A Critique of Pure Data: Part 1

Rationalism was a European philosophy popular in the 18th and 19th centuries that emphasized discovering knowledge through the use of pure reason, independent of experience. It rejected the assertion of Empiricism that no knowledge can be deduced a priori. At the center of the dispute was cause and effect–whether effects could ever be determined from causes, whether causes could ever be deduced from effects, or whether they had to be learned through experimentation. Kant, a Rationalist, observed that both positions are necessary to understanding.

Modern science descended from Empiricism, but like Kant is pragmatic, neither accepting nor rejecting either position entirely. Scientists observe nature, deduce models, make predictions using the models, and test the predictions against observations. They describe the assumptions and limits of the models, and refine the models to adapt to new observations.

The old quip says all models are wrong, but some are useful. Scientific models are are useful only to the extent they are demonstrated useful. At their simplest, they are abstract representations of the real world that are simpler and easier to comprehend than the complex phenomena they attempt to explain. They can be intuited from pure thought, or induced from observation. The benefit of models is their simplicity–they are easier to manipulate and analyze than their real-world counterparts.

Models are useful in some situations and not useful in others. Good models are fertile, meaning they apply to several fields of study beyond those originally envisioned. For example, agent models have demonstrated how cities segregate despite widespread tolerance of variation. Colonel Blotto outcomes can be applied to electoral college politics, sports, legal strategies, and screening of candidates.

To be useful, models are predictive, meaning they can infer effects from causes. For example, a model can predict that a given force (i.e. a rocket) applied to a object of a given mass (i.e. a payload) will cause a given amount of acceleration, which causes an increase in velocity over time. Models Screenshot_5_20_13_12_09_PMpredict that clocks in orbit on Earth satellites are slightly faster than those on the surface, resulting from gravitational time dilation predicted by general relativity. Models may be useful in one domain but not appropriate for another. Users have to be aware of the capabilities and their limitations.

Models give us the ability to distinguish causation from correlation. We may correlate schools running equestrian programs with higher academic performance, but we would be unwise to accept causation. We would have to create a model to show how aspects of equestrian activities improve cognitive development, and to discount the relevance of other models that may show causation to other factors. We would then search out data that can confirm or deny the affects of equestrian development on cognition. (It is more likely there are other causal factors acting on both equestrian programs and academic performance.) Whether or not a model can show causal connections to all world phenomena, they can guide us to better questions.

For this discussion we are interested in computation, and that means Alan Turing who, in 1936, devised a Universal Turing Machine (UTM) that is a simple model for a computer. Turing showed the UTM can be used to compute any computable sequence. At the time this conclusion was astonishing. The benefit of UTM lay not in its practicality–it is not a practical device–but in the simplicity of the model. In order to prove a problem is computable, you just need to demonstrate a program in the UTM. Separately, Turing also gave us the Turing Test, an approximate model of intelligence.

Those who use models to make predictions are demonstrated more accurate than experts or non-experts using intuition. This last point is the most important, and is the main reason we develop and use them.

The IT Service Management industry lacks academic rigor because it has never been modeled. Most academic research focuses on mostly vain attempts to measure satisfaction and financial returns. Lacking a model, it is impossible to predict the effect of an “ITIL Implementation Project” on an organization or how changes to the frameworks will affect industry performance. Is ITIL 2011 any better than ITIL V2? We presume it is, but we don’t know.

To be continued…

One thought on “A Critique of Pure Data: Part 1”

Comments are closed.