Algorithmic Cognition and the Computational Nature of the Mind

The idea that complexity or, its reverse, simplicity are essential concepts for cognitive psychology was already understood in the middle of the twentieth century (Mach 1914), and these concepts have remained salient ever since (Oizumi et al. 2014). As early as the 1990s, the algorithmic theory of information was referenced by some researchers in psychology, who recommended the use of algorithmic complexity as a universal normative measure of complexity. Nevertheless, the noncomputability of algorithmic complexity was deemed an insurmountable obstacle, and more often than not it merely served as a point of reference.
In recent years, we have been able to create and use more reliable estimates of algorithmic complexity using the coding theorem method (Gauvrit et al. 2014b, 2016). This has made it possible to deploy a precise and quantitative approximation of algorithmic complexity, with applications in many areas of psychology and the behavioral sciences – sometimes …


Algorithmic Cognition and the Computational Nature of the Mind

Hector Zenil , Nicolas Gauvrit

Living Reference Work Entry
Encyclopedia of Complexity and Systems Science
pp 1-9


Modelling the effects of phylogeny and body size on within-host pathogen replication and immune response

Understanding how quickly pathogens replicate and how quickly the immune system responds is important for predicting the epidemic spread of emerging pathogens. Host body size, through its correlation with metabolic rates, is theoretically predicted to impact pathogen replication rates and immune system response rates. Here, we use mathematical models of viral time courses from multiple species of birds infected by a generalist pathogen (West Nile Virus; WNV) to test more thoroughly how disease progression and immune response depend on mass and host phylogeny. We use hierarchical Bayesian models coupled with nonlinear dynamical models of disease dynamics to incorporate the hierarchical nature of host phylogeny. Our analysis suggests an important role for both host phylogeny and species mass in determining factors important for viral spread such as the basic reproductive number, WNV production rate, peak viraemia in blood and competency of a host to infect mosquitoes. Our model is based on a principled analysis and gives a quantitative prediction for key epidemiological determinants and how they vary with species mass and phylogeny. This leads to new hypotheses about the mechanisms that cause certain taxonomic groups to have higher viraemia. For example, our models suggest that higher viral burst sizes cause corvids to have higher levels of viraemia and that the cellular rate of virus production is lower in larger species. We derive a metric of competency of a host to infect disease vectors and thereby sustain the disease between hosts. This suggests that smaller passerine species are highly competent at spreading the disease compared with larger non-passerine species. Our models lend mechanistic insight into why some species (smaller passerine species) are pathogen reservoirs and some (larger non-passerine species) are potentially dead-end hosts for WNV. Our techniques give insights into the role of body mass and host phylogeny in the spread of WNV and potentially other zoonotic diseases. The major contribution of this work is a computational framework for infectious disease modelling at the within-host level that leverages data from multiple species. This is likely to be of interest to modellers of infectious diseases that jump species barriers and infect multiple species. Our method can be used to computationally determine the competency of a host to infect mosquitoes that will sustain WNV and other zoonotic diseases. We find that smaller passerine species are more competent in spreading the disease than larger non-passerine species. This suggests the role of host phylogeny as an important determinant of within-host pathogen replication. Ultimately, we view our work as an important step in linking within-host viral dynamics models to between-host models that determine spread of infectious disease between different hosts.


Modelling the effects of phylogeny and body size on within-host pathogen replication and immune response
Soumya Banerjee, Alan S. Perelson, Melanie Moses
Published 15 November 2017.DOI: 10.1098/rsif.2017.0479


How Self-Organization Happens

Self-organization refers to natural processes of human relating, that are similar at all scales of order in the natural world.The dynamics of self-organization are much more rich and complex than the simple patterns we use to model them.Being able to make sense of these dynamics enables us to build new potentials in teams. The level of trust rises when we recognize our basic human capacity to collaborate with each other. Narrative-based applications can visualize some of the subtle patterns that shape a team’s potential for acting in certain ways (and not others) over time.
There isn’t one specific pattern that emerges from self-organization. The processes are so deep and fundamental to human interactions, that you cannot enforce any specific hierarchical or non-hierarchical pattern with rules.  Trust between people is an outcome of allowing people to freely self-organize. Complex networks of trust emerge and change as people continuously negotiate their relationships.


Analytical framework for the study of epidemic models on activity driven networks

Network theory has greatly contributed to an improved understanding of epidemic processes, offering an empowering framework for the analysis of real-world data, prediction of disease outbreaks, and formulation of containment strategies. However, the current state of knowledge largely relies on time-invariant networks, which are not adequate to capture several key features of a number of infectious diseases. Activity driven networks (ADNs) constitute a promising modelling framework to describe epidemic spreading over time varying networks, but a number of technical and theoretical gaps remain open. Here, we lay the foundations for a novel theory to model general epidemic spreading processes over time-varying, ADNs. Our theory derives a continuous-time model, based on ordinary differential equations (ODEs), which can reproduce the dynamics of any discrete-time epidemic model evolving over an ADN. A rigorous, formal framework is developed, so that a general epidemic process can be systematically mapped, at first, on a Markov jump process, and then, in the thermodynamic limit, on a system of ODEs. The obtained ODEs can be integrated to simulate the system dynamics, instead of using computationally intensive Monte Carlo simulations. An array of mathematical tools for the analysis of the proposed model is offered, together with techniques to approximate and predict the dynamics of the epidemic spreading, from its inception to the endemic equilibrium. The theoretical framework is illustrated step-by-step through the analysis of a susceptible–infected–susceptible process. Once the framework is established, applications to more complex epidemic models are presented, along with numerical results that corroborate the validity of our approach. Our framework is expected to find application in the study of a number of critical phenomena, including behavioural changes due to the infection, unconscious spread of the disease by exposed individuals, or the removal of nodes from the network of contacts.


An analytical framework for the study of epidemic models on activity driven networks
Lorenzo Zino Alessandro Rizzo Maurizio Porfiri
Journal of Complex Networks, cnx056,


Stream Graphs and Link Streams for the Modeling of Interactions over Time

Graph theory provides a language for studying the structure of relations, and it is often used to study interactions over time too. However, it poorly captures the both temporal and structural nature of interactions, that calls for a dedicated formalism. In this paper, we generalize graph concepts in order to cope with both aspects in a consistent way. We start with elementary concepts like density, clusters, or paths, and derive from them more advanced concepts like cliques, degrees, clustering coefficients, or connected components. We obtain a language to directly deal with interactions over time, similar to the language provided by graphs to deal with relations. This formalism is self-consistent: usual relations between different concepts are preserved. It is also consistent with graph theory: graph concepts are special cases of the ones we introduce. This makes it easy to generalize higher-level objects such as quotient graphs, line graphs, k-cores, and centralities. This paper also considers discrete versus continuous time assumptions, instantaneous links, and extensions to more complex cases.


Stream Graphs and Link Streams for the Modeling of Interactions over Time
Matthieu Latapy, Tiphaine Viard, Clémence Magnien