A Survey of Neo-Schumpeterian Simulation Models:

Review and Prospects

Paul Windrum

presented at DIME-ETIC

‘The Economy As A Complex Evolving System’,

UNU-MERIT, Maastricht, 15 - 19 October 2007

References:

‘Empirical Validation of Agent-Based Models’, Special Issue of Computational Economics, Computational Economics, 2007 Vol. 30 (3), Chris Birchenhall, Giorgio Fagiolo and Paul Windrum (editors).

Windrum, P., 2007, ‘Neo-Schumpeterian simulation models’, in The Edward Elgar Companion to Neo-Schumpeterian Economics, H. Hanusch and A. Pyka (eds.), Cheltenham: Edward Elgar.

Windrum, P., Fagiolo, G., and Moneta, A., 2007, ‘Empirical validation of agent-based models: alternatives and prospects’, Journal of Artificial Societies and Social Simulation, 10(2) 8, <http://jasss.soc.surrey.ac.uk/10/2/8.html>.

Windrum, P., 1999, ‘Simulation models of technological innovation: a review’, American Behavioral Scientist, 42 (10), pp.1531-1550. ISSN: 0002-7642.

Neo-Classical (Type 1) and Schumpeterian (Type 2) Models

Different views about the world in which real economic agents operate.

Type 1 world can, in principle, be known and understood.

In the Type 2 world the set is unknown, and agents must engage in an open-ended search for new objects.

Primary interest of models differ Type 1 models: learning that leads to improvements

in allocative efficiency. Type 2 models: open-ended search of dynamically

changing environments. Due to (i) ongoing introduction of novelty & generation of new patterns of behaviour (Knightian uncertainty), and (ii) complexity of interactions between heterogeneous agents.

Equilibrium versus non-equilibrium

Type 1 models view the underlying structure of the economic system as an equilibrium structure.

In Type 2 models, aggregate regularities are not equilibrium properties but emergent properties that arise from an evolutionary process: process in which variety generation and selection interact

A bottom-up perspective Heterogeneity The evolving complex system (ECS) approach Non-reductionism Non-linearity Direct (endogenous) interactions Bounded rationality The nature of learning ‘True’ dynamics (irreversibility) Endogenous and persistent novelty Selection-based market mechanisms

Early Neo-Schumpeterian (Type 2) Models

1. ‘Stylised Man’ of early models (Nelson & Winter, Dosi et al., Silverberg-Verspagen).

Focus: develop models containing key evolutionary mechanisms that can generate ‘stylised facts’ observed at industry/macro level.

Key algorithms: variety generation (search) and selection algorithms

Key elements: heterogeneity of agents, stochastic processes (notably of innovation), interaction between agents, feedbacks between decision making and emergent properties (path dependency), absence of perfect foresight and learning as a process of open-ended search (constrained rationality/ myopia).

Success of Early Models

Agenda setting: a viable, neo-Schumpeterian alternative.

Method: Nelson & Winter 2-step method to approach to empirical validation of simulation models

Demonstration: demonstration of the feasibility of the new approach using simulation.

Models generated outputs accorded with empirically observed phenomena and evolutionary neo-Schumpeterian explanations for these phenomena.

Innovation: open-ended search as basis for learning in worlds with Knightian uncertainty.

Limitations of Early Models

Generality of the studies A very limited range of agents were considered &

and the agent representations were highly stylised Reports conducted on very few simulation runs

(illustrations from handful of individual runs) High dimensionality of the models (random walk?) Lack of sensitivity analysis on key variables and

parameters (what’s really driving the model) Lack of rigorous testing procedures for model

outputs. No comparison of alternative theories or models.

More Recent Neo-Schumpeterian Models

Motivations:

1. To address the limitation of the early models

2. Exploit new algorithms, procedures developed in computer sciences, statistics etc., and make use of improved software / hardware.

Will consider 2 examples:

Malerba-Nelson-Orsenigo-Winter, and

Windrum-Birchenhall models.

‘History friendly modelling’ Methodology

suggests tying down simulation models to carefully specified, empirical ‘histories’ of individual industries.

Detailed empirical data to inform the simulation work:

1. Act as a guide when specifying the representations of agents (their behaviour, decision rules, and interactions), and the environment in which they operate.

2. Assist in identification of particular parameters on key variables likely to generate the observed history.

3. *** Enable more demanding tests on model outputs to be specified - evaluate a model by comparing its output (‘simulated trace history’) with the actual history of an industry.

Issues Regarding the ‘History friendly’ Method

Category 1: implementation issues.

1. Modelling a special case: Malerba et al. (1999, 2001) is not informed by a history of the computer industry as whole, but of one particular company: IBM. The research questions addressed are not relevant to others in the industry.

2. IBM account is itself highly stylised and subjective

3. Lack of data on industry as a whole empirical data regarding R&D spend, market shares, and profitability of computer firms. No empirical data on key variables: relative sizes of network externalities and branding in the mainframe and PC markets.

Question: is the empirical data required by the method readily obtained in practice?

4. Other factors influencing the modelling choices: modelling is informed by theory as well as available

empirical data Empirical data is itself informed by theory

5. Do not present a rigorous sensitivity analysis of the initial seedings or the random parameter values used in the 50 simulation runs reported.

Cheapness

Quality

Mainframe

PC

Attractor state

Compare with :

Windrum, P. and Birchenhall, C., 2005, ‘Structural change in the presence of network externalities: a co-evolutionary model of technological successions’, Journal of Evolutionary Economics, 15(2), pp.123-148.

This paper opens up:

1. ‘quality’ is unpacked – a complex multi-dimensional variable its own right

2. relationship between heterogeneous consumer demand and different sets of characteristics offered by old & new technology products

- some performance characteristics of old technology are better than those of new technology

- some performance characteristics of new technology are better than those of old technology

- some performance characteristics of offered by the new technology are NOT offered by the old technology

- some performance characteristics of offered by the old technology are NOT offered by the new technology

Now can address the question of co-evolution new consumer groups with different preferences demanding new technology products with new characteristics (a ‘succession’).

Can distinguish between successions and substitutions

Rigorous sensitivity analysis is conducted on 1000 runs using different parameter values of variables and different seedings.

Then a statistical model is used to test which variables affect the probability of a succession occurring (here to predict the probability of a succession occurring).

Results: successions occur if ‘direct’ utility of new tech product > old tech product

(product characteristics) indirect utility of new tech product > old tech product

(price: production economies & efficiency of production techniques)

Important: initial new design(s) needs to be highly competitive & have a supporting ‘new’ customer group(s)

rate of innovative improvements of new tech firms > rate of innovative improvements of new tech firms (sail ship effect is possible).

** Complex interplay between quality, price and cost is NOT open to investigation in Malerba et al model.

Results:

Time

1. Time old technology firms have to improve performance, price and cost of their designs prior to new technology firms arriving in the market (entry)

2. Time the new firms have to improve to undertake innovation and improve their product & process performance (degree of competition in the market – strength of replicator OR degree to which new consumer preference are distinguished from old consumer preferences).

Issues: comparing the models

1. Both Malerba et al and Windrum-Birchenhall consider sequential competitions, and the conditions under which old technology and established firms may be replaced by new technologies and new firms.

2. But the models are very different in terms of: what they are trying to explain: Malerba et al want to

understand the conditions under which established firms can survive by switching production from old to new technology

the elements used in each model the empirical data they draw upon when building their

models (input) the empirical data that is selected as the ‘stylised facts’

that each model is expected to reproduce

Category 2: Methodological Issues.

Can history to be the final arbiter in theoretical and modelling debates? (as suggested by Malerba et al, and Brenner)

E.H Carr (1961): History itself is neither simple nor uncontested

1. Records that exist are fortuitously bequeathed.

2. In-built biases – Yin (1994) verbal reports are subject to problems of bias, poor recall and inaccurate articulation

3. Missing data

4. Contestability of events, current and past.

5. Process of writing academic history is open-ended; process in which many pieces of ‘data’, bequeathed from the past, are filtered by the historian. Some data accorded status of ‘facts’ by the community of historians. But this status is open to review.

Upshot:

Need to develop high quality accounts, open to critical scrutiny.

On the basis of these accounts, guidance is taken on particular modelling choices, on parameter testing, and output evaluation.

In recognising the limitations of any historical account, we simultaneously recognise the limitations of decisions based on that account.

*** Applies to all historical / empirical approaches to modelling

Further Issues.

1. goal of modelling: what is the advantage of having a very accurate description of one case? (Silverberg’s example: perfect description of the fall of an individual leaf from a tree, or Brownian motion equations)

2. unconditional objects and alternative model testing (Brock, 1999)

3. alternative methods of sensitivity analysis

4. counterfactuals (Cowan & Foray, 2002)

5. ergodicity (what if the system is non-ergodic?)

Further Issues cont.

6. structural change - the relationship of statistical data to evolutionary models, timing effects and lag structures in simulation models

7. calibration – alternative ways to calibrate initial conditions and parameters:

‘Indirect calibration’

First validate, then indirectly calibrate the model by focusing on the parameters that are consistent with output validation.

‘Werker-Brenner approach’

Step 1: use existing empirical knowledge to calibrate initial conditions and the ranges of model parameters Step 2: empirically validate the outputs for each of the model specifications derived from Step 1. This reduces the plausible set of models still further.

Step 3 further round of calibration on surviving set of models and, where helpful, recourse to expert testimony from historians (so-called ‘methodological abduction’).

Conclusions and Forward Look Rapid development & consistent reappraisal of the

boundaries of research, both with respect to the range of phenomena studied and model content.

Development of distinctive features which set neo-Schumpeterian models apart from other models, and which gives them a collective coherence.

1. A distinctive view about the type of world in which real economic agents operate.

2. An identifiable set of algorithms that make up a neo-Schumpeterian simulation model: a search algorithm, a selection algorithm, and a population of objects in which variation is expressed and on which selection operates.

Limitations of the early models are starting to be addressed in various ways.

Malerba et al. have put forward a new methodology and a new model structure.

BUT key issues relating to use of data - applicable to all historical / empirically based modelling

1. unconditional objects and alternative model testing

2. alternative methods of sensitivity analysis

3. counterfactuals

4. ergodicity and structural change - the relationship of statistical data to evolutionary models, timing effects and lag structures in simulation models, and calibration.