Presentation is loading. Please wait.

Presentation is loading. Please wait.

Modelling Economic Evolution Eric Beinhocker McKinsey Global Institute EC Workshop on the Development of Agent Based Models for the Global Economy and.

Similar presentations


Presentation on theme: "Modelling Economic Evolution Eric Beinhocker McKinsey Global Institute EC Workshop on the Development of Agent Based Models for the Global Economy and."— Presentation transcript:

1 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.

2 1 Todays 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 2 Todays 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 3 Fact no. 1 – discontinuous economic growth World GDP per capita, constant 1992 US$ Source:J. Bradford DeLong, U. Cal. Berkeley 2.5m BC to 2000 AD15,000 BC to 2000 AD1750 to 2000

5 4 Fact no. 2 – increased order and complexity 10 2 SKU economy From SKU economy To... Wal-Mart 100,000 SKUs Cable TV 200+ channels 275 breakfast cereals

6 5 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

7 6 Fact no. 4: economies are physical systems subject to the laws of thermodynamics Economic activity is fundamentally an order creating process (Georgescu-Roegen) Interacting agents Low 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)

8 7 Fact no. 5 – no one is in charge

9 8 Todays 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 9 A paradigm shift Neoclassical economicsComplexity 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 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 Networks Explicitly account for agent-to- agent interactions and relationships Assume agents only interact indirectly through market mechanisms

11 10 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 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 Networks Explicitly account for agent-to- agent interactions and relationships Agent-based models typically good at this Do we also need this?

12 11 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

13 12 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

14 13 A generic model of evolution Design spaceSchema Environment Schema Reader – Builder Interactor

15 14 Evolution creates complexity from simplicity Information World Physical World Design encoded in a schemaInteractor in an environment Rendering of design Feedback on fitness Variation, selection, amplification Order, complexity Energy

16 15 Applying a computational view to social systems Schema Reader – BuilderSchema BUSINESS PLAN MegaCorp Design space Design A Design B Design E Design D Design C Physical artefacts Social structures Economic designs

17 16 Who designed the modern bicycle?

18 17 The reality – evolution through deductive-tinkering

19 18 Technologies evolve

20 19 Economic evolution occurs in three design spaces Physical technologies Social technologies Business plans

21 20 Business plan evolution works at three levels Individual mindsOrganizationsMarkets Independent booksellers A? E? D? 6? A+C? B+D+E? A? D? C?E? B?

22 21 What would economic evolution predict? Periods of stasis/bursts of innovation Spontaneous self organization Increasing economic order (non-monotonic), increasing pollution

23 22 Todays 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 23 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…

25 24 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?

26 25 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?

27 26 Remember... Evolution is cleverer than we are Orgelss second rule


Download ppt "Modelling Economic Evolution Eric Beinhocker McKinsey Global Institute EC Workshop on the Development of Agent Based Models for the Global Economy and."

Similar presentations


Ads by Google