So ten years on from the Black Saturday fire we’re taking a national moment to remember the unstinting heroism displayed in the face of the hell that was Black Saturday day and all that was lost. Of course we’re not quite so diligent in remembering that we haven’t prevented people rebuilding in high risk areas, nor in improving the fire resistance of people’s homes, nor yet in managing down the risk through realistic burn off programs, nor for that matter have we noticed that the fuel burden in 2018 is back up to the same levels as it was ten years ago. So perhaps instead we should reflect on how we’ve squandered the opportunity for reform. And perhaps we should remember that a fire will come again, to burn our not so clever country .
Archives For Risk
What is risk, how dow we categorise it and deal with it.
…it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.
When you look at the safety performance of industries which have a consistent focus on safety as part of the social permit, nuclear or aviation are the canonical examples, you see that over time increases in safety tend to plateau out. This looks like some form of a learning curve, but what’s the mechanism, or mechanisms that actually drives this process? I believe there are two factors at play here, firstly the increasing marginal cost of improvement and secondly the problem of learning from events that we are trying to prevent.
Increasing marginal cost is simply an economist’s way of stating that it will cost more to achieve that next increment in performance. For example, airbags are more expensive than seat-belts by roughly an order of magnitude (based on replacement costs) however airbags only deliver 8% reduced mortality when used in conjunction with seat belts, see Crandall (2001). As a result the next increment in safety takes longer and costs more (1).
The learning factor is in someways like an informational version of the marginal cost rule. As we reduce accident rates accidents become rarer. Now one of the traditional ways in which safety improvements occur is through studying accidents when they occur and then to eliminate or mitigate identified causal factors. Obviously as the accident rate decreases this likewise the opportunity for improvement also decreases. When accidents do occur we have a further problem because (definitionally) the cause of the accident will comprise a highly unlikely combination of factors that are needed to defeat the existing safety measures. Corrective actions for such rare combination of events therefore are highly specific to that event’s context and conversely will have far less universal applicability. For example the lessons of metal fatigue learned from the Comet airliner disaster has had universal applicability to all aircraft designs ever since. But the QF-72 automation upset off Learmouth? Well those lessons, relating to the specific fault tolerance architecture of the A330, are much harder to generalise and therefore have less epistemic strength.
In summary not only does it cost more with each increasing increment of safety but our opportunity to learn through accidents is steadily reduced as their arrival rate and individual epistemic value (2) reduce.
1. In some circumstances we may also introduce other risks, see for example the death and severe injury caused to small children from air bag deployments.
2. In a Popperian sense.
1. Crandall, C.S., Olson, L.M., P. Sklar, D.P., Mortality Reduction with Air Bag and Seat Belt Use in Head-on Passenger Car Collisions, American Journal of Epidemiology, Volume 153, Issue 3, 1 February 2001, Pages 219–224, https://doi.org/10.1093/aje/153.3.219.
Update to the MH-370 hidden lesson post just published, in which I go into a little more detail on what I think could be done to prevent another such tragedy.
The search for MH370 will end next tuesday with the question of it’s fate no closer to resolution. There is perhaps one lesson that we can glean from this mystery, and that is that when we have a two man crew behind a terrorist proof door there is a real possibility that disaster is check-riding the flight. As Kenedi et al. note in a 2016 study five of the six recorded murder-suicide events by pilots of commercial airliners occurred after they were left alone in the cockpit, in the case of both the Germanwings 9525 or LAM 470 this was enabled by one of the crew being able to lock the other out of the cockpit. So while we don’t know exactly what happened onboard MH370 we do know that the aircraft was flown deliberately to some point in the Indian ocean, and on the balance of the probabilities that was done by one of the crew with the other crew member unable to intervene, probably because they were dead.
As I’ve written before the combination of small crew sizes to reduce costs, and a secure cockpit to reduce hijacking risk increases the probability of one crew member being able to successfully disable the other and then doing exactly whatever they like. Thus the increased hijacking security measured act as a perverse incentive for pilot murder-suicides may over the long run turn out to kill more people than the risk of terrorism (1). Or to put it more brutally murder and suicide are much more likely to be successful with small crew sizes so these scenarios, however dark they may be, need to be guarded against in an effective fashion (2).
One way to guard against such common mode failures of the human is to implement diverse redundancy in the form of a cognitive agent whose intelligence is based on vastly different principles to our affect driven processing, with a sufficient grasp of the theory of mind and the subtleties of human psychology and group dynamics to be able to make usefully accurate predictions of what the crew will do next. With that insight goes the requirement for autonomy in vetoing of illogical and patently hazardous crew actions, e.g ”I’m sorry Captain but I’m afraid I can’t let you reduce the cabin air pressure to hazardous levels”. The really difficult problem is of course building something sophisticated enough to understand ‘hinky’ behaviour and then intervene. There are however other scenario’s where some form of lesser AI would be of use. The Helios Airways depressurisation is a good example of an incident where both flight crew were rendered incapacitated, so a system that does the equivalent of “Dave! Dave! We’re depressurising, unless you intervene in 5 seconds I’m descending!” would be useful. Then there’s the good old scenario of both the pilots falling asleep, as likely happened at Minneapolis, so something like “Hello Dave, I can’t help but notice that your breathing indicates that you and Frank are both asleep, so WAKE UP!” would be helpful here. Oh, and someone to punch out a quick “May Day” while the pilot’s are otherwise engaged would also help tremendously as aircraft going down without a single squawk recurs again and again and again.
I guess I’ve slowly come to the conclusion that two man crews while optimised for cost are distinctly sub-optimal when it comes to dealing with a number of human factors issues and likewise sub-optimal when it comes to dealing with major ‘left field’ emergencies that aren’t in the QRM. Fundamentally a dual redundant design pattern for people doesn’t really address the likelihood of what we might call common mode failures. While we probably can’t get another human crew member back in the cockpit, working to make the cockpit automation more collaborative and less ‘strong but silent’ would be a good start. And of course if the aviation industry wants to keep making improvements in aviation safety then these are the sort of issues they’re going to have to tackle. Where is a good AI, or even an un-interuptable autopilot when you really need one?
1. Kenedi (2016) found from 1999 to 2015 that there had been 18 cases of homicide-suicide involving 732 deaths.
2. No go alone rules are unfortunately only partially effective.
Kenedi, C., Friedman, S.H.,Watson, D., Preitner, C., Suicide and Murder-Suicide Involving Aircraft, Aerospace Medicine and Human Performance, Aerospace Medical Association, 2016.
One of the perennial problems we face in a system safety program is how to come up with a convincing proof for the proposition that a system is safe. Because it’s hard to prove a negative (in this case the absence of future accidents) the usual approach is to pursue a proof by contradiction, that is develop the negative proposition that the system is unsafe, then prove that this is not true, normally by showing that the set of identified specific propositions of `un-safety’ have been eliminated or controlled to an acceptable level. Enter the term `hazard’, which in this context is simply shorthand for a specific proposition about the unsafeness of a system. Now interestingly when we parse the set of definitions of hazard we find the recurring use of terms like, ‘condition’, ‘state’, ‘situation’ and ‘events’ that should they occur will inevitably lead to an ‘accident’ or ‘mishap’. So broadly speaking a hazard is a explanation based on a defined set of phenomena, that argues that if they are present, and given there exists some relevant domain source (1) of hazard an accident will occur. All of which seems to indicate that hazards belong to a class of explanatory models called covering laws. As an explanatory class Covering laws models were developed by the logical positivist philosophers Hempel and Popper because of what they saw as problems with an over reliance on inductive arguments as to causality.
As a covering law explanation of unsafeness a hazard posits phenomenological facts (system states, human errors, hardware/software failures and so on) that confer what’s called nomic expectability on the accident (the thing being explained). That is, the phenomenological facts combined with some covering law (natural and logical), require the accident to happen, and this is what we call a hazard. We can see an archetypal example in the Source-Mechanism-Outcome model of Swallom, i.e. if we have both a source and a set of mechanisms in that model then we may expect an accident (Ericson 2005). While logical positivism had the last nails driven into it’s coffin by Kuhn and others in the 1960s and it’s true, as Kuhn and others pointed out, that covering model explanations have their fair share of problems so to do other methods (2). The one advantage that covering models do possess over other explanatory models however is that they largely avoid the problems of causal arguments. Which may well be why they persist in engineering arguments about safety.
1. The source in this instance is the ‘covering law’.
2. Such as counterfactual, statistical relevance or causal explanations.
Ericson, C.A. Hazard Analysis Techniques for System Safety, page 93, John Wiley and Sons, Hoboken, New Jersey, 2005.
We are hectored almost daily basis on the imminent threat of islamic extremism and how we must respond firmly to this real and present danger. Indeed we have proceeded far enough along the escalation of response ladder that this, presumably existential threat, is now being used to justify talk of internment without trial. So what is the probability that if you were murdered, the murderer would be an immigrant terrorist?
In NSW in 2014 there were 86 homicides, of these 1 was directly related to the act of a homegrown islamist terrorist (1). So there’s a 1 in 86 chance that in that year if you were murdered it was at the hands of a mentally disturbed asylum seeker (2). Hmm sounds risky, but is it? Well there was approximately 2.5 million people in NSW in 2014 so the likelihood of being murdered (in that year) is in the first instance 3.44e-5. To figure out what the likelihood of being murdered and that murder being committed by a terrorist we just multiply this base rate by the probability that it was at the hands of a `terrorist’, ending up with 4e-7 or 4 chances in 10 million that year. If we consider subsequent and prior years where nothing happened that likelihood becomes even smaller.
Based on this 4 in 10 million chance the NSW government intends to build a super-max 2 prison in NSW, and fill it with ‘terrorists’ while the Federal government enacts more anti-terrorism laws that take us down the road to the surveillance state, if we’re not already there yet. The glaring difference between the perception of risk and the actuality is one that politicians and commentators alike seem oblivious to (3).
1. One death during the Lindt chocolate siege that could be directly attributed to the `terrorist’.
2. Sought and granted in 2001 by the then Liberal National Party government.
3. An action that also ignores the role of prisons in converting inmates to Islam as a route to recruiting their criminal, anti-social and violent sub-populations in the service of Sunni extremists.