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Deduction, induction and abduction – or how not to keep doing what you always did and getting what you always got

The saying ‘keep doing what you always did and you’ll get what you always got’ is a great way to make people stop and think. You learned one approach and it worked, so that’s what you always do. But was the outcome you got, the last few times, actually what you wanted? If not, wouldn’t it be good to do something else and get a different result? Depending on experience and what worked in the past may not be the best way to approach a problem.

I have been pondering this. Because it’s actually not even true that if you do what you always did, the results will be what you always got… The world is changing so fast that it’s probably truer to say that you won’t even get what you used to get – you’ll get something less satisfactory, less useful.

So what to do?

The problem is that just knowing that you need to change your approach doesn’t actually tell you what to do instead. So you are thrown back on your capability to work it out. And the first thing you’ll do, because you are a human being, with the same wiring in your brain and as everyone else, is to apply logic to the situation.

Let’s see… if A is happening, then I probably need to do B. Elementary, my dear Watson! It’s obvious! If your car runs out of petrol, it will stutter and stop. Every time! If you forget to turn off the tap, the bath will overflow. Count on it! If I see a problem I can usually see the cause – and that’s all I need to fix it!

That’s called Deductive reasoning.


Using deduction is fine in a world where everything works logically and consistently, where causality is transparently clear and we can always understand what’s needed to be done. The trouble is, the world isn’t like that. Stuff happens and sometimes the obvious solutions don’t work. They can even make things worse.

And sometimes we think there is a causal connection, but it turns out, there isn’t. The sales figures are down again this month. It must be the sales director’s fault. So he is sacked and a new sales director comes in – and the figures continue to slide. Turns out it wasn’t the sales director’s fault – there’s a new competitor who is taking away your customers. You should have spent a bit more time finding out what was really going on, analysing the situation, digging around in the undergrowth to uncover the root causes and then working out how the pattern fits together.

And that’s called Inductive reasoning.


If my research suggests that B happened because of A, I can make the hypothesis that if you do A again, you’ll get B again. That’s called learning from experience and that’s how we function. We analyse the past, work out the ‘why’, try it out and repeat what worked. If B was an undesirable occurrence, like a train crash, the enquiry will identify the cause and recommend strategies to stop it happening again.

Which is fine if your research identified a true causal link and didn’t confuse the symptom with the underlying cause. And if the world around you doesn’t keep changing, so that what caused B last time won’t make it happen again this time.

We see this when social workers fail to stop a baby being abused and a government enquiry makes recommendations for more controls and improvements in the management procedures to prevent it happening again. But it does – again and again. The underlying causes of the failure will be complex and may not have been fully understood. They will be context-specific and, as circumstances changed, new contributory factors will influence the outcome. And the very act of increasing controls may actually make the social workers’ job more difficult, adding to the problem rather than reducing the risks.

We can use inductive reasoning to make sense of the world, provided we don’t forget that it’s reductionist in nature – we simplify in order to reduce the data to something we can understand and use. And provided we recognise that it’s only based on what happened in the past. Retrospective analysis will often uncover useful insights – patterns which look causal and suggest worthwhile conclusions, but these patterns won’t necessarily repeat themselves in future. In fact they are almost certain not to do so.

So if we can’t count on inductive reasoning, based on experience, logical enquiry and analysis, how are we to decide what to do differently to get an improved outcome?

There is always intuition, of course… leaps of faith… gut feeling…

The technical term for this is Abduction


This is where we make a connection between two occurrences which are not causally connected. We are all very good at doing this. That’s how conspiracy theories arise. We put 2 and 2 together, and make 5. We see order where there is none. We assign meaning to coincidences.

Sometimes this is a good talent to have. It can help us gain a sense of purpose and create a feeling that we are in control, and not subject to random chance and the chaos of the world around us. It allows us to be creative, imaginative. But mostly it leads us into false assumptions and invalid conclusions.

The trouble is, the more uncertain the situation we face, the more we appear to depend on our abductive reasoning powers. The less we know, the more we convince ourselves that our view is right. This is the territory of prejudice, born out of ignorance and our overwhelming need to be able to come up with an answer that means we feel in control.

Unsurprisingly, until recently, scientists and disciples of management theory derided the abductive process and confined themselves to deductive and inductive methods of enquiry and decision-making. Occasionally discoveries and successes came from ‘accidents’ and ‘luck’ – but these were not capable of being repeated, so as a methodology, it was of little value.

But there are well-known problems with the scientific and empirical use of inductive and deductive reasoning, too.

Ask someone a question and, as any researcher worth his salt will tell you, you have immediately bounded the answer within your own frame of reference. So the answer will not be the ‘truth’ you sought, but just a version of the truth, biased by you in the very act of asking the question.

It gets worse: ask the question of someone and the answer you get will also reflect: how they feel about you at the time, how they think you want them to answer the question, their own mood and well-being at that moment… etc.

Of course, a well-managed research project will always work to reduce these influences, but they can never be eliminated. And by imposing confidence limits and statistical averaging on the data, you devalue the exercise – many potentially interesting opportunities to make useful connections are lost.

And remember, analysis of what has happened in the past can only tell us something about the past – often quite a long time past. By the time we have collected enough data, analysed it, drawn conclusions and decided what needs to change, the occurrences could be months or years old.

If we want to get the result we want or to prevent something happening in the future, we need a method of enquiry that doesn’t have this built-in time lag. Is that possible? Is there such a method?

Some work that is going on in the use of unstructured story-telling in research promises to overcome these fundamental problems and offers a new way to gain early and unbiased feedback and insights into what is happening around us in real time.


As we saw, traditional research and analysis methods tend to be limited by their dependence on the ‘expert’s role in framing the questions and interpreting the results. These issues are specifically addressed by SenseMaker®, which has been developed by Cognitive Edge (, a firm founded by Wiltshire-based Dr David Snowden, the former Director of Knowledge Management at IBM.

SenseMaker® applies the principles of unstructured, fragmented data, disintermediation (avoiding expert bias) and network intelligence (the wisdom but not the stupidity of crowds). It offers an innovative means of gathering and analysing feedback, which is quite different from conventional methodologies, such as surveys, focus groups or interviews.

Expert bias is minimised through the use of stakeholder-derived ‘signifiers’, which enable the respondents themselves to add layers of meaning to their own narratives.  That enables the findings and conclusions to be defined, not through the interpretation of an intermediary expert, but directly by the users themselves.

A significant advantage of the use of SenseMaker® is its ability to identify “weak signals”. These occur when small clusters of narratives emerge, sharing particular patterns of response.  These weak signals may indicate emergent trends towards beneficial or adverse patterns of activity; their early identification enables you to take early, small‑scale action either to dampen down negative effects or to encourage the development of beneficial outcomes. No built-in time lag.

So SenseMaker® supports abductive reasoning – identifying relationships between factors that would not normally be considered linked, or where relationships are counter-intuitive. And it provides a method of enquiry that can respond quickly to current and ongoing situations.

In summary

Wouldn’t it be good to do something different and get a different result? Well, now you can. Rather than just depending on analysis of what worked in the past, you can gather information in the form of stories, which will point to new and sometimes surprising approaches to a problem.

Here’s a great story to sum up:

A drunk, scrabbling about on the pavement under a streetlight, is approached by a policeman. “What are you doing?” “I’m looking for my keys. I lost them somewhere over there in the dark”. “Well, why are you looking for them here, under the streetlight?” ”I can see better over here.”

We all tend to look where our experience and methods provide the best approach – but they won’t necessarily provide the solutions we need.

December 16, 2010 Posted by | analysis, business, business change management, problem-solving, storytelling | 1 Comment