Because in military situations, as well as for self-driving cars, information must be processed faster than humans can achieve, determination of context computationally, also known as situational assessment, is increasingly important. In this article, we introduce the topic of context, and we discuss what is known about the heretofore intractable research problem on the effects of interdependence, present in the best of human teams; we close by proposing that interdependence must be mastered mathematically to operate human-machine teams efficiently, to advance theory, and to make the machine actions directed by AI explainable to team members and society. The special topic articles in this issue and a subsequent issue of AI Magazine review ongoing mature research and operational programs that address context for human-machine teams.

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Key concepts: We review interdependence theory measured by entropic forces, findings in support, and several examples from the field to advance a science of autonomous human-machine teams (A-HMTs) with artificial intelligence (AI). While theory is needed for the advent of autonomous HMTs, social theory is predicated on methodological individualism, a statistical and qualitative science that neither generalizes to human teams nor HMTs. Maximum interdependence in human teams is associated with the performance of the best teams when compared to independent individuals; our research confirmed that the top global oil firms maximize interdependence by minimizing redundant workers, replicated for the top militaries in the world, adding that impaired interdependence is associated with proportionately less freedom, increased corruption, and poorer team performance. We advanced theory by confirming that the maximum interdependence in teams requires intelligence to overcome obstacles to maximum entropy production (MEP; e.g., navigating obstacles while abiding by military rules of engagement requires intelligence). Approach: With a case study, we model as harmonic the long-term oscillations driven by two federal agencies in conflict over closing two high-level radioactive waste tanks, ending when citizens recommended closing the tanks. Results: While contradicting rational consensus theory, our quasi-Nash equilibrium model generates the information for neutrals to decide; it suggests that HMTs should adopt how harmonic oscillations in free societies regulate human autonomy to improve decisions and social welfare.

a b s t r a c tThe concept of social sustainability is discussed in a wide range of literatures, from urban planning to international development. Authors agree a notion of social sustainability is difficult to define, comprising numerous component parts (criteria), such as community cohesion, human wellbeing, effective dialogue and the access that citizens have to those that make important decisions on their behalf. The definition and measurement of these criteria and the role of social sustainability in energy decision making is a contentious issue. We argue that a community led, asset based approach is required to achieve any sense of how social sustainability can be defined in a community setting within the context of energy developments. We propose a conceptual framework based on a process of community group prioritization and visioning. Our earlier research on public participation and the role of dialogue for nuclear energy development in the UK, US and Japan is used to demonstrate barriers to be overcome if our systemic model of social sustainability is to become a reality. We highlight the importance of fairness and justice, place based approaches and socio-energy systems, concluding that these are necessary to promote a community and institutional awareness of social sustainability for large energy developments.