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1 Evolutionary modelling and the Laboratory for Simulation Development PhD Eurolab on Simulation of Economic Evolution (SIME) University of Strasbourg,

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Presentation on theme: "1 Evolutionary modelling and the Laboratory for Simulation Development PhD Eurolab on Simulation of Economic Evolution (SIME) University of Strasbourg,"— Presentation transcript:

1 1 Evolutionary modelling and the Laboratory for Simulation Development PhD Eurolab on Simulation of Economic Evolution (SIME) University of Strasbourg, April 2004 Esben Sloth Andersen DRUID and IKE, Aalborg University, Denmark

2 2 KISS and TAMAS: Conflicting principles? KISS = Keep It Simple, Stupid! A slogan from the US army during World War II Generally acknowledged by scientific modellers TAMAS = Take A Model, Add Something! Variant for Lsd modellers: TAMAM = Take A Model, Add Marco! Principle for cumulative modelling KISS = TAMAS? Not when the initial model is complex and ill structured! In this case we need a new principle! TAMAKISS = Take A Model And Keep It Simpler, Stupid!

3 3 The history of evolutionary economics 1. Old evolutionary economics: No KISS and TAMAS Adam Smith, Marx, Menger, Marshall, Schumpeter, Hayek, … 2. The “dark ages”: KISS and TAMAS kill evolution! Crowding out by the formalist revolution from about 1930 3. Starting new evolutionary economics with KISS and TAMAS Breakthrough I: Nelson and Winter’s book on An Evolutionary Theory of Economic Change (1982) Breakthrough II: The follow-up on Maynard Smith’s book on Evolution and the Theory of Games (1982) Breakthrough III: The computational study of evolving dynamical systems (e.g. the Santa Fe Institute) 4. Developing new evolutionary economics Normal evolutionary science with TAMAS or new start with TAMAKISS?

4 4 Population thinking as the starting point Typological thinking is anti-evolutionary It suggests that heterogeneity is not essential – just disturbing It wants to find the common type or the “representative agent” Population thinking is pro-evolutionary Here heterogeneity is the driver of evolution The outliers are of crucial importance The “representative firm” must be supplemented by population statistics (including the variance of behaviour) Literature Population thinking is explained by Ernst Mayr (evolutionary biologist) and Stan Metcalfe (evolutionary economist)

5 5 Nelson and Winter’s population thinking Nelson and Winter’s evolutionary synthesis including: Behavioural patterns and their transmission Creation of new behavioural patterns Different types of selection mechanisms More specifically, they combined: Simon’s work on routines and satisficing behaviour Nelson’s and other Schumpeterian work on innovation and imitation Alchian’s and Winter’s work on natural selection

6 6 The structure of the Nelson-Winter book Part I: Overview and Motivation Part II: Organization-Theoretic Foundations of Economic Evolutionary Theory The part that has made Nelson and Winter famous in leading business schools and in leading business economics journals Part III: Textbook Economics Revisited Includes KISS models of the selection process Part IV: Growth Theory Micro-founded endogenous growth theory, but no KISS Part V: Schumpeterian Competition Core contribution to evolutionary theory and to a realistic industrial dynamics, but no KISS Part VI: Economic Welfare and Policy Part VII: Conclusion

7 7 The Nelson-Winter family of simulation models NWch6, NWch7 and NWch10: Theoretical KISS models of the selection process NWch9 reproduces Solow’s growth data in a more ‘realistic’ way than through Solow’s own growth model NWch12: the competition between innovators and imitators in a process of 'Schumpeterian competition' (Schumpeter Mark II) NWch13: how concentration and macroproductivity are influenced by the conditions of innovation and imitation, and by investment strategies NWch14: the trade-off between static efficiency and dynamic efficiency (based on some degree of market power) XNW1984: Winter’s study of Schumpeterian competition in alternative technological regimes XNW1999: history-friendly modelling of the computer industry

8 8 The basic set-up of Nelson-Winter models The models can be extended by introducing new evolving variables into the state space E.g. R&D intensity in the models by Silverberg and Verspagen

9 9 Naive simulation of the Nelson-Winter model of Schumpeterian competition

10 10 The structure of the transformation mechanism in Nelson-Winter models 1. Short-run process 2. Capital accumulation 3. Technical change KiKi AiAi

11 11 The need for simulation tools The areas of simulation Simple models (like replicator dynamics) can be studied by mathematical analysis But simulation helps mathematical intuition Complex models can only be studied by simulation The need for tools To perform the simulations The present the results graphically To perform statistical analysis of the results To document the simulation model and share it with others

12 12 Typical simulation tasks - I

13 13 Tasks - II

14 14 First step: install and start Lsd

15 15 Second step: select a model

16 16 Third step: start the model

17 17 Fourth step (a): load configuration file

18 18 Fourth step (b): inspect configuration file

19 19 Fourth step (c): revise configuration file

20 20 Fifth step: run simulation and study plot

21 21 Sixth step: make data analysis

22 22 Seventh step (a): automatic documentation

23 23 Seventh step (b): Lsd equations as model specification Equations are written in a rather simple language and in an arbitrary sequence Example: EQUATION("Q") /* Q(t) = K(t-1) * A(t-1) Quantity is is computed as capital times productivity, both with lagged values */ RESULT(VL("K",1)*VL("A",1))

24 24 How to do it in practice?

25 25 Rethinking simulation models: The avoidance of the monopolistic trend The core of the Nelson-Winter model Replicator dynamics in a homogeneous selection environment Such dynamics lead to monopoly It is even worse when we include cumulative innovation NW solution: monopolistic investment restraint In the end selection is more or less switched off Variance is sustained and innovation dominates Not a fully satisfactory solution! Alternatives are the introduction of market niches and/or large spin-offs from large firms (inheriting the productivity level from the mother firm)

26 26 Step-wise analysis of the transformation mechanism in Nelson-Winter models Define four regime parameters Regime_inno Determines whether and how innovation takes place Regime_imi Determines whether and how imitation takes place Regime_restraint Determines whether investment restraint is present Regime_fission Determines whether spin-offs from large firms takes place

27 27 Specifying the regimes Regime_imi - Imitative regime of the industry 0: no imitation 1: imitation of industry's best productivity 2: imitation of industry's mean productivity Regime_inno - Innovative regime of the industry 0: no innovation 1: innovation from industry's mean productivity 2: innovation from firm's present productivity Regime_restraint - Monopolistic behaviour O: no monopolistic behaviour 1: monopolistic restraint due to mark-up pricing Regime_fission - Splitting of large firms 0: no fission of large firms 1: fission of large firms

28 28 Defining and calculating statistics Population information for two points of time Initial capacity share of each firm Reproduction coefficient of each firm Productivity of each firm and its change Simple statistics Mean reproduction coefficient (abs. fitness) Change in mean productivity Variance of productivities Covariance of reproduction coefficients and productivities Regression of reproduction coefficients on productivities

29 29 George Price’s interpretation of the statistics Developed in biology in the beginning of the 1970s Surprisingly fruitful for any evolutionary analysis The format of Price’s equation (identity) Total evolutionary change  Selection effect + Innovation effect Metcalfe (2002): “For some years now evolutionary economists have been using the Price equation without realising it.”

30 30 Price’s definition of evolutionary change Total evolutionary change with respect to a particular characteristic of a population = the change in the mean of the individual values of that characteristic, i.e. This definition is directly applicable to simple population analysis and multi-level population analysis

31 31 Definition of selection by covariance Selection effect = the covariance of relative reproduction coefficients and values of the characteristic The meaning of this definition of selection: The exploitation of variance in pre-selection population to change the mean characteristic of post-selection population Elements of pure selection (i.e. no innovation) Basically selection is seen as the covariance between relative reproduction coefficients (fitnesses) and characteristics The efficiency of selection is the regression of fitnesses on characteristics

32 32 Definition of innovation by a mean effect Innovation effect = the mean of the product of the change of the values of the characteristic and the relative reproduction coefficients, i.e. Measuring pure innovation (i.e. no selection) e.g. the weighted mean of the firm-internal change in productivity “Innovation” is any local change in the “units of selection” It includes imitation among units and learning within units It can often be decomposed into within-unit selection and low-level innovation

33 33 Price’s partitioning of evolutionary change Total evolutionary change  Selection effect + Innovation effect Alternative formulation for further partitioning Remark that the LHS is structurally like the RHS expectation The innovation effect is often the outcome of both selection and innovation within higher-level “units of selection”

34 34 The meaning of Price’s equation The innovation effect is the creative part It takes place within the units, e.g. the firms It may be due to innovation, imitation, learning, … It may also be due to intra-firm selection, e.g. of plants The selection effect means that some entities are promoted while other entities shrink It represents Schumpeter’s “creative destruction” Firms may try to avoid selection by imitation and learning The selection pressure sets the agenda for firms The Price equation ignores ecological effects Thus it is a form of short-term evolutionary analysis But short-term evolution is the starting point!

35 35 Understanding Nelson-Winter models through the TAMAKISS principle The simplest situation: No innovation/imitation and no monopolistic restraint Then we have a simple replicator dynamics Result: Monopoly of the productivity leader

36 36 The movement of mean productivity

37 37 The movement of capital shares

38 38 The covariance between reproduction coefficients and productivity

39 39 The regression of reproduction coefficients on productivity

40 40 Introducing monopolistic restraint The monopoly paradox in evolutionary models Not really a paradox in the highly simplified environment with a homogeneous product, etc. But in reality monopolies are seldom We also would like some permanent competition for using the model for exploring evolution Nelson-Winter solution: monopolistic restraint Large firms recognise that they do not maximise profits by expanding capacity But a full monopoly would be more profitable! Alternative solution: new firms by spin-offs but presently we shall stick to N&W

41 41 The movement of mean productivity

42 42 The movement of capital shares

43 43 The regression of reproduction coefficients on productivity

44 44 Introducing innovation into simple replicator dynamics Innovation strengthens monopolistic tendencies Innovation is costly, so in the short run it reduces capital accumulation In the long run, innovation is more profitable for large than for small firms Reason 1: There are fixed probabilistic costs of producing an innovation, but large firms have a larger effect of the innovation (it is immediately used throughout the firm) Reason 2: Under the cumulative regime, productivity leaders have better innovations than others Consequence There are further reasons to introduce monopolistic restraint (see below)

45 45 The movement of productivities

46 46 The movement of capital shares

47 47 Innovation and monopolistic restraint Monopolistic restraint totally change the outcome It means that firms moves profits away from capital accumulation within the industry Therefore, they make room for other firms Oligopoly The result is an oligopoly, since only a limited number of firms can survive in the productivity race Productivity growth is smaller than in (unrealistic) replicator dynamics with innovation

48 48 The movement of productivities

49 49 The movement of capital shares

50 50 Results about the monopoly paradox The core of the Nelson-Winter model Replicator dynamics in a homogeneous selection environment Such dynamics lead to monopoly It is even worse when we include cumulative innovation Stabilisation of diversity by investment restraint A simple solution that creates an environment in which many evolutionary processes can be tested Not a fully satisfactory solution Alternatives are the introduction of market niches and/or large spin-offs from large firms (inheriting the productivity level)

51 51 Introducing fission in replicator dynamics

52 52 Andersen’s early lack of TAMAKISS Building on complex rather than simple simulation models Not making a demand specification for his model version Not making a sufficiently detailed model design Although lots of verbal descriptions, flow charts, pseudo-code,... Not developing the program step by step Much too often the simulation program did not run correctly! Not performing systematic simulation experiments Not making deep statistical analysis Not making a cumulative an extensive documentation

53 53 Overcoming the difficulties and failures Development of TAMAKISS versions of Nelson-Winter models and AL models starting from replicator dynamics Complexity is introduced in a step-wise manner Development of concrete models to explain concrete phenomena Like macroeconomic demand satiation and the stylised history of the software industry Use of the Lsd system for simulation models Makes gradual model development and statistical analysis easy Gives easy tools for model documentation and distribution Use Price’s evometrics to understand the dynamics of simulations and to start exploration of empirical data

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