Polya’s bees: A model of decentralized decision-making

Golman, Russell; Hagmann, David; Miller, John H.

doi:10.1126/sciadv.1500253

Cited by 10 publications

(3 citation statements)

References 61 publications

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“…Much attention has been paid to optimisation of speed-accuracy trade-offs in such situations (e.g. [10][11][12][13][14]) but theory shows that where decisions makers are rewarded by the value of the option they select, rather than simply whether or not it was the best available, managing speed-accuracy trade-offs may not help to optimise overall decision quality [15]. Here we analyse a value-sensitive decisionmechanism inspired by cross-inhibition in house-hunting honeybee swarms [5,6].…”

Section: Introductionmentioning

confidence: 99%

Model of the best-of-Nnest-site selection process in honeybees

et al. 2017

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The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modelled and theoretically analysed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision process dynamics qualitatively change. In this work, we extend previous analyses of a valuesensitive decision-making mechanism to a decision process among N nests. First, we present the decisionmaking dynamics in the symmetric case of N equal-quality nests. Then, we generalise our findings to a best-of-N decision scenario with one superior nest and N -1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signalling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signalling behaviours. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signalling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signalling ratio.

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Section: Introductionmentioning

confidence: 99%

Model of the best-of-Nnest-site selection process in honeybees

et al. 2017

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“…The design of an effective human-collective system must enable the human-collective team to fulfill primary objectives, without hindering other metrics, such as decision time and accuracy. Devoting more time to ensure high task performance is a common trade-off [18]. Expedited decisions may have occurred if higher valued targets were more observable further away from other objects (less clutter), making them more salient, or if impatient operators predicted future collective behaviors and influenced collectives more to make faster decisions.…”

Section: Discussionmentioning

confidence: 99%

Transparency's Influence on Human-Collective Interactions

Roundtree¹,

Cody²,

Leaf³

et al. 2020

Preprint

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Collective robotic systems are biologically inspired and advantageous due to their apparent global intelligence and emergent behaviors. Many applications can benefit from the incorporation of collectives, including environmental monitoring, disaster response missions, and infrastructure support. Transparency research has primarily focused on how the design of the models, visualizations, and control mechanisms influence humancollective interactions. Traditionally most evaluations have focused only on one particular system design element, evaluating its respective transparency. This manuscript analyzed two models and visualizations to understand how the system design elements impacted human-collective interactions, to quantify which model and visualization combination provided the best transparency, and provide design guidance, based on remote supervision of collectives. The consensus decision-making and baseline models, as well as an individual agent and abstract visualizations, were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. Both models and visualizations provided transparency and influenced human-collective interactions differently. No single combination provided the best transparency.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); User studies.

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“…We use a model of sequential sampling and evidence accumulation to specify the cognitive basis of strategic deliberation and decision making. Growing out of evidence accumulation models capturing perception, categorization, lexical decision making, and memory (Gold & Shadlen, 2007; Nosofsky & Palmeri, 1997; Ratcliff, 1978; Ratcliff, Gomez, & McKoon, 2004; Usher & McClelland, 2001), models based on sampling and accumulation have been quite successfully applied to nonstrategic multiattribute, intertemporal, and risky choice (Bhatia, 2013, 2014, 2017; Bhatia & Mullett, 2016; Clithero, 2018; Dai & Busemeyer, 2014; Diederich, 1997; Fudenberg, Strack, & Strzalecki, 2018; Golman, Hagmann, & Miller, 2015; Krajbich, Armel, & Rangel, 2010; Noguchi & Stewart, 2018; Roe et al, 2001; Trueblood, Brown, & Heathcote, 2014; Tsetsos, Chater, & Usher, 2012; Turner et al, 2018; Usher & McClelland, 2004; Webb, 2018). Preferences (defined as propensities to choose the available choice options 4 ) can be represented as activation strengths in network nodes corresponding to the choice options.…”

Section: Preference Accumulation Modelsmentioning

confidence: 99%