Tuesday 8 December 2015

Norman Fenton at Maths in Action Day (Warwick University)

Today Norman Fenton was one of the five presenters at the Mathematics in Action Day at Warwick University - the others included writer and broadcaster Simon Singh and BBC presenter Steve Mould (who is also part of the amazing trio Festival of the Spoken Nerd which features Queen Mary's Matt Parker). The Maths in Action day is specifically targeted at A-Level Maths students and their teachers.

Norman says:
This was probably the biggest live event I have spoken at - an audience of 550 in the massive Butterworth Hall (which has recently hosted Paul Weller and the Style Council, Jools Holland) - so it was quite intimidating. My talk was on "Fallacies of Probability and Risk" (the powerpoint slides are here). I hope to get some photos of the event uploaded shortly.
Butterworth Hall (hopefully some real photos from the event to come)

Friday 27 November 2015

Another international award for the BBC documentary co-presented by Norman Fenton

Earlier this month I reported that the BBC documentary "Climate Change by Numbers" (that I co-presented) won the American Association for the Advancement of Science (AAAS) Science Journalism Gold Award for "best in-depth TV reporting".

Now the programme has won another prestigious award: the European Science TV and New Media Award for the best Science programme on an environmental issue, 2015.

The new award (see photo below) was presented to BBC Executive Director Jonathan Renouf at a ceremony in Lisbon on 25 November 2015. Jonathan thanked the team involved in the programme, saying:

"I'm absolutely delighted to see the film gain such widespread international recognition. It really is a tribute to the way you managed to bring fresh television insight to a very well trodden subject, and to do it in a way that was genuinely entertaining as well as so innovative. Everyone I've spoken to out here is so impressed with the film. Thank you again for all your hard work, passion and commitment in making the show."

The programme has also recently been screened on TV in a number of other countries. Here is a comprehensive review that appeared in La Monde.   

The European Science TV and New Media Award

Wednesday 11 November 2015

BBC Documentary co-presented by Norman Fenton wins AAAS Science Journalism Gold Award for "best in-depth TV reporting"

1 Dec Update: the programme has now won another award.

In March I reported on my experience of presenting the BBC documentary "Climate Change by Numbers". The programme has won the American Association for the Advancement of Science (AAAS) Science Journalism Gold Award for "best in-depth TV reporting". The summary citation says:
The Gold Award for in-depth television reporting went to a BBC team for a documentary that used clever analogies and appealing graphics to discuss three key numbers that help clarify important questions about the scale and pace of human influence on climate. The program featured a trio of mathematicians who use numbers to reveal patterns in data, assess risk, and help predict the future.
Jonathan Renouf Executive Producer at BBC Science said (to those involved in the making of the programme):
It’s a huge honour to win this award; it’s a global competition, open to programmes in every area of science, and it’s judged by science journalists. I can’t think of a finer and more prestigious endorsement of the research and journalistic rigour that you brought to bear in the film. We all know how difficult it is to make programmes about climate change that tread the line between entertainment, saying something new, and keeping the story journalistically watertight. I’m really thrilled to see your efforts recognised in top scientific circles.
Full details of the awards can be found on the AAAS website.

Friday 6 November 2015

Update on the use of Bayes in the Netherlands Appeal Court

In July I reported about the so-called Breda 6 case in the Netherlands and how a Bayesian argument was presented in the review of the case. My own view was that the Bayesian argument was crying out for a Bayesian network representation (I provided a model in my article to do that).

Now Richard Gill has told me the following:
Finally there has been a verdict in the 'Breda 6' case. The suspects were (again) found guilty. The court is somewhat mixed with respect to the Bayesian analysis: On the one hand they ruled that Frans Alkmeye had the required expertise, and that he was rightly appointed as a 'Bayesian expert'. On the other hand they ruled that a Bayesian analysis is still too controversial to be used in court. Therefore they disregarded 'the conclusion' of Frans's report. This is a remarkable and unusual formulation in verdicts, the normal wording is that report has been disregarded.
This unusual wording is no accident: If the court would say that they had disregarded the report, they would lie, since actually quite a lot of the Bayesian reasoning is included in their judgment. A number of considerations from Frans's report are fully paraphrased, and sometimes quoted almost verbatim.
Also I noticed that the assessment of certain findings is expressed in a nicely Bayesian manner.
However: Contrary to Frans's assessment, the court still thinks that the original confessions of three of the suspects contain strong evidence. Unfortunately, the case is not yet closed, but has been taken to the high court.
Frans Alkmeye has also been appointed as a Bayesian expert in yet another criminal case.

The ruling that the Bayesian analysis is too controversial is especially disappointing since we have recently been in workshops with Dutch judges who are very keen to use Bayesian reasoning - and even Bayesian networks (in the Netherlands there are no juries so the judges really do have to make the decisions themselves). These judges - along with Frans Alkemade - will be among many of the world's top lawyers, legal scholars, forensic scientists, and mathematicians participating in the Isaac Newton Institute Cambridge Programme on Probability and Statistics in Forensic Science that will take place July-Dec 2016. This is a programme that I have organised along with David Lagnado, David Balding, Richard Gill and Leila Schneps. It derives from our Bayes and the Law consortium which states that, despite the obvious benefits of using Bayes:

The use of Bayesian reasoning in investigative and evaluative forensic science and the law is, however, the subject of much confusion. It is deployed in the adduction of DNA evidence, but expert witnesses and lawyers struggle to articulate the underlying assumptions and results of Bayesian reasoning in a way that is understandable to lay people. The extent to which Bayesian reasoning could benefit the justice system by being deployed more widely, and how it is best presented, is unclear and requires clarification.
One of the core objectives of the 6-month programme is to address this issue thoroughly. Within the programme there are three scheduled workshops:
  1. "The nature of questions arising in court that can be addressed via probability and statistical methods", Tue 30th Aug 2016 - Tue 30th Aug 2016
  2. "Bayesian networks in evidence analysis", Mon 26th Sep 2016 - Thurs 29th Sep 2016
  3. "Statistical methods in DNA analysis and analysis of trace evidence", Mon 7th Nov 2016 - Mon 7th Nov 2016

Monday 26 October 2015

Cyber security risk of nuclear facilities using Bayesian networks

Scientists from Korea (Jinsoo Shin, Hanseong Son, Rahman Khalilur, and Gyunyoung Heo) have published an article describing their Bayesian network model for assessing cyber security risk of nuclear facilities (using the AgenaRisk tool). It is based on combining two models - one which is process based (considers how well security procedures were followed) and the other which is considers the system architecture (considering vulnerabilities and controls). The full paper is here:

Shin, J., Son, H., Khalil ur, R., & Heo, G. (2015). Development of a cyber security risk model using Bayesian networks. Reliability Engineering & System Safety, 134, 208–217. doi:10.1016/j.ress.2014.10.006

Bayesian Networks for Risk Assessment of Public Safety and Security Mobile Service

A new paper by Matti Peltola and Pekka Kekolahti of the Aalto University (School of Electrical Engineering) in Finland uses Bayesian Networks and the AgenaRisk tool to gain a deeper understanding of the availability of Public Safety and Security (PSS) mobile networks and their service under different conditions. The paper abstract states:
A deeper understanding of the availability of Public Safety and Security (PSS) mobile networks and their service under different conditions offers decision makers guidelines on the level of investments required and the directions to take in order to decrease the risks identified. In the study, a risk assessment model for the existing PSS mobile service is implemented for both a dedicated TETRA PSS mobile network as well as for a commercial 2G/3G mobile network operating under the current risk conditions. The probabilistic risk assessment is carried out by constructing a Bayesian Network. According to the analysis, the availability of the dedicated Finnish PSS mobile service is 99.1%. Based on the risk assessment and sensitivity analysis conducted, the most effective elements for decreasing availability risks would be duplication of the transmission links, backup of the power supply and real-time mobile traffic monitoring. With the adjustment of these key control variables, the service availability can be improved up to the level of 99.9%. The investments needed to improve the availability of the PSS mobile service from 99.1 % to 99.9% are profitable only in highly populated areas. The calculated availability of the PSS mobile service based on a purely commercial network is 98.8%. The adoption of a Bayesian Network as a risk assessment method is demonstrated to be a useful way of documenting different expert knowledge as a common belief about the risks, their magnitudes and their effects upon a Finnish PSS mobile service.
Full reference details:
Peltola, M. J., & Kekolahti, P. (2015). Risk Assessment of Public Safety and Security Mobile Service. In 2015 10th International Conference on Availability, Reliability and Security (pp. 351–359). IEEE. doi:10.1109/ARES.2015.65

Sunday 18 October 2015

What is the value of missing information when assessing decisions that involve actions for intervention?

This is a summary of the following new paper:

Constantinou AC, Yet B, Fenton N, Neil M, Marsh W  "Value of Information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences". Artif Intell Med. 2015 Sep 8  doi:10.1016/j.artmed.2015.09.002. The full pre-publication version can be found here.

Most decision support models in the medical domain provide a prediction about a single key unknown variable, such as whether a patient exhibiting certain symptoms is likely to have (or develop) a particular disease.

However we seek to enhance decision analysis by determining whether a decision based on such a prediction could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to the decision. In particular we wish to incorporate interventional actions and counterfactual analysis, where:
  • An interventional action is one that can be performed to manipulate the effect of some desirable future outcome. In medical decision analysis, an intervention is typically represented by some treatment, which can affect a patient’s health outcome.
  • Counterfactual analysis enables decision makers to compare the observed results in the real world to those of a hypothetical world; what actually happened and what would have happened under some different scenario.
The method we use is based on the underlying principle of Value of Information. This is a technique initially proposed in economics for the purposes of determining the amount a decision maker would be willing to pay for further information that is currently unknown within the model.

The type of predictive decision support models to which our work applies are Bayesian networks. These are graphical models which represent the causal or influential relationships between a set of variables and which provide probabilities for each unknown variable.

The method is applied to two real-world Bayesian network models that were previously developed for decision support in forensic medical sciences. In these models a decision maker (such as a probation officer or a clinician) has to determine whether to release a prisoner/patient based on the probability of the (unknown) hypothesis variable: “individual violently reoffends after release”. Prior to deciding on release, the decision maker has the option to simulate various interventions to determine whether an individual’s risk of violence can be managed to acceptable levels. Additionally, the decision maker may have the option to gather further information about the individual. It is possible that knowing one or more of these unobserved factors may lead to a different decision about release.

We used the method to examine the average information gain; that is, what we learn about the importance of the factors that remain unknown within the model. Based on six different sets of experiments with various assumptions we show that:
  1. the average relative percentage gain in terms of Value of Information ranged between 11.45% and 59.91% (where a gain of X% indicates an expected X% relative reduction of the risk of violent reoffence);
  1. the potential amendments in Decision Making, as a result of the expected information gain, ranged from 0% to 86.8% (where an amendment of X% indicates that X% of the initial decisions are expected to have been altered).
The key concept of the method is that if we had known that the individual was, for example, a substance misuser, we would have arranged for a suitable treatment; whereas without having information about substance misuse it is impossible to arrange such a treatment and, thus, we risk not treating the individual in the case where he or she is a substance misuser.

The method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from seeking information about that particular set of risk factors.

This summary can also be found on the Atlas of Science

Thursday 15 October 2015

Talk: Bayesian networks: why smart data is better than big data

Bayesian networks: why smart data is better than big data
by Prof. Norman Fenton from the School of Electronic Engineering and Computer Science (QMUL)
WHEN: Fri, 16th October 2 - 3 pm
WHERE: People's Palace PP2 (Mile End Campus)

"This talk will provide an introduction to Bayesian networks which, due to relatively recent algorithmic breakthroughs, has become an increasingly popular technique for risk assessment and decision analysis. I will provide an overview of successful applications (including transport safety, medical, law/forensics, operational risk, and football prediction). What is common to all of these applications is that the Bayesian network models are built using a combination of expert judgment and (often very limited) data. I will explain why Bayesian networks ‘learnt’ purely from data – even when ‘big data’ is available - generally do not work well."

All are welcome. The seminar consists of an app. 45 min long lecture and discussion.
In case of any questions, feel free to contact me.
Hope to see you tomorrow,

Judit Petervari
Judit Petervari
PhD Student

Biological and Experimental Psychology Group
School of Biological and Chemical Sciences
Queen Mary University of London
Mile End Road
E1 4NS London
United Kingdom

E-mail: j.petervari@qmul.ac.uk
Office: G.E. Fogg Building, Room 2.16

Friday 2 October 2015

Beware a 'journal' called FSS (Forensic Science Seminars): publishing papers without authors' permission

Screenshot of our unpublished draft paper that somehow got published in the 'journal' FSS
I have previously reported here and here on cases of our work being plagiarised in the most brazen way. Now comes a more unusual case - our unpublished work has been published in a 'journal' without our permission. And it seems the journal's articles may all be obtained in this way.

A forensic scientist in the Netherlands contacted me this week to say that he had found a copy of one of his papers in a journal he had never heard of, namely ‘Forensic Science Seminar’. In the same journal, he found this article by us. In fact, that article is an exact copy of this unpublished draft article that appears on my website. The only difference is that the article on my website (which is dated Jan 2012) says the following very clearly on the front:
Much of the work in this unpublished draft paper has subsequently been published in the following (which should be cited):
  • Fenton, N. E., D. Berger, D. Lagnado, M. Neil and A. Hsu, (2014). "When ‘neutral’ evidence still has probative value (with implications from the Barry George Case)", Science and Justice, 54(4), 274-287 http://dx.doi.org/10.1016/j.scijus.2013.07.002 
  • Fenton, N. E., Neil, M., & Hsu, A. (2014). "Calculating and understanding the value of any type of match evidence when there are potential testing errors". Artificial Intelligence and Law, 22. 1-28 . http://dx.doi.org/10.1007/s10506-013-9147-x 
The 'journal' is Printed and Published by "ZolCat® Academic House". Their other titles include:"Science and Nature", "Frontiers of Engineering Journal", "Journal of Computer Sciences".

Tuesday 29 September 2015

Yet another flawed statistical study attracts massive unquestioning attention

The Guardian, 29 Sept 2015
A very widely reported story in today’s news (see, for example, the report in the Guardian and this Press release) claims that companies in which there is at least one female executive on the Board (‘gender diverse’ companies) in the US, UK and India outperform companies with male-only executives by a staggering US$655 billion per year. The story is based on a study by Grant Thornton whose representative Francesca Lagerberg concludes:
“The research clearly shows what we have been talking about for a while: that diversity leads to better decision-making”.
As is typical when the results of a statistical study fit a popular narrative, the story attracted massive, unquestioning attention. Unfortunately, while I am sure that most people agree that greater gender diversity in the Boardroom is a worthy objective, based on the ‘full report’ – and in the absence of other data - Lagerberg's claim is simply not supported. In fact, the study exemplifies some of the classic misuses of statistics that we wrote about in the first chapter of our book and highlights yet again the need for proper causal/explanatory models to be used in statistical studies such as these*.

Moreover, using the data in Lagerberg's study it is possible to construct a simple causal model (a Bayesian network) that replicates the results but with provably opposite conclusions: diversity decreases performance.

The full report and BN model are provided here. The model can be run in the free version of AgenaRisk.

*Making such an approach both universally feasible and acceptable is the major objective of the EU-funded programme BAYES-KNOWLEDGE.

Monday 31 August 2015

Doctoring Data: Review of a must read book

Book Review:

Review by Norman Fenton and Martin Neil (a pdf version of this article can be found here)

This is an extremely important (and also entertaining) book that should be mandatory reading not just for anybody interested in finding out about what data-driven medical studies really mean, but also for anybody engaged in any kind of empirical work. What Kendrick shows brilliantly is the extent to which the vast majority of medical recommendations and guidelines are based on data-driven studies that are fundamentally flawed and often corrupt. He highlights how the resulting recommendations and guidelines have led (world-wide) to millions of unnecessarily early deaths, millions of people suffering unnecessary pain, and widespread use of drugs and treatments that do more harm than good (example: statins), as well as wasting billions of taxpayer dollars every year.

As researchers who have been involved in empirical studies in a very wide range of disciplines over many years we believe that much of what he says is also relevant to all of these disciplines (which include most branches of the physical, natural and environmental sciences, computer science, the social sciences, and law). Apart from the cases of deliberate corruption and bias (of which Kendrick provides many medical examples) most of the flaws boil down to a basic misunderstanding of statistics, probability and the scientific method.

There are two notable quotes that Kendrick uses, which we believe sum up most of the problems he identifies:
  1. When a man finds a conclusion agreeable, he accepts it without argument, but when he finds it disagreeable, he will bring against it all the forces of logic and reason” Thucydides.
  2.  “I know that most men, including those at ease with problems of the greatest complexity, can seldom accept even the simplest and most obvious truth if it be such as would oblige them to admit the falsity of conclusions which they have delighted in explaining to colleagues, which they have proudly taught to others, and which they have woven, thread by thread, into the fabric of their lives.” Leo Tolstoy
The first sums up the extent to which results of empirical work are doctored to suit the pre-conceived biases and hopes of those undertaking it (a phenomenon also known as ‘confirmation bias’). The second sums up the extent to which there are ideas that represent the ‘accepted orthodoxy’ in most disciplines that are impossible to challenge even when they are wrong. Those brave enough to challenge the accepted orthodoxy risk ruining their careers in their discipline. Hence, most researchers and practitioners simply accept the orthodoxy without question and help perpetuate flawed or useless ideas in order to get funding and progress their careers. Kendrick describes how these problems lie at the heart of the fundamentally fraudulent peer review system in medicine – which applies to both submitting articles to journals and submitting research grant applications. Once again, we believe that all of the areas of research where we have worked (maths, computer science, forensics, law, and AI) suffer from the same flawed peer review system.

Kendrick is not afraid to challenge the leading figures in medicine, often exposing examples of hypocrisy and corruption. Of special interest to us, however, is that he also challenges the attitude of revered figures in our own discipline. For example, Kendrick highlights two quotes in a recent article by Nobel prize-winner Daniel Kahneman, whose work in the psychology of decision theory and risk is held in the highest esteem.:
  1.  “The way scientists try to convince people is hopeless because they present evidence, figures, tables, arguments, and so on. But that’s not how to convince people. People aren’t convinced by arguments, they don’t believe conclusions because they believe in the arguments that they read in favour of them. They’re convinced because they read or hear the conclusions from people they trust. You trust someone and you believe what they say. That’s how ideas are communicated. The arguments come later.”
  2.  “Why do I believe global warming is happening? The answer isn’t that I have gone through all the arguments and analysed the evidence – because I haven’t. I believe the experts from the Academy of Sciences. We all have to rely on experts.
Kendrick notes the problem here:
“In one breath he states that people aren’t convinced by arguments; they’re convinced because they read or hear conclusions from people they trust. Then he says that we all have to rely on experts. But he does not link these two thoughts together to ask the obvious question. Just how, exactly, did the experts come to their conclusions?”
Having presented the BBC documentary on Climate Change by Numbers we also got an insight into the extent to which problems exist there.

As good as the book is (and indeed because of how good it is), we feel the need to highlight some points where we believe Kendrick gets it wrong. There are some statistical/probability errors and over-simplifications, which mostly seem to stem from a lack of awareness of Bayesian probability. For example, he says:
“… although association cannot prove causation, a lack of association does disprove causation”.
This is not true as can be proven by the simple counter example we provide below using a Bayesian network*.

Next we believe Kendrick’s faith in randomised control trials (RCTs) as being the (only) reliable empirical basis for medical decision making is misplaced. Because of Simpson’s paradox and the impossibility of accounting for all confounding variables there is, in principle, no solid basis for believing that the result of any RCT is ‘correct’. As is shown in the article here it is possible, for example, that an RCT can find a drug to be effective compared to a placebo in every possible categorisation of trial participants, yet the addition of a single confounding variable can result in an exact reversal of the results.

So, if we are saying that even RCTs cannot be accepted as valid empirical evidence, does that mean that we are even more pessimistic than Kendrick about the possibility of any useful empirical research? No - and this brings us to our final major area of disagreement with Kendrick’s thesis. In contrast to what Kendrick proposes we believe there is an important role for expert judgment in critical decision-making. In fact, we believe expert judgement is inevitable even if every attempt is made to remove it from an empirical study (it is, for example, impossible to remove expert judgment from the very problem of framing the study and choosing the variables and data to collect). Given the inevitability of expert judgment, we feel it should be made obvious, transparent, and open to refutation by experiment. Any scientist should be as open and honest about their judgment as possible and be prepared to make predictions and be contradicted by data.

By combining expert judgment with data it is possible to get far more reliable empirical results with much less data and effort than required for an RCT. This is essentially what we proposed in our book and which is being further developed in the EU project BayesKnowledge.

*Refuting the assertion “If there is no association (correlation) then there cannot be causation”.

Consider the two hypotheses:
  • H1: “If there is no association (correlation) then there cannot be causation”.
  • H2: “If there is causation there must be association (correlation).
Kendrick’s assertion (H1) is, of course, equivalent to H2. We can disprove H2 with a simple counter-example using two Boolean variables a, and b, i.e. whose states are True or False. We do this by introducing a third, latent, unobserved Boolean variable c. Specifically we define the relationship between a,b, and c via the following Bayesian network :

By definition b is completely causally dependent on a. This is because, when c is True the state of b will be the same as the state of a, and when c is False the state of b will be the opposite of the state of a.

However, suppose - as in many real-world situations – that c is both hidden and unobserved (i.e. a typical confounding variable). Also, assume that the priors for the variables a and c are uniform (i.e. 50% of the time they are False and 50% of the time they are True).

Then when a is False there is a 50% chance b is False and a 50% chance b is True. Similarly, when a is True there is a 50% chance b is False and a 50% chance b is True. In other words, what we actually observe is zero association (correlation) despite the underling mechanism being completely (causally) deterministic.

The above BN model can be downloaded here and run using the free version of AgenaRisk

Sunday 30 August 2015

Using Bayesian networks to assess and manage risk of violent reoffending among prisoners

Fragment of BN model
Probation officers, clinicians, and forensic medical practitioners have for several years sought improved decision support for determining whether and when to release prisoners with mental health problems and a history of violence.  It is critical that the risk of violent re-offending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established 'risk predictors' in this area of research are typically based on statistical regression models and their results are less than convincing. But recent work undertaken at Queen Mary University of London has resulted in Bayesian network (BN) models that not only have much greater accuracy, but which are also much more useful for decision support. The work has been developed as part of a collaboration between the Risk and Information Management group and the medical practitioners of the Violence Prevention Research Unit (VPRU) of the Wolfson Institute of Preventative Medicine.

The (BN) model, called DSVM-P (Decision Support for Violence Management – Prisoners) captures the causal relationships between risk factors, interventions and violence.  It also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. These decision support features are not available in the previous generation of models used by practitioners and forensic psychiatrists.

Full reference:
Constantinou, A., Freestone M., Marsh, W., Fenton, N. E. , Coid, J. (2015) "Risk assessment and risk management of violent reoffending among prisoners", Expert Systems With Applications 42 (21), 7511-7529.  Published version: http://dx.doi.org/10.1016/j.eswa.2015.05.025.
Download Pre-publication draft.

Thursday 27 August 2015

Modelling crime linkage with Bayesian networks

When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. A new paper:
de Zoete, J, Sjerps, M, Lagnado,D,  Fenton, N.E. (2015), "Modelling crime linkage with Bayesian Networks" Law, Science & Justice, 55(3), 209-217.
shows how Bayesian networks  can be used to model different evidential structures that can occur when linking crimes, and how they help in understanding how evidence that is obtained in one case can be used in another and vice versa. The flip side of this is that the intuitive decision to "unlink" a case in which exculpatory evidence is obtained leads to serious overestimation of the strength of the remaining cases.

Download the full article.