No abstract

General Collective Intelligence (GCI) are software platforms that organize groups into a single collective intelligence with general problem solving ability. In doing so a GCI has the potential to give groups vastly more ability to address collective challenges such as the SDGs. A GCI is a significant infrastructure investment. The Collective Intelligence based Program to Accelerate Achievement of the Sustainable Development Goals (CIPAA-SDGs) however is designed to implement a GCI in phases so that the cost to any single project is far outweighed by the potential benefits. The phasing of that GCI development in the CIPAA-SDGs program design is used here as a case study for collectively intelligent program design.

Where systems thinking approachesare often different and sometimes irreconcilable because they are based on the reasoning of one individual or another, Human-Centric Functional Modeling aims to become universal through looking inwards to the way all human beings perceive. HCFM provide a methodology that enables first person observations within each of the senses of the body or within the emotions, mind, or consciousness as functional systems to be represented as forming mathematical spaces that in turn enable all possible systems thinking approaches to be represented.This creates the possibility of comparing all systems thinking approaches to determine which is most “fit” in each context in which it is used, and it radically reduces the barriers to reusing the best components of each approach.

A recently developed framework for modelling cognition defines General Collective Intelligence or GCI as Collective Intelligence (CI) with general problem solving ability. Where CI uses the intelligence of crowds to optimize decision-making, a GCI must also optimize the choice of problem to solve. This framework represents a GCI as an adaptive problem solving system with problem solving segmented across a hierarchy of problem solving domains, one of which is adaptation through cooperation between functional components of the system. This domain defines how functionality is segmented across different components of a system in order to maximize outcomes, or in summary, balances centralization with decentralization. Where one function is more important to overall fitness than another, centralized cooperation prioritizes that function so that overall outcomes can be maximized. Decentralized cooperation maximizes outcomes for all participating components equally to remove the barriers that align decision-making with the interests of a subset of the group, which forces groups to solve the wrong problems. One such group problem is design and manufacturing for sustainability. Recent work has challenged the idea that process improvements will yield technologies with enough of an increase in efficiency to permit green growth while still reducing climate and other environmental impact. This paper proposes that designing all products and services according to the principles of GCI gives sufficient competitive advantage to businesses that cooperate to reduce consumption and increase sustainability, to make green growth not only possible but reliably achievable. This paper also provides an overview of what GCI based design and manufacturing of products and services for sustainability looks like. Leveraging GCI to achieve sustainability is explored as an example of biomimicry, and nature is shown to use the same approach to design living things. From this perspective, organisms are a collection of cells that cooperate to optimize functional designs according to well-defined principles in order to maximize the sustainability of the organism as a collective. Sustainability is represented as a mathematical pattern of stability implemented through these principles, a pattern which the 3.5 billion year history of the earth has thoroughly tested, and which therefore is robust enough to be replicated in all products and services, and once launched is stable enough as a pattern of cooperation to be sustainable in all organizations.

The lockdown of economic activity in many countries as a measure to stop the spread of the COVID-19 pandemic has led to high levels of unemployment and other indicators of a potentially upcoming economic crisis. As a gauge of the seriousness of these concerns some have suggested that current levels of some of these indicators have not been seen in the US since the time of the great depression. This paper explores how General Collective Intelligence, a recent innovation in group decision-making systems, might reliably generate the economic growth needed to avert such a crisis where not reliably achievable otherwise. Current group decision-making systems, whether choosing a human decision-maker, consensus voting on decisions, or automated decision-systems such as conventional collective intelligence, have been suggested to lack the capacity to maximize more than a very few group outcomes simultaneously due to specific limitations. Since impact on collective well-being is determined by impact on an open (unbounded) set of outcomes, this implies lack of the capacity to maximize the necessary range of impacts on well-being for groups if that range is too broad. If so, the breadth of impact required to achieve sustainable “green” economic development while simultaneously solving hunger, solving the environmental degradation that consensus has linked to climate change, as well as providing maximal access to healthcare, education, and other resources, may not be reliably possible with current decision systems. General Collective Intelligence or GCI replicates the adaptive problem solving mechanisms by which nature has demonstrated the ability to optimally respond to an unlimited set of problems, and by which nature has demonstrated the ability to potentially increase sustainability per unit of resources by orders of magnitude so that life is reliably self-sustaining. This paper explores why GCI can potentially be used to reliably drive self-sustaining economic growth to revive economies in the aftermath of the COVID-19 pandemic, and why GCI has the potential to reliably drive a transformation to sustainable green economies while doing so.