Mapping Economic Complexity
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Increasing interconnectedness and growing complexity of economic and financial systems raise the question of how economic growth and strength should be measured. New economic metrics and economic theories borrow from complexity studies and other disciplines, creating a new paradigm to think about economic development and relations.


  • Several new institutions want to research the fundamentals of complex, adaptive systems and their behavior, such as the Santa Fe Institute and the Institute of New Economic Thinking. Their aim is to integrate other disciplines, like psychology, physics, computer sciences, biology, anthropology, in the study of economic systems.
  • The Atlas of Economic Complexity measures how the knowledge of the society is translated into its production. Complex economies export not only highly complex products, but also a large number of different products. Japan is the most complex economy of the world, followed by Switzerland and Germany.
  • New Economic Metrics tries to encode the intangible assets of economic systems, like social capital or the quality of export products, using machine-learning tools. Their approach perceives economic outcomes as the result of adaptive and evolutionary processes, and it develops metrics on the ‘fitness’ of the national economy in the globalized and interconnected world economy.


The concept of economic complexity can help us understand some of the flaws and limitations of the models that are used in mainstream economics. Complexity studies in general study systems that consist of many components that interact and adapt to each other, but whose outcomes are hard to model because the many and insecure independencies, relationship, and interactions of the parts that form the whole. They exist at almost every scale: the universe, societies, the human brain, living cells. In these systems, outcomes change continuously and equilibria are often unstable and without steady state convergence. As a result, the understanding of complex systems requires a holistic point of view by seeing how order and stable patterns emerge in these systems, e.g. collective human behavior, diseases, coherent human thought (like philosophy), or the remarkable stability and order in the observable universe.

These complex systems have distinct characteristics that transcend disciplines and objects of study.

These complex systems have distinct characteristics that transcend disciplines and objects of study. The first is non-linearity, or the idea that the change in the input is not proportional to the change in the output, like self-reinforcing waves, revolutions, or general relativity. Another key concept is ‘emergence’: properties of larger entities that are not possessed by its smaller organism, like conscious biological life or a piece of art. An idea related to this is ‘spontaneous order’ or ‘self-organization’, where order arises from the behavior of individual parts, which is nonetheless not planned or (centrally) orchestrated. Examples are the evolution of life on earth, behavior on social media platforms, free market economies, or the structure of crystals. Lastly, ‘feedback loops’ is a concept where the output of a system is at the same time the input of a new (causal) process, like in biospheres, electronic engineering, or stock markets. These concepts show that understanding complex systems require a multidisciplinary approach, and that it’s practically impossible to model all the possible inputs, relationships, and interdependencies.

This paradigm is already fashionable in natural sciences, like astrophysics, (quantum)physics and biology, and is currently introduced in heterodox economics. New concepts are still needed for understanding the complex nature of economic growth in the globalized and digitized economy. For example, when gauging the strength of an economy, we need to look at its ‘resilience’: how well it can overcome shocks and overcome periods of stress, like financial market turbulence, wars, or changing trade patterns. Does an economy generate oscillating dynamics with more unstable equilibria, or does the system have self-regenerating capacity to return to a stable order? Another term is the ‘elasticity’ of an economy’s growth capacity: how well it can respond and adapt to changing economic conditions. For example, a research paper on the fitness of the BRIC economies shows that the higher growth paths of China and India can be explained from the growing complexity of their industrial and technological development curves vis-à-vis the Russian and Brazil economies. Furthermore, the embedded and interdependent nature of complex economic systems within other systems, like social or cultural systems, perceives service economies as distributed computer networks that process information, inputs and data flows into new products and services. Economics in this sense is a social learning process, in which output is dependent on the quality of other inputs and information within the system. For example, how well the economy reacts to new developments and whether it has the capacity to benefit from technological innovations, depends on whether an economy has the required types of capital: social, financial, human, or infrastructural


  • Complex systems require an approach that combine quantitative data research with qualitative human judgement and understanding. In this way, economic complexity studies are the opposite of reductionist paradigms, which reduce their object of study to one aspect, and require meta-analyses and a multidisciplinary approach.
  • Understanding economies like distributed computer networks provides a new take on the strengths and weaknesses of the economy. For example, Japanese export products (which are highly complex) have much to benefit from globalization, as there are few other countries that are able to compose the necessary intangible assets for producing these goods.