Market, innovation, competition: An evolutionary model of industrial dynamics

Kwaśnicki, Witold; Kwaśnicka, Halina

doi:10.1016/0167-2681(92)90042-a

Cited by 51 publications

(32 citation statements)

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“…The values of the initial conditions and parameters in this setting are chosen because they represent plausible conditions. Thus, we assume that all firms start out from a positive profit situation; R&D costs are significantly lower than production costs; parameters λ i , r i are taken such that the growth rate of the national stock of scientists ( λ i w i (t) r i ) is plausible; and, finally, in accordance with previous contributions Winter, 1982 or Kwasnicki andKwasnicka, 1992), we have chosen plausible values for r i , θ, δ, k i (0) and A i (0).…”

Section: The Sources Of Industrial Leadershipmentioning

confidence: 99%

Industrial leadership in science-based industries: A co-evolution model

Fatás-Villafranca

Jarne

Chóliz

2009

Journal of Economic Behavior & Organization

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In this paper, we seek to analyse the role of national university systems in combination with technological and market factors as sources of industrial leadership and industry growth in sciencebased industries. We propose a model in which national university systems and their respective national firms and industries are considered as co-evolving. National firms compete on a worldwide level and they rely on the progress of science and the availability of scientists to innovate. As the global industry develops, firms try to mold their national university systems, but they achieve different degrees of success. Apart from highlighting the role of institutional responsiveness as a source of competitive advantage, our model points to the access to essential inputs for production, the technological and strategic characteristics of firms, the international diffusion of knowledge, and the initial distribution of market demand as key sources of leadership and industry growth. The international mobility of scientists seems to foster the emergence of industrial leadership shifts.

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Section: The Sources Of Industrial Leadershipmentioning

confidence: 99%

Industrial leadership in science-based industries: A co-evolution model

Fatás-Villafranca

Jarne

Chóliz

2009

Journal of Economic Behavior & Organization

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“…These algorithms are not introduced to represent faithfully the exact learning mechanism of agents but to just take into account, in the least ad hoc way, the presence of learning (see Marengo (1992) for a precursory application of evolutionary algorithms to models of learning). The models developed by Kwasnicki (see Kwasnicki & Kwasnicka (1992) and Kwasnicki (1998)) use a representation of the learning of firms already very close to genetic algorithms (see Figure 1 and Goldberg (1991)). Even if the R&D strategies of firms do not directly result from learning and adaptive processes, the result of this R&D (i.e.…”

Section: Towards Richer Adaptation: the Emergence Of A Unified Modellmentioning

confidence: 99%

Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

Yıldızoğlu¹

2001

E.J.E.S.S.

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In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of "internal model". The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982).

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“…Alternatively, selection may be modelled as a two-stage or a multi-level process: internal and external to the firm. Internal selection concerns selection of routines at the level of a firm, while external selection is typically understood in terms of market selection (Kwasnicki and Kwasnicka 1992;Lazaric and Raybaut 2005). For instance, in Kwasnicki and Kwasnicka (1992) each firm searches for new routines (or new combinations of existing routines) to increase its overall competitiveness.…”

Section: Selectionmentioning

confidence: 99%

“…Alternatively, a firm can be treated as a multi-operation unit (e.g., Kwasnicki and Kwasnicka 1992;Chiaromonte and Dosi 1993;Dosi et al 1994bDosi et al , 2006. For instance, in Kwasnicki and Kwasnicka's (1992) model of industry dynamics, each firm is characterized by two types of routines: active ones employed in everyday practice, and latent ones stored but not actually applied. Routines here are modelled with genetic algorithms.…”

Section: F(a; T a It ) Where A It Is Firm I's Current Productivitymentioning

confidence: 99%

“…The string representation offers a convenient way to code consumer preferences (Aversi et al 1997), production designs Birchenhall 1998, 2005), firm routines (Kwasnicki and Kwasnicka 1992), production rules in cobweb models (Arifovic 1994(Arifovic , 1995Dawid and Kopel 1998;Frenke 1998), production functions (Birchenhall 1995;Birchenhall et al 1997), pricing strategies (Curzon Price 1997), or strategies in a Prisoners Dilemma (Axelrod 1987;Miller 1996).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

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Evolutionary models in economics: a survey of methods and building blocks

2009

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This paper assesses methods and components of formal evolutionary-economic modelling. Methods are broadly classified into evolutionary game theory and selection dynamics, evolutionary computation and multi-agent models, each with relevant subcategories. The components or building blocks are organized into diversity, innovation, selection, bounded rationality, diffusion, path dependency and lock-in, coevolution, multilevel and group selection, and mechanisms of growth. The number of alternatives that has been proposed for each category is vast, making it difficult to comprehend the variety of assumptions and formalizations underlying existing evolutionary-economic models. Our survey aims to clarify for each model component the choice range, formal expressions, associated assumptions, and possible techniques for formalization. Our study is unique in that it provides more information about the formal details of specific model components and is considerably more inclusive than earlier reviews.

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Market, innovation, competition: An evolutionary model of industrial dynamics

Cited by 51 publications

References 1 publication

Industrial leadership in science-based industries: A co-evolution model

Industrial leadership in science-based industries: A co-evolution model

Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

Evolutionary models in economics: a survey of methods and building blocks

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