Concepts and Models of a Quantitative Sociology

Weidlich, Wolfgang; Haag, Günter

doi:10.1007/978-3-642-81789-2

Cited by 347 publications

(127 citation statements)

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“…The model draws on Haken's synergetic approach (Haken, 1978) and is based on a Master-Equation algorithm (see Weidlich and Haag, 1983). The other notable feature of the model is its evolutionary character.…”

Section: Modelling the Self-organisation Of Strategic Alliancesmentioning

confidence: 99%

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The self-organisation of strategic alliances

Pyka

Windrum

2003

Economics of Innovation and New Technology

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Strategic alliances form a vital part of today's business environment. The sheer variety of collaborative forms is notable -which include R&D coalitions, marketing and distribution agreements, franchising, coproduction agreements, licensing, consortiums and joint ventures. Here we define a strategic alliance as a cooperative agreement between two or more autonomous firms pursuing common objectives or working towards solving common problems through a period of sustained interaction. A distinction is commonly made between 'formal' and 'informal' inter-firm alliances. Informal alliances involve voluntary contact and interaction while in formal alliances cooperation is governed by a contractual agreement. The advantage of formal alliances is the ability to put in place IPR clauses, confidentially agreements and other contractual measures designed to safeguard the firm against knowledge spill-over. However, these measures are costly to instigate and police. By contrast, a key attraction of informal relationships is their low co-ordination costs. Informal know-how trading is relatively simple, uncomplicated and more flexible, and has been observed in a number of industries.A number of factors affecting firms' decisions to cooperate or not cooperate within strategic alliances have been raised in the literature. In this paper we consider three factors in particular: the relative costs of coordinating activity through strategic alliances vis-à-vis the costs of coordinating activity in-house, the degree of uncertainty present in the competitive environment, and the feedback between individual decision-making and industry structure. Whereas discussion of the first two factors is well developed in the strategic alliance literature, the third factor has hitherto only been addressed indirectly. The contribution to this under-researched area represents an important contribution of this paper to the current discourse. In order to focus the discussion, the paper considers the formation of horizontal inter-firm strategic alliances in dynamic product markets. These markets are characterised by rapid rates of technological change, a high degree of market uncertainty, and high rewards (supernormal profits) for successful firms offset by shortening life cycles.

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Section: Modelling the Self-organisation Of Strategic Alliancesmentioning

confidence: 99%

“…2 See Weidlich/Haag (1983). The state space x describes the particular distribution of cooperative firms n 1 and non-cooperative firms n 2 .…”

Section: Mean Field Analysismentioning

confidence: 99%

The self-organisation of strategic alliances

Pyka

Windrum

2003

Economics of Innovation and New Technology

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“…A third interesting development in the area of dynamic discrete choice modelling can be found in the so-ealled master-equation approach advocated among others by Weidlich and Haag (1983). A master equation describes the evolution of the probability distribution function, representing the transition probabilities for well defined states of a dynamic micro-based spatial system of actors.…”

Section: Outlook: Alternative Approaches To Modelling the Dynamics Ofmentioning

confidence: 99%

“…This increased attention is due to methodological advances in the area of (in)stability and (dis)equilibrium analysis (e.g., Nicolis and Prigogine, 1977, Thom, 1975and Weidlich and Haag, 1983, to the progress made in the statistical, mathematical and computerized handling of non-linear dynamic models, and to the general awareness that the economies of most countries are going through a stage of structural (i.e., non-linear dynamic) change.…”

Section: Introductionmentioning

confidence: 99%

From static towards dynamic discrete choice modelling

Fischer

Nijkamp

1987

Regional Science and Urban Economics

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The main aim of the present paper is to survey sorae major trends in current research in the field of discrete choice modelling, with particular emphasis on dynamic approaches. The paper is organized as follows. Section 2 provides a brief overview of static disaggregate choice modelling and random utility maximimation, based inter alia on multinomial logit and/or probit models, generalized extreme value models, and nested logit models. Particular attention is given here to model representation issues, sampling and estimation issues and model performance issues. Next, section 3 is devoted to some recent developments in the rapidly growing new field of dynamic discrete choice modelling. In contrast to stochastic panel data models of buying behaviour, dynamic discrete choice models incorporate explanatory variables and take adaptive behaviour explicitly into account (i.e. the effect of past experience on choice behaviour). Several dynamic discrete choice model approaches are summarized. Special attention is paid to the seminal work of Heekman. In the final section, complementary and alternative approaches to dynamic choice modelling are discussed, such as the human activity constraint approach, the computational process modelling approach and the master equation approach. It is concluded that contextual effects, multi-actor or synergetic interactions and shifting individual preferences based-on learning principles are of primary importance in dynamic discrete choice modelling.

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“…In particular, economie theory and modelling has adopted various concepts and research strategies from the natural sciences, for example from biology, ecology and classical physics. Consequently, we may classify the various non-linear dynamic models developed in the past decade and which are capable of exhibiting bifurcations, oscillations and also self-organising structures, according to the following prototypes: a) models based on logistic laws of biological populations (see, e.g., many analyses based on Harris and Wilson's (1978) models) ; b) models based on the dissipative structures emerging from physics and chemistry (see, e.g., Allen, 1982); c) models derived from the interaction laws in synergetics (see e.g., Weidlich and Haag, 1983); d) models based on ecological/biological processes of a prey/predator type (see, e.g., Dendrinos and Mullally, 1981); e) models derived from ecological theories of innovation diffusion (see, e.g., Blommestein andSonis, 1987).…”

Section: Introductionmentioning

confidence: 99%

Space-Time Dynamics, Spatial Competition and the Theory of Chaos

Nijkamp¹,

Reggiani²

1993

Structure and Change in the Space Economy

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In economics and regional science we observe recently a growing interest in the use of non-linear dynamic models. In particular nonlinear deterministic models appear quite appealing, since they may exhibit both oscillating and chaotic behaviour and hence may provide an endogenous explanation of the periodicity and/or irregularity observed in the dynamics of spatial economie systems.In this paper the aim is to analyse consequences of spatial dynamic competition in regional systems including the possibility of chaotic behaviour. Non-linear deterministic models often used in ecology and biology will be investigated in this framework. Although these models by definition do not display oscillating behaviour, it will be shown that the particular case of a 'chaotic' evolution of May-type in one region will lead to an oscillating divergent behaviour in the entire system, if the intrinsic growth rates of the competing regions exceed a critical value (beyond which instability begins).The analysis will be illustrated by means of simulation experiments for both the cases of two and three competing regions.

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Concepts and Models of a Quantitative Sociology

Cited by 347 publications

References 0 publications

The self-organisation of strategic alliances

The self-organisation of strategic alliances

From static towards dynamic discrete choice modelling

Space-Time Dynamics, Spatial Competition and the Theory of Chaos

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