Interdependent Self-Organizing Mechanisms for Cooperative Survival.

Cooperative survival "games" are situations in which, during a sequence of catastrophic events, no one survives unless everyone survives. Such situations can be further exacerbated by uncertainty over the timing and scale of the recurring catastrophes, while the resource management required for survival may depend on several interdependent subgames of resource extraction, distribution, and investment with conflicting priorities and preferences between survivors. In social systems, self-organization has been a critical feature of sustainability and survival; therefore, in this article we use the lens of artificial societies to investigate the effectiveness of socially constructed self-organization for cooperative survival games. We imagine a cooperative survival scenario with four parameters: scale, that is, n in an n-player game; uncertainty, with regard to the occurrence and magnitude of each catastrophe; complexity, concerning the number of subgames to be simultaneously "solved"; and opportunity, with respect to the number of self-organizing mechanisms available to the players. We design and implement a multiagent system for a situation composed of three entangled subgames-a stag hunt game, a common-pool resource management problem, and a collective risk dilemma-and specify algorithms for three self-organizing mechanisms for governance, trading, and forecasting. A series of experiments shows, as perhaps expected, a threshold for a critical mass of survivors and also that increasing dimensions of uncertainty and complexity require increasing opportunity for self-organization. Perhaps less expected are the ways in which self-organizing mechanisms may interact in pernicious but also self-reinforcing ways, highlighting the need for some reflection as a process in collective self-governance for cooperative survival.

Expertise, Social Influence, and Knowledge Aggregation in Distributed Information Processing.

In many social, cyber-physical, and socio-technical systems, a group of autonomous peers can encounter a knowledge aggregation problem, requiring them to organise themselves, without a centralised authority, as a distributed information processing unit (DIP). In this article, we specify and implement a new algorithm for knowledge aggregation based on Nowak's psychological theory Regulatory Theory of Social Influence (RTSI). This theory posits that social influence consists of not only sources trying to influence targets, but also targets seeking sources by whom to be influenced and learning what processing rules those sources are using. A multi-agent simulator SMARTSIS is implemented to evaluate the algorithm, using as its base scenario a linear public goods game where the DIP's decision is a qualitative question of distributive justice. In a series of experiments examining the emergence of expertise, we show how RTSI enhances the effectiveness of the multi-agent DIP as a social group while conserving each agent's individual resources. Additionally, we identify eight criteria for evaluating the DIP unit's performance, consisting of four conflicting pairs of systemic drivers, and discuss how RTSI maintains a balanced tension between the four driver pairs through the emergence and divergence of expertise. We conclude by arguing that this shows how psychological theories like RTSI can have a crucial role in informing agent-based models of human behaviour, which in turn may be critically important for effective knowledge management and reflective self-improvement in both cyber-physical and socio-technical systems.

The Disruption of Trust in the Digital Transformation Leading to Health 4.0.

The specification and application of policies and guidelines for public health, medical education and training, and screening programmes for preventative medicine are all predicated on trust relationships between medical authorities, health practitioners and patients. These relationships are in turn predicated on a verbal contract that is over two thousand years old. The impact of information and communication technology (ICT), underpinning Health 4.0, has the potential to disrupt this analog relationship in several dimensions; but it also presents an opportunity to strengthen it, and so to increase the take-up and effectiveness of new policies. This paper develops an analytic framework for the trust relationships in Health 4.0, and through three use cases, assesses a medical policy, the introduction of a new technology, and the implications of that technology for the trust relationships. We integrate this assessment in a set of actionable recommendations, in particular that the trust framework should be part of the design methodology for developing and deploying medical applications. In a concluding discussion, we advocate that, in a post-pandemic world, IT to support policies and programmes to address widespread socio-medical problems with mental health, long Covid, physical inactivity and vaccine misinformation will be essential, and for that, strong trust relationships between all the stakeholders are absolutely critical.

Fitness for Purpose in Online Communities: Community Complexity Framework for Diagnosis and Design of Socio-Technical Systems.

Recent discourse on Information and Communication Technologies' (ICT) impact on societies has been dominated by negative side-effects of information exchange in huge online social systems. Yet, the size of ICT-based communities also provides an unprecedented opportunity for collective action, as exemplified through crowdfunding, crowdsourcing, or peer production. This paper aims to provide a framework for understanding what makes online collectives succeed or fail in achieving complex goals. The paper combines social and complexity sciences' insights on structures, mechanics, and emergent phenomena in social systems to define a Community Complexity Framework for evaluating three crucial components of complexity: multi-level structuration, procedural self-organization, and common identity. The potential value of such a framework would be to shift the focus of efforts aimed at curing the malfunctions of online social systems away from the design of algorithms that can automatically solve such problems, and toward the development of technologies which enable online social systems to self-organize in a more productive and sustainable way.

Thinking about Trust: People, Process, and Place.

This brief paper is about trust. It explores the phenomenon from various angles, with the implicit assumptions that trust can be measured in some ways, that trust can be compared and rated, and that trust is of worth when we consider entities from data, through artificial intelligences, to humans, with side trips along the way to animals. It explores trust systems and trust empowerment as opposed to trust enforcement, the creation of trust models, applications of trust, and the reasons why trust is of worth.

A real-time architecture for time-aware agents.

This paper describes the specification and implementation of a new three-layer time-aware agent architecture. This architecture is designed for applications and environments where societies of humans and agents play equally active roles, but interact and operate in completely different time frames. The architecture consists of three layers: the April real-time run-time (ART) layer, the time aware layer (TAL), and the application agents layer (AAL). The ART layer forms the underlying real-time agent platform. An original online, real-time, dynamic priority-based scheduling algorithm is described for scheduling the computation time of agent processes, and it is shown that the algorithm's O(n) complexity and scalable performance are sufficient for application in real-time domains. The TAL layer forms an abstraction layer through which human and agent interactions are temporally unified, that is, handled in a common way irrespective of their temporal representation and scale. A novel O(n2) interaction scheduling algorithm is described for predicting and guaranteeing interactions' initiation and completion times. The time-aware predicting component of a workflow management system is also presented as an instance of the AAL layer. The described time-aware architecture addresses two key challenges in enabling agents to be effectively configured and applied in environments where humans and agents play equally active roles. It provides flexibility and adaptability in its real-time mechanisms while placing them under direct agent control, and it temporally unifies human and agent interactions.