Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades
19981.3k citationsSushil Bikhchandani, David Hirshleifer et al.The Journal of Economic Perspectivesprofile →
A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
19921.1k citationsSushil Bikhchandani, David Hirshleifer et al.Journal of Political Economyprofile →
Countries citing papers authored by Sushil Bikhchandani
Since
Specialization
Citations
This map shows the geographic impact of Sushil Bikhchandani'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 Sushil Bikhchandani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sushil Bikhchandani more than expected).
Fields of papers citing papers by Sushil Bikhchandani
This network shows the impact of papers produced by Sushil Bikhchandani. 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 Sushil Bikhchandani. The network helps show where Sushil Bikhchandani may publish in the future.
Co-authorship network of co-authors of Sushil Bikhchandani
This figure shows the co-authorship network connecting the top 25 collaborators of Sushil Bikhchandani.
A scholar is included among the top collaborators of Sushil Bikhchandani 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 Sushil Bikhchandani. Sushil Bikhchandani is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bikhchandani, Sushil, David Hirshleifer, & Ivo Welch. (2008). Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades. Deep Blue (University of Michigan).37 indexed citations
7.
Bikhchandani, Sushil. (2006). Ex post implementation in environments with private goods. Theoretical Economics. 1(3). 369–393.40 indexed citations
Bikhchandani, Sushil. (2004). The Limits of Ex Post Implementation Revisited. RePEc: Research Papers in Economics.5 indexed citations
11.
Bikhchandani, Sushil, S. Chatterji, & Arunava Sen. (2003). Incentive Compatibility in Multi-unit Auctions. eScholarship (California Digital Library). 1.11 indexed citations
Bikhchandani, Sushil & Sunil Sharma. (2001). Comportamiento gregario o de rebaño en los mercados financieros: una reseña. RePEc: Research Papers in Economics. 23–42.1 indexed citations
Bikhchandani, Sushil, David Hirshleifer, & Ivo Welch. (1998). Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades. The Journal of Economic Perspectives. 12(3). 151–170.1250 indexed citations breakdown →
Bikhchandani, Sushil, David Hirshleifer, & Ivo Welch. (1992). A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. Journal of Political Economy. 100(5). 992–1026.1124 indexed citations breakdown →
20.
Bikhchandani, Sushil. (1986). Market games with few traders. UMI Dissertation Information Service eBooks.3 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.