On measuring complexity in a post-industrial economy: the ecosystem’s approach

Ivanova, Inga A.; Smorodinskaya, Nataliya V.; Leydesdorff, Loet

doi:10.1007/s11135-019-00844-2

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Overall, new metrics have improved ECI's capacity to explain different social variables of relevance (to cite some of them: (Tacchella et al, 2012(Tacchella et al, , 2013Cristelli et al, 2013;Ivanova et al, 2017Ivanova et al, , 2019Ivanova et al, , 2020Gala et al, 2018)). ECI variants continue to show their capacity to make useful predictions on economic growth (Hidalgo, 2015;Hausmann et al, 2014;Ourens, 2012;Poncet & de Waldemar, 2013;Chávez et al, 2017;Tacchella et al, 2018;Domini, 2019;Stojkoski et al, 2016), income inequality (Hartmann et al, 2017;Zhu et al, 2020;Sbardella et al, 2017;Fawaz & Rahnama-Moghadamm, 2019) and even environmental performance (Neagu & Teodoru, 2019;Can & Gozgor, 2017;Mealy & Teytelboym, 2020;Romero & Gramkow, 2021;Boleti et al, 2021).…”

Section: Economic Complexitymentioning

confidence: 99%

“…As previously mentioned, some attempts to improve ECI were proposed, such as the Fitness and product Complexity Index (FCI) (Tacchella et al, 2012), the Patent Complexity Index (PatCI) (Ivanova et al, 2017) or the Modified Economic Complexity Index (Ivanova et al, 2020). However, these multivariable approaches might be potentially subject to the typical problems of missing data, bias, weighting, or underlying factors identification.…”

Section: Improved Economic Complexity Indexmentioning

confidence: 99%

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Economic Complexity and Information Society Paradigms: A Hybrid Contribution to Explain Economic Growth

Jurado-González

Gómez-Barroso

2022

Technological and Economic Development of Economy

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In the last decades, the Information Society (IS) paradigm has brought together various research lines related to the diffusion and adoption of Information and Communication Technologies (ICTs) and their contribution to economic, social and human development. On the other hand, other research lines have recently explored the phenomenon of Economic Complexity (EC), providing new metrics to quantify this decisive factor for the prediction of economic growth and other human relevant variables. This article explores these two trends with the construction of two composite indexes. Then, it evaluates their explanatory power to predict economic growth, first separately, and afterwards together. The results show they can predict economic growth and improve their predictive capacity by working combined. In the conclusions, some difficulties and challenges for the development of these metrics are analyzed.

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Section: Economic Complexitymentioning

confidence: 99%

Section: Improved Economic Complexity Indexmentioning

confidence: 99%

Economic Complexity and Information Society Paradigms: A Hybrid Contribution to Explain Economic Growth

Jurado-González

Gómez-Barroso

2022

Technological and Economic Development of Economy

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“…In this paper, we propose a new model that studies the behavior of an IoT network with the predator–prey approach with external intromission of a third species (smart building maintenance staff). Although the LV approach is often used in animal ecosystems, there are some successful cases of the application of the LV model to other fields such as economy [20,21,22], chemistry [23,24,25], and infectious diseases [26,27] to name but a few. We have conducted an in-depth study of some of the most transferable applications in the industry, demonstrating that our approach is novel in the field of IoT and cannot be compared to other similar techniques.…”

Section: Proposed Approachmentioning

confidence: 99%

Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka–Volterra Ecosystem Model

Casado‐Vara

Canal-Alonso

Rey

et al. 2019

Sensors

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Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges encountered by IoT networks is that the the data they collect may be unreliable since IoT devices can lose accuracy for several reasons (sensor wear, sensor aging, poorly constructed buildings, etc.). The aim of our work is to study the evolution of IoT networks over time in smart buildings. The hypothesis we have tested is that, by amplifying the Lotka–Volterra equations as a community of living organisms (an ecosystem model), the reliability of the system and its components can be predicted. This model comprises a set of differential equations that describe the relationship between an IoT network and multiple IoT devices. Based on the Lotka–Volterra model, in this article, we propose a model in which the predators are the non-precision IoT devices and the prey are the precision IoT devices. Furthermore, a third species is introduced, the maintenance staff, which will impact the interaction between both species, helping the prey to survive within the ecosystem. This is the first Lotka–Volterra model that is applied in the field of IoT. Our work establishes a proof of concept in the field and opens a wide spectrum of applications for biology models to be applied in IoT.

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“… 5 5 Several other related efforts are noteworthy, including Cristelli et al (2013) who propose a statistical approach to relate the competitiveness of countries to the complexity of their products based on coupled nonlinear maps, and Ivanova et al (2020) who propose the Modified Economic Complexity Index as a possible refinement to two relevant complexity measures: the ECI and the Fitness and Complexity Index. …”

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confidence: 99%

Innovation‐adjusted economic complexity and growth: Do patent flows reveal enhanced economic capabilities?

Lybbert

2021

Review Development Economics

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Understanding economic growth has been a primary pursuit for generations of economists. Hidalgo and Hausmann (2009) (Proceedings of the National Academy of Sciences, 106, 10570–10575) charted new territory in this pursuit by using bipartite international trade networks to characterize the economic complexity of a country based on the capabilities implied by the products it exports. The resulting Economic Complexity Index (ECI) correlates strongly with income levels and predicts subsequent economic growth with some statistical precision. We propose an innovation‐adjusted ECI (i‐ECI) that uses a high‐resolution patent–trade concordance to construct trade networks weighted by domestic and international patent application flows. The i‐ECI thus reflects firms’ decisions about where to seek patent protection on industry‐specific inventions, allows for heterogeneity in the relative importance of patenting across industries, and implicitly captures firms’ perceived innovation potential in the recipient countries. Although the i‐ECI is highly correlated with the ECI (correlation coefficient greater than 92%), it is a stronger and more statistically precise predictor of economic growth than the unadjusted ECI.

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On measuring complexity in a post-industrial economy: the ecosystem’s approach

Cited by 15 publications

References 18 publications

Economic Complexity and Information Society Paradigms: A Hybrid Contribution to Explain Economic Growth

Economic Complexity and Information Society Paradigms: A Hybrid Contribution to Explain Economic Growth

Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka–Volterra Ecosystem Model

Innovation‐adjusted economic complexity and growth: Do patent flows reveal enhanced economic capabilities?

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