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Theory of games and economic behavior
Open accessAbstract
This is the classic work upon which modern-day game theory is based. What began more than sixty years ago as a modest proposal that a mathematician and an economist write a short paper together blossomed, in 1944, when Princeton University Press published Theory of Games and Economic Behavior. In it, John von Neumann and Oskar Morgenstern conceived a groundbreaking mathematical theory of economic and social organization, based on a theory of games of strategy. Not only would this revolutionize economics, but the entirely new field of scientific inquiry it yielded--game theory--has since been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations. And it is today established throughout both the social sciences and a wide range of other sciences.
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Cited by
Prospect theory: An analysis of decision under risk
Kahneman, D., & Tversky, A. (1988) · Decision, Probability and Utility · doi:10.1017/cbo9780511609220.014 · Cited by 32,474
Hardin, G. (1968) · Science · doi:10.1126/science.162.3859.1243 · Cited by 25,167
(1994) · Cited by 5,513
Statistical physics of social dynamics
Castellano, C., Fortunato, S., & Loreto, V. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.591 · Cited by 4,119
Selforganization of matter and the evolution of biological macromolecules
Eigen, M. (1971) · Die Naturwissenschaften · doi:10.1007/bf00623322 · Cited by 3,788
Metadata
- Venue
- 100 Years of Math Milestones
- Publisher
- American Mathematical Society
- Published
- 2019-06-12
- Pages
- 163-167
- OpenAlex
- W2144846366
- Cited by
- 16,632
- References
- 0
- FWCI
- 618.77
- Type
- book chapter
- Language
- en
- Open access
- bronze
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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.