Social systems are always exposed to critical processes in which their organization, or part of it, is questioned by the society that demands solutions through different critical saliences. The traditional approach to such social crises has mainly focused on their anticipation and management, implying that the focus is on trying to deal with crises once they occur, rather than delving in their essential characteristics that seemingly depend on the adaptive nature of the system and the increase in its internal complexity. (...) To address this issue, we propose a dual approach that utilizes both qualitative and quantitative methods in order to delve into the relationship between the complexity of the social system, its adaptation, and critical episodes. Our analysis shows how an explosive economic growth affects a social system, increasing its complexity. This complexity produces different demands from the system itself. These demands manifest signatures of complexity such as a heterogeneous and rich social structure, which emerges during moments when the society acts strongly. (shrink)

There is a growing concern about the effects that the relationship between the activity of society in the physical world and in the digital world could have. In this study, we address this question in a context of social crisis. Our quantitative and qualitative analysis of the data associated with the critical process suggests a deep and nontrivial relationship between both worlds. Perhaps the most important result refers to the leading role of language, its meaning, and symbolism in the development (...) of social transformation processes linked to the complexity of the social system and its adaptive nature. (shrink)

This work responds to a criticism of effective complexity made by James McAllister, according to which such a notion is not an appropriate measure for information content. Roughly, effective complexity is focused on the regularities of the data rather than on the whole data, as opposed to algorithmic complexity. McAllister’s argument shows that, because the set of relevant regularities for a given object is not unique, one cannot assign unique values of effective complexity to considered expressions and, therefore, that algorithmic (...) complexity better serves as a measure of information than effective complexity. We accept that problem regarding uniqueness as McAllister presents it, but would not deny that if contexts could be defined appropriately, one could in principle find unique values of effective complexity. Considering this, effective complexity is informative not only regarding the entity being investigated but also regarding the context of investigation itself. Furthermore, we argue that effective complexity is an interesting epistemological concept that may be applied to better understand crucial issues related to context dependence such as theory choice and emergence. These applications are not available merely on the basis of algorithmic complexity. (shrink)