We introduce a minimal model of a thermodynamic agent capable of maintaining far-from-equilibrium states by actively harvesting and storing free energy from its environment. Inspired by minimal models of autonomy like Bittorio (Varela et al., 1991), our agent —labelled Thermina —gives shape to a theoretical framework for studying the interplay between thermodynamics and autonomy. By analytically studying the nonequilibrium steady state of the system, we distinguish between regions of ‘autonomous’ states —sustaining themselves out-of-equilibrium by harvesting free energy from the environment— and regions of ‘non-autonomous’ states —close to thermodynamic equilibrium and with very low chances of gathering free energy. Furthermore, we inspect the adaptive mechanisms that allow an agent to regulate its interaction with the environment to robustly maintain its nonequilibrium state. Studying in detail the behaviour of the system, we aim to provide insights into the broader question of how thermodynamic processes contribute to the emergence and maintenance of complex, adaptive behaviour in natural and artificial systems.

Current success of Artificial Intelligence (particularly in the application of Deep Learning techniques) is bringing some of its methods closer to Artificial Life and re-opening old questions, social fears and envisioned applications. The concept of autonomy has long guided research and progress in Artificial Life. We explore how this concept can contribute to evaluate the autonomy of contemporary AI systems.

There has been a revival of the notion of habit in the embodied and situated cognitive sciences. A habit can be understood as ‘a self-sustaining pattern of sensorimotor coordination that is formed when the stability of a particular mode of sensorimotor engagement is dynamically coupled with the stability of the mechanisms generating it’ (Barandiaran, 2008, p. 281). This view has inspired models of biologically-inspired homeostatic agents capable of establishing their own habits (Di Paolo and Iizuka, 2008). Despite recent achievements in this field, there is little written about how social habits can be established from this modelling perspective. We hypothesize that, when the stability of internal behavioural mechanisms is coupled to the stability of a behaviour and other agents are present during this behaviour, a social interdependence of behaviour takes place: a social habit is established. We provide evidence for our hypothesis with an evolutionary robotics simulation model of homeostatic plasticity in a phototactic behaviour. Agents evolved to couple internal homeostasis to behavioural fitness display social interdependencies in their behaviour. The social habit of these agents was not interrupted when blindness to phototactic stimuli was introduced as long as social perception remained active. This did not happen when internal homeostasis was not coupled to the fitness of the agent. The results allow us to propose a possible conjecture about the character of social habits and to offer a potential theoretical framework to understand how habits develop from neurodynamics to the level of social interaction.

By Artificial Democratic Life we mean the design and deployment of artificial (digital) infrastructures aimed at enhancing or improving social democratic life. Artificial Life, as a discipline and as a community, has much to contribute to the contemporary challenge of redesigning democracy in the network era, in understanding and designing democracy as a form of life: one that evolves into increasingly higher complexity and diversity while preserving homeostatic invariants and designing the infrastructures capable to resiliently enhance it. We identify some opportunities and specific challenges that can be faced using Alife simulation techniques and conceptual resources.