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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 15,000 BC to 2000 AD 1750 to 2000 7000 7000 7000 6000 6000 6000 5000 5000 5000 4000 4000 4000 3000 3000 3000 2000 2000 2000 1000 1000 1000 0 -2500000 -1500000 -500000 Source: J. Bradford DeLong, U. Cal. Berkeley 0 -15000 -10000 -5000 0 5000 0 1700 1800 1900 2000 2100 3 Fact no. 2 – increased order and complexity From . . . 102 SKU economy To . . . 1010 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 Economic activity is fundamentally an order creating process (Georgescu-Roegen) Disordered outputs – waste products, heat, gases (entropy exported – universally increasing) 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 Neoclassical economics Complexity economics Economies are closed, static, linear systems in equilibrium Economies are open, dynamic, non-linear systems far from equilibrium 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 Assume agents only interact indirectly through market mechanisms Explicitly account for agent-toagent interactions and relationships Emergence Treats micro and macroeconomics as separate disciplines Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions Evolution Contains no endogenous mechanism for creating novelty or growth in order and complexity Evolutionary process creates novelty and growing order and complexity over time Dynamics Agents 9 Do we need evolution in agent-based models? Complexity economics Economies are open, dynamic, non-linear systems far from equilibrium Dynamics Agents Agent-based models typically good at this Networks Explicitly account for agent-toagent interactions and relationships Sees no distinction between micro- and macroeconomics; macro patterns emerge from micro behaviors and interactions Emergence Evolution Heterogeneous agents • Mix deductive/inductive decision-making • Subject to errors and biases • Learn and adapt over time Do we also need this? Evolutionary process creates novelty and growing order and complexity over time 10 Evolution as a form of computation Algorithms Search algorithms Evolutionary search algorithms Biological evolution Physical technologies Human social evolution Social technologies Business Plans Coevolution Other types of algorithms Non-evolutionary search algorithms Other evolution Culture? Other? 11 Evolution is a search algorithm for ‘fit designs’ Create a variety of experiments Variation Select designs that are ‘fit’ Selection Amplify fit designs, de-amplify unfit designs Amplification Repeat 12 A generic model of evolution Design space Schema Schema Reader – Builder Environment 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0 Interactor 0 0 13 Evolution creates complexity from simplicity Information World Rendering of design Physical World Order, complexity 1 0 1 1 Energy 0 Variation, selection, amplification 0 1 0 0 Feedback on fitness 0 Design encoded in a schema Interactor in an environment 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 Organizations A? A? B? D? C? 6? Markets A+C? D? E? B+D+E? 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 agentbased 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 agentbased 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