Nisarg Shah
- Economics and Econometrics top 1%
- Management Science and Operations Research top 1%
- Computer Networks and Communications top 5%
- Artificial Intelligence top 5%
- Information Systems top 5%
- Co-authors
- Ariel D. ProcacciaIoannis CaragiannisDavid C. ParkesDavid KurokawaHervé MoulinJunxing WangIan A. KashRupert Freeman
- Topics
- Game Theory and Voting Systems (53 papers)Auction Theory and Applications (43 papers)Game Theory and Applications (18 papers)
- Cited by
- Management Science and Operations ResearchGeneral Decision SciencesEconomics and Econometrics
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Nisarg Shah
72 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 73
- Economics and Econometrics 687
- Management Science and Operations Research 627
- Computer Networks and Communications 239
- Artificial Intelligence 235
- Information Systems 198
Countries citing papers authored by Nisarg Shah
This map shows the geographic impact of Nisarg Shah'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 Nisarg Shah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nisarg Shah more than expected).
Fields of papers citing papers by Nisarg Shah
This network shows the impact of papers produced by Nisarg Shah. 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 Nisarg Shah. The network helps show where Nisarg Shah may publish in the future.
Co-authorship network of co-authors of Nisarg Shah
This figure shows the co-authorship network connecting the top 25 collaborators of Nisarg Shah. A scholar is included among the top collaborators of Nisarg Shah 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 Nisarg Shah. Nisarg Shah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 3 | |
| 6 | 12 | |
| 7 | 6 | |
| 8 | Group Fairness for Indivisible Goods Allocation | 2 |
| 9 | Efficient and Thrifty Voting by Any Means Necessary | 11 |
| 10 | 7 | |
| 11 | 5 | |
| 12 | Truthful univariate estimators | 8 |
| 13 | 11 | |
| 14 | 1 | |
| 15 | Electing the most probable without eliminating the irrational: voting over intransitive domains | 7 |
| 16 | 5 | |
| 17 | 0 | |
| 18 | 41 | |
| 19 | 10 | |
| 20 | 4 |
About Nisarg Shah
Nisarg Shah is a scholar working on Management Science and Operations Research, Economics and Econometrics and General Decision Sciences, having authored 77 papers that have together received 1.2k indexed citations. Recurring topics across this work include Game Theory and Voting Systems (53 papers), Auction Theory and Applications (43 papers) and Game Theory and Applications (18 papers). The work is most often cited by research in Management Science and Operations Research (627 citations), General Decision Sciences (49 citations) and Economics and Econometrics (687 citations). Nisarg Shah has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Ariel D. Procaccia, Ioannis Caragiannis, David C. Parkes, David Kurokawa, Hervé Moulin, Junxing Wang, Ian A. Kash, Rupert Freeman, Vincent Conitzer and Ariel D. Procaccia. Their work appears in journals such as Management Science, Operations Research and Artificial 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.