TECHNOLOGY AND ECONOMIC EVOLUTION AN ALGORITHMIC PERSPECTIVE

Transcription

1 TECHNOLOGY AND ECONOMIC EVOLUTION AN ALGORITHMIC PERSPECTIVE Eric Beinhocker Getting Inside the Black Box Workshop Santa Fe Institute August 14, 213

2 Themes from workshop discussions Search in combinatorial spaces (Arthur, Farmer, Hausmann, Lobo, Pietronero, Strumsky) Recipes, codes, routines, artifacts (Auerswald, Dosi) Synthesis? Change through evolutionary processes (Arthur, Dosi, Erwin)

3 Historic approach to economic and technology evolution Evolutionary Biology Evolutionary Economics Darwin Wallace Mendel Haldane Wright Fisher Etc What is similar? What is different? Mandeville Marshall Menger Veblen Schumpeter Nelson & Winter Etc

4 A synthesizing perspective evolution as algorithm rather than biology 4

5 An ontological stance Algorithms Search algorithms Other types of algorithms Evolutionary search algorithms Non evolutionary search algorithms Biological evolution Human social evolution Other evolution (e.g. computer) Technological evolution Economic evolution Political evolution Cultural evolution Etc Co evolution

6 Today s discussion Articulate a substrate neutral (i.e. generic ) algorithmic model of evolution Apply the generic model to the evolution of technologies, institutions, firms, and economies

7 A generic algorithmic model of evolution (1) Design An underlying scheme that governs functioning, developing, or unfolding (Merriam Webster) Ordered, low entropy versus its environment Has purpose, function Modular combines components in an architecture Nested modules are components of other designs

8 A generic algorithmic model of evolution (2) Schema Encodes a design as information AND encodes process for rendering the design in physical form by Schema Reader Builder (more later) Examples: DNA, cookie recipe, IKEA assembly instructions, Intel chip design and manufacturing documents Storage: computer bits, blue prints, paper, songs, human memory, deoxyribonucleic acid Storage of schema can be distributed amongst people, computers, artifacts, organisms

9 A generic model of evolution (3) Schema Reader Builder Read schema Manipulate matter, energy, information to physically render a design ( devo process) Examples: dinosaur egg, female elephant womb, carpenter, Intel engineer, computer code object Schema need not be complete, only sufficient to be reliably rendered by its Reader Builder Schemata and Reader Builders coevolve (biological schema code their own Reader Builders)

10 A generic model of evolution (4) Interactors Physical manifestation of the design (e.g. phenotype) Rendered as an ordered pattern of matter, and/or energy, and/or information Following their designs, interactors are modular with architecture and components Rendering subject to constraints (e.g. laws of physics, scarcity) Interacts with some environment including other interactors Interactors collectively form a population Interactors subject to fitness pressures (more on this later)

11 A generic model of evolution (5) Design Space Combinatorial space of all possible schema Any schema can be coded as a bit string, flipping bits gives variants Space is finite but can grow Size at point in time defined by longest bit string Bit strings cannot be infinite Reader Builder cannot read render an infinite string in finite time Space expands as bit strings grow Examples: Borges Library of Babel, Dennett Library of Mendel Fitness Landscape Each point in Design Space has associated fitness Evolution is an algorithm for searching landscape for relatively higher fitness

12 A generic model of evolution (6) Important A general what this algorithm does NOT for say: searching fitness landscapes Variation does not have to be random (mutation) or recombinant just requisite variety for effective search Schema transmission not specified not necessarily inheritance or descent no sex! Not necessarily Darwinian or Lamarkian (but could be either) Definition of species undefined, not necessary (though ex post categorizations, taxonomies possibly useful) Selection mechanism not specified just distinguishes relative fitness Not necessarily replication amplification a more generic term, implies mechanism that increases relative frequency in a population Nothing VARIATION about directed evolution SELECTION or not Create a variety of experiments Select designs that are fit AMPLIFICATION Amplify fit designs, de amplify unfit designs Again, this is NOT about biolgy biology is just one possible instantiation REPEAT

13 Putting it all together (1) Design space Schema Schema Reader Builder Environment Interactor

14 Putting it all together (2) Information World Rendering of design Physical World Order, complexity 1 Energy 1 1 Variation, selection, amplification 1 Feedback on fitness Design encoded in a schema Interactor in an environment

15 Some benefits of this approach Can be formalized computationally and using tools from information theory Schema characterized by Kolmogorov complexity Schema reader builders are Turing machines Fitness landscapes can be characterized (statistically, topologically, Hamming distances) Evolutionary search algorithms well studied Fundamentally connects energy and entropy

16 Today s discussion Articulate a substrate neutral (i.e. generic ) algorithmic model of evolution Apply the generic model to the evolution of technologies, institutions, firms, and economies

17 There are three essential Design Spaces in the economy Physical technologies Business plans Social technologies Co evolution Beinhocker (26)

18 Physical Technologies Physical Technologies are methods and designs for transforming matter, energy and information from one state into another in pursuit of a goal or goals Schemata: natural language, equations, blueprints, diagrams, algorithms, etc. (all can be converted to bit strings) Storage: human minds, stories, songs, stone tablets, papyrus scrolls, books, computers, etc. Schema Reader Builders: people (e.g. hunter, farmer, stone mason, carpenter, metallurgist, engineer, programmer) Interactors: artifacts (products) and experiences (services) Beinhocker (211, 26) building on Nelson (23, 25), Mokyr (199, 2) and Ziman (2)

19 Social Technologies Social Technologies are methods and designs for organizing people in pursuit of a goal or goals Schemata: natural language, equations, diagrams, algorithms, etc. (all can be converted to bit strings) Storage: human minds, stories, songs, stone tablets, papyrus scrolls, books, computers, etc. Schema Reader Builders: people (e.g. hunter, tribal chief, priest, general, IBM executive, sports captain, workshop chair) Interactors: human teams and organizations Examples Hunting band Money Law Democracy Markets Science Limited liability corporation Production line Marketing dept Beinhocker (211, 26) building on Nelson (23, 25), North (199)

20 Business Plans NOTE: A business is a person, or an organized group of people, who transform(s) All three matter, definitions energy are and forms information of transformation from one state into another processes with the goal of making a profit* Schemata: natural language, diagrams, financial statements, etc. (all can be converted to bit strings) All three are designs and methods for transformations that create order (use energy to lower entropy) Storage: human minds, stories, songs, stone tablets, papyrus scrolls, books, computers, etc. Schema Reader Builders: people (e.g. hunter, farmer, craftsman, entrepreneur, business executive) Interactors: businesses *In pre money societies profit can be caloric or other stores of value Strategies combine Physical and Social Technologies in a design in Business Plans Firms are entities defined by STs containing one or more Businesses All three are in pursuit of some set of goals serving human needs Technologies are designs and methods for transformations that create order that serves human needs Beinhocker (211, 26) building on Hannan and Freeman (1977) and Hodgson and Knudsen (26)

21 Evolutionary search in PT, ST, and BP space DEDUCTIVE TINKERING CHOICE COMMITMENT VARIATION Create a variety of experiments SELECTION Select designs that are fit AMPLIFICATION Amplify fit designs, de amplify unfit designs REPEAT

22 Deductive tinkering generates variety NOTE: Provides a role for human agency, intentionality, creativity and foresight in social evolution - not blind evolution Building Different on Campbell search (196) pattern and on Simon fitness (1996) landscape than biology People pursue - different goals when mix of searching structure PT, and ST, stochasticity and BP space a better mousetrap, a better soccer team, a better IBM But vast Mix search between space, deduction interactors and too tinkering complex, can fitness change function over time only partially known - Science, institutional innovations radically increased Can t deductively deductive find best hit rate design from first principles Go as far as creativity and deduction will go generate candidate designs Then tinker, experiment, get feedback from the environment, iterate, learn

23 Choice provides selection mechanism In all three spaces agents make choices, e.g. PTs: designers, engineers, innovators, consumers STs: employees, voters, team members, leaders BPs: employees, managers, shareholders, consumers Agents choose interactors embodying designs based on perceived relative fitness Fitness function is multi dimensional, e.g. functionality, quality, aesthetics, cost, reputation, values, social perception Fitness function co evolves with design space through niche construction and filling Fitness can only be observed retrospectively through differential selection Does not pre suppose choice process (e.g. utility) But cognitive science insights on choice process might be useful

24 Choice example tablets

25 Commitments provide amplification Agents commit to their choices Commitments are hard to reverse investments of scarce resources (e.g. money, labor, energy, materials, time, reputation) Examples PT: Buy an ipad, incorporate Pratt & Whitney engine in Boeing Dreamliner ST: Adopt just in time inventory, going with formation in soccer match BP: Investing in a new business Commitments increase frequency of a design expressed in a population of interactors

26 What might such an evolutionary theory predict, explain or illuminate? Super exponential growth designs create the possibilities for new designs But non monotonic growth periods of radiation and extinction (creative destruction) Technology epochs dominant designs PT ST BP co evolution (and possibly physical ecosystem co evolution) Niche construction/filling Rising order/complexity and energy flows

27 An evolutionary theory of value? A pattern of matter, energy, and information has economic value if the following three conditions are jointly met: 1. Irreversibility all value creating economic transformations and transactions are thermodynamically irreversible (i.e. costly or impossible to reverse). 2. Entropy all value creating economic transformations and transactions reduce entropy locally within the economic system, while increasing entropy globally in the environment. 3. Fitness all value creating economic transformations and transactions produce artifacts and or actions that are fit for human purposes Value = fit order = knowledge Beinhocker (26)

28 Evolution is cleverer than we are. ORGEL S SECOND LAW

29 THANK YOU For more information please see