We propose quantum Boolean networks, which can be classified as deterministic reversible asynchronous Boolean networks. This model is based on the concept of quantum Boolean functions. A quantum Boolean network is a Boolean network where the functions associated with the nodes are quantum Boolean functions. We study some properties of this novel model and, using a quantum simulator, we study how the dynamics change in function of the connectivity of the network and the set of operators we allow. For some configurations, the behavior of this model resembles that of reversible Boolean networks (RevBN), while for other configurations a more complex dynamics can emerge. For example, cycles larger than 2 N were observed. Additionally, using a scheme akin to one used previously with random Boolean networks, we computed the average entropy and complexity of the networks. As opposed to classic random Boolean networks, where “complex” dynamics are restricted mainly to a connectivity close to a phase transition, quantum Boolean networks can exhibit stable, complex, and unstable dynamics independently of their connectivity.

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.

Self-organization has been an important concept within a number of disciplines, which Artificial Life (ALife) also has heavily utilized since its inception. The term and its implications, however, are often confusing or misinterpreted. In this work, we provide a mini-review of self-organization and its relationship with ALife, aiming at initiating discussions on this important topic with the interested audience. We first articulate some fundamental aspects of self-organization, outline its usage, and review its applications to ALife within its soft, hard, and wet domains. We also provide perspectives for further research.

Measurements of coordinated motion in flocks are necessary to evaluate their performance. In this work, a set of quantitative metrics to evaluate the performance of the spatial features exhibited by flocks are introduced and applied to the well-known boids of Reynolds. Our metrics are based on quantitative indicators that have been used to evaluate fish schools. These indicators are revisited and extended as a set of three new metrics that can be used to evaluate and design flocks.

We apply formal information measures of emergence, self-organization and complexity to scale-free random networks, to explore their association with structural indicators of network topology. Results show that the cumulative number of nodes and edges coincides with an increment of the self-organization and relative complexity, and a loss of the emergence and complexity. Our approach shows a complementary way of studying networks in terms of information.