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Learning in Cournot Oligopoly - an Experiment
Abstract
This experiment was designed to test various learning theories in the context of a Cournot oligopoly. We derive theoretical predictions for the learning theories and test these predictions by varying the information given to subjects. The results show that some subjects imitate successful behaviour if they have the necessary information, and if they imitate, markets are more competitive. Other subjects follow a best reply process. On the aggregate level we find that more information about demand and cost conditions yields less competitive behaviour, while more information about the quantities and profits of other firms yields more competitive behaviour.
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Citations
Cited by
Imitation and luck: An experimental study on social sampling
Offerman, T., & Schotter, A. (2009) · Games and Economic Behavior · doi:10.1016/j.geb.2008.03.004 · Cited by 52
Apesteguia, J., Oechssler, J., & Weidenholzer, S. (2020) · Management Science · doi:10.1287/mnsc.2019.3508 · Cited by 54
Self-citationTrust, Leniency, and Deterrence
Bigoni, M., Fridolfsson, S.-O., Le Coq, C., & Spagnolo, G. (2015) · Journal of Law, Economics, and Organization · doi:10.1093/jleo/ewv006
Algorithmic Collusion by Large Language Models
Fish, S., Gonczarowski, Y. A., & Shorrer, R. I. (2024) · arXiv · doi:10.48550/arxiv.2404.00806 · Cited by 10
Metadata
- Authors
- Huck, S., Normann, H.-T., & Oechssler, J.
- Venue
- The Economic Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-03
- Volume
- 109
- Issue
- 454
- Pages
- 80-95
- ISSN
- 0013-0133 (ISSN-L)
- 1468-0297
- OpenAlex
- W1534829722
- Cited by
- 279
- References
- 0
- FWCI
- 26.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Learning in Cournot Oligopoly - an Experiment. https://app.cochat.ai/papers/10.1111/1468-0297.00418
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- Huck, S., Normann, H.-T., & Oechssler, J. (1999). Learning in Cournot Oligopoly - an Experiment. The Economic Journal. https://doi.org/10.1111/1468-0297.00418
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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.
