Abductive reasoning is the best tool for discovery.

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When dealing with human systems (social issues), we are dealing with complexity. These types of problems present us with surprises. There is very little predictability in them. When solving such problems, we are embarking on a path of discovery. Adductive reasoning tool is excellent for discovery and visual SenseMaking.

Abductive reasoning is a process that seeks the ‘best’ explanation for the given situation facing us. It starts with an incomplete set of observations and moves on to the likely possible explanation where the conclusion does not follow with certainty from the premises (evidence).

Deductive and Inductive reasoning are the two main arguments we use in the scientific method. The problem is that they both fail when we have to evaluate unfam iliar conditions, and when we are dealing with variables that are not clear or even unknown, such as in complex adaptive systems (see here). These two types of reasoning are inadequate to deal with discovery (on deduction and induction see here).

The deductive process is imperfect when dealing with uncertainty, but it is good at reviewing theories and refining them. Induction is equally inadequate as it deals with issues whose characteristics are known. Both will put limitations on what we will see.

You must have heard about the experiment the invisible Gorilla. The video confirms: We do not see what is there to be seen, but what we expect to see, are ready to see, or are directed to see. Both deductive and inductive reasoning will create these conditions – not exactly ideal when we are trying to understand something we do not know.

Abduction tool for discoveries

Abduction tool for discovery

Abductive reasoning is a practical approach to explore, construct and make sense of data when the problem is complex, and emergence is a constant.

It begins with an observation or set of observations and then seeks to find the simplest and most likely explanation for the observations

This approach allows us to tell an unfolding story of our understanding as the problem evolves. When faced with new or surprising facts, we then decide how best to address them. We create an initial explanation, then test it against all our observations to see if it works. If a single observation does not support the current explanation, we know that the account is not acceptable. We then proceed by formulating a mini (working) hypothesis and explore further into a broader scope of data. Within each of these cycles, our explanations become broader, more general and abstract.

How abductive reasoning works?

We know that certain things are true (premises)

Then we ask why these things are true? (explanation)

What makes something a reasonable explanation?

  1. The more the explanation fits in with the observations, the better it tends to be. 
  2. A simple explanation is better than a complicated one – Occam’s Razor.

Abductive arguments do not guarantee their conclusions. Such cases are said to be ampliative (adding to that which is already known). They offer an inference to the best explanation.

Their conclusions are the best explanation (more likely) for the premises – they address the why?

A way to challenge an abductive argument is to come up with a better explanation of the data. In complex systems, we do not know what the endpoint is. We can only nudge the system in the direction we want to go. But we need to be prepared to change course if the patterns emerging from the data are very different from what we have seen before because the data is providing clues as to how to move forward.

The process is cyclical

Take 3 minutes to look at a snippet from the TV series ‘House’ to see abduction at work. If you have not seen any of the House series, we recommend you watch at least one episode. You will be able to see the full process including the multiple mini-hypothesis, tests, collection of data, dead ends, restarts and the layering – the twists and turns in the process - working its way towards sound reasoning. All based on observations and no theory from the start of the process.

When a problem deals with ‘humans’, we are dealing with complexity (see here). The challenge will, by definition, have to deal with uncertainty, and this means that abductive reasoning is the only process that will effectively enable us to address the issues of such a system. Induction and deduction will fail us when dealing with social problems – if we were dealing with a complicated problem like building a spaceship, then inductive and deductive reasoning will be the methods to use, not abductive logic.

TV series ‘House’ - it takes a little while to load!

When dealing with abduction, we are focusing on probability – we are in the realm of plausibility, giving us insights and building theory as we go. The patterns that we observe help us understand anomalies and discrepancies we find along the way.

Abduction explanations do not have certainty, and the testing of the working hypothesis is knowledge statements of plausibility, not of confidence as with deduction.

Often one is asked to solve problem ‘A’, but as you progress, you find that the problem is looking like ‘B’. In the beginning, no-one knew that ‘B’ even existed, so what do you do?

Your problem starting point is then a question for which we do not have a clear answer. Often we start with the inductive frame of mind – coming in with a theoretical framework of problem ‘A’ – then, as we observe the patterns, we see that we need to start from scratch. We then need to ‘construct’ an answer to the situation emerging before us.

It is not difficult—all we need is to have a change in mindset.


  • Bamberger, P. A. (2016). The quantitative discovery: what is it and how to get it published. Academy of Management Discoveries, Vol. 2, No. 1, 1-6
  • Isaacson, W. (2007). Einstein: His life and universe. New York: Simon & Schuster.
  • Okhuysen, G. & Behfar, K. (2017). On the “Too Much Theory” Problem: Using Data to Surface Phenomenon, Generate Plausible Explanation, and Offer Insight into Anomalies. Organizational Science.
  • Paavola, S., & Hakkarainen, K. (2005). Three abductive solutions to the Meno Paradox—with instinct, inference, and distributed cognition. Studies in Philosophy and Education, 24(3–4).
  • Peirce, C. S. (1931–1935). Collected papers, edited by C. Hartshorne & P. Weiss. Cambridge, MA, USA: Harvard University Press.
  • Peirce, C. S. (1955a). The criterion of validity in reasoning. In J. Buchler (Ed.), Philosophical writing of Peirce (pp.120–128). New York: Dover.
  • Peirce, C. S. (1955b). Abduction and induction. In J. Buchler (Ed.), Philosophical writing of Peirce (pp. 150–156). New York: Dover.
  • Peirce, C. S. (1992). Reasoning and the Logic of Things. Cambridge, Massachusetts, Harvard University Press.
  • Melrose, R. (1995). The seduction of abduction: Peirce's theory of signs and indeterminacy in language, Journal of Pragmatics, Vol. 23, (5). (pp.493-507).

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