Dealing with Big Numbers: Representation and Understanding of Magnitudes Outside of Human Experience

Resnick, Ilyse; Newcombe, Nora S.; Shipley, Thomas F.

doi:10.1111/cogs.12388

Cited by 36 publications

(39 citation statements)

References 44 publications

(78 reference statements)

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“…Large numbers add weight to a statement, imply a “huge” problem and elicit a state of urgency, since laypersons are usually not able to fully comprehend the implications of such large numbers 33,34. Furthermore, images which demonstrate the extension of the phenomenon are created by terms such as “smog” or “nebulous swarm of pollution.”…”

Section: Resultsmentioning

confidence: 99%

On the Creation of Risk: Framing of Microplastics Risks in Science and Media

2019

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The public is concerned about plastic pollution, while clear‐cut scientific evidence for an environmental risk of microplastics is absent. This contrast between incomplete scientific knowledge and public risk perception is an interesting case for investigating how “environmental risk” is transformed in science communication. This study examines how microplastics risks are framed in peer‐reviewed publications and online newspaper articles, respectively. It also analyzes if the contents conveyed by the frames used in science and the media are consistent. The results show that most scientific studies (67%) frame microplastics risks as hypothetical or uncertain, while 24% present them as established. In contrast, most media articles reporting on microplastic impacts (93%) imply that risks of microplastics exist and harmful consequences are highly probable. The creation of simple narratives (journalists) and the emphasis on potentially negative impacts (scientists) contribute to this inconsistency. The transformation of an uncertain risk into an actual risk is further caused by two inconsistent risk conceptions, namely risk being the probability of a negative outcome (environmental scientists) or being the uncertainty of a negative outcome itself (public). Although the latter differs from the risks identified “objectively” by scientific methods, it allows understanding the risk perception of the public and decision‐makers.

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Section: Resultsmentioning

confidence: 99%

On the Creation of Risk: Framing of Microplastics Risks in Science and Media

2019

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“…This effect, originally unveiled and discussed in [21], has a valuable effect on collective performance in human groups. The authors argued that because people have "difficulties to reason about magnitudes outside of human perception" [32], they may assess the reliability of relatively low numbers more easily than they would very high numbers, making them more likely to discard low estimates compared to high estimates. A concomitant explanation is that people usually know that the quantities they were asked to estimate are supposed to be large, even if they have a poor idea of the actual value.…”

Section: Discussionmentioning

confidence: 99%

Impact of sharing full versus averaged social information on social influence and estimation accuracy

Jayles

Kurvers

2020

Preprint

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The digital revolution has fundamentally changed social information exchange and vastly increased exposure to the opinions of others. However, it is unclear whether exchanging such large amounts of information benefits decision making. Exchanging a moderate amount or aggregated forms of social information may indeed avoid information overload and foster better decisions. We performed experiments in which participants were asked to estimate quantities twice, before and after receiving either all of their peers' estimates or the geometric mean thereof. We find that second estimates were more accurate when participants observed all estimates than when they saw their geometric mean. Using a model, we predict that accuracy improves most when about twelve estimates are exchanged, independent of group size. Taken together, our results thus suggest that to optimize collective decisions, individuals should receive all decisions from a moderate number of group members, rather than aggregated opinions of large crowds.

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“…But why do people weigh information asymmetrically? A possible explanation could be that it results from "people's di culty to reason about magnitudes outside of human perception" [53]. Indeed, people generally deal with small quantities, typically below one thousand (e.g.…”

Section: Discussionmentioning

confidence: 99%

Crowd control: Reducing individual estimation bias by sharing biased social information

Jayles

Sire²,

Kurvers

2019

Preprint

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Collective decision-making is ubiquitous across biological systems. However, biases at the individual level can impair the quality of collective decisions. One such prime bias is the human tendency to underestimate quantities. Former research on social influence in human estimation tasks has generally focused on the exchange of single estimates, showing that randomly exchanging single estimates does not reduce the underestimation bias. Here we performed estimation experiments to test whether leveraging prior knowledge about this bias when designing the structure of information exchange can attenuate its effects. Participants had to estimate a series of quantities twice. After providing a personal estimate, they received estimates from one or several group members, and could revise their personal estimate. Our purpose was threefold: (i) to investigate whether restructuring the information exchange can reduce the underestimation bias, (ii) to study how the number of estimates exchanged affects accuracy, and (iii) to shed light on the mechanisms underlying the integration of multiple pieces of social information. Our results show that leveraging prior knowledge about the underestimation bias allows to select and exchange the estimates that are most likely to attenuate its effects. Crucially, this exchange method operates without any reference to the truth. Moreover, we find that exchanging more than one estimate also reduces the underestimation bias. Underlying these results are a human tendency to herd, to trust large numbers more than small numbers, and to follow disparate social information less. Using a computational modelling approach, we demonstrate that these effects are indeed key to explain our experimental results. We then use the model to explore the conditions under which estimation accuracy can be improved further. Overall, our results show that existing knowledge on biases can be used to boost collective decision-making, paving the way for combating other cognitive biases threatening collective systems.

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Dealing with Big Numbers: Representation and Understanding of Magnitudes Outside of Human Experience

Cited by 36 publications

References 44 publications

On the Creation of Risk: Framing of Microplastics Risks in Science and Media

On the Creation of Risk: Framing of Microplastics Risks in Science and Media

Impact of sharing full versus averaged social information on social influence and estimation accuracy

Crowd control: Reducing individual estimation bias by sharing biased social information

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