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Based on what we know about thermodynamics of synergy, we explored the relationship between countries socio-cultural order (negentropy), estimated through their constitutions, indicators of Rule of Law and their academic development; with countries indicators of Free Energy (amount of useful work, productivity, socioeconomic health). The analysis of 219 indicators unveiled strong correlations between estimates of the Rule of Law and the number of Academic Publications, with the socioeconomic health indicators: GDP, Human Development Index and Infant Mortality. In contrast, correlations with the length of constitutions (number of words and of articles), suggest that the proliferation of legal rules hinders the rule of law and socioeconomic development, or that under-development and/or the lack of the rule of law foments the proliferation of legal rules. These findings suggest that not any order favors productivity (Free Energy) and that excess regulations and state tutelage increase social entropy decreasing socioeconomic health., Comment: Working paper

Understanding the factors underpinning COVID-19 infection and mortality rates is essential in order to implement actions that help mitigate the current pandemic. Here we evaluate how a suit of 15 climatic and socio-economic variables influence COVID-19 exponential growth-phase infection and mortality rates across 36 countries. We found that imports of goods and services, international tourism and the number of published scientific papers are good predictors of COVID-19 infection rates, indicating that more globalized countries may have experienced multiple and recurrent introductions of the virus. However, high-income countries showed lower mortality rates, suggesting that the consequences of the current pandemic will be worse for globalized low-income countries. International aid agencies could use this information to help mitigate the consequences of the current pandemic in the most vulnerable countries.

We argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable criterion levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and determining sample sizes much more directly than significance testing does; but none of the statistical tools should replace significance testing as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, or implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.

Data mining revealed a cluster of economic, psychological, social and cultural indicators that in combination predicted corruption and wealth of European nations. This prosperity syndrome of self-reliant citizens, efficient division of labor, a sophisticated scientific community, and respect for the law, was clearly distinct from that of poor countries that had a diffuse relationship between high corruption perception, low GDP/capita, high social inequality, low scientific development, reliance on family and friends, and languages with many words for guilt. This suggests that there are many ways for a nation to be poor, but few ones to become rich, supporting the existence of synergistic interactions between the components in the prosperity syndrome favoring economic growth. No single feature was responsible for national prosperity. Focusing on synergies rather than on single features should improve our understanding of the transition from poverty and corruption to prosperity in European nations and elsewhere., Comment: 22 pages, 4 figures

In complex systems, many different parts interact in non-obvious ways. Traditional research focuses on a few or a single aspect of the problem so as to analyze it with the tools available. To get a better insight of phenomena that emerge from complex interactions, we need instruments that can analyze simultaneously complex interactions between many parts. Here, a simulator modeling different types of economies, is used to visualize complex quantitative aspects that affect economic dynamics. The main conclusions are: 1- Relatively simple economic settings produce complex non-linear dynamics and therefore linear regressions are often unsuitable to capture complex economic dynamics; 2- Flexible pricing of goods by individual agents according to their micro-environment increases the health and wealth of the society, but asymmetries in price sensitivity between buyers and sellers increase price inflation; 3- Prices for goods conferring risky long term benefits are not tracked efficiently by simple market forces. 4- Division of labor creates synergies that improve enormously the health and wealth of the society by increasing the efficiency of economic activity. 5- Stochastic modeling improves our understanding of real economies, and didactic games based on them might help policy makers and non specialists in grasping the complex dynamics underlying even simple economic settings.