"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
http://www.sheffield.ac.uk/psychology/prospectivepg/funding
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
results.
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
http://www.tomstafford.staff.shef.ac.uk/
- Co-supervisor: James Marshall, Department of Computer Science
http://staffwww.dcs.shef.ac.uk/people/J.Marshall/lab/About_Us.html
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
http://www.abrg.group.shef.ac.uk/
- Co-supervisor: Roderich Gross, Automatic and Control Systems Engineering
http://naturalrobotics.group.shef.ac.uk/
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
http://www.shef.ac.uk/management/staff/binner
- Co-supervisor: Eleni Vasilaki, Department of Computer Science :
http://staffwww.dcs.shef.ac.uk/people/E.Vasilaki/site/Profile.html
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@shef.ac.uk
Room 2.27
Tel +44 (0) 114 22 26620
http://www.tomstafford.staff.shef.ac.uk/
Our special topic at Frontiers is now accepting submissions:
http://www.frontiersin.org/Cognitive_Science/researchtopics/Intrinsic_motivations_and_open/1326
NOTES FOR UNDERGRADUATE STUDENTS: please read this before emailing me
http://tomstafford.staff.shef.ac.uk/email.htm
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