As AI and robots become more widespread, their social and ethical nature will become more important. The study of behavioral models that follow us, humans, as social creatures, can be expected as one of the solutions. We assume that human sociality including punishments toward free-riders, which is necessary for a stable society, are deeply related to emotions, especially emotions toward others and empathy. Based on this idea, we incorporated social emotions into the active inference, a human behavior model in cognitive science, and investigated the social behavior of the models using a virtual game.

Cooperation has been widely studied in multi-agent foraging tasks. However, the impact of agent-environment interactions on the longer term and the achievement of sustainability have been largely unexplored in this context. This work contributes to the development of a testbed for exploring social dynamics between agents: the ‘sustainable foraging problem’. This testbed explores the effect of agent behaviour and the agent’s dilemma of choosing between individual reward and collective long-term goals for sustainable resource management. To incorporate varied levels of replenishment rates in this testbed, forest, pasture and desert environment types are formulated. A co-evolving deliberative loop with neuro-evolution that asks the agents to act with greedy or moderate behaviour is demonstrated. This deliberative layer is shown to be insufficient in situations of social dilemma where the agents learn to increase their individual rewards instead of collectively increasing these rewards through the sustainability of the environment. A simple reflective governor based on the notion of the agent’s self-awareness is illustrated to allow the agents to occasionally reason about the long-term impacts of their immediate actions on future resource availability in the environment, which may eventually ensure sustainability.

Innovation dynamics in social and technological systems are strongly linked to urban systems and their multi-scale properties. Understanding underlying processes is crucial for sustainable territorial planning. We introduce a multi-scalar model for innovation dynamics in systems of cities, coupling a macroscopic innovation diffusion and urban dynamics model with mesoscopic models for local innovation clusters. The model parameter space is explored, and we apply a bi-objective optimisation algorithm with objectives across scales. Implementing indicators for downward causation, we finally investigate with a diversity search algorithm the diverse regimes of emergence the model can produce. This suggests strong emergence is captured, confirming the relevance of multi-scale approaches to artificial societies and urban simulation.

We draw from the recent enactivist literature to articulate an operational definition of Wittgensteinien forms of life as a self-productive collection of constraints over collective behavior. We propose that humans integrate and enact those account through the Active Inference of shared “regimes of attentions”, which are experienced as embedded normativity within direct engagement with a shared sociocultural niche. Given those elements, we discuss how sociocultural lifeforms “encode information” in the material niche, and discuss how this information may be recovered by cognitive archaeologists.