How the Taxonomy of Products Drives the Economic Development of Countries

We introduce an algorithm able to reconstruct the relevant network structure on which the time evolution of country-product bipartite networks takes place. The significant links are obtained by selecting the largest values of the projected matrix. We first perform a number of tests of this filtering procedure on synthetic cases and a toy model. Then we analyze the bipartite network constituted by countries and exported products, using two databases for a total of almost 50 years. It is then possible to build a hierarchically directed network, in which the taxonomy of products emerges in a natural way. We study the influence of the structure of this taxonomy network on countries' development; in particular, guided by an example taken from the industrialization of South Korea, we link the structure of the taxonomy network to the empirical temporal connections between product activations, finding that the most relevant edges for countries' development are the ones suggested by our network. These results suggest paths in the product space which are easier to achieve, and so can drive countries' policies in the industrialization process.Comment: 16 pages, 8 figure

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course. A proof-of-principle study

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients

Economic Complexity

Nowadays economic systems have evolved to become globalized, largely financialized and in- terconnected. This observation leads directly to another: any approximation that regards them as the sum of many independent parts is going to fail. In other words nowadays economic systems are Complex Systems and they are much more than the sum of their single parts, for the fact that they show emergent chaotic behaviours, that we are not able to predict nor to ex- plain within the mainstream framework. As the set of relevant interactions among economic entities is much wider than what has been used to build present-day’s economic theories, we can’t build up on these theories anymore. What we need is probably more similar to a brand new start. If we want our theories to be predictive in such a complex scenario we need to link them tightly to data and let our models go beyond overly simplified linear approaches. More importantly, we need to state clearly the limits of these theories and to discriminate situations where we can actually make predictions from those where we can’t. The methods, approaches, data and ideas proposed in this work and the way these ingredi- ents are combined together, embody our view on how a brand new start in Macroeconomic modeling should look like, for two reasons. The first is our concern in remaining constantly linked to quantitative data. Of course we make assumptions about some underlying mecha- nisms and general principia, but we constrain their validity to the extent of the ability of our methods to perform quantitative predictions. The second is the fact that we always try to use the right method for the right problem. Examples of this are provided in chapters 2 and 4 where methods derived from dynamical systems and machine learning are used and tested in the field of economic prediction. Mainstream economic modeling is often rigid and sim- plistic from a methodological point of view, with a predominant and pervasive use of linear regressions and descriptive statistics. These tools have strict validity limits and, from a con- ceptual point of view, provide a low level of falsifiability, unless a comprehensive theoretical framework is available. Thus, since reductionism is made hard in economics by the impossibil- ity to repeat experiments and control the environment, our goal is always to look at concrete predictions and we value our results on the basis of their accuracy. The philosophy that drives our approach is the similarity that exists between economic and ecologic systems. As discussed in chapter 3 economy and ecology are close not only for shar- ing the same root as a word, but also in the very essence of the interactions they describe: sets of individual competing and cooperating for the allocation of common resources. Once this connection is put in focus it is easy to move concepts and ideas among the two fields and start to observe similarities in the data that we have about economic networks and ecologic ones. In particular we explore the concept of nestedness in interaction networks, observed in ecology and economics: namely when specialized entities only interact with generalist ones. This means that only generalists have access to exclusive relationships. Both in economics and ecology this provides an obvious advantage and leads to the identification of the concept of diversification with a measure of fitness, intended as the ability to perform well and survive in the ecosystem. The presence of a nested structure also suggest the presence of a precise under- lying dynamics in the formation of such networks, and allows us to discover very informative taxonomic structures, as described in the final chapter. We open our discussion in chapter 1 with an empirical observation about the structure of the bipartite network defined by countries and the products they export. The nestedness of this network is exploited to build a quantitative measure of complexity for countries’ national economies and for products, in a self-consistent way. This measure of complexity is then used in chapter 2 to introduce a framework in which we are able to predict the Macroeconomic development of a set of countries. By using an approach derived from dynamical systems we are able to define quantitatively the level of predictability of a country’s development, sim- ilar in spirit to a weather forecast, where turbulent areas are much harder to predict. The parallelism with biological systems is studied more in detail in chapter 3, where we observe how the build-up of diversity seems to follow some universal features. We propose a minimal scheme to model this dynamics and we introduce the concept of Usefulness which reveals to be a crucial feature if we want to picture real-world diversity as the combination of smaller Building Blocks. Finally in chapter 4 we look at how the export data can be used to infer tech- nological relations among products. These relations are relevant for countries’ development, as we show that countries move in recurrent paths when developing. The accuracy of our methods in predicting new links in the countries-products network is quantitatively assessed and results to be as high as 16% in a-priori selected subsets

Measuring the intangibles: a metrics for the economic complexity of countries and products

We investigate a recent methodology we have proposed to extract valuable information on the competitiveness of countries and complexity of products from trade data. Standard economic theories predict a high level of specialization of countries in specific industrial sectors. However, a direct analysis of the official databases of exported products by all countries shows that the actual situation is very different. Countries commonly considered as developed ones are extremely diversified, exporting a large variety of products from very simple to very complex. At the same time countries generally considered as less developed export only the products also exported by the majority of countries. This situation calls for the introduction of a non-monetary and non-income-based measure for country economy complexity which uncovers the hidden potential for development and growth. The statistical approach we present here consists of coupled non-linear maps relating the competitiveness/fitness of countries to the complexity of their products. The fixed point of this transformation defines a metrics for the fitness of countries and the complexity of products. We argue that the key point to properly extract the economic information is the non-linearity of the map which is necessary to bound the complexity of products by the fitness of the less competitive countries exporting them. We present a detailed comparison of the results of this approach directly with those of the Method of Reflections by Hidalgo and Hausmann, showing the better performance of our method and a more solid economic, scientific and consistent foundation

Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control

Formal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this pa- per we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to ver- ify various safety properties using the tool NEVER2, a new framework that integrates learning and verification in a sin- gle easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning

A network analysis of countries' export flows: firm grounds for the building blocks of the economy

In this paper we analyze the bipartite network of countries and products from UN data on country production. We define the country-country and product-product projected networks and introduce a novel method of filtering information based on elements' similarity. As a result we find that country clustering reveals unexpected socio-geographic links among the most competing countries. On the same footings the products clustering can be efficiently used for a bottom-up classification of produced goods. Furthermore we mathematically reformulate the "reflections method" introduced by Hidalgo and Hausmann as a fixpoint problem; such formulation highlights some conceptual weaknesses of the approach. To overcome such an issue, we introduce an alternative methodology (based on biased Markov chains) that allows to rank countries in a conceptually consistent way. Our analysis uncovers a strong non-linear interaction between the diversification of a country and the ubiquity of its products, thus suggesting the possible need of moving towards more efficient and direct non-linear fixpoint algorithms to rank countries and products in the global market.Comment: 17 pages, 5 figure

Growth scenarios for sub-Saharan countries in the framework of economic complexity

We present a comparative analysis of the medium-long term perspectives of development for sub-Saharan countries in the framework of economic complexity. This analysis is made in comparison with the development of Asian tigers. Economic complexity is a data-driven framework which aims at providing a more scientific basis for the economic theory and it has a specific focus on understanding the determinants of growth by means of two new economic dimensions: the country fitness and the product complexity. We argue that the fitness of countries is a quantitative assessment of those intangible assets, which drive the growth. The comparison of this measure for intangibles with monetary figures provides effective insights on the growth potential of countries and defines the fitness-income plane. The analysis of the dynamics in this plane reveals that most sub-Saharans get stuck in a pre-industrial regime which can be thought as a generalized poverty trap where both income and fitness dimensions are considered. Only Senegal, Kenya, Tanzania, Madagascar and Uganda show a behavior compatible with the early steps of a long term stable and sustained growth, which resembles the one of Vietnam and Malaysia at the beginning of the nineties. As expected, South Africa is the most mature economy of the southern part of Africa. However, its trajectory highlights the concrete risk of an incomplete development of its productive system in terms of diversification, which might concretely jeopardize South Africa’s chance to reach the level of wealth of fully developed countries and put the country at risk of getting stuck in the so-called middle-income trap