How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams

O'Bryan, Lisa R.; Beier, Margaret E.; Salas, Eduardo

doi:10.3390/jintelligence8010009

Cited by 18 publications

(14 citation statements)

References 106 publications

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“…Empirical substantiation of such claims has proven difficult, however. Most investigations rely heavily on laboratory-derived summaries or “snapshots” of individual and collective behavior that flatten the complexity of local scale interactions [ 20 ] and make it difficult to examine causal relationships between individual scale mechanisms and collective behavior as they typically unfold in real world settings [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

An Active Inference Model of Collective Intelligence

Kaufmann¹,

Gupta

Taylor

2021

Entropy

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Collective intelligence, an emergent phenomenon in which a composite system of multiple interacting agents performs at levels greater than the sum of its parts, has long compelled research efforts in social and behavioral sciences. To date, however, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between autonomous sub-system components (individuals) and global-scale behavior of the composite system (the collective). In this paper we use the Active Inference Formulation (AIF), a framework for explaining the behavior of any non-equilibrium steady state system at any scale, to posit a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence. We explore the effects of providing baseline AIF agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model 2), Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4). These stepwise transitions in sophistication of cognitive ability are motivated by the types of advancements plausibly required for an AIF agent to persist and flourish in an environment populated by other highly autonomous AIF agents, and have also recently been shown to map naturally to canonical steps in human cognitive ability. Illustrative results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents’ local and global optima. Alignment emerges endogenously from the dynamics of interacting AIF agents themselves, rather than being imposed exogenously by incentives to agents’ behaviors (contra existing computational models of collective intelligence) or top-down priors for collective behavior (contra existing multiscale simulations of AIF). These results shed light on the types of generic information-theoretic patterns conducive to collective intelligence in human and other complex adaptive systems.

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Section: Introductionmentioning

confidence: 99%

An Active Inference Model of Collective Intelligence

Kaufmann¹,

Gupta

Taylor

2021

Entropy

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“…In this context, swarm intelligence provides tools and a theory to understand the dynamics of opinion emergence, spreading, and competition in networks of highly correlated individuals (O’Bryan et al, 2020). Therefore, it can help explain how information entering such networks can eventually bias an entire population (Lorenz et al, 2011; Moussaïd et al, 2015), or how the interaction dynamics within a population can lead to the emergence of more accurate decisions in some cases (Kao and Couzin, 2014; Mann, 2018; Marshall et al, 2019), but also of groupthink and opinion polarization in others (Hegselmann and Krause, 2002; Janis, 1982).…”

Section: From Condorcet To Swarm Intelligence: a Brief Historical Per...mentioning

confidence: 99%

We the swarm—Methodological, theoretical, and societal (r)evolutions in collective decision-making research

Garnier

Moussaïd

2022

Collective Intelligence

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Collective decision-making constitutes a core function of social systems and is, therefore, a central tenet of collective intelligence research. From fish schools to human crowds, we start by interrogating ourselves about the very definition of collective decision-making and the scope of the scientific research that falls under it. We then summarize its history through the lenses of social choice theory and swarm intelligence and their accelerating collaboration over the past 20 or so years. Finally, we offer our perspective on the future of collective decision-making research in 3 mutually inclusive directions. We argue (1) that the possibility to collect data of a new nature, including fine-grain tracking information, virtual reality, and brain imaging inputs, will enable a direct link between plastic individual cognitive processes and the ontogeny of collective behaviors; (2) that current theoretical frameworks are not well suited to describe the long-term consequences of individual plasticity on collective decision-making processes and that, therefore, new formalisms are necessary; and finally (3) that applying the results of collective decision-making research to real-world situations will require the development of practical tools, the implementation of monitoring processes that respect civil liberties, and, possibly, government regulations of social interventions by public and private actors.

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“…Observations of animal behavior [14], [26], [29], as well as experiments in humans [5] have demonstrated that dynamic switching between social and individual learning improves performance of the collaborative decision making process. Shifting from group driven activity to individual work is conjectured to act like brainstorming sessions, allowing to identify a wider range of possible solutions, since agents are not influenced by each other at all times.…”

Section: Dynamic Learning Rulesmentioning

confidence: 99%

Optimizing the efficiency of collective decision making in groups

Turalska

Mel

Lickorish

et al. 2021

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III

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The complexity of modern military operations create a demand for efficient collaborative decision making and problem solving. Additionally, as military units operate in increasingly dynamic environments, the ability to respond to changing circumstances becomes paramount for mission success. An effective response rests on correct dissemination and transfer of information across the command and control structure, and thus is critically linked to the network of human interactions.In this paper, we take an agent-based modeling approach to collective problem solving. We investigate three key factors affecting the performance in collaborative environments: (1) the structure of network used to share information between agents, (2) the search strategies adopted by agents, and (3) the complexity of problems facing the group. In particular we study how the trade-off between exploitation of known solutions and exploration for novel ones is related to the efficiency of collective search. Additionally we consider the role of agent behavior: propensity for risk-taking and trustworthiness, as well as the dynamic nature of social connections. Finally, we outline the directions for future work regarding the efficiency of problem solving on military-like command and control structures.

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How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams

Cited by 18 publications

References 106 publications

An Active Inference Model of Collective Intelligence

An Active Inference Model of Collective Intelligence

We the swarm—Methodological, theoretical, and societal (r)evolutions in collective decision-making research

Optimizing the efficiency of collective decision making in groups

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