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Ecological rationality of choices between gambles


   Centre for Accountable, Responsible and Transparent AI

  , Prof Ralph Hertwig  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

What is optimal choice under uncertainty? This is a fundamental question in artificial intelligence, as well as in economics and in psychology. The standard answer comes from expected utility theory but assumes an environment in which possible outcomes of actions, and their probabilities, are known. But what if this is not the case (as is commonly true in the real world)? When potential outcomes and their likelihoods have to be learned from experience (through sampling and exploration), depending on the exploration strategies being used, the representation of the outcome space may be systematically incomplete and the beliefs about event likelihoods may be systematically biased. How does an agent make good decisions when knowledge is biased and incomplete?

This project aims to develop decision methods (including simple heuristics) that enable competitive choices in environments ruled by uncertainty. The choices are among two or more options that are typically characterised by potential monetary outcomes (but can also use other types of outcomes, including those related to health) as well as the respective probabilities with which the outcomes occur. In economics literature, these types of choices are known as choices between monetary gambles.

The project will explore choices between gambles as a function of the structure of the environment. The project may draw inspiration from (and seek understanding of) how people make such choices under various environments, including which pieces of information are being used and how they are combined to arrive at a particular choice.

Proposed methods of decision making will be evaluated not only in their accuracy but also on their transparency. A primary objective of the research project is to develop decision methods that are easy to understand and to explain.

The project will be jointly supervised by Dr Özgür Şimşek at the Department of Computer Science at the University of Bath (Bath, UK) and Prof. Hertwig at the Center for Adaptive Rationality (ARC) at the Max Planck Institute for Human Development (Berlin, Germany).

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. 

The student is expected to spend extended time at the Center for Adaptive Rationality in Berlin (for example, years 1 and 4 in Bath, and years 2 and 3 in Berlin). The working language of the Center is English, and knowledge of German is not necessary for living in Berlin and enjoying the active life and cultural riches of this city. 

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

This project is associated with the UKRI Centre for Doctoral Training (CDT) in Accountable, Responsible and Transparent AI (ART-AI). We value people from different life experiences with a passion for research. The CDT's mission is to graduate diverse specialists with perspectives who can go out in the world and make a difference.

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. Enquiries about the application process should be sent to .

Start date: 2 October 2023.


Funding Notes

ART-AI CDT studentships are available on a competition basis and applicants are advised to apply early as offers are made from January onwards. Funding will cover tuition fees and maintenance at the UKRI doctoral stipend rate (£17,668 per annum in 2022/23, increased annually in line with the GDP deflator) for up to 4 years.
We also welcome applications from candidates who can source their own funding.

References

Spiliopoulos, L., & Hertwig, R. (in press). A map of ecologically rational heuristics for uncertain strategic worlds. Psychological Review.
Lichtenberg, J. M., & Şimşek, Ö. (2019). Regularization in directable environments with application to Tetris. Proceedings of the 36rd International Conference on Machine Learning (ICML), in PMLR 97:3953-3962.
Hertwig, R., Pleskac, T. J., Pachur, T., & The Center for Adaptive Rationality (2019). Taming uncertainty. Cambridge, MA: MIT Press.
Pachur, T., Suter, R. S., & Hertwig, R. (2017). How the twain can meet: Prospect theory and models of heuristics in risky choice. Cognitive Psychology, 93, 44-73.
Şimşek, Ö., Algorta, S., & Kothiyal, A. (2016). Why most decisions are easy in Tetris—and perhaps in other sequential decision problems, as well. Proceedings of the 33rd International Conference on Machine Learning (ICML), in PMLR 48:1757-1765.
Şimşek, Ö. (2013). Linear decision rule as aspiration for simple decision heuristics. In Advances in neural information processing systems, pp. 2904-2912.
Gigerenzer, G., Hertwig, R., & Pachur, T. (Eds.). (2011). Heuristics: The foundations of adaptive behavior. Oxford: Oxford University Press.

How good is research at University of Bath in Computer Science and Informatics?


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