Reaction network is a promising framework for representing complex systems of diverse and even interdisciplinary types. In this approach, complex systems appear as self-maintaining structures emerging from a multitude of interactions, similar to proposed scenarios for the origin of life out of autocatalytic networks. The formalism of chemical organization theory mathematically specifies under which conditions a reaction network is stable enough to be observed as a whole complex system. Such conditions specify the notion of organization, crucial in COT. In this (...) paper, we show that the structure and operation of organizations can be advanced towards a formal framework of resilience in complex systems. That is, we show that there exist three fundamental types of change defined for reaction networks, and that these perturbations not only provide a general representation of perturbations in the context of resilience but also pave the ground to formalize different forms of resilient responses. In particular, we show that decomposing the network’s operational structure into dynamically decoupled modules allows to formalize what is the impact of a perturbation and to what extent any potential compensation to that perturbation will be successful. We illustrate our approach with a toy model of a farm that operates in a sustainable way producing milk, eggs, and/or grains from other resources. With the help of simulations, we analyze the different types of perturbations and responses that the farm can undergo and how that affects its sustainable operation. (shrink)

Emergence is defined as a process which cannot be described by a fixed model, consisting of invariant distinctions. Hence emergence must be described by a meta‐model, representing the transition of one model to another one by means of a distinction dynamics. The dynamics of distinctions is based on the processes of variation and selection, resulting in an invariant distinction, which constrains the variety and thus defines a new system. A classification of emergence processes is proposed, based on the following criteria: (...) amount of variety, internality/ externality of variation and selection, number of levels, and contingency of constraint. It is argued that traditional formal and computational models are incapable of representing the more general types of emergence, but that it is possible to generalize them on the basis of the dynamics of distinctions. (shrink)

Purpose: This is an attempt to define constructivism in a pluralistic way. It categorizes constructivist work within a three-dimensional space rather than along one dimension only. Practical implications: The interdisciplinary definition makes it possible to perceive the rather heterogeneous constructivist community as a coherent and largely consistent scientific effort to provide answers to demanding complex problems. Furthermore it gives authors of Constructivist Foundation the opportunity to locate their own position within the community. Conclusion: I offer a catalogue of ten points (...) that outline the constructivist program. Each of these aspects invites authors to extensively reflect on it and to approach it from their disciplinary background to do work in any of the types of investigations the journal covers. (shrink)

Given that knowledge consists of finite models of an infinitely complex reality, how can we explain that it is still most of the time reliable? Survival in a variable environment requires an internal model whose complexity (variety) matches the complexity of the environment that is to be controlled. The reduction of the infinite complexity of the sensed environment to a finite map requires a strong mechanism of categorization. A measure of cognitive complexity (C) is defined, which quantifies the average amount (...) of trial-and-error needed to find the adequate category. C can be minimized by "probability ordering" of the possible categories, where the most probable alternatives ("defaults") are explored first. The reduction of complexity by such ordering requires a low statistical entropy for the cognized environment. This entropy is automatically kept down by the natural selection of "fit" configurations. The high probability, "default" cognitive categorizations are then merely mappings of environmentally "fit" configurations. (shrink)

Why has the problematic of complexity appeared so late? And why would it be justified?