David Bourgin
Impact in
- General Decision Sciences top 10%
- Decision-Making and Behavioral Economics
-
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
Papers in
-
- Bayesian Modeling and Causal Inference 1
- AI-based Problem Solving and Planning 1
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- Forecasting Techniques and Applications 2
- Co-authors
- Thomas L. Griffiths (9 shared papers)Daniel Reichman (5 shared papers)Joshua C. Peterson (3 shared papers)Mayank Agrawal (1 shared paper)Jordan W. Suchow (2 shared papers)Robert L. Diaz (1 shared paper)Halyna R. Shcherbata (1 shared paper)Bradford Stadler (1 shared paper)
- Journals
- Cognitive Science (3 papers)Cell Cycle (1 paper)Science (1 paper)Trends in Cognitive Sciences (1 paper)Nature Human Behaviour (1 paper)
- Partner nations
- United StatesIsraelTaiwan
In The Last Decade
David Bourgin
9 papers receiving 317 citations
Peers
Comparison fields: 5 of 101
- General Decision Sciences 27
- Cancer Research 89
- Computer Science Applications 23
- Cognitive Neuroscience 43
- Artificial Intelligence 69
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 | 2009 | 119 | |
| 2 | 2021 | 115 | |
| 3 | 2019 | 36 | |
| 4 | 2017 | 23 | |
| 5 | Empirical Evidence for Markov Chain Monte Carlo in Memory Search. | 2014 | 18 |
| 6 | 2019 | 12 | |
| 7 | 2017 | 5 | |
| 8 | 2018 | 2 | |
| 9 | 2023 | 1 | |
| 10 | 2025 | 0 | |
| 11 | 2020 | 0 |
About David Bourgin
David Bourgin is a scholar working on Artificial Intelligence, Management Science and Operations Research, General Decision Sciences, Experimental and Cognitive Psychology and Computer Science Applications, having authored 11 papers that have together received 331 indexed citations. Recurring topics across this work include Decision-Making and Behavioral Economics (3 papers), Forecasting Techniques and Applications (2 papers), Teaching and Learning Programming (2 papers), Mental Health Research Topics (1 paper), Cognitive and psychological constructs research (1 paper), Bayesian Modeling and Causal Inference (1 paper), Software Testing and Debugging Techniques (1 paper) and AI-based Problem Solving and Planning (1 paper). The work is most often cited by research in General Decision Sciences (27 citations), Cancer Research (89 citations), Computer Science Applications (23 citations), Cognitive Neuroscience (43 citations) and Artificial Intelligence (69 citations). David Bourgin has collaborated with scholars based in United States, Israel and Taiwan. Frequent co-authors include Thomas L. Griffiths, Daniel Reichman, Joshua C. Peterson, Mayank Agrawal, Jordan W. Suchow, Robert L. Diaz, Halyna R. Shcherbata, Bradford Stadler, Irena L. Ivanovska and Julie Mathieu. Their work appears in journals such as Cognitive Science, Cell Cycle, Science, Trends in Cognitive Sciences and Nature Human Behaviour.
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.