Collective minds: social network topology shapes collective cognition

Human cognition is not solitary, it is shaped by collective learning and memory. Unlike swarms or herds, human social networks have diverse topologies, serving diverse modes of collective cognition and behaviour. Here, we review research that combines network structure with psychological and neural experiments and modelling to understand how the topology of social networks shapes collective cognition. First, we review graph-theoretical approaches to behavioural experiments on collective memory, belief propagation and problem solving. These results show that different topologies of communication networks synchronize or integrate knowledge differently, serving diverse collective goals. Second, we discuss neuroimaging studies showing that human brains encode the topology of one's larger social network and show similar neural patterns to neural patterns of our friends and community ties (e.g. when watching movies). Third, we discuss cognitive similarities between learning social and non-social topologies, e.g. in spatial and associative learning, as well as common brain regions involved in processing social and non-social topologies. Finally, we discuss recent machine learning approaches to collective communication and cooperation in multi-agent artificial networks. Combining network science with cognitive, neural and computational approaches empowers investigating how social structures shape collective cognition, which can in turn help design goal-directed social network topologies. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

Deducing the Properties Required by General Collective Intelligence Platforms

A functional modeling approach is used to derive the properties that must be possessed by a platform with the capacity to significantly increase the general collective intelligence or c factor of groups. Such platforms have been termed “General Collective Intelligence” or GCI platforms. Having general problem-solving ability, a GCI potentially enables groups to execute any collective reasoning process, including abstracting (generalizing) a reasoning process so it might be reused in any other domain where it applies. A GCI can be shown to have the potential to exponentially increase the capacity of a group to create generalizations and other relationships, and capacity to store and exchange those relationships. Since relationships are concepts, and since the number of relationships between concepts better specify the location of any concept in conceptual space and therefore increases the density of conceptual space as a whole, GCI represents a phase change in collective cognition at which the collective conceptual space can expand exponentially in size and density. Each reasoning process connecting this far larger space of concepts has outcomes, making it potentially possible through these additional concepts to accumulate far greater impact on any outcome in the world. Because this phase change is not believed to have been possible at any point before in history, and is believed cannot occur again until the advent of another system with general problem-solving ability, such as a second order GCI or an Artificial General Intelligence (AGI), and because both AGI and second order GCI are believed to require GCI, GCI is proposed here to be the most important innovation in the history and immediate future of human civilization.

Understanding the effects of partisan identity on climate change

Partisan and ideological identities are a consistent barrier to the adoption of climate change mitigation policies, especially in Anglophone countries where fossil fuel reliance is the highest. We review how understanding collective cognition may help overcome such barriers by changing norms, promoting cooperation, downplaying partisan identities, or leveraging other identities to promote pro-climate change beliefs and behaviors. We also highlight several gaps in the literature and lay out a brief roadmap for future research. In sum, this review highlights the important role that social identity plays, both in terms of a barrier and a potential solution, in aid of promoting climate change mitigation.

Collective Intelligence

The chapter provides an introduction to the social science of ‘collective intelligence’, the aggregation of individual judgments for purposes of collective decision making. It starts from the basic logic of the Condorcet jury theorem and summarises the main determinants of the accuracy of collective cognition. The recent research has focused on developing and refining formal aggregation methods beyond majority voting. The chapter presents the main findings on the two general approaches, surveying and prediction markets. It then contrasts these techniques with informal deliberation as a basic and prevalent aggregation mechanism. One conclusion is that while deliberation is prone to herding and can distort collective judgment, it is also more versatile and robust than formal mechanisms.

General Collective Intelligence vs the Innate Collective Intelligence Factor

INTRODUCTION: Groups of individuals of species exhibiting collective behaviours have been suggested to have some innate general collective intelligence. General Collective Intelligence or GCI has been described as a platform that organizes individual humans into a single collective intelligence with the potential capacity for exponentially greater general problem-solving ability.OBJECTIVES: To explore whether a functional modelling approach might have the capacity to represent any system of organization resulting in a general collective intelligence factor. And to explore what functionality might be required for a GCI to exponentially increase it.METHODS: An analysis of the meaning of general problem-solving ability in the functional state space of a system of cognition or collective cognition is used to assess whether GCI has the potential to exponentially increase increase that ability.RESULTS: GCI has the potential to exponentially increase increase impact on all general outcomes where limited by general problem-solving abilityCONCLUSION: While an innate general collective intelligence factor might exist, and while conventional CI solutions might have significant impact on specific collective outcomes, a GCI is required to exponentially general problem-solving ability, and therefore to exponentially increase collective outcomes. This capacity has the potential to be disruptive.

General Collective Intelligence and the Transition to Collective Super-Intelligence

General Collective Intelligence has been defined as a system that combines individuals into a single collective cognition with the potential for vastly greater intelligence than any individual in the group [1], [2]. A novel Human Centric Functional Modeling approach [3] has been used define a model for this collective cognition, and for individual cognition [4], as well as for the intelligence of those systems of cognition, in order to quantify this potential increase in intelligence as exponential. Where other approaches assume the functions of cognition are implemented through mechanisms that are not yet confirmed, these functional models are defined from first principles and simply reflect all observed functionality rather than assuming any implementation at all. Here we show that from the perspective of these functional models, the transition from animal intelligence to a human intelligence capable of a sufficient level of abstraction to develop science and other concepts, and capable of exchanging and accumulating the value of those abstractions to achieve exponentially greater impact on the external world, is a well-defined phase change [5]. The transition from human intelligence to GCI, the transition from GCI to second order GCI, and so forth to Nth order GCI are hypothesized to be subsequent phase changes that may or may not occur [5]. The functional modeling approach is used to clarify the fundamentally different nature of the general problem-solving ability provided by GCI as opposed to the problem solving ability of tools such as computation or computing methods [6] that can be applied to any general problem, and why even super computers without general problem-solving ability are limited to the problems their designers can define, and to the solutions those designers can envision [7]. This model suggests that entire categories of problems cannot reliably be solved without this phase change to General Collective Intelligence, and since this exponential increase in problem-solving ability applies to physics, mathematics, economics, health care, sustainable development, and every other field of human study where intelligence applies. In addition, since this model suggests that any exponential increase in ability to impact the external world possible through GCI cannot have been possible before at any time in human civilization, and since another such increase cannot be possible again until the advent of AGI or the transition to a second order GCI. the implications of GCI are profound [8].

Towards Cognitive Communication and Other Applications of Cognitive Computing

The term cognitive communications has been used to describe “human-centric” communication systems that adapt to different behaviors, expectations and preferences. This paper explores a more general use of the term by attempting to enumerate all communication functions that might benefit through being executed by systems of individual or collective cognition. Systems of individual cognition might be represented by intelligent agents (based on some subset of the functionality suggested to be required for Artificial General Intelligence) with the capacity to change any property of communication. Communication functions executed by such systems optimize individual outcomes. Systems of collective cognition might be represented by collective intelligence solutions (based on some subset of functionality suggested to be required for General Collective Intelligence) with the capacity to enable such intelligent agents to self-assemble into communication networks using any combination of network topology, protocols, spectrum or other properties. Communication functions executed by such systems optimize collective outcomes. From this perspective, cognitive communication is explored as a specific case that might be generalized to apply to any number of other sectors, such as cognitive power generation and distribution, cognitive agriculture, cognitive healthcare, etc.

Human Intelligence and General Collective Intelligence as Phase Changes in Animal Intelligence

The hypothesis that human intelligence represents a phase transition in animal intelligence is explored, as is the hypothesis that General Collective Intelligence (GCI), which has been defined as a system that organizes groups into a single collective cognition with the potential for vastly greater general problem-solving ability than that of any individual in the group, represents a phase transition in human intelligence. At these phase transitions, cognition can be demonstrated to gain the capacity for exponentially greater general problem-solving ability. If valid, then when generalized as an Nth order pattern, these N phase transitions represent successively more powerful super-intelligences, where each of these super-intelligences can potentially be implemented as an Artificial General Intelligence (AGI), or as a General Collective Intelligence (GCI).