Maximum-Entropy Tools for Economic Fitness and Complexity

Krantz, Ruben; Gemmetto, Valerio; Garlaschelli, Diego

doi:10.3390/e20100743

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The maximum entropy principle is extensively applied in many disciplines [ 28 , 29 , 30 , 31 , 32 ]. In information theory, if there is a discrete random variable, X , with possible values of { x1, x2, …, xn }, and probability mass function, P(X ), the entropy, H, of X is defined as follows:

where E is the expectation operator, r is the logarithmic base, which generally takes a value of two [ 33 , 34 , 35 ] (in this study, r = 2).…”

Section: Methodsmentioning

confidence: 99%

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

Chen

Jin

Shang

et al. 2019

Entropy

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Assembly accuracy and accuracy stability prediction are significant research directions for improving the reliability and efficiency of precision assembly. In this study, an improved method for assembly accuracy stability prediction, based on the contact-pressure distribution entropy, is presented. By using the contact-pressure distribution as the evaluation parameter instead of the strain-energy distribution, the improved method can not only predict the assembly accuracy of precision assembly more efficiently, but also predict the stability of the assembly accuracy with variations in the ambient temperature. The contact pressure has a clearer mechanical significance than strain energy density in the assembly process, which can be used to distinguish the actual contact area from the contact surface. Hence, the improved method is more efficient and accurate than the original. This study utilizes the same case used in the original method and an additional case from the actual production process to verify the improved method. The correctness and validity of the improved method are proved by these case studies.

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where E is the expectation operator, r is the logarithmic base, which generally takes a value of two [ 33 , 34 , 35 ] (in this study, r = 2).…”

Section: Methodsmentioning

confidence: 99%

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

Chen

Jin

Shang

et al. 2019

Entropy

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“…The index introduced by Balassa, named Revealed Comparative Advantage (RCA) and extensively employed in the field of Economic Complexity [1][2][3][4]7], does not follow from any formal derivation, being simply deduced from a quite general rationale. In fact, it can be shown that Balassa's RCA is a comparison between the empirical value of a country-specific product export and its expected counterpart under a null-model preserving the total export of each country and the total export of each product [8][9][10]. It is also worth mentioning that the application of RCA has gone far beyond international trade, as it has been applied (sometimes with a different name) to analyze patenting data for technologies [11,12], employment data for sectoral studies [13] and scientific publication for science production [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Third, we overcome the intrinsic limitation of Balassa's recipe, which prescribes comparing an empirical value with its expected counterpart (as noticed in [10], an overall poor validation from a statistical point of view). Specifically, using the BiWCM's probability distributions, we calculate the p-value associated with each country-specific product export and perform a proper test of hypothesis.…”

Section: Introductionmentioning

confidence: 99%

Inferring comparative advantage via entropy maximization

Bruno,

Mazzilli,

Patelli

et al. 2023

J. Phys. Complex.

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We revise the procedure proposed by Balassa to infer comparative advantage, which is a standard tool in Economics to analyze specialization (of countries, regions, etc). Balassa’s approach compares a country’s export of a given product with what would be expected from a benchmark based on the total volumes of countries and product flows. Based on results in the literature, we show that implementing Balassa’s idea leads to conditions for estimating parameters conflicting with the information content of the model itself. Moreover, Balassa’s approach does not implement any statistical validation. Hence, we propose an alternative procedure to overcome such a limitation, based upon the framework of entropy maximization and implementing a proper test of hypothesis: the ‘key products’ of a country are, now, the ones whose production is significantly larger than expected, under a null-model constraining the same amount of information defining Balassa’s approach. What we found is that country diversification is always observed, regardless of the strictness of the validation procedure. Besides, the ranking of countries’ fitnesses is only partially affected by the details of the validation scheme employed for the analysis while large differences are found to affect the rankings of product complexities. The routine for implementing the entropy-based filtering procedures employed here is freely available through the official Python Package Index PyPI.

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Bow-tie structures of twitter discursive communities

Mattei

Pratelli

Caldarelli

et al. 2022

Sci Rep

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Bow-tie structures were introduced to describe the World Wide Web (WWW): in the direct network in which the nodes are the websites and the edges are the hyperlinks connecting them, the greatest number of nodes takes part to a bow-tie, i.e. a Weakly Connected Component (WCC) composed of 3 main sectors: IN, OUT and SCC. SCC is the main Strongly Connected Component of WCC, i.e. the greatest subgraph in which each node is reachable by any other one. The IN and OUT sectors are the set of nodes not included in SCC that, respectively, can access and are accessible to nodes in SCC. In the WWW, the greatest part of the websites can be found in the SCC, while the search engines belong to IN and the authorities, as Wikipedia, are in OUT. In the analysis of Twitter debate, the recent literature focused on discursive communities, i.e. clusters of accounts interacting among themselves via retweets. In the present work, we studied discursive communities in 8 different thematic Twitter datasets in various languages. Surprisingly, we observed that almost all discursive communities therein display a bow-tie structure during political or societal debates. Instead, they are absent when the argument of the discussion is different as sport events, as in the case of Euro2020 Turkish and Italian datasets. We furthermore analysed the quality of the content created in the various sectors of the different discursive communities, using the domain annotation from the fact-checking website Newsguard: we observe that, when the discursive community is affected by m/disinformation, the content with the lowest quality is the one produced and shared in SCC and, in particular, a strong incidence of low- or non-reputable messages is present in the flow of retweets between the SCC and the OUT sectors. In this sense, in discursive communities affected by m/disinformation, the greatest part of the accounts has access to a great variety of contents, but whose quality is, in general, quite low; such a situation perfectly describes the phenomenon of infodemic, i.e. the access to “an excessive amount of information about a problem, which makes it difficult to identify a solution”, according to WHO.

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Maximum-Entropy Tools for Economic Fitness and Complexity

Cited by 4 publications

References 16 publications

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

Inferring comparative advantage via entropy maximization

Bow-tie structures of twitter discursive communities

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