Daniel M. Reeves

1.2k total citations
27 papers, 614 citations indexed

About

Daniel M. Reeves is a scholar working on Management Science and Operations Research, Marketing and Economics and Econometrics. According to data from OpenAlex, Daniel M. Reeves has authored 27 papers receiving a total of 614 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Management Science and Operations Research, 12 papers in Marketing and 12 papers in Economics and Econometrics. Recurrent topics in Daniel M. Reeves's work include Auction Theory and Applications (22 papers), Consumer Market Behavior and Pricing (12 papers) and Game Theory and Applications (10 papers). Daniel M. Reeves is often cited by papers focused on Auction Theory and Applications (22 papers), Consumer Market Behavior and Pricing (12 papers) and Game Theory and Applications (10 papers). Daniel M. Reeves collaborates with scholars based in United States and United Kingdom. Daniel M. Reeves's 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 and has published in prestigious journals such as Journal of Economic Theory, Decision Support Systems and Algorithmica.

In The Last Decade

Daniel M. Reeves

25 papers receiving 570 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel M. Reeves United States	16	374	220	127	121	92	27	614
Rica Gonen Israel	8	334 0.9×	124 0.6×	135 1.1×	134 1.1×	165 1.8×	21	491
Esther David Israel	11	275 0.7×	50 0.2×	115 0.9×	115 1.0×	100 1.1×	40	463
Rajdeep K. Dash United Kingdom	13	312 0.8×	104 0.5×	160 1.3×	80 0.7×	198 2.2×	23	544
Shaddin Dughmi United States	17	556 1.5×	176 0.8×	99 0.8×	190 1.6×	222 2.4×	45	677
Andrew Byde United Kingdom	12	227 0.6×	55 0.3×	146 1.1×	113 0.9×	109 1.2×	26	393
Carmine Ventre United Kingdom	11	287 0.8×	273 1.2×	51 0.4×	28 0.2×	74 0.8×	66	564
Azarakhsh Malekian United States	12	206 0.6×	90 0.4×	42 0.3×	168 1.4×	80 0.9×	44	468
Maria Polukarov United Kingdom	11	250 0.7×	184 0.8×	76 0.6×	32 0.3×	82 0.9×	45	397
Renato Paes Leme United States	17	695 1.9×	178 0.8×	117 0.9×	421 3.5×	139 1.5×	65	858
S. Matthew Weinberg United States	15	600 1.6×	135 0.6×	115 0.9×	430 3.6×	216 2.3×	52	867

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-authorship network of co-authors of Daniel M. Reeves

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Reeves. A scholar is included among the top collaborators of Daniel M. Reeves based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel M. Reeves. Daniel M. Reeves is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lambert, Nicolas, John Langford, Jennifer Wortman Vaughan, et al.. (2014). An axiomatic characterization of wagering mechanisms. Journal of Economic Theory. 156. 389–416. 14 indexed citations

Vorobeychik, Yevgeniy, Daniel M. Reeves, & Michael P. Wellman. (2012). Constrained Automated Mechanism Design for Infinite Games of Incomplete Information. arXiv (Cornell University). 400–407. 3 indexed citations

Vorobeychik, Yevgeniy, Daniel M. Reeves, & Michael P. Wellman. (2011). Constrained automated mechanism design for infinite games of incomplete information. Autonomous Agents and Multi-Agent Systems. 25(2). 313–351. 6 indexed citations

Goel, Sharad, Daniel M. Reeves, Duncan J. Watts, & David M. Pennock. (2010). Prediction without markets. 357–366. 30 indexed citations

Othman, Abraham, Tüomas Sandholm, David M. Pennock, & Daniel M. Reeves. (2010). A practical liquidity-sensitive automated market maker. 377–386. 36 indexed citations

Goel, Sharad, Jake M. Hofman, John Langford, David M. Pennock, & Daniel M. Reeves. (2009). CentMail: Rate Limiting via Certified Micro-Donations. 1 indexed citations

Goel, Sharad, Daniel M. Reeves, & David M. Pennock. (2009). Collective revelation. 265–274. 20 indexed citations

Vorobeychik, Yevgeniy & Daniel M. Reeves. (2008). Equilibrium analysis of dynamic bidding in sponsored search auctions. International Journal of Electronic Business. 6(2). 172–172. 16 indexed citations

Vorobeychik, Yevgeniy & Daniel M. Reeves. (2007). Automated Mechanism Design in Infinite Games of Incomplete Information: Framework and Applications.. National Conference on Artificial Intelligence. 76–84.

10.

Reeves, Daniel M., et al.. (2007). Yootopia!. ACM SIGecom Exchanges. 6(2). 1–26. 2 indexed citations

11.

Wellman, Michael P., et al.. (2005). Approximate strategic reasoning through hierarchical reduction of large symmetric games. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 502–508. 29 indexed citations

12.

Reeves, Daniel M. & Michael P. Wellman. (2005). Generating trading agent strategies: Analytic and empirical methods for infinite and large games.. Deep Blue (University of Michigan). 17 indexed citations

13.

Wellman, Michael P., et al.. (2005). Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 0–449. 11 indexed citations

14.

MacKie–Mason, Jeffrey K., et al.. (2004). Price Prediction Strategies for Market-Based Scheduling. Deep Blue (University of Michigan). 244–252. 26 indexed citations

15.

Reeves, Daniel M. & Michael P. Wellman. (2004). Computing best-response strategies in infinite games of incomplete information. arXiv (Cornell University). 470–478. 35 indexed citations

16.

Reeves, Daniel M., et al.. (2004). Exploring bidding strategies for market-based scheduling. Decision Support Systems. 39(1). 67–85. 51 indexed citations

17.

Cheng, Shih-Fen, Daniel M. Reeves, Yevgeniy Vorobeychik, & Michael P. Wellman. (2004). Notes on Equilibria in Symmetric Games. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 71–78. 83 indexed citations

18.

Reeves, Daniel M., Benjamin N. Grosof, & Michael P. Wellman. (2003). Toward a Declarative Language for Negotiating Executable Contracts. 24 indexed citations

19.

Reeves, Daniel M., Michael P. Wellman, & Benjamin N. Grosof. (2002). Automated Negotiation from Declarative Contract Descriptions. Computational Intelligence. 18(4). 482–500. 22 indexed citations

20.

Reeves, Daniel M., Michael P. Wellman, & Benjamin N. Grosof. (2001). Automated negotiation from declarative contract descriptions. 51–58. 17 indexed citations

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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