are”
Orgels’s second rule