I saw it on a computer so it must be true

The nature of proof

This is an interesting thread. It may also be a hopeful sign of increasing maturity within the ITSM world.

It is understandable to want proof; empirical evidence based on controlled environments and observations. But, as Karl Popper taught us, searching for empirical proof is a trap. Without proper methods, empirical observations will lead you astray. For example:

- The problem of induction: how do we know that what we have observed suffices enough to figure out other properties? No matter how many white swans we count, as Hume so cleverly put it, it does not prove the non-existence of black swans (they were later discovered in Australia).

- Confirmation bias/errors: we focus on pre-selected segments of the observed and generalize it to the unobserved. "If org performance improves after an ITIL implementation, then ITIL raises org performance."

- The narrative fallacy: we fool ourselves with false causality because it makes sense and fills our thirst for a good story, as Taleb explained. "The ten most successful CIOs implemented ITIL. Ergo, if you want to be a successful CIO, implement ITIL."

…and there are several others.

Why don't managers effectively learn from experience? Why do they keep falling into traps like short-term thinking, firefighting and quality erosion? A key part of the answer lies in how our minds interpret data from complex systems. For example, there is the human tendency to assume cause and effect are closely related in time and space. We attribute events such as customer complaints, low org performance, or over-budget projects to poor tools, poor work habits or attitudes of employees, rather than the system itself. We falsely attribute to dispositional rather than situational factors. It more often turns out to be not the case. [See your thread on process improvement.]

You are correct on models, though. By definition, they are all wrong; a subset of the real deal. But every good model does one thing: it predicts. Popper also taught us the difference between science and pseudo-science: Falsifiability. The idea of Falsifiability claims that a model or hypothesis is scientific if and only if it has the potential to be refuted by some possible observation. In other words, it has to leave itself open to being tested and being proven wrong. It must take a risk. This is how science, or any field for that matter, progresses. Good models are eventually proven wrong and new ones built. Bad models are never proven wrong.

If a model takes no risks at all, because it is compatible with every possible observation, then it is pseudo-science. Astronomy is science; Astrology is pseudo-science. Many (aw heck, most) of the IT and ITSM models I see floating around are pseudo-science.

When Mendeleev crafted the periodic table of elements, his model opened itself up to falsifiability by predicting the existence and properties of several new elements. His model didn't "prove" anything but when its predictions came true it passed from pseudo-science to science.

Asserting a model is highly useful, pragmatic or that others agree, of course, is not proof - only forms of confirmation bias. When the assertions are particularly adamant, I sometimes call it snake oil. At one time many people agreed that the world was flat - a model which failed its falsifiability tests.

So, if someone is putting necks on the line with models that take risks by predicting something that can be proven wrong, I'd call that science.

If instead, as so often happens in IT, models are put forth that don't assert anything more than "it's correct” or “it's good for you", then that, my friend, is pseudo-science.