Science as a public good: improving how we do research with game theory and computation

Science is a public good. The benefits of knowledge are or should be available to everyone, but the way this knowledge is produce often responds to individual incentives [1]. Scientist are not only cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational models to desing institutions to do science better: these may include contests in grant-seeking, review process for publications, ways to diseminate scientific findings and other mechanisms that are part of everyday scientific practice [2].

We will use a combination of game theoretical models, AI and simulation techniques, as well as available data from scientific publications, citations, and science career trajectories. The goal is to design better incentives for scientists to produce their best work.

Our research group studies how groups of agents can learn to cooperate. Most of our research focuses on social dilemmas, i.e., situations where poor group outcomes arise from optimal individual choices. We use this framework to study: Multi-agent Systems and AI, Social Systems, and Models in Biology and Evolution. Please check our publications for more details: http://garciajulian.com

[1] “Empirical Agent Based Models of Cooperation in Public Goods Games | Proceedings of the Fourteenth ACM Conference on Electronic Commerce.” Accessed September 10, 2021. https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/10.1145/2482540.2482586.

[2] Fortunato, Santo, Carl T. Bergstrom, Katy Börner, James A. Evans, Dirk Helbing, Staša Milojević, Alexander M. Petersen, et al. “Science of Science.” Science 359, no. 6379 (March 2, 2018): eaao0185. https://doi.org/10.1126/science.aao0185.