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Platforms and methods for agent-based modeling

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, May 2002
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Readers on

mendeley
355 Mendeley
citeulike
2 CiteULike
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Article details
Title
Platforms and methods for agent-based modeling
Published in
Proceedings of the National Academy of Sciences of the United States of America, May 2002
DOI 10.1073/pnas.072079499
Pubmed ID
Authors

Nigel Gilbert, Steven Bankes

Abstract

The range of tools designed to help build agent-based models is briefly reviewed. It is suggested that although progress has been made, there is much further design and development work to be done. Modelers have an important part to play, because the creation of tools and models using those tools proceed in a dialectical relationship.

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 355 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 6 2%
United Kingdom 6 2%
Brazil 5 1%
Germany 4 1%
Austria 2 <1%
Australia 2 <1%
Canada 2 <1%
Sweden 2 <1%
Portugal 2 <1%
Other 20 6%
Unknown 304 86%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 101 28%
Researcher 76 21%
Student > Master 34 10%
Professor > Associate Professor 25 7%
Other 21 6%
Other 68 19%
Unknown 30 8%
Readers by discipline
Readers by discipline Count As %
Computer Science 60 17%
Environmental Science 39 11%
Social Sciences 37 10%
Engineering 36 10%
Business, Management and Accounting 32 9%
Other 102 29%
Unknown 49 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 25 April 2023.
All research outputs
#9,526,254
of 34,517,649 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#75,089
of 119,103 outputs
Outputs of similar age
#71,943
of 178,814 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#390
of 639 outputs
Altmetric has tracked 34,517,649 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 119,103 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.1. 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 178,814 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 59% of its contemporaries.
We're also able to compare this research output to 639 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.