Transcript Eric Beinhocker
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.
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 1
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
Fact no. 1 – discontinuous economic growth
World GDP per capita, constant 1992 US$
2.5m BC to 2000 AD
7000
15,000 BC to 2000 AD
7000
1750 to 2000
7000 6000 5000 4000 3000 2000 1000 0 -2500000 -1500000 -500000 6000 5000 4000 3000 6000 5000 4000 3000 2000 1000 2000 1000 0 -15000 -10000 -5000 0 5000 0 1700 1800 1900 2000 2100 Source: J. Bradford DeLong, U. Cal. Berkeley 3
Fact no. 2 – increased order and complexity From . . .
To . . .
10 2 SKU economy 10 10 SKU economy
• • •
Wal-Mart 100,000 SKUs Cable TV 200+ channels 275 breakfast cereals
4
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
5
Fact no. 4: economies are physical systems subject to the laws of thermodynamics Low order inputs Interacting agents Ordered outputs
– goods and services (entropy locally decreased) • Food calories • Fossil fuels • Raw materials • Information
Disordered outputs
increasing) – waste products, heat, gases (entropy exported – universally
Economic activity is fundamentally an order creating process (Georgescu-Roegen)
6
Fact no. 5 – no one is in charge
7
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 8
A paradigm shift Dynamics Agents Networks Emergence Evolution
• • •
Neoclassical economics
Economies are closed, static, linear systems in equilibrium Homogeneous agents Only use rational deduction Make no mistakes/no biases Already perfect, so why learn?
Assume agents only interact indirectly through market mechanisms Treats micro and macroeconomics as separate disciplines Contains no endogenous mechanism for creating novelty or growth in order and complexity
Complexity economics
Economies are open, dynamic, non-linear systems far from equilibrium • • • Heterogeneous agents Mix deductive/inductive decision-making Subject to errors and biases Learn and adapt over time Explicitly account for agent-to agent interactions and relationships Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions Evolutionary process creates novelty and growing order and complexity over time 9
Do we need evolution in agent-based models?
Complexity economics Dynamics
Economies are open, dynamic, non-linear systems far from equilibrium
Agents Networks Emergence Evolution Agent-based models typically good at this Do we also need this?
• • • Heterogeneous agents Mix deductive/inductive decision-making Subject to errors and biases Learn and adapt over time Explicitly account for agent-to agent interactions and relationships Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions Evolutionary process creates novelty and growing order and complexity over time 10
Evolution as a form of computation Algorithms
Search algorithms Other types of algorithms Biological evolution Evolutionary search algorithms Human social evolution Physical technologies Social technologies Business Plans Non-evolutionary search algorithms Other evolution Culture?
Other?
Co evolution 11
Evolution is a search algorithm for ‘fit designs’ Create a variety of experiments Select designs that are ‘fit’ Amplify fit designs, de-amplify unfit designs Variation Selection
Repeat
Amplification
12
A generic model of evolution
Design space Schema 1 0 1 1 0 0 1 0 0 0 Schema Reader – Builder 1 0 1 1 0 0 1 0 0 0 Interactor Environment 13
Evolution creates complexity from simplicity
Energy Information World Rendering of design Physical World 1 0 0 0 1 0 0 1 0 1 Design encoded in a schema Variation, selection, amplification Feedback on fitness Interactor in an environment Order, complexity 14
Applying a computational view to social systems
Design space Schema Schema Reader – Builder Design A
BUSINESS PLAN
MegaCorp Physical artefacts Social structures Economic designs 15
Who designed the modern bicycle?
16
The reality – evolution through ‘deductive-tinkering’
17
Technologies evolve
18
Economic evolution occurs in three ‘design spaces’
Physical technologies Business plans Social technologies
19
Business plan evolution works at three levels Individual minds Markets
A?
C?
B?
D?
E?
Organizations
A+C?
A?
D?
E?
6?
B+D+E?
Independent booksellers 20
What would economic evolution predict?
•
Periods of stasis/bursts of innovation
•
Spontaneous self organization
•
Increasing economic order (non-monotonic), increasing pollution
21
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
22
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 23
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?
24
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?
25
Remember . . .
“Evolution is cleverer than we are”
Orgels’s second rule 26