[Comp-neuro] 3 PhD studentships: "Decision making under uncertainty: brains, swarms and markets"

Tom Stafford t.stafford at sheffield.ac.uk
Fri Dec 21 15:48:36 CET 2012

"Decision making under uncertainty: brains, swarms and markets"

The cross-disciplinary neuroeconomics network at the University of 
Sheffield is seeking applications for PhD studentships as part of the 
project: "Decision making under uncertainty: brains, swarms and markets"

- Tutition fees at UK/EU rate, annual maintenance at the standard RCUK 
rate (£13,726 for 2013-14), and a contribution towards research and 
travel expenses of £1,000 p.a.
- World-leading research environment https://www.shef.ac.uk/
- Deadline for applications 15 February, to start between August 1st and 
December 1st 2013
- Initial enquiries via 

Project overview:

How do we make decisions in uncertain situations? And what is the right 
thing to learn from the outcome of such decisions? Most of our decisions 
involve insufficient knowledge and a certain degree of risk. To study 
such decisions comprehensively is the goal of 'neuroeconomics', which 
brings to bear the insights of computational theory, neuroscientific 
evidence and behavioural experiment. We have assembled a local team of 
internationally renowned experts in a diversity of disciplines (Computer 
Science, Automatic Control and Systems Engineering, Psychology and 
Management). Together we will combine theoretical insights with tests in 
practical domains to advance the field. Strategically, the study of 
brain systems in decision-making has potential benefits in engineering 
and the digital economy in particular. The network therefore presents a 
unique opportunity for multi-disciplinary post-graduate training in a 
topic of increasing interest with multiple applications inside and 
outside academia. The common thread to all three projects is 
understanding decision making using computational models of information 
processing. Our methodology will involve the validation of three 
specific hypothesis, by (i) translating psychophysical experiments to 
computational models, (ii) using computational models to interpret 
financial data and (iii) further test decision-making hypotheses in 
embodied (robotic) systems. This work extends to a number of different 
areas, i.e. psychophysics experiments, high level modelling, finance and 
robotics, offering a unique possibility for synchronized interaction of 
all these leading experts in a topic whose timeliness requires fast 

This is a chance to receive postgraduate training in an exciting and 
important field. You will interact with academics from multiple fields 
and be required to integrate insights from different literatures, as 
well as develop the research skills appropriate for your project.

Applicants should have, or expect to achieve, a first or upper second 
class UK honours degree or equivalent qualifications gained outside the 
UK in an appropriate area of study.

Awards are open to UK, EU and international applicants.  International 
applicants will be required to prove that they have sufficient funds to 
cover the difference between the UK/EU and Overseas tuition fees.  For 
exceptional international candidates there may be opportunities for 
additional fee waivers (these will be subject to the policies of the 
individual departments involved in each project).

* Project 1: "Experimental validation of a new computational theory of 
adaptive decision-making."
- Principle Supervisor: Tom Stafford, Department of Psychology
- Co-supervisor: James Marshall, Department of Computer Science

All behaviour involves selecting one option over others, or over the 
option of doing nothing. It is therefore of fundamental interest how 
this selection process operates in our own brains. Tightly controlled 
experimental investigations can look at measures such as how fast 
decisions are made, or how often the decision is incorrect, to constrain 
theories of the underlying processes which generate these decisions. 
Additional evidence is available from neuroscientists who can 
investigate the brain structures and connections that might support 
decision-making, and make recordings of brain cell activity during 
decision-making. A powerful alternative perspective on decision-making 
is from computational theory, which can refine our understanding of how 
decisions should be made, separately from how decisions actually are 
made. This proposed studentship focuses on using behavioural experiments 
to test a new theory of how decision-making should be made.

Recent work on the computational theory of decisions has focussed on an 
algorithm called the Sequential Probability Ration Test (SPRT). This 
algorithm is provably optimal, in the sense of allowing the ideal 
combination of incoming evidence concerning a decision to make the 
fastest and least likely to be wrong decision. There are circumstances, 
however, where this "information optimal" decision-making may not be the 
best strategy. An important example is when the available options are 
closely matched and both acceptable. In such circumstances all time 
spend trying to resolve the difference between the options is time lost 
to enjoying one of them. Our computational theory suggests that an 
evolutionary optimal decision maker, such as we suppose the human brain 
to be, should be able to switch between modes of decision making 
depending on circumstance. This studentship will develop experiments 
that generate and define these circumstances.

By doing this we will advance the general theory of decision making, as 
well as revealing new facts about the operation of decision making in 
the human brain. The work will also make an important scientific 
contribution with potential high impact, because it will support a major 
reconceptualisation of a dominant theory of human decision-making.

* Project 2: "'Herding cats': Visually guided decision making with 
target swarms"
- Principle Supervisor: Kevin Gurney, Department of Psychology
- Co-supervisor: Roderich Gross, Automatic and Control Systems Engineering

How do we decide 'what to do next'? We are constantly bombarded by a 
plethora of sensory information and have to decide, moment-to-moment, 
how to act in order to achieve our goals. One key aspect of this process 
is that we must have access to the relevant sensory information; if we 
were approaching traffic lights and were completely colour blind it 
would be harder to make the right driving decision. Another key aspect 
of decision-making is that we must be able to map sensory information 
onto the right actions. Thus, if we could see the traffic light colours 
perfectly well, but had not learned the code (red is stop etc) then we 
could not make correct decision at all.

In this project we aim to investigate both aspects of decision-making in 
a naturalistic setting based on shepherding-flock relationships using 
artificial (robotic) agents. Here, multiple moving agents form a 'crowd' 
or 'swarm' that must be 'shepherded' by a single agent that is trying to 
coax them to safety. The swarm will be in constant motion and provide a 
visual sensory 'flow field' to the shepherding agent. This is of 
particular interest because there are specific areas of the brain 
devoted to the analysis of such optic flow. We will investigate the 
perceptual 'bonus' for decision-making supplied by having optic flow 
detection. We will also see if there is advantage in having special 
purpose optic flow detectors 'tuned' to the swarm's motion, rather than 
some set of standard, 'off the shelf' detectors.

Our decision-making mechanisms will mimic those in the brain which are 
based on a set of structures lying underneath the cortex called the 
basal ganglia. We will use our existing models of basal ganglia to see 
if the shepherding agent can learn to use the visual motion information 
to decide which, out of a range of possible 'shepherding actions' it 
should deploy in each situation. This project will make specific 
contributions to application areas requiring monitoring and action with 
dynamic flows of people and animals, including: evacuation scenarios and 
large-scale public events, and large scale animal husbandry, This work 
will contribute to our understanding of decision making in the brain, 
and, in particular, the way we use our senses to help make decisions.

* Project 3: Reinforcement learning and the equity premium puzzle
- Principle Supervisor: Jane Binner, Accounting and Financial Management
- Co-supervisor: Eleni Vasilaki, Department of Computer Science :

Humans often make decisions based on their desire to maximize profit 
orreward. Such decision take place within changing environments, where 
optimal choices in the past may differ from those in the present. For 
example, choosing a tracker-rate mortgage might have been at some time 
in the past a better option than a fixed-rate but today this may have 
changed. Moreover, these choices are typically made under uncertain 
situations and involve a degree of risk. Though the specifics of 
decision-making mechanisms are still not fully understood, it is evident 
that fundamentally the human brainis able to identify information 
sequences that could also correlate with reward.

Interestingly investors, and in particular low to intermediate income 
investors make decisions based on short horizons of information and in 
what is in essence a naïve "reinforcement learning" approach, i.e. a 
profitable action in the past will lead again to profit. They expect 
that investments profitable in the near past are likely to be profitable 
in the future, attributing often their gain or loss to random factors, 
fluctuations etc.

We propose to study and develop a data driven framework for 
understanding decision-making types of investors, and the key 
ingredients of making successful investment decisions. We hypothesise 
that investor profiles have a component of naïve reinforcement learning 
principles and a component of more sophisticated reinforcement learning 
principles. We ask the question whether the choices of successful 
investors have indeed a higher component of sophisticated principles 
versus the unsuccessful investors, and whether different mixtures of the 
two models can account for different investor strategies. We anticipate 
that the system of investors may not be well described by memory-less 
components, as typically assumed in many modelling approaches, and in 
our approach, we will also employ novel reinforcement learning 
techniques that are not restricted by this limitation.

We anticipate that our results would be of immediate interest to finance 
institutions that may want to use our models to extract information 
about their clients' profiles in order to provide customized financial 
training or making decisions about investor loans.

Further details are available upon request

Tom Stafford
Lecturer in Psychology and Cognitive Science
Department of Psychology, University of Sheffield
Western Bank, Sheffield, S10 2TP, UK

t.stafford at shef.ac.uk

Room 2.27
Tel +44 (0) 114 22 26620


Our special topic at Frontiers is now accepting submissions:

NOTES FOR UNDERGRADUATE STUDENTS: please read this before emailing me 


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