History-friendly Model Research Articles

Simulating the industrial revolution: a history-friendly model

Simulating the industrial revolution: a history-friendly model

Simulating the Industrial Revolution: A History-Friendly Model

Simulating the Industrial Revolution: A History-Friendly Model

크리스탈 사이클과 수요, 기술의 공진화: TFT-LCD 산업의 역사 친화적 모형

This paper aims at building a history-friendly model which replicates the history of the Thin Film Transistor-Liquid Crystal Display (TFT-LCD) industry focusing on the industrial business cycle(crystal cycle) and co-evolution of demand and technology. The model is distinctive compared to existing models addressing industrial business cycles in two points: 1) endogenous demand and technological change are incorporated 2) industrial cycle affects evolution of demand and technology as well as are caused by the change of demand and technology, that is, we consider industrial business cycle as a medium of co-evolution of demand and technology. The simulation results show that it can successfully replicate the crystal cycle, and investment-production delay, price delay and endogenous change of demand are factors of the crystal cycle. Moreover, we can see positive feedback between demand expansion and technological advance through medium of the cycle.

Demand-led catch-up: a history-friendly model of latecomer development in the global green economy

AbstractThis article examines the role played by demand in catching up and in leadership changes in green industries, motivated by the belief that demand-led catch-up is a prevalent pathway in such industries. The article first examines stylized cases of sectoral green catch-up by China in which the local market and domestic demand played an important role before the sector started expanding globally. In particular, the focus is on three industries: wind, biomass and hydropower. Then, it uses a history-friendly model to study the effects of a major increase in domestic demand (a “demand window”) in a green industry. The baseline simulation first examines the effects of a demand window in promoting learning and capability building by latecomers and in triggering a catch-up process. Then, the counterfactual simulations show that (i) a technological discontinuity which takes place after the demand window could reduce the effectiveness of the demand window in the catch-up process; (ii) the specific timing of the demand window could significantly alter the dynamic patterns of catch-up; (iii) protectionism is a necessary condition for the demand window to have its effect; and (iv) regimes of slow capability accumulation could turn out to be beneficial for the latecomer when a technological discontinuity follows the demand window. These results can help policymakers in identifying key conditions related to demand-led catch-up strategies.

Modelling the Coevolution of the Fuel Ethanol Industry, Technology System, and Market System in China: A History-Friendly Model

The interaction among the fuel ethanol industry, the technology system, and the market system has a substantial effect on the growth of the fuel ethanol industry which plays a key role in the formation of a sustainable energy system in China. However, we know little about the relationships among them and it is difficult to explore the nexus using econometric method due to the lack of statistics on China’s fuel ethanol industry. This paper develops a history-friendly coevolutionary model to describe the relationships among the fuel ethanol industry, the technology system, and the market system in China. Based on the coevolutionary model, we further assess the impacts of entry regulations, production subsidies, R&D subsidies, and ethanol mandates on the growth of the fuel ethanol industry in China using a simulation method. The results of historical replication runs show that the model can appropriately reflect the multidirectional causalities between the fuel ethanol industry, the technology system, and the market system. We also found that entry regulation is conducive to weakening the negative economic impacts induced by the growth of the grain-based fuel ethanol industry without affecting the long-term total output of the industry; production subsidies to traditional technology firms are helpful for the expansion of the fuel ethanol industry, but they also impede technology transfer in the industry; only when firms inside the industry are not in the red can R&D subsidies promote technological progress and then further accelerate the growth of the fuel ethanol industry; the ethanol mandate has a significant impact on industrial expansion only when a production subsidy policy is implemented simultaneously. Our findings suggest that more attention could be paid to consider the cumulative effects caused by coevolutionary mechanisms when policymakers assess the effects of exogenous policies on the growth of the fuel ethanol industry. More attention also could be paid to the conditions under which these policies can work effectively.

The long march to catch-up: A history-friendly model of China’s mobile communications industry

This paper develops a history-friendly model of the process of catch-up by Chinese firms in the mobile communications industry. It aims to explain how the sectoral environment in terms of segmented markets and generational technological change facilitated the catch-up of domestic firms with respect to foreign multinationals. Segmented markets provided a nurturing environment in peripheral markets for the survival of domestic firms starting with low level capabilities in their infant stage. Generational technological change opened windows of opportunities for domestic firms to catch-up with foreign multinationals in new product segments. Segmented markets and generational technological change allowed domestic firms to leverage their initial advantages in peripheral markets to catch-up in core markets. Counterfactual simulations highlight that the process of catch-up was facilitated by relatedness across technological generations. This paper contributes to the literature on catch-up and industry evolution by illustrating the role of technological change and market regimes in the process of catching-up.

A History-friendly Model of China’s Mobile Communications Industry

This paper develops a history-friendly model of China’s mobile communications industry to explain how the sectoral environments (e.g., segmented markets and generational technological changes) of the industry facilitated domestic firms’ catching-up with respective to foreign multinationals. In a nutshell, segmented markets provided domestic firms with a nurturing ground in the peripheral (rural) market for surviving their infant ages, whereas generational technological changes opened windows of opportunities for domestic firms to catch-up with foreign multinationals in new product segments. Segmented markets, together with generational technological changes, allowed domestic firms to transform their initial comparative advantages in the rural market into rapid catching-up in the urban market. This paper mainly contributes to the literature on industry evolution by illuminating the role of market/technological regimes in the dynamic catching-up process.

A history-friendly model of the successive changes in industrial leadership and the catch-up by latecomers

Successive changes in industrial leadership between both firms and countries (described here as catch-up cycles) have been common in several sectors. This article develops a history-friendly model to explore the role played by technological conditions in the emergence of such leadership changes. The model is inspired by two cases where the emergence of disruptively novel technology played an important role: mobile phones and semiconductors. In the baseline setting the model is able to generate the benchmark case of three cycles with two leadership changes. In particular, the simulation analysis reveals that: (a) the more disruptive the new technology and the lower the incumbents’ capabilities, the greater the shake-up of market shares between incumbents and latecomers; (b) leadership change is more likely to occur when it coincides with certain responses by the actors to the technological disruption, such as a high lock-in behaviour on the part of incumbents; and (c) a technology-driven change of industrial leadership is more likely to occur in the presence of increasing returns to technological investments. The counterfactual experiments show that different catch-up dynamics can emerge depending on the magnitude of technological disruption, the degree of lock-ins, the shape of technological landscape, and incumbents’ initial capabilities. In particular, four other types of catch-up cycle are generated – the aborted cycle, persistent leadership, return of the old leadership, and coexistence in leadership between latecomers and incumbents. Each of these cycles is identified with a specific historical case of catch-up.

A Simulation Model of the Evolution of the Pharmaceutical Industry: A History-Friendly Model

This paper examines the evolution of the pharmaceutical industry. After a brief discussion of the main stylised facts about the industry, we present a history-friendly model that aims at capturing the underlining mechanisms and the logic that guides the evolution of this industry. Simulation results show the mechanisms and dynamic processes linking the patterns of innovation, demand structure, and concentration.

Una aproximación amistosa con la historia a la competitividad de los departamentos y a las disparidades regionales en Colombia

Desde hace algún tiempo, algunas organizaciones especializadas han venido publicando informes sobre la competitividad de los departamentos colombianos, en los que se destaca, en primer lugar, la existencia de grandes disparidades en sus niveles de competitividad, las cuales no sólo se han conservado, sino que, incluso, se han ampliado; y, en segundo lugar, que en las mediciones son unos mismos departamentos los que ocupan una y otra vez las mejores y peores posiciones, lo que parece configurar una situación estructural. Particularmente, estos informes han destacado la posición de liderazgo de Antioquia –aunque significativamente después de Bogotá/ Cundinamarca–, pero no se han ocupado demasiado en interpretar el fenómeno, ciñéndose a cuantificarlo y describirlo. Dado que los indicadores de competitividad departamental se correlacionan positivamente con el nivel relativo de desarrollo de los departamentos y sus políticas de gestión, en este artículo se ofrece un Modelo Amistoso con la Historia para interpretar los niveles de competitividad de los departamentos colombianos, particularmente el nivel alcanzado por Antioquia.

Vertical integration and disintegration of computer firms: a history-friendly model of the coevolution of the computer and semiconductor industries

In this article, we present a history-friendly model of the changing vertical scope of computer firms during the evolution of the computer and semiconductor industries. The model is history-friendly, in that it attempts at replicating some basic, stylized qualitative features of the evolution of vertical integration on the basis of the causal mechanisms and processes, which we believe can explain the history. These factors are identified in the coevolution of capabilities, the size of markets, and the structure of industries. In particular, the basic assumption is that the principal force behind the patterns of vertical integration and disintegration of computer firms was the differential development of capabilities for designing and producing semiconductors among firms. On this basis, the changing boundaries of firms are analyzed in the context of dynamic and uncertain technological and market environments, characterized by periods of technological revolutions punctuating periods of relative technological stability and smooth technical progress. The model illustrates how the patterns of vertical integration and specialization in the computer industry change as a function of the evolving levels and distribution of firms' capabilities over time and how they depend on the coevolution of the upstream and downstream sectors. Specific conditions in each of these markets-the size of the external market, the magnitude of the technological discontinuities, the lock-in effects in demand-exert critical effects and feedbacks on market structure and on the vertical scope of firms as time goes by. Copyright 2008 , Oxford University Press.

Validating agent-based marketing models through conjoint analysis

Agent-based modelers in the field of marketing research have paid little attention to validation issues. This paper provides a definition of validation relevant for this community of modelers. On the basis of a history-friendly model for simulation calibration [Malerba, F., Nelson, R., Orsenigo, L., and Winter, S. (1999). ‘History-friendly’ models of industry evolution: the computer industry. Industrial and corporate change, 8(1), 3–40.], the authors demonstrate how conjoint analyses can be used to instantiate and calibrate an agent-based marketing model. Methods for model instantiation using conjoint partworths and model calibration using the conjoint first-choice rule are demonstrated. When the model matches the results of the first-choice rules for consumer preferences, the modeler can feel more confident that calibration has been achieved. When verification replicates stylized facts on a macro-level, the model is one step closer to validation. Because conjoint data results are meaningful on an individual level as well as on an aggregate level, this type of empirical data collection is ideal for agent-based marketing models.

Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model

This paper is a first attempt at modelling the long-term dynamics of market structure and innovation in the pharmaceutical industry in a history-friendly way. The model examines the relationships between the nature of the search space, demand, the patterns of competition, and industry evolution in the age of random screening and in the age of molecular biology, and shows that concentration in the pharmaceutical industry is shaped by lack of cumulativeness in innovative activities and market fragmentation. The model conforms to our appreciative understanding and responds to changes in parameters concerning demand, costs, economies of scale, opportunity conditions and the relative advantages of new biotechnology firms (NBFs) vis-a-vis incumbents. With the exception of cost increases, the model is quite robust to these changes in its essential features: it is quite difficult to raise substantially concentration and to have NBFs displacing incumbents.