Point Lookout: a free weekly publication of Chaco Canyon Consulting
Volume 21, Issue 20;   May 19, 2021:

Pre-Decision Discussions: Reasoning

by

When we meet to resolve issues related to upcoming decisions, we sometimes rely on reasoning to help find solutions. Contributions to these discussions generally use mixtures of deductive, inductive, and abductive reasoning. How do they differ, and what are their strengths and risks?
Sherlock Holmes and Doctor Watson

Sherlock Holmes and Doctor Watson, in an illustration by Sidney Paget, captioned "Holmes gave me a sketch of the events." The illustration was originally published in 1892 in The Strand magazine to accompany a story called The Adventure of Silver Blaze by Sir Arthur Conan Doyle. It is in this story that the following dialog occurs:

Gregory (Scotland Yard detective): "Is there any other point to which you would wish to draw my attention?"

Holmes: "To the curious incident of the dog in the night-time."

Gregory: "The dog did nothing in the night-time."

Holmes: "That was the curious incident."

From this, Holmes concludes that the dog's master was the villain. This is an example of what I here call abductive reasoning.

Original book illustration, courtesy Wikimedia.

In Part I and Part II of this exploration of contributions to pre-decision discussions, I examined how we use facts and emotions in discussion contributions. We can think of facts and emotions as the raw materials of the discussion. Reasoning is a tool for combining the raw materials into a comprehensible structure. So in this part I examine how the three different kinds of reasoning can build new components that help us find paths to final decisions.

Three forms of reasoning are available for use in pre-decision discussions. Most uses of reasoning in organizational settings are informal, but even in informal reasoning there are some refining attributes. Those attributes distinguish the three forms of reasoning.

Deductive reasoning
We use deductive reasoning to proceed from premises to conclusions in a sequence of steps, each step following from its predecessors by implication. Deductive reasoning provides strong validation for its conclusion. For example, if we know that testing is the only way to be certain that our software application works as intended, and we also know that we haven't tested our software application in a given release of an operating system, then it follows that we don't know for certain whether our software application works in that release of the operating system. The software might work, or it might not work, but we can be certain that we don't know for certain.
Deductive reasoning based on validated facts and evidence provides a validated conclusion. That is its appeal. But rarely do we have validated facts and evidence, because we rarely have the time and resources required for validating those facts and evidence.
Inductive reasoning
Inductive reasoning also proceeds from premises to a conclusion, but it establishes the conclusion as a generalization of the premises. When the basis for the generalization is data regarding large numbers of examples, the generalization is statistical. When the basis for the generalization is a limited number of cases deemed typical of a large class, the generalization is anecdotal. Neither kind of generalization leads with certainty to a valid conclusion. For this reason, inductive reasoning is less likely to provide strong validation for the conclusion.
But inductive reasoning is still useful. Continuing with our software example, suppose we know that the operating system release in question is a minor update of the previous release, and that our software worked well in four previous minor updates. Reasoning inductively, we then have reason to believe that the software will work in this new minor release. It might work, or it might not work. We don't know for certain, but based on past experience, we believe there is a strong chance that the software will work.
Abductive reasoning
Abductive reasoning is neither deductive nor inductive, but, in a weird way, it can be both. Distinguishing abductive reasoning and deductive reasoning can be difficult, because what Sherlock Holmes (Arthur Conan Doyle actually) calls deductive is in the modern terminology, abductive.
When we're Reasoning is a tool for combining the
raw materials of a discussion — facts
and emotion — into a comprehensible
structure that helps us
find paths to decisions
reasoning abductively, we begin by gathering data about the situation. We then formulate an explanation. That is, we apply principles that we believe pertain to that situation to provide an explanation for what we observe about the situation.
To illustrate, consider yet another extension of the software example. Suppose we actually test our software on 22 machines running the minor update of the operating system. And further suppose that on three of the 22 machines, the software fails. Examining all 22 machines, we notice that the three that failed were also running an old version of a popular word processing program. The machines on which our software operated correctly were running an updated version of that word processing program. Using abductive reasoning we suggest that an unanticipated interaction could be occurring between our software and the word processor. Engineers then investigate further, and they do discover the problem and install a repair.

Using deductive reasoning, we find a conclusion by starting with premises, and creating a chain of implications connecting them to the conclusion. Using inductive reasoning we create a generalization from the premises to reach a highly plausible conclusion. And using abductive reasoning we create an explanation that fits all available observations. Noticing the kinds of reasoning in use in your organization can help you reach more solid conclusions more rapidly. First in this series  Go to top Top  Next issue: Even  Next Issue

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