Transformation Networks: A study of how technological complexity impacts economic performance (original) (raw)
Related papers
arXiv preprint arXiv:1112.4708, 2011
Abstract: A transformation network describes how one set of resources can be transformed into another via technological processes. Transformation networks in economics are useful because they can highlight areas for future innovations, both in terms of new products, new production techniques, or better efficiency. They also make it easy to detect areas where an economy might be fragile. In this paper, we use computational simulations to investigate how the density of a transformation network affects the economic performance, as ...
Reconciling contrasting views on economic complexity
Nature Communications, 2020
Summarising the complexity of a country’s economy in a single number is the holy grail for scholars engaging in data-based economics. In a field where the Gross Domestic Product remains the preferred indicator for many, economic complexity measures, aiming at uncovering the productive knowledge of countries, have been stirring the pot in the past few years. The commonly used methodologies to measure economic complexity produce contrasting results, undermining their acceptance and applications. Here we show that these methodologies – apparently conflicting on fundamental aspects – can be reconciled by adopting a neat mathematical perspective based on linear-algebra tools within a bipartite-networks framework. The obtained results shed new light on the potential of economic complexity to trace and forecast countries’ innovation potential and to interpret the temporal dynamics of economic growth, possibly paving the way to a micro-foundation of the field.
Structural change and economic dynamics: Rethinking from the complexity approach
Journal of Dynamics & Games, 2019
Economic systems have evolved through time thereby changing the structure that characterizes them. These changes respond to technological changes that transform economies into highly interconnected systems. The modifications in the norms that guide the behaviour of organizations and, therefore the functioning of the economy, are a first case of this transformation. The industrialization process, through the incorporation of increasing returns to scale in different sectors, and the introduction of service activities are other examples. Another form to represent structural change is the change of the values of the variables that characterize the state space of an economic system. This research article is an effort to put together and compare, from the complexity approach, different approaches for structural change and dynamics of economic systems. We start by briefly presenting the complexity approach in general and in economics. Then, we put forward three approaches highlighting structural change.
The building blocks of economic complexity
In ref. 4, Maddison presents GDP per capita measures for 60 countries since 1820. In that year, the ratio of the 95th to the 5th percentile was 3.18 but it increased to 17.82 by the year 2000. Today, the U.S. GDP per capita is Ͼ60 times higher than Malawi's. This article contains supporting information online at www.pnas.org/cgi/content/full/ 0900943106/DCSupplemental.
Structural Change and Economic Dynamics, 2018
A wide range of economic development theoreticians has discussed the manufacturing sector's properties as an engine for economic growth. More recently, the sophisticated services sector began to share similar characteristics with the industrial sector as a driver for economic growth, particularly as a locus of technological innovation. This paper considers the symbiotic relationship between these two sectors, and assesses their importance in the technological development of countries. More precisely, this study uses economic complexity analysis and input-output matrices to assess the importance of employment creation in advanced sectors of countries. Results show that in the long-run economic complexity depends on the effort and the ability of countries to generate employment in manufacturing and sophisticated services sectors.
The dynamics of economic complexity and the product space over a 42 year period
2009
How does the productive structure of countries' changes over time? In this paper we explore this question by combining techniques of networks science with 42 years of trade data and find that, while the Product Space remains relatively stable during this period, the dynamics of countries' productive structures is characterized by a few highly dynamic economies. In particular we identify Brazil,
The Economy as an Evolving Complex System, III
2005
In September 1987 twenty people came together at the Santa Fe Institute to talk about "the economy as an evolving, complex system." Ten were theoretical economists, invited by Kenneth J. Arrow, and ten were physicists, biologists and computer scientists, invited by Philip W. Anderson. The meeting was motivated by the hope that new ideas bubbling in the natural sciences, loosely tied together under the rubric of "the sciences of complexity," might stimulate new ways of thinking about economic problems. For ten days, economists and natural scientists took turns talking about their respective worlds and methodologies. While physicists grappled with general equilibrium analysis and noncooperative game theory, economists tried to make sense of spin glass models, Boolean networks, and genetic algorithms. The meeting left two legacies. The first was a volume of essays, The Economy as an Evolving Complex System, edited by Arrow, Anderson and David Pines. The other was the founding, in 1988, of the Economics Program at the Santa Fe Institute, the Institute's first resident research program. The Program's mission was to encourage the understanding of economic phenomena from a complexity perspective, which involved the development of theory as well as tools for modeling and for empirical analysis. To this end, since 1988, the Program has brought researchers to Santa Fe, sponsored research projects, held several workshops each year, and published several dozen working papers. And since 1994, it has held an annual summer school for economics graduate students. This volume, The Economy as an Evolving Complex System II, represents the proceedings of an August, 1996 workshop sponsored by the SFI Economics Program. The intention of this workshop was to take stock, to ask: What has a complexity perspective contributed to economics in the past decade? In contrast to the 1987 workshop, almost all of the presentations addressed economic problems, and most presenters were economists by training. In addition, while some of the work presented was conceived or carried out at the Institute, some of the participants had no previous relation with SFI-research related to the complexity perspective is under active development now in a number of different institutes and university departments. But just what is the complexity perspective in economics? That is not an easy question to
Agent-Based Modelling of Innovation Networks – The Fairytale of Spillover
Understanding Complex Systems, 2009
Springer Complexity is an interdisciplinary program publishing the best research and academic-level teaching on both fundamental and applied aspects of complex systems-cutting across all traditional disciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and computer science. Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous formation of distinctive temporal, spatial or functional structures. Models of such systems can be successfully mapped onto quite diverse "real-life" situations like the climate, the coherent emission of light from lasers, chemical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of the internet, earthquake statistics and prediction, freeway traffic, the human brain, or the formation of opinions in social systems, to name just some of the popular applications. Although their scope and methodologies overlap somewhat, one can distinguish the following main concepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems, catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptive systems, genetic algorithms and computational intelligence. The two major book publication platforms of the Springer Complexity program are the monograph series "Understanding Complex Systems" focusing on the various applications of complexity, and the "Springer Series in Synergetics", which is devoted to the quantitative theoretical and methodological foundations. In addition to the books in these two core series, the program also incorporates individual titles ranging from textbooks to major reference works.