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On the distribution of the number of internal equilibria in random evolutionary games

Overview of attention for article published in Journal of Mathematical Biology, August 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
On the distribution of the number of internal equilibria in random evolutionary games
Published in
Journal of Mathematical Biology, August 2018
DOI 10.1007/s00285-018-1276-0
Pubmed ID
Authors

Manh Hong Duong, Hoang Minh Tran, The Anh Han

Abstract

The analysis of equilibrium points is of great importance in evolutionary game theory with numerous practical ramifications in ecology, population genetics, social sciences, economics and computer science. In contrast to previous analytical approaches which primarily focus on computing the expected number of internal equilibria, in this paper we study the distribution of the number of internal equilibria in a multi-player two-strategy random evolutionary game. We derive for the first time a closed formula for the probability that the game has a certain number of internal equilibria, for both normal and uniform distributions of the game payoff entries. In addition, using Descartes' rule of signs and combinatorial methods, we provide several universal upper and lower bound estimates for this probability, which are independent of the underlying payoff distribution. We also compare our analytical results with those obtained from extensive numerical simulations. Many results of this paper are applicable to a wider class of random polynomials that are not necessarily from evolutionary games.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 27%
Other 1 9%
Student > Doctoral Student 1 9%
Professor 1 9%
Researcher 1 9%
Other 1 9%
Unknown 3 27%
Readers by discipline Count As %
Physics and Astronomy 4 36%
Engineering 2 18%
Computer Science 1 9%
Unknown 4 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 08 August 2018.
All research outputs
#6,330,851
of 23,098,660 outputs
Outputs from Journal of Mathematical Biology
#119
of 665 outputs
Outputs of similar age
#109,630
of 331,041 outputs
Outputs of similar age from Journal of Mathematical Biology
#5
of 21 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 665 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 81% of its peers.
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 331,041 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.