Modelling the Modeller

Something I’ve struggled with on and off over the 20 years that I have been making mathematical models is explaining those models to others. I have tried to bring people along and develop their understanding. But mainly what I observed was that, some people just got it and others did not.

I have certainly improved my own skill at explaining. This comes down to having streamlined stories and simpler take-home messages. Telling a clearer story certainly improves my audiences’ self-satisfaction, but ultimately some of them get the whole message and others do not.

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Predictive Models

I have mentioned, in the past, that I am a huge fan of Nate Silver. Something which he used to repeat quite frequently, on their podcast, is a sort of predictive modelling tautology:

The best prediction of the future is no change.

Nate Silver [Paraphrasing]

This concept has even got a probabilistic and philosophical theory behind it. All other things being equal, over the long history of time, the next moment from now is not likely to be any different from right now. If we repeat this process often enough then we will be right more often than we are wrong. In essence, we are accepting that there is continuity (and perhaps causality) in our experience of the natural world. Political scientist David Runciman even explored the concept in his recent work of political theory.

I originally took this statement in the manner in which, I hope, it was intended. But behind every great phrase there is often an enticing problem. Thinking over this phrase has led me to realise that there are three basic types of predictive models and each one of them has a fundamentally different purpose and indeed parameterisation.

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Narrative and Decision Models

Since I managed to break my writers’ block on decision making models last week I want to follow-up with a brief discussion on the use of Narrative in presenting decision models to an audience.

In my first article on decision making models I emphasized that a model must serve a purpose. In explaining our models to others I want to highlight that there are two purposes behind explaining a model; the first is to convince the audience; the second is to convey insights into the model. This is the opposite ordering of how scientifically-trained modellers typically think about communicating results, but it is by far-and-away the prioritisation of most top scientific communicators around the world.

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Decision Making Using Models 3.0

This is my third attempt, over the course of 9 months, to write this article. The first attempt foundered on my desire to go into detail on whether explanation or explanability is a good characteristic of a model or not. I confess, this was overly motivated by my personal frustration at having worked with somebody who, “never let the facts get in the way of a good story.” The second attempt got lost in a forest of anecdotes from previous projects. I was trying so hard to knit them together that I failed to make a point. Today, I want to focus on the single most important thing that I have learned about developing decision making models.

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New hosting

I have had to move my website hosting this week. I was hosted by the computer society at National University of Ireland, Galway for many years. Their hardware is now on its last legs and the building it is hosted in has been turned into a field hospital for Covid patients. It was time to move on.

Many thanks to Compsoc at NUI Galway for the years of hosting.

There is no such thing as a manager

I had the opportunity to interview for a senior position at a very big company recently. The entire process was fascinating for what it says about human nature and about large companies. The outcome of the process is unclear at the time of writing this but I am expecting the intrinsic misalignments in the process will lead it into the reeds from which it is unlikely to emerge. I am as close to the perfect candidate for the role that they will ever interview, but the internal parties are not all aligned around the very existence of the position.

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Talks cancelled – Talks online

I had quite a nice spring season of talks planned for 2020. I was invited to deliver a keynote on AI in Healthcare at Biovaria. And, I was one of the invited speakers for the Dynamics of Immune Repertoires conference where I would also have given a workshop, in Dresden. Covid-19 struck and the rest is history.

Emergencies lead to quick changes of plans. Anthony Kelly from AI in Action reached out to me asking me to take part in a special on AI in Healthcare.

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Writing quickly

Every blog I read eventually contains a post about i) navigating the blog, and ii) the author’s policy on writing. Consider this my attempt at the latter.

I have mentioned before that I find that writing benefits my long-term thought processes. It is meditative. I am forced to formalise my thoughts and chase-up loose ends. I have never considered myself to be good at writing – I failed English in school – but I find my confidence growing as I get older.

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The era of the solo contributor is dead

I have been reconnecting with some of my academic friends. We all belong more or less to the same age cohort. In recent weeks, I have been watching them interacting with one another on Twitter and through various other media. They each have achieved considerable degrees of success in their chosen fields – all have tenure at global top-50 ranked institutions. Through my observations, I have come to the realisation that the era of the solo contributor is dead.

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