Daniel M. Reeves

25 papers receiving 570 citations

Peers

Daniel M. Reeves
Comparison fields: 5 of 59
  • Management Science and Operations Research 374
  • General Decision Sciences 25
  • Marketing 121
  • Safety Research 85
  • Economics and Econometrics 220
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Countries citing papers authored by Daniel M. Reeves

Since Specialization
Citations

This map shows the geographic impact of Daniel M. Reeves'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 Daniel M. Reeves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel M. Reeves more than expected).

Fields of papers citing papers by Daniel M. Reeves

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Reeves. 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 Daniel M. Reeves. The network helps show where Daniel M. Reeves may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel M. Reeves, 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 Daniel M. Reeves Line = papers co-authored together Daniel M. Reeves links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200386
2
Notes on Equilibria in Symmetric Games
200483
3 200451
4 200949
5 201036
6 200435
7 201030
8
Approximate strategic reasoning through hierarchical reduction of large symmetric games
200529
9
Price Prediction Strategies for Market-Based Scheduling
200426
10
Toward a Declarative Language for Negotiating Executable Contracts
200324
11 200222
12 200920
13
Generating trading agent strategies: Analytic and empirical methods for infinite and large games.
200517
14 200117
15 200816
16 201315
17 201414
18 200511
19 20039
20
Empirical Game-Theoretic Analysis of the TAC Market Games
20069

About Daniel M. Reeves

Daniel M. Reeves is a scholar working on Management Science and Operations Research, Marketing, Economics and Econometrics, Safety Research and Artificial Intelligence, having authored 27 papers that have together received 614 indexed citations. Recurring topics across this work include Auction Theory and Applications (22 papers), Consumer Market Behavior and Pricing (12 papers), Game Theory and Applications (10 papers), Sports Analytics and Performance (6 papers), Game Theory and Voting Systems (5 papers), Experimental Behavioral Economics Studies (5 papers), Multi-Agent Systems and Negotiation (3 papers) and Decision-Making and Behavioral Economics (2 papers). The work is most often cited by research in Management Science and Operations Research (374 citations), General Decision Sciences (25 citations), Marketing (121 citations), Safety Research (85 citations) and Economics and Econometrics (220 citations). Daniel M. Reeves has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Michael P. Wellman, Jeffrey K. MacKie–Mason, Yevgeniy Vorobeychik, Shih-Fen Cheng, David M. Pennock, Sowmya Swaminathan, Benjamin N. Grosof, Sharad Goel, Tüomas Sandholm and Abraham Othman. Their work appears in journals such as Decision Support Systems, Algorithmica, Autonomous Agents and Multi-Agent Systems, Journal of Economic Theory and Computational Intelligence.

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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