Applications are invited for participation in a workshop on: 

INFO-GAP ANALYSIS OF ENGINEERING SYSTEMS: ROBUST DECISIONS UNDER SEVERE
UNCERTAINTY

to be held on 29-30th September 2005 University of Newcastle-upon-Tyne

Organisers:  	
Prof Jim Hall, University of Newcastle-upon-Tyne
Prof Keith Worden, University of Sheffield
Dr Nick Alexander, University of Bristol

This workshop will bring together international researchers who have
been progressing info-gap applications, in most cases independently of
one another, and ask them to present demonstrations of the maturing
theory and reflect upon future challenges. They will be joined by
selected participants working in uncertainty analysis of engineering
systems that have potential for application of info-gap theory. Each
member of this latter group will be invited to present a poster
summarising their current work and identifying potential info-gap
applications to be discussed during the workshop. 
 
The purpose of the workshop is:
i.	to share experiences of application on info-gap to a range of
engineering applications, and 
ii.	to identify and examine future opportunities and challenges for
info-gap. 
This will be achieved by a combination of 
*	presentations by leading exponents of info-gap theory followed
by plenary discussion, and 
*	a poster session to address open problems raised by other
participants in the workshop and to develop potential info-gap
applications.

The main speakers are (subject to confirmation): 
1.	Prof Yakov Ben-Haim (Yitzhak Moda'i Chair in Technology and
Economics Faculty of Mechanical Engineering, Technion - Israel Institute
of Technology)
2.	Dr. Francois Hemez (Engineering Sciences and Applications, Los
Alamos National Laboratory, USA)
3.	Dr. Scott Cogan (Faculte des Sciences, Laboratoire de Mecanique
Appliquee, Universite de Franche-Comte, France) 
4.	Prof. Yoshihiro Kanno (Dept. of Urban and Environmental
Engineering, Kyoto University, Japan) 
5.	Prof. Chris Pantelides (Department of Civil and Environmental
Engineering, University of Utah, USA) 
6.	Dr Miriam Zacksenhouse (Faculty of Mechanical Engineering,
Technion - Israel Institute of Technology)
7.	Prof. Jim Hall (Professor of Earth Systems Engineering, School
of Civil Engineering and Geosciences, University of Newcastle-upon-Tyne,
UK)
8.	Prof. Keith Worden (Professor of dynamical systems, Department
of Mechanical Engineering, University of Sheffield, UK)
9.	Dr Gareth Pierce (Research Associate, Department of Mechanical
Engineering, University of Sheffield, UK) 

INVITATION TO PARTICIPANTS
There are places for between fifteen and twenty participants at the
workshop, in addition to the main speakers. No prior experience of
info-gap analysis is necessary but each participant will be expected to
present a poster summarising aspects of their current work and
identifying key areas of uncertainty that have potential for info-gap
analysis. 

We are able to pay the registration fees as well as accommodation and
dinner on the night of Thursday 29 September for each participant. We
cannot contribute to travel expenses. 

If you would like to attend, please contact 
Meg Buckley 
by 29 July 2005 with your name, full contact
details, affiliation, current position and a short summary of the
proposed contents of your poster. In selecting applicants we will seek a
balance between industrial and university participants and between
academic staff and graduate students. Participants from outside the UK
are welcome. We will notify successful applicants by 8 August 2005.

BACKGROUND TO INFO-GAP ANALYSIS
Design and planning decisions often employ quantitative models of
various phenomena. Typically, these phenomena are complex and poorly
understood, so these models are accompanied by tremendous uncertainty.
This uncertainty is of two sorts: aleatoric and epistemic. Aleatoric
uncertainty is randomness which is usually modelled by probability
distributions. Epistemic uncertainty is a knowledge gap: our
understanding of the phenomena is incomplete or erroneous, so models of
the phenomena are uncertain. Often the random (aleatoric) elements of
the phenomena are poorly understood, so probability models are
themselves subject to epistemic uncertainty as well. Info-gap theory is
a method for modelling epistemic uncertainty and for evaluating and
selecting between plans and designs in terms of effectiveness and
robustness to both aleatoric and epistemic uncertainties.

Information-gap decision theory was initiated and developed by Prof
Yakov Ben-Haim and has its origins in the early 1980s in convex
modelling of materials, mechanical and dynamical problems. A system
model is parameterised so that system response to loading is represented
by nested sets containing excursions of system behaviour. Of particular
interest is the level at which system behaviour exceeds some failure
criterion. In work with Isaac Elishakoff, Ben-Haim demonstrated how
diligently applied probabilistic methods can result in disturbingly
inaccurate estimates of the probability of failure of safety-critical
systems, whilst convex analysis identified more reliable bounds on
system behaviour (Ben-Haim and Elishakoff, 1990). This work was
cultivated into a theory of non-probabilistic robust reliability
(Ben-Haim, 1996) and subsequently into a complete theory of
decision-making under severe uncertainty (Ben-Haim, 2001, 2005). 

An info-gap analysis has three components: a system model, an info-gap
uncertainty model and performance requirements. The system model
describes the structure and behaviour of the system in question, using
as much information as is reasonably available. The system model may,
for example, be in the form of a set of partial differential equations,
a network model, or indeed a probabilistic model such as a Poisson
process. The uncertainty in the system model is parameterised with an
uncertainty parameter \alpha (a positive real number), which defines a
family of nested sets that bound regions or clusters of system
behaviour. When \alpha = 0 the prediction from the system model
converges to a point, which is the anticipated system behaviour, given
current available information. However, it is recognised that the system
model is incomplete so there will be a range of variation around the
nominal behaviour. Uncertainty, as defined by the parameter \alpha, is
therefore a range of variation of the actual around the nominal. No
further commitment is made to the structure of uncertainty. \alpha is
not normalised and has no betting interpretation, so is clearly distinct
from a probability. 

Next, two contrasting consequences of uncertainty are introduced:
'catastrophic failure' and 'windfall success'. Two immunity functions, a
robustness function and an opportunity function, describe the variation
of \alpha with the magnitude of the unfavourable and favourable
consequences. Info gap theory therefore seeks to gain from favourable
excursions in uncertain system behaviour as well as developing robust
strategies that guard against the effects of unfavourable excursions.
Excessive emphasis on failure can result in a loss of opportunity, but
the two are not always mutually exclusive.

Analysis of robustness and development of strategies for
robust-decision-making under severe uncertainty are now amongst the most
pressing problems in the management of engineering systems. Recent
events, from terrorist attacks to the Asian tsunamis illustrate the
importance of designing systems that are robust to unexpected loadings.
This is a concept that is straightforward at an intuitive level, but
formalising a theory that can deal with truly unexpected loads and
states of near total ignorance (situations where probabilistic
approaches falter or fail completely) is certainly non-trivial. Info-gap
theory shows great promise yet needs to be tested and applied in order
to prove its fitness for purpose. The proposed workshop aims to
contribute to the evaluation of info-gap theory in practice and the
definition of problems that have to be overcome if info-gap is to gain
widespread acceptance in engineering practice. 

Ben-Haim, Y. and Elishakoff, I. Convex Models of Uncertainty in Applied
Mechanics. Elesevier, Amsterdam, 1990.
Ben-Haim, Y. Robust Reliability in the Mechanical Sciences,
Springer-Verlag, Berlin, 1996.
Ben-Haim, Y. Information-gap Decision Theory: Decisions Under Severe
Uncertainty, Academic Press, San Diego. 2001.
Ben-Haim, Y. Info-gap Decision Theory For Engineering Design. Or: Why
'Good' is Preferable to 'Best', appearing as chapter 11 in Engineering
Design Reliability Handbook, Edited by Efstratios Nikolaidis, Dan
M.Ghiocel and Surendra Singhal, CRC Press, Boca Raton, 2005.

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