Computational Management Science

477 papers and 7.1k indexed citations i.

About

The 477 papers published in Computational Management Science in the last decades have received a total of 7.1k indexed citations. Papers published in Computational Management Science usually cover Management Science and Operations Research (195 papers), Finance (135 papers) and Economics and Econometrics (125 papers) specifically the topics of Risk and Portfolio Optimization (130 papers), Stochastic processes and financial applications (66 papers) and Economic theories and models (48 papers). The most active scholars publishing in Computational Management Science are Masao Fukushima, Richard Loulou, Jong‐Shi Pang, Maryse Labriet, Jean‐Paul Watson, Anna Nagurney, David L. Woodruff, Michal Kaut, Sandra Paterlini and Holger Heitsch.

In The Last Decade

Fields of papers published in Computational Management Science

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Computational Management Science. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Computational Management Science.

Countries where authors publish in Computational Management Science

Since Specialization
Citations

This map shows the geographic impact of research published in Computational Management Science. 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 papers published in Computational Management Science with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computational Management Science more than expected).

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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2025