A Turing test for collective motion

Herbert‐Read, James E.; Romenskyy, Maksym; Sumpter, David J. T.

doi:10.1098/rsbl.2015.0674

Cited by 15 publications

(25 citation statements)

References 19 publications

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“…The challenges of using self-propelled particle models for modeling collective animal behaviour are also known [43,44]. The tractable models are usually oversimplified and too general and thus lack flexibility required to reflect the features of a particular phenomena.…”

Section: Discussionmentioning

confidence: 99%

Body size affects the strength of social interactions and spatial organization of a schooling fish (Pseudomugil signifer)

et al. 2017

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While a rich variety of self-propelled particle models propose to explain the collective motion of fish and other animals, rigorous statistical comparison between models and data remains a challenge. Plausible models should be flexible enough to capture changes in the collective behaviour of animal groups at their different developmental stages and group sizes. Here, we analyse the statistical properties of schooling fish (Pseudomugil signifer) through a combination of experiments and simulations. We make novel use of a Boltzmann inversion method, usually applied in molecular dynamics, to identify the effective potential of the mean force of fish interactions. Specifically, we show that larger fish have a larger repulsion zone, but stronger attraction, resulting in greater alignment in their collective motion. We model the collective dynamics of schools using a self-propelled particle model, modified to include varying particle speed and a local repulsion rule. We demonstrate that the statistical properties of the fish schools are reproduced by our model, thereby capturing a number of features of the behaviour and development of schooling fish.

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Section: Discussionmentioning

confidence: 99%

Body size affects the strength of social interactions and spatial organization of a schooling fish (Pseudomugil signifer)

et al. 2017

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“…Our experimental methodology was based on that of [27], but with in-person (as opposed to online) participants. The first trial tested the ability of participants to distinguish between real and simulated royalsocietypublishing.org/journal/rsos R. Soc.…”

Section: Methodsmentioning

confidence: 99%

A Turing test for crowds

Webster

Amos

2020

R. Soc. open sci.

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The accuracy and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Accuracy concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we address both issues via a so-called ‘Turing test’ for crowds, using movies generated from both accurate simulations and observations of real crowds. The fundamental question we ask is ‘Can human observers distinguish between real and simulated crowds?’ In two studies with student volunteers ( n = 384 and n = 156), we find that non-specialist individuals are able to reliably distinguish between real and simulated crowds when they are presented side-by-side, but they are unable to accurately classify them. Classification performance improves slightly when crowds are presented individually, but not enough to out-perform random guessing. We find that untrained individuals have an idealized view of human crowd behaviour which is inconsistent with observations of real crowds. Our results suggest a possible framework for establishing a minimal set of collective behaviours that should be integrated into the next generation of crowd simulation models.

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“…In politics, the forecasts of prediction markets (19,20) are now commonly reported alongside opinion polls during elections. Scientists are also turning to crowdsourcing collective wisdom as a validation tool (21)(22)(23). However, as highlighted by the failure of financial and prediction markets to foresee the results of recent elections in the United Kingdom and the United States, collective wisdom is not a guaranteed property of a distributed system (2), partly because of herding effects (24,25).…”

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confidence: 99%

Optimal incentives for collective intelligence

Mann

Helbing

2017

Proc. Natl. Acad. Sci. U.S.A.

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Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate one's peers. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that incentives may play in maintaining useful diversity. We show that market-based incentive systems produce herding effects, reduce information available to the group, and restrain collective intelligence. Therefore, we propose an incentive scheme that rewards accurate minority predictions and show that this produces optimal diversity and collective predictive accuracy. We conclude that real world systems should reward those who have shown accuracy when the majority opinion has been in error.collective intelligence | game theory | democracy | diversity | markets T he financial crisis and its aftermath have reopened longstanding debates about the collective wisdom of our societal organizations (1-3). Financial and prediction markets seem unable to foresee major economic and political upheavals, such as the credit crunch or Brexit. This lack of collective foresight could be the result of insufficient diversity among decisionmaking individuals (4). Diversity has been identified as a key ingredient of successful groups across many facets of collective behavior (5-7). It is a crucial condition for collective intelligence (6-10) that can be more important than the intelligence of individuals within a group (11). Because collective behavior ultimately results from individual actions, incentives play a major role for diversity and collective performance (12, 13). Although most previous research has focused on explaining how collective intelligence emerges (14), less is known about how to optimize the wisdom of crowds in a quantitative sense.Harnessing collective wisdom is important. Global systems of communication, governance, trade, and transport grow rapidly in complexity every year. Many of these real world problems have a large number of contributing factors. For example, predicting future economic fluctuations requires integrating knowledge about credit markets and supply chains across the world as well as the ramifications of political developments in different countries and the shifting sentiments of individual investors and consumers. Political developments are themselves the result of many factors: both direct (e.g., political parties' strategies) and indirect (e.g., technological change). Scientific questions are also increasingly complex. For instance, building a complete model of an ecosystem requires bringing together expertise on many scales from individual animal behavior to complex networks of predation and codependency (15). In each case, knowledge about the diverse contributing factors is dispersed. For these highdimensional problems, it is becoming impossible for any single individual or agency to gather and process...

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A Turing test for collective motion

Cited by 15 publications

References 19 publications

Body size affects the strength of social interactions and spatial organization of a schooling fish (Pseudomugil signifer)

Body size affects the strength of social interactions and spatial organization of a schooling fish (Pseudomugil signifer)

A Turing test for crowds

Optimal incentives for collective intelligence

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