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Algorithms for AI inspired by the bounded rationality of humans

   Centre for Accountable, Responsible and Transparent AI

This project is no longer listed on and may not be available.

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  Dr Özgür Şimşek  No more applications being accepted  Self-Funded PhD Students Only

About the Project

This project will develop new algorithms for AI inspired by the bounded rationality of humans, in other words, by how people are able to make good decisions when they have limited time, information, and computation in environments that are complex, dynamic, and deeply uncertain. In contrast, today’s AI algorithms generally require extensive time, information, and computation and are effective in environments that are highly confined, stable, and predictable. Advances in the speed of computation will continue to extend the capabilities of our AI systems. But computational speed can take us only so far. For fundamental shifts in the capabilities of our AI systems, we need fundamentally new ideas in our learning algorithms. 

This project will use as a foundation the large, interdisciplinary scientific literature on bounded rationality. In designing boundedly-rational AI algorithms, it will pay particular attention to the fit between “the computational capacities of the actor and the structure of the environment,” as noted by Herbert Simon. Algorithmic focus will be on reinforcement learning and supervised learning. The algorithmic approaches explored will depend on the background and interests of the student. The objective is to improve the state-of-the art in both accuracy (and similar metrics) and explainability. The project will be supervised by Dr Özgür Şimşek at the Department of Computer Science at the University of Bath. Examples of earlier work by the PI are listed in the references. 

The University of Bath is located in the UNESCO World Heritage city of Bath, providing a vibrant research environment in one of the most beautiful areas in the UK.

Informal enquires are welcome and should be directed to Dr Özgür Şimşek ().

Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous. Desirable qualities in candidates include intellectual curiosity, a strong background in mathematics and programming experience.

Formal applications should be accompanied by a research proposal and made via the University of Bath’s online application form. Further information about the application process can be found here.

Start date: Between 8 January and 30 September 2024.

Funding Notes

We welcome applications from candidates who can source their own funding. Tuition fees for the 2023/4 academic year are £4,700 (full-time) for Home students and £26,600 (full-time) for International students. For information about eligibility for Home fee status: View Website.


[1] K. Katsikopoulos, Ö. Şimşek, M. Buckmann, and G. Gigerenzer (In press). Classification in the wild: The science and art of transparent decision making. MIT Press, Cambridge, MA.
[2] M. J. Lichtenberg and Ö. Şimşek (2019). Regularization in directable environments with application toTetris. International Conference on Machine Learning (ICML).
[3] Ö. Şimşek, S. Algórta, and A. Kothiyal (2016). Why most decisions are easy in Tetris—and perhaps in other sequential decision problems, as well. International Conference on Machine Learning (ICML).
[4] Ö. Şimşek and D. Jensen (2008). Navigating networks by using homophily and degree. Proceedings of the National Academy of Sciences of the USA (PNAS), 105(35):12758–12762.

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