Tuesday, 24 November 2015

The Red Button Problem

Liverpool University Professor Simon Maskell created the 'red button problem' as a simple but messy conundrum that can be used to elucidate the similarities and differences between different approaches to handling uncertainty, such as Bayesian inference, Dempster-Shafer, frequentism, 'fuzzy logic' and so on. We'll outline the problem here, and present our answer, which comes from a squarely Bayesian angle.

The Red Button Problem

You are in charge of security for a building (called 'B' in the problem, but let's call it 'The Depot'). You are concerned about the threat of a VBIED (vehicle-borne improvised explosive device, or car bomb) being used against The Depot. 

An individual (called 'A' in the problem, but let's call him 'Andy'), has previously been under surveillance because of 'unstable behaviour'. He drives a white Toyota.

A white Toyota of the kind that may be owned by 'Andy'

10 minutes ago, a white Toyota was spotted on a video camera on a road 200m away from The Depot. An analyst ('Analyst 1' - let's call him 'Juan') with ten years' experience views the video footage and states that Andy is 'probably' near the Depot. An automated image recognition system, analysing the number plate, states that there is a 30% probability that the Toyota in the image is Andy's car. 

5 minutes ago, a white Toyota was spotted on a video camera on a road 15km away from The Depot. A second analyst ('Analyst 2', who we'll call 'Tahu'), who is new in post, reviews the footage an concludes that it is 'improbable' that Andy is 'near The Depot'.

There is a big red button in your office, that if pressed will order The Depot to be evacuated.

A big red button yesterday

Based on the evidence you have, should you press the button? 

Key Uncertainties

This problem is deliberately full of ambiguity. There are many questions that, in real life, you would want to know, and probably would already know or could easily resolve, including:

  • Is Andy's 'unstable behaviour' of a kind that is in any way empirically linked to VBIED attacks? Why was he under surveillance?
  • How many other individuals like Andy pose a security concern?
  • Which country and city is The Depot in, how easy is it to obtain the materials to manufacture a VBIED, and how prevalent are VBIED attacks?
  • Is The Depot in some way a salient target, in general and for Andy?
  • Do we have video feeds from the streets adjacent to The Depot, so we can scan them for the white Toyota?
  • Are analysts on the lookout for Andy?
  • What kind of analysis is Juan experienced in? 
  • To what extent is Juan already factoring in the NPR data?
  • When Tahu says he thinks it's improbable that Andy is near The Depot, does he mean that 15km away (where he spotted the Toyota) is not near, and that he thinks it's Andy's car, or that he thinks 15km is near, but that it isn't Andy's car?
  • Given the roads and traffic concerned, could Andy conceivably have driven at 180kmph - a fair lick, but not physically inconceivable - and thus be in both videos? Is this even possible in a Toyota?
  • Under what circumstances is it safer to evacuate The Depot rather than stay inside or perhaps retreat to the basement? 
  • How many people work in The Depot, and how costly is evacuation?

Thankfully, instead of requiring concrete answers or forcing us to make cavalier assumptions, the Bayesian approach to tackling these sorts of uncertainties is simply to expose and quantify each of them, based on reasonable comparison with similar situations and drawing on all the available evidence including the existence of the problem itself. For example, although we don't know anything about Andy's 'unstable behaviour', the fact that there are analysts keeping an eye out for him, and that we (the person in charge of the red button) are involved in the problem at all, suggests that he is at least more likely than the average person to commit a VBIED attack. Similar kinds of reasoning can be applied to think about likely answers to each of the questions above, so these uncertainties can be factored into our response. In this case, though, it's largely unnecessary to do this for all of the questions above, as we gain most of what we want for a decision through order-of-magnitude judgements, using a simple model.

Start with the Decision

All analysis, particularly in the realm of business or government, adds value because it reduces uncertainty and therefore risk. The more closely one's object of analysis - the key uncertainty, or target hypothesis - maps to one's decisions, the more valuable the analysis will be. In most cases, this means that analysing the decision needs to precede the analysis of the problem

In the case of the Red Button Problem, the decision is a simple binary one: evacuate, or don't. If we evacuate, there are two stages to think about - during the evacuation, and after the evacuation. While people are filing out of The Depot, one assumes to a remote assembly point, they might be more vulnerable to a VBIED attack. The US National Counterterrorism Centre helpfully provides some guidance on how to respond to VBIED threats. In the case of an explosive-laden sedan (e.g. a Toyota) the guidance suggests that sheltering in place is the optimal strategy at a radius of 98m or more (98m is curiously, and implausibly, specific here - why not say 100m?) but that 560m is the safest distance. Closer than 98m, evacuation is always optimal. If The Depot is located in the centre of a compound, staying inside would therefore be better. But if it's roadside, it would be better to evacuate, presumably because being inside a building at that radius would be as dangerous as being caught outside during a blast.

The decision is therefore time- and distance-sensitive. If there's a VBIED closer than 100m to The Depot, evacuation is best under any circumstances. If there's a VBIED between 100m and 560m from The Depot, sheltering in place would be best if it's going to explode soon, but evacuation would be better if there's enough time to get people to a safe distance. This might take in the order of ten minutes. Beyond 560m, you certainly shouldn't evacuate as people will be safer inside.   

So we have three mutually-exclusive hypotheses that we're interested in here:

H1. There is no VBIED.
H2. There is a VBIED that is timed to explode in roughly the next ten minutes.
H3. There is a VBIED that is timed to explode in more than ten minutes. 

If The Depot is near the road, evacuation is optimal under H2 and H3. If The Depot is more than 100m from the road (but less than 560m), evacuation is optimal under H3. Under all other circumstances, evacuation isn't optimal.

Costs and Benefits

Optimal decision-making involves at the very least a comparison of costs and benefits with probabilities. A bomb might be very unlikely, but if the risk of staying put is significantly higher than the cost of evacuating, it might still be right to press the button. 

In this case, the cost of evacuating, whether or not a bomb goes off, will be equal to the lost productivity from workplace absence. How much is that worth? Depending on what sort of work The Depot does, it won't be too far off £10-100 an hour per person. Assuming, in the event of a false alarm, that the area can be confirmed safe (and people return to their desks) in about an hour, and that there are (say) 100 or so people at The Depot, the cost of an evacuation would be of the order of £1,000-10,000. 

What if we don't evacuate - or we evacuate at the wrong time, and people are still walking to their muster point - and a bomb goes off? There are two things we need to know: how many deaths are likely, and how much is a death worth? 

It's hard to find statistics about numbers of people killed by car bombs at various distances. But we have a few reference points. The Oklahoma bomb killed 168 people, in an office building that accommodated about 500. It was a massive bomb, and close to the building. As an upper limit, this suggests that something like one third of The Depot's occupants would be killed. Then you have other costs, such as ongoing medical costs for those injured. If The Depot were further away from the road, this figure would be lower, and we can assume that at 500m or so, the probability of being killed is negligible. So we are perhaps looking at something around 10% fatalities from a nearby VBIED if The Depot were not evacuated. For the trickier situation in the 'shelter-in-place' zone, we may have to do some handwavey guesswork if the decision turns on the relative probability of a bomb in the next 10 minutes (the evacuation time) compared to a bomb afterwards.

How much is a life worth? Although some organisations avoid putting explicit values on human lives, the 'yuck' factor aside, you have to do it somehow or you won't be able to make a decision. If you use lifetime productivity (at the lower end) or willingness-to-pay to avoid death (at the upper end), estimates of the value of a life are typically in the order of £1m-10m. Assuming there are around 100 staff, and that not evacuating when there's a bomb next to The Depot would lead to around 10% fatalities, this gives us an order-of-magnitude estimate for the maximum cost of an un-acted-upon roadside bomb of around £10m-100m.

The ratio between the two figures - the cost of evacuation and the cost of failing to evacuate when there is a bomb - could therefore be somewhere between 1000:1 and 100,000:1. This ratio - between costs and benefits - gives us a 'critical probability' of a bomb, above which we should evacuate, and below which we should sit tight, for those situations in which evacuation may be optimal.

On to the Probability

The decision analysis has given us some useful information about roughly what we need to establish. If The Depot is close to the road, we should certainly evacuate if the evidence suggests a bomb probability of more than 1 in 1000, and certainly not evacuate if it's less than 1 in 100,000. If it's in between, we need to think a bit harder but we might take the risk-averse option and evacuate anyway. If The Depot is further from the road, but less than about 500m away, we have a trickier decision that will depend on our assessment of bomb timing and relative safety inside or outside the building.

In the usual Bayesian fashion, we'll take the approach of splitting our probability estimate into a prior probability, typically using background frequencies, and a set of conditional probabilities that take into account the evidence. This provides a useful audit trail for our estimate, to identify key sensitivities in our assessment, and to focus discussion on the main areas of disagreement.

First, then, what's the background frequency of VBIED attacks? Well, clearly it depends. In Iraq, there were over 800 VBIED attacks in 2014 alone, out of around 1400 worldwide (according to the ever-useful Global Terrorism Database). But assuming The Depot isn't in a troublespot - Iraq, Yemen, Nigeria etc. - the prior probability of a VBIED will be minuscule. There are - generously - a few dozen such attacks in stable countries worldwide. There are a number of back-of-the-envelope ways we could derive a prior probability from this, but it would be of the order of 1,000,000-to-1 a year that a VBIED attack would hit a particular office building, and therefore of around 1,000,000,000-to-1 or less that a VBIED was parked outside a particular building and timed to go off within the next (say) eight hours. 

The question, then, turns on the power of the evidence - the relative likelihood of that evidence under the 'bomb' and 'no bomb' hypotheses. Is this evidence sufficiently powerful to raise the odds of an imminent VBIED by a factor of 10,000 or more - to at least 100,000-to-1 - as would be needed to make evacuation potentially optimal?

First, we will discount Tahu's evidence entirely. We don't even know what he means. Does he mean that the car he saw 5 minutes ago (15km away) was Andy's car, and that 15km is 'not near', or that although 15km is 'near', the car he saw wasn't Andy's car and so Andy is unlikely to be near The Depot simply because there's no reason to think he would be? We don't know. Both interpretations seem equally likely, and they pull in different directions: if the car was Andy's car, we can pretty much rule out his presence at the Depot, but if it wasn't Andy's car, the other evidence becomes more important. Action point: Tahu to be enrolled on an intelligence analysis communications course.
The more of these you have, the less diagnostic they are
The next piece of evidence is the video of the white Toyota, possibly Andy's, spotted 200m from The Depot ten minutes ago. We'll combine it with the evidence of Andy's previous instability to form a single piece of evidence:
  • E: "An individual with a history of instability was in a car near The Depot five minutes ago."
In fact, this evidence should only believed with a probability of either 30%, or whatever Juan means by 'probably', or some other number depending on how credible the NPR system or the analyst are. But we're going to pretend that E is known with certainty. This is the most diagnostic case. If it's still insufficient to push the probability into the 'evacuate' zone, then we don't need to worry about the finer points.

So, how likely is E if there's going to be a VBIED attack? Let's assume it's pretty close to 1. Some VBIED attacks will be carried out by individuals not known to have been unstable, but let's not worry about that. The key question here is the second probability - how likely is E under the assumption that there isn't going to be a VBIED attack? The lower this probability, the more powerful the evidence.

What this boils down to is how likely it is that an unstable person will be 'near' The Depot during the vast majority of time that there isn't about to be a VBIED attack. According to this article, the Security Service ('MI5') are 'watching' 3000 potential 'Jihadists' in the UK. Let's assume, including other types of threat, that there are something like 6000 people of 'concern' in the UK. This is about 1 in 10,000 people. The security infrastructure supporting The Depot may well have a similar proportion of people covered - after all, they have intelligence analysts, collection assets and so on. 

Finally, we need to guess roughly how many people are 'near' The Depot every day, and how probable it is that one of them is an individual of concern. Is it in the middle of a town, or out in the countryside? Let's give it the benefit of the doubt and assume that it's somewhere quiet, which again increases the power of the evidence. Let's say one car a minute is on the road nearby. This equates to about 500 cars during a working day.

And here's the key point: even with just 500 cars going past a day, each with just one occupant, you expect to see an individual of concern on average every twenty days. In an average eight hour stretch, the probability of seeing one of these individuals is about 5%. Even in the worst case - with a set of assumptions that make the evidence particularly diagnostic - the evidence presented raises the probability of an attack by a factor of just 20 or so - nowhere near the factor of 10,000 that would be needed to make evacuation optimal at any distance.


Don't press the button. Andy might be nearby, but the probability that he's about to conduct a VBIED attack is negligible. Instances of 'unstable person near a building' are far, far more frequent than instances of 'VBIED attack', to a multiple that greatly exceeds the ratio of costs and risks associated with the decision problem itself. The nature of the evidence is such that it simply cannot be diagnostic enough. In real life, you'd perhaps want to pursue further investigation - perhaps eyeball the street outside to see if the car's there. But ordering an immediate evacuation would be very jumpy indeed, and your tenure as head of security would probably not be a long one.

Some professional analysts would baulk at the approach taken above. It seems too full of assumptions and guesswork. It is, of course - in real life you would have a lot more information that would help guide the decision. But the broad approach taken - to start by analysing the decision, then ask whether the evidence could conceivably be sufficient to change it - is a robust one, and might save analytical resources that would otherwise be used up on estimating a probability that would not, in fact, make any difference to anything.

Friday, 6 November 2015

Sharm el-Sheikh and Terrorism Risks

The British government's choice to suspend flights from Sharm el-Sheikh, based on 'intelligence reports', makes an interesting subject for decision-analysis. On the benefit side is the (assumed) avoidance of an unacceptable risk of fatalities either by moving passengers geographically (to another airport) or temporally (by delaying flights for some period of time), and the longer-term reduction in risk through pressure on the Egyptians to improve airport security. On the cost side are the potential increased risk to those passengers through remaining in Sharm el-Sheikh, and the material costs (time, money) of delayed return to the UK. By making a few back-of-the-envelope calculations, we can get a sense of what the scales of these various risks and costs are, and draw some inferences about the UK government's decision calculus.

Background Risks

Before considering the specifics of the case, it's useful to look at the data on background terrorism risk levels. The Global Terrorism Database is the go-to source for this kind of information, even though the latest full database only runs up to the end of 2014 and therefore doesn't include recent events such as the bombing of the Italian consulate in Cairo or the downing of Flight 9268 (if this were to prove the work of IS).

Despite the conclusions our reptilian brains might draw from the background noise of an 'ever more dangerous world', for people living outside countries such as Syria, Iraq, Nigeria and Afghanistan, terrorism risks are minuscule. In the UK in 2010 (the latest year for which comprehensive cause-of-death data are available), no-one died in any terrorist attacks, but 34 died of bullous pemphigoid - we'd never heard of it either. Even in Iraq, an average of 5,400 people a year were killed in terrorist incidents in 2010-2014, compared to a reported 10,000 from road traffic accidents in 2014.

You're pretty safe here

Between 2010-2014, around 130 people a year died in terrorist attacks in Egypt. Given Egypt's population (80m), this means that a day spent in Egypt would have exposed you to around a 1-in-250,000,000 chance of dying in a terrorist attack. We might suppose that tourists were particularly likely to be targeted, but this is not so - only 3% of attacks targeted tourists, and of these, not a single one was fatal in the whole five year period. 

What about terrorism against non-military aircraft? Throughout that period, there were no terrorist attacks against such aircraft in Egypt, and there were only four such attacks throughout the region between 2010-2014. With such low-frequency events, we get a better idea of background risk looking at the global figures. The wider trend is clear: aviation is safer than ever. Looking at terrorism risks in particular, between 2010-2014 there were just 17 attacks of any kind on non-military aircraft, with 306 fatalities, but 298 of these were in a single incident: MH17. The risk of dying in a terrorist attack during a flight is therefore probably around 1-in-50,000,000. Five times more dangerous than the terrorism risk of spending a day in Egypt, but still negligible, particularly in comparison with terrestrial means of getting home.

Finally, given that the stranded passengers will be hanging around in the airport for longer, we should consider the background risk of terrorist attacks on those targets. Perhaps unsurprisingly, attacks on airports are more frequent, but considerably less deadly, than those on aircraft. 227 people were killed in 118 attacks on airports worldwide between 2010-2014. (Two of those attacks were in Egypt, with one fatality between them.) On average, this is a terrorism death once every eight days in airports worldwide. Perhaps around 16,000,000 hours are spent by people in airports per day (assuming 8,000,000 flights and 2 hours of airport-time per flight), so this works out at around one terrorism death every 130,000,000 hours spent in airports. To put it another way, about 150 minutes in an airport is as risky as the flight you take, and 24 hours in an airport exposes you to about a 1-in-5,000,000 chance of dying in a terrorist attack. It would certainly be safer to make your way back to the centre of town for a mint tea as most of the passengers at Sharm seem to have done.

Some ways of exposing yourself to a 1 in 50m risk
of dying in a terrorist attack

'Intelligence Reports'

With these figures serving as anchor points, we can speculate as to what intelligence the UK government might have received, and consider its possible implications for the relative risks of postponing the flights versus allowing them to continue.

'It was a bomb'

The media are suggesting that the intelligence reports related to Flight 9268, and specifically that it was destroyed by a bomb. If this is all the intelligence reports suggested, then the only implication for future terrorism risk is if there is a clustering effect. The data for Egypt suggest a small clustering effect, such that the risk of a terrorist attack (of any kind) is around 15% higher on the same day as another attack, but this effect disappears thenceforth. Using the figures above, there might therefore be a rationale for sending passengers home on the day that Flight 9268 went down - a Saturday - but the travel ban was imposed on the following Wednesday. Assuming the UK government is acting to minimise risks, we might be able to assume that the intelligence also related to raised future threat levels.

'There is a general threat'

If the intelligence suggested a higher general threat level, this might appear to justify the travel ban. But if a 'general' threat increase applies to all terrorism risks in Egypt, including against flights, the figures presented above suggest that the best thing to do would be to fly passengers home as quickly as possible: the flight is unavoidable, and getting back to the UK would definitely be safer than spending time in the airport or roaming around Egypt, even at (albeit negligible) background risk levels.

'There is a specific threat'

It's possible the intelligence related to a specific threat against certain flights in a certain area or at certain defined times. If this is so, then the extent of increased risk is impossible to guess, but if the intelligence were credible enough it could easily justify the temporary travel ban.

Benefits and Costs

Scarier than a terrorist

The decision may not entirely have been driven by short-term risk minimisation. The UK government may hope that the Egyptians will be pressured into improving airport security, for a longer-term gain. But the decision is also costly - for the stranded people, for the Egyptian tourist industry, and (one assumes) for the Egyptian government in terms of increased security spending. The reduction in risk would have to be very significant or very long term, if this were the only goal of the UK's decision. Since 1970, an average of around 60-70 aircraft passengers a year have been killed in terrorist attacks worldwide. If we include indirect deaths from airborne terrorism (e.g. the people in the World Trade Centre) then this average figure would roughly double, but would still be lower than the number of people killed each year by either hippos, lions or buffalo. In other words, the risk from aviation terrorism is already so low that there are probably few gains left to be had from improved security. 

Thursday, 5 November 2015

Cause-Effect diagramming in focus

Another example of a popular and effective low-tech analytical tool is the 'Cause-Effect diagram' (aka Herringbone or Ishikawa diagram). As the name suggests they are appropriate for identifying and delineating possible causal relationships. Examples include the ways a plan might fail, the causes of an accident or bottlenecks in processes. As with the previous post examining SWOT analysis we won’t provide yet another ‘how to’ - there are lots of excellent guides out there, but to look a little further into what is going on when an analyst goes about using such a method, to add robustness to the approach and encourage adaptation.

So what’s going on? Lets examine some of the things that seem to be happening when this method is used. There seem to be a few mechanisms in play:
  • Selecting and specifying the subject. 
  • Choosing the framework - selecting top-level headings to suit the question.
  • 'Ideation' - identifying and recording 'factors' relating to the question
  • Sorting and categorising ideas
  • Turning the output into action

Specifying the question: As observed in the previous SWOT post, there is an important process of electing a manageable but useful scope to address. I won’t go into more detail on this (it is probably worthy of it’s own post). However selection of an appropriate framework of headings is an important element of this method which is informed by the scope/questions selection.

Choosing the framework: Various guides suggest different collections of top level headings (choose from 5 Ms or 8 Ms in manufacturing, 7 Ps in marketing or 5 Ss in service industry) to provide the first level of features. These top level headings provide broadly (woolly) defined causes, as additional layers of detail are added these causes become more clearly defined. These top level headings serve as a set of categories for sorting factors.

Naturally, it is important to choose headings which you consider likely to give you good coverage of the scope decided in the step above, so, for example, if human error is though to be a root cause of the error under examination then 'PEOPLE' (or other appropriate category) should be included. Be aware, that the framework may limit ideation: Any prescriptive set of categories may limit ideation, so, these stock frameworks should be used with discretion, they can provide useful suggestions of things to think about, but do not feel pressured to 'force' ideas in order to satisfy an empty heading and likewise, feel free to add more categories if those pre-selected don't fit your ideas.

Presenting this outline structure which assists ideation. There seems to be a mode of thought while 'generating' ideas which is assisted by considering a single branch (category) at a time. This reduction in scope, seems to make it less cognitively demanding in some way. It is easier often to come up with an answer a series of questions, "What 'people'/'environment'/'equipment'... factors caused this?" than to answer the more general "What factors caused this?".

Guides for this method often suggest asking the question “what are the causes for this?” or “why?” to prompt ideas. This seems to play on the power of the human mind for generating narrative style explanations for observed phenomena. The categories already present provide a subject for the guiding question, and the question prompts you to seize at available possible causes from experience or imagination. The method seems to be making good use of the Availability Heuristic to reduce the burden of ideation. However, watch out, the first thing to come to mind is not necessarily the most important factor. To assume this is to succumb to a natural and powerful bias (the 'dark side' of the heuristic). Try asking your self "Why else?" to generate more ideas on the theme until it becomes exhausted.

It’s clear that when you are ‘generating ideas’ (or perhaps, recalling things you already know) the visible categories help guide you to bits of your memory that contain useful/relevant concepts. It seems to me that this plays to the strengths of the associative nature of the human memory, you might ask yourself “What are the possible PEOPLE causes of this problem” and by recalling the PEOPLE concept in your mind there are other ‘proximate’ concepts which come readily to mind. And as with SWOT the diagram and pre-selected categories provide a handrail to both prompt and sort ideas.

The ideas already present as well as the categories themselves (and the associated 'conceptual baggage') provide a useful prompt to the brainstormers. In this sense it is similar to SWOT, as mental short cuts can be used to find which are similar factors and add them to the diagram with less cognitive load than if they were generated from fresh.

It is important that the question asked is relevant to the task. Furthermore, to prevent the output being ambiguous and confusing the question should be and consistent. Avoid mixing cause-effect questions such as 'why?' with other relationships. There may be a temptation to try to explicit capture temporal relationships - perhaps by asking "What came before?", If this in relevant and interesting, consider a different, separate analysis.

When a participant comes up with an idea, and then wants to place it on the board, they will generally already have a category in mind - the category was the prompt. The guiding question invites a critical analysis of the category, and from that an idea is generated.

In some cases, ideas come out which are not driven by the guiding question. In these cases there is a need to switch between ‘creative' and a ‘critical’ mode of thought to assign the idea to an appropriate limb. If they can’t find a suitable category to ‘hang’ the idea off, they may be dissuaded and assume that the idea is ‘wrong’ in some way. A good facilitator will invite people to put any ideas up, using the white space around the diagram, and worry about categorising/judging ideas later.  The categorisation scheme can be used to sort ideas by looking for parent classes that the thing in mind is NOT like, just as much as finding it an appropriate parent.  By elimination of candidates a home may be found.

Although this form of categorisation feels like concepts go though a process of gross simplification (and therefor data loss), this is only surface deep.  By associating concepts with a parent class they inherit a great deal of information in return. The association with other siblings, and a proper place in the hierarchy (e.g. level relative to other items) tells you a great deal, implicitly, about the item in the tree.

The order in which one chose to do things is again a matter of preference, but the the two ‘modes’ (creative/critical) of thought seem to occur at some point whatever approach the participants take to the task. So, mechanistically, it is very similar to a SWOT: Ideas are generated (or recalled) and then sorted. Where this process differs significantly from SWOT is the structure into which the ideas are placed. Consider two parts to the structure of the ideas:
  • Visual Structure of the diagram and 
  • Logical Structure of the guiding questions
Logical Structure: The guiding question is clearly a powerful aid in ideation, but more than this it provides a prototype for the relationship between all the factors captured in the diagram. By asking a guiding question such as 'why?' means that a level of ‘meaning’ woven into the output: information about the causal relationship between any ‘bone’ or branch and its subordinates is implicit in the relationship between the parent-child branches in the hierarchy. The link between any bone and the it's superior is the answer to the question: 'BRANCH...because...SUB-BRANCH'.

This is extra information, that the structure (layout) helps you visualise and capture. So, a doctrinally pure Cause and Effect diagram represents an understanding of sequential causality, and hence implicitly includes temporal information in the relationship between the concepts: the small bones must exist before their bigger parent bones. With each step ‘down’ from branch to sub-branch you are trading breadth of scope for specificity while moving back through time and sibling sub-branches share an implicit relationship via the parent branch’s broader meaning.

Visual Structure: The SWOT diagram invites only one level of categories, which is fixed at 4 all ideas must be made to fit into on of the ‘holes’. With this method we have potentially many more. For example, we might at the start, have 1 of 8 Ms to choose, but as we drill down adding more layers, we potentially have more and more sub-categories which in turn may have their own sub-categories. And if the situation arises that ideas don’t ‘fit’ into any existing categories, then new ones can be appended. So, in this way the visual structure is extensible both horizontally (number of bones) and vertically (number of levels of sub-bones).

The fish bone shape, with distinct hierarchy and slanting bones and a ‘head’ makes use of the semiotic 'baggage', there is a sense of ‘pointing’ forward from which the human reader infers the temporal/causal relationship. You are left with a sense that a number of sub-things lead to the higher thing. The completed diagram represents a set of stories which tell how various factors fell into sequence to cause the main focus of the analysis.

Keep in mind that the fish bone shape aids this inference. For some problems this may not be appropriate. For example if you are not looking for temporal or causal relationships between factors then you should question if the fish shape it appropriate - does the shape complement the logic of the question that drives the relationship between elements and their parents and children?

When completed and agreed as a group it represents a shared and accepted vision of events. This is often a useful consensus to build for subsequent group action.

Commonalities: What is clear is that there are certain common mechanistic activities which are present in both SWOT and cause-effect diagramming:
  • Specifying the question: Selecting a usefully broad, but answerable narrow scope.
  • Coming up with and recording ideas: Ideation, recall and capture.
  • Sorting ideas: Critical consideration and classification.
As we have discussed, it is important not to allow the structure and choice of categories to be too prescriptive. This is a theme which we shall examine further in the next post providing a critical examination of another common visual analytical method, Mind Mapping.