Aaron Archer

2.3k citations
30 papers · 1.3k indexed · h-index 17

Aaron Archer

30 papers receiving 1.3k citations

Peers

Aaron Archer
Comparison fields: 5 of 97
  • Management Science and Operations Research 559
  • Computer Networks and Communications 624
  • Marketing 178
  • Ecological Modeling 82
  • Industrial and Manufacturing Engineering 142
Replace Thomas Erlebach with:
Thomas Erlebach United Kingdom
Steven L. Lytinen United States
Pierre Courtois France
Mirco Nanni Italy
Jakob Krarup Denmark
Duanbing Chen China
Shengkai Zhang China
Xiangrong Tong China
José M. Vidal United States
Xianjia Wang China
Aaron Archer relative to Thomas Erlebach United Kingdom Thomas Erlebach's profile →
Citations per field
00.5×10×16.4×
Thomas Erlebach · 1×
Citations per year

Countries citing papers authored by Aaron Archer

Since Specialization
Citations

This map shows the geographic impact of Aaron Archer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aaron Archer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aaron Archer more than expected).

Fields of papers citing papers by Aaron Archer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aaron Archer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aaron Archer. The network helps show where Aaron Archer may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Aaron Archer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Aaron Archer Line = papers co-authored together Aaron Archer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201911
2 201541
3 201152
4
Combining predictors for recommending music: the false positives' approach to KDD Cup track 2
20112
5 20107
6 2010176
7 2008118
8 20085
9 200829
10 200842
11 20073
12 200416
13
Mechanisms for discrete optimization with rational agents
200422
14 2004122
15 200491
16 200375
17 200325
18 200341
19 200012
20 199930

About Aaron Archer

Aaron Archer is a scholar working on Management Science and Operations Research, Business and International Management, Computational Theory and Mathematics, Marketing and Safety Research, having authored 30 papers that have together received 1.3k indexed citations. Recurring topics across this work include Auction Theory and Applications (9 papers), Game Theory and Voting Systems (7 papers), Complexity and Algorithms in Graphs (6 papers), Optimization and Search Problems (4 papers), Caching and Content Delivery (4 papers), Consumer Market Behavior and Pricing (4 papers), Experimental Behavioral Economics Studies (3 papers) and Game Theory and Applications (3 papers). The work is most often cited by research in Management Science and Operations Research (559 citations), Computer Networks and Communications (624 citations), Marketing (178 citations), Ecological Modeling (82 citations) and Industrial and Manufacturing Engineering (142 citations). Aaron Archer has collaborated with scholars based in United States, Australia and South Africa. Frequent co-authors include Éva Tardos, David Applegate, Kunal Talwar, K. K. Ramakrishnan, Christos H. Papadimitriou, Brian Kinghorn, David G. Mayer, Vijay Gopalakrishnan, Seungjoon Lee and Paul Williams. Their work appears in journals such as Games and Economic Behavior, SIAM Journal on Computing, ACM Transactions on Algorithms, Biological Conservation and Ecological Applications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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