Abstract
For grasping the relationship between novelty creating activities of agents and growth of economic aggregates, a multi-level approach is suggested. The first level specifies the triggering conditions for novelty creating activities for the agents, i.e. firms. Here the behavioral elements and the modes of actions for the firms are portrayed using an agent-based approach (Section2). On the second level, the consequences of successful innovations and imitations in a given sector of economic activities are dealt with (Section 3). This depends on the frequency of successful novelties and on the way they diffuse in that sector. We use an agent-related functional approach, applying difference equations for depicting the stylized facts of the diffusion dynamics. Only if these different levels of economic dynamics are distinguished as well as related to each other, is it possible to derive aggregate effects of novelties for the whole economy. This will be done by way of computer simulations (Section 4). Conclusions are drawn in Section5.
Reprinted from Journal of Evolutionary Economics 22(3), 513–542, Springer (2012).
This research was supported by the Volkswagenstiftung. Comments by an anonymous referee and programming assistance by Ramón Briegel are gratefully acknowledged.
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Notes
- 1.
In this article, we distinguish the notions of “novelty creation”, “imitation” and “innovation”. By the generic term “novelty creation” we integrate all aspects of the creation of new products in firms. This encompasses radical product innovation (simply called “innovation” in the sequel) as well as, according to the definition of OECD, Eurostat (2005, p. 46), “imitations” which comprise innovations that are new at the level of the firm, but not for the market. Our definition of novelty creation takes a broader scope than the definition provided by Witt (2009, p. 312), who refers to fundamental novelty. On the other hand, our notion is narrower than Witt’s as we refer only to product innovations. (These may involve physical products and/or services.)
- 2.
The so-called endogenous growth theory is the most prominent example for such an endeavor.
- 3.
A mode of action is defined by a particular way to care about information, to discriminate between alternatives and to link different practical operations.
- 4.
In the given context, ‘individual’ means ‘one firm’ and ‘cooperative’ means at least two firms interacting directly.
- 5.
Apart from that tackling a technologically embodied change is not an easy analytical task for which a well defined solution recipe exists (Silverberg and Verspagen 1995, p. 9).
- 6.
Product innovation was introduced in a Nelson/Winter framework for the first time by Gerybadze (1982). Recently, Dawid and Reimann 2010 used an approach similar to the one suggested in the present contribution in a model on product diversification. In their model, the demand potential of a sub market is functionally dependent on the firms innovative efforts (measured in terms of r&d investments). This potential determines the attractiveness of a product variant. The attractiveness of each product variant changes according to a function in which time enters as an independent variable.
- 7.
This procedure manifests the importance of the multi-scale property for analyzing the economy as a ‘complex adaptive system’ (Arthur et al. 1997).
- 8.
According to this caveat, the novelty creating process is totally conjectural without anything to generalize. Due to the idiosyncratic nature of the processes, as well as of the persons involved in innovations, some authors see only a limited possibility for an after-the-fact analysis on an aggregated level (Vromen 2001). For a critical discussion of these assumptions cf. Beckenbach and Daskalakis (2003 pp. 3)
- 9.
This focus allows for deriving characteristics on the firm level from characteristics at the individual level.
- 10.
- 11.
For a discussion of the risk conceptions of managers, see March and Shapira (1987).
- 12.
We use the term in a broad sense, including the notions of cooperation, national and regional innovation networks, clusters, industrial districts, and so on.
- 13.
The expected profit is calculated by a linear regression on past profits weighted by a parameter reflecting angent-specific degrees of optimism/pessimism.
- 14.
The expected costs are calculated by multiplying the given costs per time step and the average time for novelty creating processes. According to empirical findings, it is assumed that the costs of an imitation project are lower than the costs of an innovation project.
- 15.
If the novelty creating process is intended as an imitation, the knowledge about another firm’s product (improvement) which appeared recently in the market is required as an additional feasibility condition.
- 16.
For reasons of model fine tuning, these relationships are formalized as elasticities (power of ε) with different weights (w).
- 17.
The knowledge reserves (kr) are operationalized as the relation of the number of sharable knowledge domains (see below) of the agent to the total number of sharable knowledge domains; the financial resources (fr) are operationalized as the share of the current profit in relation to current turnover.
- 18.
Setting the preservation force as a constant is no restriction of generality since the absolute values of the forces F i do not matter; it is only the ratio between them which determines the action mode. There are three special or exceptional cases in which the selection mechanism mentioned above is not applied (or even not applicable) two of which concern start up firms and one concerns firms with a negative or zero profit. (See Beckenbach et al. 2009 for a more detailed discussion.)
- 19.
At the beginning, each firm is assigned randomly a set of supplier firms, being fixed for the whole simulation.
- 20.
The absorptive capacity is conceptualized as a given probability weight for the cooperation to happen.
- 21.
If the right hand side of the equation is negative, the probability is set to 0. We assume 0≤tr≤1 and 0≤ac ≤1.
- 22.
Independently of the knowledge transfer described above, there are two probabilistic “knowledge destruction” processes taking place in each time step: a decay (forgetting) of knowledge for each firm and a general devaluation (depreciation) of knowledge for all firms as a global effect of technological change.
- 23.
Cf., however, Geroski (2000) for an exposition of how a basic diffusion model can be enriched to incorporate heterogeneity on the part of the adopting agents.
- 24.
An intermediate outcome emerges if y(t0) = yts(t). In this case, the demand approaches the threshold value yts(t).
- 25.
In order to achieve this sectoral outcome, the demand facing the individual firm is rescaled by a scaling factor: \(\mbox{sf(t)}=\frac{\mbox{Y(t)}+(1-su)\;\mbox{W(t}+1)}{\mbox{Y(t)}+\mbox{W(t}+1)}\).
- 26.
These shares for the behavioral types are taken from findings of an empirical investigation in the region of Northern Hesse in Germany (Beckenbach et al. 2009).
- 27.
Values in Fig. 7 are cumulated average values of the respective indicators at the end of the simulation run.
- 28.
For each combination of values for these parameters, a Monte Carlo simulation with 16 runs with different seed values for the random number generator has been run. The darker the grey, the higher the respective indicator.
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Appendix: Core of the Simulation Model (Pseudo-Code)
Appendix: Core of the Simulation Model (Pseudo-Code)
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Beckenbach, F., Daskalakis, M., Hofmann, D. (2013). Agent-Based Modelling of Novelty Creating Behavior and Sectoral Growth Effects—Linking the Creative and the Destructive Side of Innovation. In: Buenstorf, G., Cantner, U., Hanusch, H., Hutter, M., Lorenz, HW., Rahmeyer, F. (eds) The Two Sides of Innovation. Economic Complexity and Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-01496-8_7
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