Several
years ago when we started serious modelling of legal arguments using
Bayesian networks we hit a problem that we felt would be easily solved.
We had a set of mutually exclusive events such as "X murdered Y, Z
murdered Y, Y was not murdered" that we needed to model as separate
variables because they had separate causal pathways and evidence.
It
turned out that existing BN modelling techniques cannot capture the
correct intuitive reasoning when a set of mutually exclusive events need
to be modelled as separate nodes instead of states of a single node.
The standard proposed ’solution’, which introduces a simple constraint
node that enforces mutual exclusivity, fails to preserve the prior
probabilities of the events and is therefore flawed.
In
2012 myself (and the co-authors listed below) produced an initial novel
and simple solution to this problem that works in a reasonable set of
circumstances, but it proved to be difficult to get people to understand
why the problem was an important one that needed to be solved. After
many changes and iterations this work has finally been published and, as
a 'gold access paper' it is free for anybody to download in full (see
link below).
During the current Programme "
Probability and Statistics in Forensic Science"
that I am helping to run at the Isaac Newton Institute for Mathematical
Sciences, Cambridge,
18 July - 21 Dec 2016, it has become clear that the mutual exclusivity
problem is critical in any legal case where there are diverse
prosecution and defence narratives. Although our solution does not work
in all cases (and indeed we are working on more comprehsive approaches)
we feel it is an important start.
Norman Fenton,
Martin Neil,
David Lagnado,
William Marsh, Barbaros Yet,
Anthony Constantinou,
"How to model mutually exclusive events based on independent causal pathways in Bayesian network models", Knowledge-Based Systems,
Available online 17 September 2016
http://dx.doi.org/10.1016/j.knosys.2016.09.012