Modelling Economic Evolution LOX-ZYJ201-20060923-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.

Today’s discussion Facts – five empirical observations to be explained LOX-ZYJ201-20060923-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

Today’s discussion Facts – five empirical observations to be explained LOX-ZYJ201-20060923-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

Fact no. 1 – discontinuous economic growth LOX-ZYJ201-20060923-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

Fact no. 2 – increased order and complexity LOX-ZYJ201-20060923-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

Fact no. 3: evolutionary patterns in technology LOX-ZYJ201-20060923-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

LOX-ZYJ201-20060923-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)

Fact no. 5 – no one is in charge LOX-ZYJ201-20060923-SGEB-P1 Fact no. 5 – no one is in charge

Today’s discussion Facts – five empirical observations to be explained LOX-ZYJ201-20060923-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

A paradigm shift Neoclassical economics Complexity economics Dynamics LOX-ZYJ201-20060923-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

Do we need evolution in agent-based models? LOX-ZYJ201-20060923-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?

Evolution as a form of computation LOX-ZYJ201-20060923-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

Evolution is a search algorithm for ‘fit designs’ LOX-ZYJ201-20060923-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

A generic model of evolution LOX-ZYJ201-20060923-SGEB-P1 A generic model of evolution Schema Reader – Builder 1 Interactor Design space Schema 1 Environment

Evolution creates complexity from simplicity LOX-ZYJ201-20060923-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

Applying a computational view to social systems LOX-ZYJ201-20060923-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

Who designed the modern bicycle? LOX-ZYJ201-20060923-SGEB-P1 Who designed the modern bicycle?

The reality – evolution through ‘deductive-tinkering’ LOX-ZYJ201-20060923-SGEB-P1 The reality – evolution through ‘deductive-tinkering’

LOX-ZYJ201-20060923-SGEB-P1 Technologies evolve

Economic evolution occurs in three ‘design spaces’ LOX-ZYJ201-20060923-SGEB-P1 Economic evolution occurs in three ‘design spaces’ Physical technologies Business plans Social technologies

Business plan evolution works at three levels LOX-ZYJ201-20060923-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

What would economic evolution predict? LOX-ZYJ201-20060923-SGEB-P1 What would economic evolution predict? Periods of stasis/bursts of innovation Spontaneous self organization Increasing economic order (non-monotonic), increasing pollution

Today’s discussion Facts – five empirical observations to be explained LOX-ZYJ201-20060923-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

Should we include innovation processes in agent-based models? LOX-ZYJ201-20060923-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

Options for modelling innovation LOX-ZYJ201-20060923-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?

Can we incorporate economic evolution in agent-based modelling? LOX-ZYJ201-20060923-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?

“Evolution is cleverer than we are” Orgels’s second rule LOX-ZYJ201-20060923-SGEB-P1 Remember . . . “Evolution is cleverer than we are” Orgels’s second rule