Decision Mechanics

Insight. Applied.

And if thine data offend Excel, change it

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genetic code

I’ve written numerous articles on issues with spreadsheets and why they shouldn’t play any part in a data analytics pipeline. However, trying to persuade people to stop defaulting to Excel is a thankless task.

So, it was no surprise (but very depressing) to read that geneticists changed their naming conventions to prevent Excel mangling their data.

My dismay was alleviated, partially, by a great quote in the article that originally alerted me to this travesty.

[W]e won’t be putting anything into production that relies on data supplied to us as spreadsheets.

The anti-spreadsheet manifesto in a single sentence.

A passion for decision analysis

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When discussing the value we offer to clients, the focus is often on technical aspects—experience, understanding the psychology, skill with tools, processes, etc.

However, it occurs to me that often much of the value we bring to the table is energy and a fresh set of eyes. In many circumstances decision-makers have decision fatigue. It might be due to the number of decisions they have to make, or how long they have spent mulling over this particular decision.

When you are exhausted you make poor decisions.

Engaging the support of a decision scientist gets you someone who isn’t drained. In fact, it gets you someone who is excited about looking at your decision in detail, and from all angles. This work isn’t a burden to the consultant. It’s their job.

No-one likes to admit they are just plain exhausted/exasperated. But you can often see the weight of the world lifting off the shoulders of decision-makers when we come in to help.

Photo by Ian Schneider

JavaScript for data science?

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Arquero is a data manipulation library written in JavaScript. It’s modelled on the excellent R dyplr library.

JavaScript isn’t known as a data science language. However, it’s probably the best language for data visualisation—and Google’s relentless optimisation of the V8 engine means it has better performance that Python or R.

The JavaScript ecosystem lacks extensive data science libraries, but Arquero coupled with, say, D3.js makes for a capable data visualisation platform.

Photo by Anton Darius

Human brains hate probability

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There was a great article published last month discussing risk communication.

Statistician Regina Nuzzo is quoted as saying

Human brains hate probability, they hate ambiguity, they hate the uncertainty. We’re just not wired to deal with this sort of thing very well.

Science journalist Tara Haelle explains that it’s difficult to separate reporting from influence. She notes that covering polls about willingness to take a vaccine set behavioural norms.

The more people question vaccines, the more people question vaccines. Vaccine hesitancy is contagious.

So, in summary, we find it difficult to think about probabilities, but are easily lead by opinion. This is why it’s increasingly essential for everyone to develop a degree of statistical literacy. With so much data flying around, and so many people commandeering it to promote their own view, we all need to be able to come to our own informed conclusions.

Photo by Naser Tamimi on Unsplash

Decision fatigue

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This Economist has an article this week on the dangers of decision fatigue.

Research suggests that people fall back into making "default" decisions when they are tired. Examples are cited from finance, law and medicine.

One thing that isn’t discussed is the obvious benefits of automated decision-making—computers don’t suffer from exhaustion. The more we can have computers advise decision-makers on routine decisions, the more humans can devote their limited energy to more complex cases.

The article notes that there may be value using software to monitor decisions and nudging people when the pattern of their decision-making changes. This is an interesting approach—have the computer critique the decision-making process rather than the decision itself.

Photo by Luis Villasmil on Unsplash