David Bourgin
Impact in
- General Decision Sciences top 10%
- Decision-Making and Behavioral Economics
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- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
Papers in
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- Decision-Making and Behavioral Economics 3
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- Forecasting Techniques and Applications 2
- Co-authors
- Thomas L. Griffiths (8 shared papers)Daniel Reichman (4 shared papers)Joshua C. Peterson (2 shared papers)Mayank Agrawal (1 shared paper)Jordan W. Suchow (2 shared papers)Bradford Stadler (1 shared paper)Jill Magnus (1 shared paper)Carol B. Ware (1 shared paper)
- Journals
- Cognitive Science (3 papers)Nature Human Behaviour (1 paper)Science (1 paper)Trends in Cognitive Sciences (1 paper)Cell Cycle (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
David Bourgin
10 papers receiving 351 citations
Peers
Comparison fields: 5 of 100
- General Decision Sciences 27
- Cancer Research 91
- Computer Science Applications 26
- Cognitive Neuroscience 46
- Artificial Intelligence 74
Countries citing papers authored by David Bourgin
This map shows the geographic impact of David Bourgin'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 David Bourgin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Bourgin more than expected).
Fields of papers citing papers by David Bourgin
This network shows the impact of papers produced by David Bourgin. 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 David Bourgin. The network helps show where David Bourgin may publish in the future.
Co-authors
The 25 scholars most cited alongside David Bourgin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 136 | |
| 2 | 2009 | 131 | |
| 3 | 2019 | 41 | |
| 4 | 2017 | 26 | |
| 5 | Empirical Evidence for Markov Chain Monte Carlo in Memory Search. | 2014 | 19 |
| 6 | 2017 | 6 | |
| 7 | 2018 | 4 | |
| 8 | 2020 | 2 | |
| 9 | 2023 | 1 | |
| 10 | 2025 | 1 |
About David Bourgin
David Bourgin is a scholar working on General Decision Sciences, Management Science and Operations Research, Computer Science Applications, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 10 papers that have together received 367 indexed citations. Recurring topics across this work include Decision-Making and Behavioral Economics (3 papers), Teaching and Learning Programming (2 papers), Forecasting Techniques and Applications (2 papers), CRISPR and Genetic Engineering (1 paper), Cognitive Science and Mapping (1 paper), AI-based Problem Solving and Planning (1 paper), Evolutionary Algorithms and Applications (1 paper) and Pluripotent Stem Cells Research (1 paper). The work is most often cited by research in General Decision Sciences (27 citations), Cancer Research (91 citations), Computer Science Applications (26 citations), Cognitive Neuroscience (46 citations) and Artificial Intelligence (74 citations). David Bourgin has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Thomas L. Griffiths, Daniel Reichman, Joshua C. Peterson, Mayank Agrawal, Jordan W. Suchow, Bradford Stadler, Jill Magnus, Carol B. Ware, Hannele Ruohola‐Baker and Junlin Qi. Their work appears in journals such as Cognitive Science, Nature Human Behaviour, Science, Trends in Cognitive Sciences and Cell Cycle.
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.