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Extrapolating weak selection in evolutionary games

Overview of attention for article published in Journal of Mathematical Biology, July 2018
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  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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Title
Extrapolating weak selection in evolutionary games
Published in
Journal of Mathematical Biology, July 2018
DOI 10.1007/s00285-018-1270-6
Pubmed ID
Authors

Zhuoqun Wang, Rick Durrett

Abstract

This work is inspired by a 2013 paper from Arne Traulsen's lab at the Max Plank Institute for Evolutionary Biology (Wu et al. in PLoS Comput Biol 9:e1003381, 2013). They studied evolutionary games when the mutation rate is so small that each mutation goes to fixation before the next one occurs. It has been shown that for [Formula: see text] games the ranking of the strategies does not change as strength of selection is increased (Wu et al. in Phys Rev 82:046106, 2010). The point of the 2013 paper is that when there are three or more strategies the ordering can change as selection is increased. Wu et al. (2013) did numerical computations for a fixed population size N. Here, we will instead let the strength of selection [Formula: see text] where c is fixed and let [Formula: see text] to obtain formulas for the invadability probabilities [Formula: see text] that determine the rankings. These formulas, which are integrals on [0, 1], are intractable calculus problems, but can be easily evaluated numerically. Here, we use them to derive simple formulas for the ranking order when c is small or c is large.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
France 1 4%
Unknown 22 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 25%
Researcher 5 21%
Student > Bachelor 3 13%
Student > Postgraduate 2 8%
Professor 2 8%
Other 4 17%
Unknown 2 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 46%
Computer Science 2 8%
Mathematics 2 8%
Unspecified 1 4%
Environmental Science 1 4%
Other 2 8%
Unknown 5 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 August 2018.
All research outputs
#15,542,250
of 23,098,660 outputs
Outputs from Journal of Mathematical Biology
#333
of 665 outputs
Outputs of similar age
#209,493
of 330,145 outputs
Outputs of similar age from Journal of Mathematical Biology
#8
of 20 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 665 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.