Collective intelligence as a public good

Leonard, Naomi Ehrich; Levin, Simon A.

doi:10.1177/26339137221083293

Cited by 16 publications

(16 citation statements)

References 160 publications

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“…There may be additional incentives for exploration for novel solutions or ideas, for example, patents, social prestige, and other rewards associated with innovations [Giraldeau et al, 2017]. Our results also suggest that studies on collective behavior should consider the discrepancies between individual and collective goals, costs and benefits [Leonard and Levin, 2022]. Further, investigating how social learning and communication evolve in tandem with individual search strategies under different contexts can shed light on general aspects of collective behavior and sociality.…”

Section: Discussionmentioning

confidence: 88%

“…The model also shows how strategies selected for foraging efficiency at the individual level may not always be beneficial for collective foraging. Instead, individual-level selection can lead to discrepancies between individual and collective search goals Leonard and Levin [2022] by favoring less-efficient exploitative strategies. We found that informed Lévy-like patterns such as those generated by ARS can restore and stabilize the balance between exploration and exploitation at both individual and group levels.…”

Section: Discussionmentioning

confidence: 99%

“…It may therefore benefit individual foragers and their individual success to be part of a group [Giraldeau and Caraco, 2018, Pitcher et al, 1982]. However, this group benefit may not confer to individuals when there are discrepancies between search behavior that is adaptive for the individual versus the group [Leonard and Levin, 2022]. Foraging with a group may also affect the search strategies that are optimal for the individual.…”

Section: Introductionmentioning

confidence: 99%

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Evolution of explorative and exploitative search strategies in collective foraging

Garg

Smaldino

Kello

2023

Preprint

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Evolutionary theories of foraging hypothesize that individual foraging strategies evolved to maximize search efficiency. Many studies have investigated the trade-offs between exploration and exploitation and how individual foragers manage them. However, for groups of foragers, these trade-offs can change and individual search strategies may evolve in response to their social environments. For instance, foragers may use social information to collectively find and harvest resources, which might increase competition and decrease the benefits of exploring new resources. Previous work has shown that when learning socially, it is optimal for groups to be composed of highly explorative strategies. However, individual and collective search efficiencies may not align if individual search strategies beneficial for the group are disadvantageous for the individuals. In the present study, we use an agent-based model to investigate the effect of collective foraging on the evolution of individual search strategies and how they relate to group search efficiency. We use a genetic algorithm to evolve L\'evy walk exponents ($\mu$) that govern the balance of explorative versus exploitative foraging. We show that groups can evolve with social learning whose collective performance is more optimal than without social learning. The model shows that exploiters have a selective advantage in scrounging off findings by other agents, but too many exploiters diminished group search efficiencies. We also show that social learning in large groups can increase the payoffs of exploration and lead to the selection of more exploratory groups. Finally, we show that area-restricted search can help explorers exploit found resources and lead to more efficient collective search. Our results demonstrate how exploration and exploitation must be balanced at both individual and collective levels, and how individual search strategies can evolve to the benefit of collective search efficiency.

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Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolution of explorative and exploitative search strategies in collective foraging

Garg

Smaldino

Kello

2023

Preprint

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“…The private benefits allow the evolutionary emergence of forecasters, who then provide public benefits to all. Collective intelligence can be measured by the relative performance of individuals and groups (Leonard and Levin, 2022). The environmental stability and increased average payoffs that forecasting types often provide can therefore be seen as a form of collective intelligence.…”

Section: Introductionmentioning

confidence: 99%

The evolution of forecasting for decision-making in dynamic environments

Tilman,

Vasconcelos,

Akçay

et al. 2023

Collective Intelligence

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Climate change and technological advances are reshaping ecosystems and societies. Strategic choices that were best yesterday may be sub-optimal tomorrow; and environmental conditions that were once taken for granted may soon cease to exist. In dynamic settings, how people choose behavioral strategies has important consequences for environmental dynamics. Economic and evolutionary theories make similar predictions for strategic behavior in a static environment, even though one approach assumes perfect rationality and the other assumes no cognition whatsoever; but predictions differ in a dynamic environment. Here we explore a middle ground between economic rationality and evolutionary myopia. Starting from a population of myopic agents, we study the evolutionary viability of a new type that forms environmental forecasts when making strategic decisions. We show that forecasting types can have an advantage in changing environments, even when the act of forecasting is costly. Forecasting types can invade but rarely overtake the population, producing a stable coexistence with myopic types. Moreover, forecasting fosters collective intelligence by providing a public good which reduces the amplitude of environmental oscillations and often increases mean payoffs to forecasting and myopic types alike. We interpret our results for understanding the evolution of different modes of decision-making such as forecasting. And we discuss implications for the management of environmental systems of societal importance.

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“…Large portions of the collective decision-making literature in swarm robotics are focused on discrete problems, such as the popular collective perception benchmark scenario [4]. Here we focus on a continuous consensus achievement problem [7], [8] in the form of a decentralized estimation scenario [9]. In our previous work we studied the effect of diverse information on the accuracy of collective estimation, which forms the exploration-exploitation tradeoff [1].…”

Section: Introductionmentioning

confidence: 99%

Speed-vs-Accuracy Tradeoff in Collective Estimation: An Adaptive Exploration-Exploitation Case

Raoufi

Hamann

Romanczuk

2021

2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

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Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

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Collective intelligence as a public good

Cited by 16 publications

References 160 publications

Evolution of explorative and exploitative search strategies in collective foraging

Evolution of explorative and exploitative search strategies in collective foraging

The evolution of forecasting for decision-making in dynamic environments

Speed-vs-Accuracy Tradeoff in Collective Estimation: An Adaptive Exploration-Exploitation Case

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