Decision-making without a brain: how an amoeboid organism solves the two-armed bandit

Reid, Chris; MacDonald, Hannelore; Mann, Richard P.; Marshall, James A. R.; Latty, Tanya; Garnier, Simon

doi:10.1098/rsif.2016.0030

Cited by 85 publications

(79 citation statements)

References 44 publications

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“…This outcome is usually referred to as maximizing (e.g., deVilliers, 1977). Similar results have been reported for great tits (Krebs, Kacelnik, & Taylor, 1978), threespined sticklebacks (Thomas, Kacelnik, & Van Der Meulen, 1985), bumblebees (Keasar, Rashkovich, Cohen, & Shmida, 2002), and individual slime mold cells (Reid et al, 2016) working in environments analogous to concurrent ratio schedules with unequal ratios in the components. To describe this outcome as maximizing implies that a mechanism different from the proportional mechanism implied by matching (Eq.…”

supporting

confidence: 83%

An evolutionary theory of behavior dynamics applied to concurrent ratio schedules

McDowell

Klapes

2018

J Exper Analysis Behavior

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An evolutionary theory of adaptive behavior dynamics was tested by studying the behavior of artificial organisms (AOs) animated by the theory, working on concurrent ratio schedules with unequal and equal ratios in the components. The evolutionary theory implements Darwinian rules of selection, reproduction, and mutation in the form of a genetic algorithm that causes a population of potential behaviors to evolve under the selection pressure of consequences from the environment. On concurrent ratio schedules with unequal ratios in the components, the AOs tended to respond exclusively on the component with the smaller ratio, provided that ratio was not too large and the difference between the ratios was not too small. On concurrent ratio schedules with equal ratios in the components, the AOs tended to respond exclusively on one component, provided the equal ratios were not too large. In addition, the AOs' preference on the latter schedules adjusted rapidly when the equal ratios were changed between conditions, but their steady-state preference was a continuous function of the value of the equal ratios. Most of these outcomes are consistent with the results of experiments with live organisms, and consequently support the evolutionary theory.

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supporting

confidence: 83%

An evolutionary theory of behavior dynamics applied to concurrent ratio schedules

McDowell

Klapes

2018

J Exper Analysis Behavior

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“…Second, many natural and artificial systems from amoebas (Reid et al, 2016) to humans (Moussaïd et al, 2010) need to implement their decision rules through local interaction rules, especially when the collectives have a decentralized structure. We will occasionally make reference to how some algorithms are implemented in distributed systems.…”

Section: Introductionmentioning

confidence: 99%

Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

2017

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The total knowledge contained within a collective supersedes the knowledge of even its most intelligent member. Yet the collective knowledge will remain inaccessible to us unless we are able to find efficient knowledge aggregation methods that produce reliable decisions based on the behavior or opinions of the collective's members. It is often stated that simple averaging of a pool of opinions is a good and in many cases the optimal way to extract knowledge from a crowd. The method of averaging has been applied to analysis of decision-making in very different fields, such as forecasting, collective animal behavior, individual psychology, and machine learning. Two mathematical theorems, Condorcet's theorem and Jensen's inequality, provide a general theoretical justification for the averaging procedure. Yet the necessary conditions which guarantee the applicability of these theorems are often not met in practice. Under such circumstances, averaging can lead to suboptimal and sometimes very poor performance. Practitioners in many different fields have independently developed procedures to counteract the failures of averaging. We review such knowledge aggregation procedures and interpret the methods in the light of a statistical decision theory framework to explain when their application is justified. Our analysis indicates that in the ideal case, there should be a matching between the aggregation procedure and the nature of the knowledge distribution, correlations, and associated error costs. This leads us to explore how machine learning techniques can be used to extract near-optimal decision rules in a data-driven manner. We end with a discussion of open frontiers in the domain of knowledge aggregation and collective intelligence in general.

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“…Ant colonies may be considered a singular brain, with each ant acting as a neuron-a suggestion made by Douglas Hofstadter [28] in 1980 and supported by cognitive scientists since. Studies of slime molds have suggested that neurons may not even be necessary for biological cognition [29]. If neurons are not necessary for cognition and collections of neurons can act collectively, then cognition is more broadly distributed than previously suggested-even within our own bodies.…”

Section: An Internet Of Processorsmentioning

confidence: 86%

Distributed Cognition: Assessing the Structure of Urban Scale Artificial Intelligence

Webb¹

2017

IRATJ

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Urban scale artificial intelligence (AI) is most frequently structured to sense and gather as much information as possible. Is this the most appropriate evaluation of intelligence? Many descriptions of so-called Smart Cities focus exclusively on their sensorial capabilities, but little is given to their cognitive capacities. Discussion of the deployment of computation within the urban environment has largely avoided notions of cognition, though a capacity for cognition is ultimately what we are asking of anything that is to be "smart". Cognition itself may be presented in a variety of ways, and determining what structure is most appropriate for urban scale AI is a critical discussion with significant implications for our collective ecological footprint. This article attempts to frame the integration of urban scale AI within a discussion of cognitive structures by building from the work of Benjamin Bratton and Edwin Hutchins to analyze the material culture of Masdar City and the Internet of Things, ultimately arguing that strategies of distributed cognition are both more feasible and more performative than a traditional Smart City model.

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Decision-making without a brain: how an amoeboid organism solves the two-armed bandit

Cited by 85 publications

References 44 publications

An evolutionary theory of behavior dynamics applied to concurrent ratio schedules

An evolutionary theory of behavior dynamics applied to concurrent ratio schedules

Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

Distributed Cognition: Assessing the Structure of Urban Scale Artificial Intelligence

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