The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
oTree—An open-source platform for laboratory, online, and field experiments
Open accessAbstract
oTree is an open-source and online software for implementing interactive experiments in the laboratory, online, the field or combinations thereof. oTree does not require installation of software on subjects’ devices; it can run on any device that has a web browser, be that a desktop computer, a tablet or a smartphone. Deployment can be internet-based without a shared local network, or local-network-based even without internet access. For coding, Python is used, a popular, open-source programming language. oTree provides the source code, a library of standard game templates and demo games which can be played by anyone.
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Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Axelrod, R., & Hamilton, W. D. (1981) · Science · doi:10.1126/science.7466396 · Cited by 20,065
The weirdest people in the world?
Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0999152x · Cited by 12,641
z-Tree: Zurich toolbox for ready-made economic experiments
Fischbacher, U. (2007) · Experimental Economics · doi:10.1007/s10683-006-9159-4 · Cited by 9,510
WEIRD languages have misled us, too
Majid, A., & Levinson, S. C. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1000018x · Cited by 4,076
Conducting behavioral research on Amazon’s Mechanical Turk
Mason, W., & Suri, S. (2012) · Behavior Research Methods · doi:10.3758/s13428-011-0124-6 · Cited by 3,172
What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?
Levitt, S. D., & List, J. A. (2007) · Journal of Economic Perspectives · doi:10.1257/jep.21.2.153 · Cited by 2,668
Gneezy, U., & Rustichini, A. (2000) · The Journal of Legal Studies · doi:10.1086/468061 · Cited by 1,816
The Online Laboratory: Conducting Experiments in a Real Labor Market∗Preprint
Zeckhauser, R. J. (2016) · Cited by 1,531
Experimenter demand effects in economic experiments
Zizzo, D. J. (2010) · Experimental Economics · doi:10.1007/s10683-009-9230-z · Cited by 1,194
Cited by
Prolific.ac—A subject pool for online experiments
Palan, S., & Schitter, C. (2018) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2017.12.004 · Cited by 3,450
Communicating sentiment and outlook reverses inaction against collective risks
Wang, Z., Jusup, M., Guo, H., Shi, L., Geček, S., Anand, M., Perc, M., Bauch, C. T., Kurths, J., Boccaletti, S., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1922345117 · Cited by 185
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.2139/ssrn.4505053 · Cited by 161
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.3386/w31422 · Cited by 161
The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice
Lehmann, C. A., Haubitz, C. B., Fügener, A., & Thonemann, U. W. (2022) · Production and Operations Management · doi:10.1111/poms.13770 · Cited by 100
Metadata
- Authors
- Chen, D. L., Schonger, M., & Wickens, C.
- Venue
- Journal of Behavioral and Experimental Finance
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 9
- Pages
- 88-97
- ISSN
- 2214-6350 (ISSN-L)
- OpenAlex
- W83993254
- Cited by
- 1,889
- References
- 33
- FWCI
- 131.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.