Journal of Mathematical Psychology

1.9k papers and 71.1k indexed citations i.

About

The 1.9k papers published in Journal of Mathematical Psychology in the last decades have received a total of 71.1k indexed citations. Papers published in Journal of Mathematical Psychology usually cover Artificial Intelligence (603 papers), Cognitive Neuroscience (452 papers) and General Decision Sciences (374 papers) specifically the topics of Decision-Making and Behavioral Economics (374 papers), Bayesian Modeling and Causal Inference (214 papers) and Economic and Environmental Valuation (172 papers). The most active scholars publishing in Journal of Mathematical Psychology are Thomas L. Saaty, H. B. Barlow, David M. Grether, R. Duncan Luce, In Jae Myung, Donald Bamber, Peter C. Fishburn, Roger N. Shepard, Amos Tversky and James T. Townsend.

In The Last Decade

Fields of papers published in Journal of Mathematical Psychology

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Journal of Mathematical Psychology. 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 Journal of Mathematical Psychology.

Countries where authors publish in Journal of Mathematical Psychology

Since Specialization
Citations

This map shows the geographic impact of research published in Journal of Mathematical Psychology. 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 Journal of Mathematical Psychology with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Journal of Mathematical Psychology 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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