(…) In this paper I review some of this recent work on the ‘stochastic thermodynamics of computation’. After reviewing the salient parts of information theory, computer science theory, and stochastic thermodynamics, I summarize what has been learned about the entropic costs of performing a broad range of computations, extending from bit erasure to loop-free circuits to logically reversible circuits to information ratchets to Turing machines. These results reveal new, challenging engineering problems for how to design computers to have minimal thermodynamic costs. They also allow us to start to combine computer science theory and stochastic thermodynamics at a foundational level, thereby expanding both.

The stochastic thermodynamics of computation
David H Wolpert
Journal of Physics A: Mathematical and Theoretical, Volume 52, Number 19
Shannon’s Information Theory 70 years on

Source: iopscience.iop.org

The theory of autopoiesis holds that an organism can be defined as a network of processes. However, an organism also has a physical body. The relationship between these two things—network and body—has been raised in this issue of Adaptive Behaviour, with reference to an extended interpretation of autopoiesis. This perspective holds that the network and the body are distinct things, and that the network should be thought of as extending beyond the boundaries of the body. The relationship between body and network is subtle, and I revisit it here from the extended perspective. I conclude that from an organism = network perspective, the body is a biological solution to the problem of maintaining both the distinctness of an organism, separate from but engaged with its environment and other organisms, and its distinctiveness as a particular individual.

The necessity of extended autopoiesis
Nathaniel Virgo
Adaptive Behavior

https://doi.org/10.1177/1059712319841557

Source: journals.sagepub.com

Epidemics may both contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex and there have been confounding observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these counter-intuitive observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. We introduce a set of mathematical models to understand the role of conflicts in epidemics. Our mathematical framework has the potential to explain the counterintuitive observations and the complex role of human conflicts in epidemics. Our work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in the conflict zone. Our approach exemplifies the role of non-linear thinking in complex systems like human societies. We view our work as a step towards a quantitative model of disease spread in conflicts.

Banerjee S. 2019. Towards a quantitative model of epidemics during conflicts. PeerJ Preprints 7:e27651v1 https://doi.org/10.7287/peerj.preprints.27651v1

Source: peerj.com