For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into 14 themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields. Relevance: Artificial life has contributed to philosophy of biology and of cognitive science, thus making it an important field related to constructivism.

In recent decades, the scientific study of complex systems (Bar-Yam 1997; Mitchell 2009) has demanded a paradigm shift in our worldviews (Gershenson et al. 2007; Heylighen et al. 2007). Traditionally, science has been reductionistic. Still, complexity occurs when components are difficult to separate, due to relevant interactions. These interactions are relevant because they generate novel informationwhich determines the future of systems. This fact has several implications (Gershenson 2013).

The extended mind hypothesis has stimulated much interest in cognitive science. However, its core claim, i.e. that the process of cognition can extend beyond the brain via the body and into the environment, has been heavily criticized. A prominent critique of this claim holds that when some part of the world is coupled to a cognitive system this does not necessarily entail that the part is also constitutive of that cognitive system. This critique is known as the “coupling-constitution fallacy.” In this paper we respond to this reductionist challenge by using an evolutionary robotics approach to create a minimal model of two acoustically coupled agents. We demonstrate how the interaction process as a whole has properties that cannot be reduced to the contributions of the isolated agents. We also show that the neural dynamics of the coupled agents has formal properties that are inherently impossible for those neural networks in isolation. By keeping the complexity of the model to an absolute minimum, we are able to illustrate how the coupling-constitution fallacy is in fact based on an inadequate understanding of the constitutive role of nonlinear interactions in dynamical systems theory.

Key words: evolutionary robotics, philosophy of mind, cognitive science, dynamical approach, extended mind hypothesis

Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic. Upshot: The limitations of materialism for studying cognition have motivated alternative epistemologies based on information and computation. I argue that these alternatives are also inherently limited and that these limits can only be overcome by considering materialism, info-computationalism, and cognition at the same time.

Purpose Autopoiesis is a concept originally used to define living systems. However, no measure for autopoiesis has been proposed so far. Moreover, how can we build systems with a higher autopoiesis value The paper aims to discuss these issues. Design/methodology/approach: Relating autopoiesis with Ashby’s law of requisite variety, self-organization is put forward as a way in which systems can be designed to match the variety of their environment. Findings: Guided self-organization has been shown to produce systems which can adapt to the requisite variety of their environment, offering more efficient solutions for problems that change in time than those obtained with traditional techniques. Originality/value: Being able to measure autopoiesis allows us to apply this measure to all systems. More “living” systems will be fitter to survive in their environments: biological, social, technological, or urban.

Key words: Adaptation, Autopoiesis, Self-organization

When we attempt to define life, we tend to refer to individuals, those that are alive. But these individuals might be cells, organisms, colonies… ecosystems? We can describe living systems at different scales. Which ones might be the best ones to describe different selves? I explore this question using concepts from information theory, ALife, and Buddhist philosophy. After brief introductions, I review the implications of changing the scale of observation, and how this affects our understanding of selves at different structural, temporal, and informational scales. The conclusion is that there is no single “best” scale for a self, as this will depend on the scale at which decisions must be made. Different decisions, different scales.