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Ecological rationality of choices between gambles

  • Full or part time
  • Application Deadline
    Monday, April 06, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

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 CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its second cohort of at least 10 students to start in September 2020. Further details of the programme can be found at:

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 maths and programming experience.

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form:

Start date: 28 September 2020.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum in 2019/20, increased annually in line with the GDP deflator) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

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

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

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