| Title |
Validating and Calibrating Agent-Based Models: A Case Study
|
|---|---|
| Published in |
Computational Economics, July 2007
|
| DOI | 10.1007/s10614-007-9097-z |
| Authors |
Carlo Bianchi, Pasquale Cirillo, Mauro Gallegati, Pietro A. Vagliasindi |
Mendeley readers
The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| Italy | 1 | <1% |
| Colombia | 1 | <1% |
| United Kingdom | 1 | <1% |
| Australia | 1 | <1% |
| Unknown | 117 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 34% |
| Researcher | 20 | 16% |
| Professor > Associate Professor | 10 | 8% |
| Student > Doctoral Student | 10 | 8% |
| Student > Master | 10 | 8% |
| Other | 19 | 15% |
| Unknown | 13 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Economics, Econometrics and Finance | 23 | 18% |
| Computer Science | 17 | 14% |
| Engineering | 16 | 13% |
| Social Sciences | 14 | 11% |
| Environmental Science | 9 | 7% |
| Other | 25 | 20% |
| Unknown | 21 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 01 January 2015.
All research outputs
#7,548,107
of 23,028,364 outputs
Outputs from Computational Economics
#46
of 190 outputs
Outputs of similar age
#24,536
of 67,523 outputs
Outputs of similar age from Computational Economics
#2
of 4 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 190 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 55% 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 67,523 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.