Social and Economic Complexity

Ettore Majorana in 1930 wrote an article entitled: “The Value of Statistical Laws in Physics and Social Sciences”. This visionary article, published posthumously in 1942, posed a problem that is particularly relevant today, especially when considered from the perspective of Big Data. The article discusses the following problem: To what extent can the rigorous methodology of physics be exported to other disciplines to make them more scientific and objective? Perhaps not all the rigor of physics is exportable, but these other disciplines, such as economics, have great importance for our society, as well as a great intellectual value. So even if the degree of rigor rate is only partial, an increase in scientific in these disciplines would be a result of the utmost importance. Furthermore, from an intellectual and scientific point of view, a link would be created between the disciplines of the so-called hard sciences and those of the socioeconomic sciences which in itself would have great cultural and scientific value. This is one of the areas that we intend to explore from the concrete perspective of the science of complexity.

The new challenges and opportunities of the hyper-connected world

In recent years, we have witnessed the emergence of completely unexpected phenomena with respect to which traditional disciplines and analyses appear completely inadequate.

  • Internet, Google, and the digital economy
  • Facebook, social media, and the dynamics of information and disinformation
  • The economic and financial crisis of the past ten years
  • The remarkable and surprising growth of the Chinese economy
  • Disintermediation and the development of the blockchain
  • Sustainability, real well-being, social inequalities, and green and circular economies
  • Smart cities and smart nations
  • Artificial Intelligence and Machine Learning
  • COVID-19, a tragic crisis but also an opportunity to rethink the economy

The common elements of the listed phenomena are an integration of connections and events on a planetary level and the speed with which they evolve and develop unpredictable emergent properties. The inadequacy of traditional concepts and analyses is evident, and the need for new scientific methodologies for the analysis, understanding, and control of these phenomena is clear. The aim is to understand and monitor society in a conscious way to transform these challenges into opportunities. The fundamental idea is to treat these phenomena with original methods, overcoming disciplinary barriers and interacting across the spectrum with academic and political institutions and with the productive world of companies.

The Science of Complexity

This scientific area is somewhat complementary to elementary particle physics which is based on a “reductionist” approach. In fact, the traditional approach of physics is to consider relatively simple and isolated systems and study them in great detail. We therefore consider the elementary “bricks” that are the constituent elements of matter. This reductionist view can be successfully applied to many situations and implies the existence of characteristic scales: the size of an atom, a molecule, or a macroscopic object. However, there are many situations in which knowledge of the individual elements is not sufficient to characterize the properties of the entire system. Moreover, when many elements interact in a nonlinear way, they can give rise to complex structures and properties that cannot be directly connected to the properties of the constituent elements. In these cases, we can think of a sort of “architecture” of nature that depends in some way on the individual elements but also manifests some properties and fundamental laws that cannot be deduced from the knowledge of the microscopic elements that compose it.

“Techno-social” systems is the term commonly used to identify socioeconomic systems in which technology blends in an original and unpredictable way with cognitive, behavioral, and social aspects of human beings. The new communication and information technologies (ICT) play an increasingly pervasive role in our culture and our daily life. This revolution obviously does not come without contraindications, and in our complex societies new global challenges are constantly emerging that constantly require new paradigms and original thinking to address.

In recent years, the science of complexity has shown that it can play an important role in understanding social and economic dynamics. However, we believe that this is only the beginning and that this field will develop in a powerful way into a new and fascinating scientific adventure with radically new transdisciplinary characteristics that are difficult to frame in traditional contexts. For this, one needs a specific point of reference with new skills and characteristics suitable for the new situation.

This opens up perspectives, unimaginable until a few years ago, that skillfully mix diverse disciplines and factors. On the one hand, we can consider the theoretical and modeling tools of the physics of complex systems connected to the ability to analyze, interpret, and visualize complex amounts of data in an original way. On the other hand, the true essence of techno-social systems provides a unique opportunity to exploit new ICT technologies to monitor and quantify the digital traces of human behavior and collective social and economic phenomena with unprecedented resolution. This situation also involves an original synergy between scientific and humanistic disciplines that aims to produce concrete and directly useful results.

Italy can play an important role in these developments for various reasons. On the one hand, the science of complex systems is well present and widely recognized. On the other hand, the elements of creativity and originality associated with these developments are also one of our strengths. Finally, these activities do not require particularly expensive infrastructures and can give rise to important scientific and practical results in a relatively short time.

An Example of the New Strategy: Economic Fitness

Economic Fitness and Complexity (EFC) consists of a radically new methodology that describes economies as evolutionary processes of ecosystems of industrial and financial technologies and infrastructures that are globally interconnected. The approach is multidisciplinary and addresses emerging phenomena in economics from various points of view: Analysis of complex systems; systems-science methods and the recent perspectives in a Big Data context (on the imprint of Google Page Rank and Machine Learning) offer new opportunities to constructively describe technological ecosystems, analyze their structures, understand their dynamics, and introduce new metrics. This approach provides a new paradigm for data-driven, non-ideological, fundamental economics.

A crucial element is a radically new approach to the Big Data problem. Big Data is often associated with “big noise” and a subjective ambiguity on how to structure data and how to assign a value that in practice corresponds to the introduction of many arbitrary parameters, often over 100 for the evaluation of a country’s industrial competitiveness. A key point of the ECF approach is to pass from 100 parameters to zero parameters and therefore to unique and testable results with a significant increase in level of the scientific approach. This is done by focusing on data where the signal-to-noise ratio is optimal and by developing iterative algorithms in spirit, but different from Google, and optimized for the economic problem in question.

The general scheme is not limited to the economy and can be adapted to other issues related to Big Data, such as the social dynamics of information and biological ecosystems, among others. In essence, the Economic Fitness method is based on a series of radically innovative elements oriented towards the goal of making these analyses reproducible, as scientific as possible and testable in detail with the available data. The main elements are:

  • Focus on connections rather than individual subjects. This is a typical feature of complex systems seen as global networks and was the basis for Google’s development of the Page Rank algorithm
  • Strategic and hierarchical selection of data with respect to the signal-to-noise ratio. Elimination of the subjective arbitrariness of the analysis to obtain scientific and verifiable results.
  • Construction of the fitness–complexity algorithm. Each problem requires the development of an appropriate algorithm. From this perspective, Big Data represents an original field where considerable creativity is required.
  • The dynamics that emerge from the PIL-Fitness space are highly heterogeneous. This implies that the analysis and predictions must be done not with the usual regressions but with methods inspired by the dynamics of complex physical systems.
  • All of this provides a better forecasting methodology than the standard money-market fund but with vastly less resources in terms of data and personnel.
  • This whole procedure can then be replicated with data from patents and scientific publications, giving rise to technological fitness and scientific fitness. These data allow a complete and original analysis of the economic competitiveness (products and services) of countries and their prospects (technology and science)
  • The product taxonomy network is built for each country, including temporal development. From this, through appropriate Machine Learning and Artificial Intelligence algorithms, the production probabilities of new products can be identified. In this way, the possible development trajectories for each country are identified.
  • Finally, radical innovations (products that have never appeared before) are identified as the combination of technologies that have never appeared before in the same product. Also, in this case, Machine Learning and Artificial Intelligence methods are used.

This series of innovative and trans-disciplinary elements has made possible an analysis and forecast of the economic growth of countries at a higher level of accuracy than traditional analyses. Over the past three years, the World Bank (IFC-World Bank) has shown great interest in these new methods and has established an intense collaboration with our group. Five people of the group have been appointed official consultants of IFC-World bank, and for some months the fitness method has been officially present on the World Bank website[1] .

 

 

Finally, the Joint Research Center of the EU Commission has recently adopted these new methodologies for the analysis of the competitiveness of EU countries and for the optimal development and planning of innovation.

A Bloomberg Views editorial commented on these developments: “New research has demonstrated that the ‘fitness’ technique systematically outperforms standard methods, despite requiring much less data.”

Recently, together with the World Bank, we presented the EFC method to a Chinese government think-tank (State Information Center) which is considering these methodologies for planning and optimizing Chinese industrial development. In fact, another important result is the analysis of China’s growth over the past 30 years. Traditional analyses over the past 20 years have been expecting this growth to collapse. Fitness, on the other hand, allows us to understand the reason for this great economic success in China and to predict several years of strong development. Since 2014, a debate has been opened on these issues with Larry Summers (former US Treasury Secretary and former Harvard Rector) who has so far confirmed the greater predictive capacity of our analyses.

We believe that, from the example of the Fitness method, it is possible to learn interesting concepts that can be applied more or less directly to the other fields mentioned at the beginning of this article. It should be noted that the most successful algorithms for Artificial Intelligence (those developed for chess and Go) are also based on methods other than brute force and structured in a way similar to Fitness. In fact, it starts with a restricted selection of the optimal data, then an algorithm is developed, and only at the end is Machine Learning used. This introspective structure of the approach also has the advantage of making the analysis relatively transparent and interpretable instead of relying blindly on a black box.

Figure 2 The figure shows the temporal evolution of wealth (GDP per capita) in the ordinate and of Fitness (abscissa) for different countries. For low Fitness values (left side of the graph) the motion is chaotic and irregular, while for countries with high Fitness (right side) a regular upward flow is observed, indicating the fact that different countries are growing in a systematic. This type of approach, different in spirit and methodology than traditional economic analyzes, allows us to make very good long-term forecasts and to indicate the optimal strategies for improving the industrial competitiveness of the various countries with optimized strategies for each country and industrial sector.

Big Data between opportunity and Myth

The immense accumulation of data in all fields represents a new phenomenon with great potential but which sometimes leads to excessive and even mythical expectations. The original strategy behind this proposal is to develop quantitative tools that go beyond standard Machine Learning to increase our ability to understand and use these immense amounts of information. The idea is to develop a scientific, transparent, and testable approach. This philosophy has already found concrete application in the development of the Economic Fitness method. The use of machine-learning algorithms and methods must be adapted and optimized for each class of problems to identify and understand the essential elements of the various phenomena. In general, the data do not speak for themselves, and there is no universally valid recipe.

The fundamental point is to overcome the mere accumulation of data but rather to focus on the optimal analysis of the available data from the perspective of the relationship between signal and noise. In fact, the idea that more data necessarily give rise to better analyses and forecasts is, in general, invalid. This phenomenon is well-known in the physics of dynamic systems in which the increase in the dimensionality of space implies an exponential increase in the necessary data. In practice, this is a very general problem: If heterogeneous data describing the productive structure of a nation, such as education and pollution, are added together, arbitrary weights must inevitably be assigned. The strategy will be to scientifically evaluate these issues. This represents an original but essential element of our approach to introduce verifiable scientific elements in the field of Big Data. In practice, the idea is to reduce or eliminate the subjective and arbitrary elements and treat the data in a hierarchical and systematic way. This leads to considerable advantages in terms of scientificity and predictive capacity, as demonstrated by the Economic Fitness method.

Reinventing Economic Theories and Practices at the Time of COVID-19

The tragic events of COVID-19 lead to a situation in which government intervention will inevitably be the protagonist of socioeconomic reconstruction. Even the staunchest supporters of the free market seem to agree on this point. The fundamental lesson we must learn from these events shows that it is illusory to seek an ideal economic theory, valid for any situation. This implies that, in the historic debate between the state and the market, it was the question that was wrong more than the possible answers. A paradigm shift therefore appears necessary in which we start from a detailed analysis of the situation and consider the possible trajectories for its development. The essential elements of this approach are a critical analysis of all past events and a scientific approach to verify ideas and hypotheses objectively and without a priori ideologies. Some of these new concepts have already been formulated: In particular, the “New Structural Economics” (NSE) developed by the Justin Lin group and our “Economic Fitness and Complexity” share some important general elements developed in different perspectives. The two approaches are indeed complementary, and their unification can give rise to a new vision for economic theories and practices. The project aims to realize these developments that have as a natural consequence an innovative and interdisciplinary analysis based on modern scientific data and the methods of the field of Complex Systems (networks, algorithms, Machine Learning, etc.) to objectively define the state of an economy and its possible development paths. The economic recovery from COVID-19 can be optimized with the methodologies that can provide scientific and informed and transparent analyses for the political decision maker and for society in general.

Over the past 50 years, many developing countries have tried to improve their economies with strong government leadership. In most cases, they have failed. But classic examples of pure free market policies, such as Chile in the 1960s and others, have also failed. So from these examples, it would appear that both theories are wrong. On the other hand, in the few successful cases, both elements of both the state and the market can be identified. An excellent example is China which, since 1978, has adopted a gradual transition from a planned economy to a market economy, in which the important roles of both the state and the free market can be clearly identified. A similar argument, albeit in a completely different context, can also be made for Silicon Valley.

Considering the inevitable role that government interventions for COVID-19 will have to play, it is particularly important to critically analyze the reasons for the numerous failures of government strategies in various past examples and learn important lessons from these. The essential point has been the inability of governments to establish effective criteria to identify the appropriate industrial sectors with respect to the actual capacities and development potential of a given country.

In fact, in an editorial in The Economist (January 9, 2016) it is argued that “growth is devilishly hard to predict”. Clearly we must expect these same difficulties to apply for planning after COVID-19, and it is therefore essential to try to remedy these problems with new scientific and interdisciplinary methods.

The new methodologies of NSE and EFC propose a paradigm shift in which the question is changed rather than looking for an answer that cannot exist. From the traditional dilemma on the search for a unique and perfect economic theory, we move on to a radically different question: “What is the economic theory and interventions that are appropriate for this country, for this industrial sector, and in this period? Just as in medicine there is no single cure that is valid for every disease, so in economics there cannot be a single theory or practice that is valid for every country and at every moment. In this sense, the various theories and strategies can all have some validity in a specific context, but none can be the correct one for each country and each period.

So the essential point is to scientifically focus and characterize the heterogeneity of the various situations and then identify the appropriate interventions that will necessarily be different and dependent on the different situations. In this way, it is proposed to overcome the ideological debate in a scientific perspective, as in medicine we have passed from miraculous potions to true medical science. The fundamental point of view for these analyses is therefore a detailed characterization of the situation of a country and its current and potential industries. Most of the industrial policies that have failed have set themselves the objectives of industrial sectors that were not compatible with the industrial ecosystem of the country in terms of comparative advantage. The possibility of a scientific analysis of these elements allows for a realistic assessment of whether it will be possible to have access to certain technologies and products and what is the gain in terms of the complexity of the economic ecosystem and its possible developments.

This project aims to create a national and international hub for the development of NSE and EFC methodologies in the direction of a paradigm shift and a new scientific approach to economic theories but also to provide concrete and scientific analyses for the development of the country, especially in the difficult post-COVID-19 period. Both the recovery of industrial capacities and the orientation towards new socioeconomic balances can obtain concrete and relevant information on the various scenarios from these studies. The idea is to create a center located in the CREF that serves as the coordinator of these activities at the national level, acquiring all the possible skills that can be useful from the university system, from research institutions and also from the private sector with which we have already developed excellent collaborations.

International Collaborations

Of course, the CREF will also be an international interlocutor with prestigious institutions that will concretely collaborate in the development of the project.

For over two years, the World Bank in Washington has been using our ECF methodologies for the development and monitoring of over 30 countries. We can therefore guarantee the very concrete involvement of the World Bank in this project, both in terms of ideas and applications but also in terms of resources and personnel. The Institute for New Structural Economics of Peking University, founded and directed by Prof. Justin Lin, is a natural partner for this project. It should be noted that this institution is particularly influential on the Chinese government’s strategies. The Joint Research Center of the EU Commission has also expressed interested in these methodologies through funding it with a special project, while starting to adopt them for its strategies.

In addition to these three main partners, we also intend to develop many other collaborations at European and international level with many research groups that already use these methodologies and with others who consider doing so.

The Dynamics of Information in the Social World

Social media has revolutionized the way we communicate and inform ourselves, becoming the main source of information for most users. Facebook has more than two billion users, who generate more than three million posts per minute, informing themselves and informing without the intermediation of journalists and experts, thus actively participating in the production and dissemination of news and content. Recent studies have shown how user groups are concentrated in echo chambers that formulate and confirm their favorite narratives, systematically countering any dissident information. In this situation, the effectiveness of fact-checking and debunking is highly questionable; instead, innovative tools are needed that address the problem of fake news using methods based on data analysis and the formulation of specific and dedicated algorithms. The proposing group intends to apply the same criteria of scientific and methodological rigor that led to the introduction of the Economic Fitness methodology to the problem of (dis-) information online, to the study of the diffusion of contents, to the analysis of the formation of echo chambers, and to study of the dynamics that lead users to the spiral into echo chambers.

Creativity and Science: An opportunity for Italy

Creativity is increasingly seen as the engine of progress in all sectors of human activity: art, science, technology, economics, business, and social policies. Creativity is clearly connected to how people explore, individually or collectively, the space of accessible possibilities. This method of exploration is the new way to create added value for oneself and for the community. The goal of this institute is to combine these creative elements with the utmost scientific rigor by overcoming the barriers inherent in individual disciplines. This situation of increased creativity can be seen as the combination of human and Artificial Intelligence.

At the same time, in fact, artificial intelligence alone is extremely powerful for “difficult” and well-posed problems, but it is difficult to imagine strategies that could be effective in a volatile and constantly evolving environment. It is even more difficult to imagine the application of Artificial Intelligence methods in situations in which the space of possibilities is not only very vast and complex but also, to some extent, indefinite. This implies a paradigm shift: inventing new solutions instead of looking for new solutions. It is evident that this situation is quite common for the problems of complex societies, for scientific research, for the development of new products and services, and clearly for economic development. Artificial Intelligence and Big Data are essential to each other, but they are optimal only for those problems that can be mathematized in a precise way, and in general this is not the case in many of the complex systems we intend to consider. We therefore propose to use them in an original way to increase our understanding of the social and economic systems for which large amounts of data are available.

In this sense, our proposal is configured in an original manner compared to those of the USA, China, Germany, France and many other countries to which enormous resources are assigned but within a relatively traditional AI vision. We therefore believe that Italy has all the elements to create something original in this field even with limited resources, as happened with the economic forecasts of the Fitness method compared to those of the International Monetary Fund (IMF).

Relations with Political Institutions and with the Productive World

The activities will be integrated with the dynamics of political action, and the Fitness analysis already allows a certain number of observations and suggestions. For a high-quality and structurally stable economy, originality and innovation are essential elements of development and growth. The Fitness analysis enables us to identify the current degree of competitiveness and its prospects for concrete development in the various industrial sectors of a country. In this sense, it is essential for the development of tomorrow’s companies as well as providing valid and concrete information support to them today.

In this sense, it is essential to have a clear separation between important and urgent issues. The economic success of a country cannot happen only with short-term financial transactions, but it is also essential to have a long-term strategic vision that focuses on the innovation and quality of future companies. To this end, we have already begun a detailed analysis of the products and services of the Italian regions to identify the best development strategies. This type of analysis also provides important information for the production world and companies and also various collaborations in this field are already active that will be systematically increased. Of course, these elements are also integrated into an analysis of both environmental and social sustainability with attention also to the green and circular economy.

Finally, the Economic Fitness method focuses on the essential elements of a country’s economic and social development and identifies them in the development of quality products and services that require long-term strategies rather than on immediate and short-term financial elements. The development of Silicon Valley and recently of China represent important examples of this interpretation.

Even for immediate perspectives, a more objective and scientific analysis is essential to understand, control, and give the appropriate weight to the various elements at stake. For example, various origins can be attributed to the current crisis in Italy: The financial crisis originated by the Lehman bankruptcy, Europe, and the Euro; China’s growth and competitiveness; bureaucracy; justice; immigration, etc. At the moment, the weights attributed to these various phenomena essentially depend on the narrative within which they are described and therefore on emotional and rhetorical elements. Instead, it would be essential to understand the real role of these various phenomena and move from an essentially narrative analysis to one that is as objective and scientific as possible. We believe that CREF can make an important contribution in this direction.

From this same perspective of scientific objectivity, we believe we can consider fundamental problems such as pollution, sustainability, social well-being, and the green and circular economies. This does not mean reducing the space for political decisions but providing an information framework that makes these decisions more based.

Present and future collaborations and synergies

At the moment, the proposing group can already count on collaborations and support from many institutions. However, it is intended to broaden the spectrum of collaborations and constitute a national and international hub for these activities at a scientific level, for political planning and for the production world and companies.

  • ISC-World Bank, Washington (Masud Cader, Leader Country analytics)
  • Sony Lab, Parigi (Vittorio Loreto, direttore)
  • CNEL, MIUR, MISE, MAE: Istituzioni politiche italiane con cui già collaboriamo
  • Joint Research Center, EU (Vladimir Sucha, direttore generale)
  • Luohan Academy e Ant Financial, Alibaba, Hangzhou (Ted Chu, Chief Economist)
  • Boston Consulting Group, New York and Paris (Martin Reeves, strategic institute)
  • Assoknowledge, Confindustria (Laura Deitinger, Presidente)
  • Incubatore di Start Up (Cristiano Esclapon, banchiere)
  • Vola Gratis (Marco Corradino, CEO, imprenditore)
  • PI-CAMPUS (Marco Trombetti, CEO, imprenditore)
  • Università Sapienza, Dipartimenti di Fisica, Informatica, Economia e Statistica
  • Università LUISS
  • Istituto dei Sistemi Complessi del CNR
  • Università Tor Vergata
  • Università Statale di Milano
  • Università Cattolica di Milano
  • Università Bocconi, Milano
  • Università Politecnica delle Marche
  • University College, London
  • Kings College, London
  • Imperial College, London
  • ETH Zurich, CH
  • University of Zurich, CH
  • University of Friburg, CH (Y.C. Zhang)
  • Complexity Science Hub Vienna (S. Thurner)
  • Columbia University, NY (Joseph Stiglitz)
  • Harvard University, Boston (Larry Summers)
  • Oxford University, UK (Eric Beinhocker)
  • Tokyo University of Technology (M. Takayasu)

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