Recently I attended the second ever Bayesian Young Statisticians’ Meeting (BAYSM`14) in Vienna, which was a really stimulating experience, and something pretty new for me, being my first non-astronomy conference. I won a prize for my talk too, which was pretty sweet!
Swanky BAYSM`14 venue at WU Vienna designed by architect Zaha Hadid
During the two-day overview of theory and a variety of applications by the newest people in the field (read about the highlights over at the blogs of Ewan Cameron and Christian Robert), we heard from a few Keynote Speakers including Chris Holmes. In his talk, he mentioned the world of rational decision makers as envisioned by Leonard J. Savage in his 1954/1972 tome The Foundations of Statistics (adding that on my ‘to read’ list), and went on to describe the application of a loss function and minimax to avoid worst-case scenarios. Minimax isn’t the only approach to decision-making; I think other approaches are more relevant to our behaviour, as I’ll describe later.
“If you lived your life according to minimax, you’d never get out of bed” – C. Holmes
In my fist was a single piece of candy wrapped in bright yellow cellophane, accompanied by an overwhelming feeling of guilt and sadness. I had just seen a little boy grab four: one for him, one each for mum and dad, and one for grandma, presumably back at home. There was a sign on the wall saying “Please Take One”, but it still didn’t feel right.
“We shouldn’t do this. Don’t you see? We’re killing him.”
“Untitled (Portrait of Ross in L.A.)”, Félix González-Torres, 1991. Multicolored candies, individually wrapped in cellophane; ideal weight 175 lb.; installed dimensions variable, approximately 92 x 92 x 92 cm (36 x 36 x 36 in.)
Children are very good at science. They start with broad priors (anything is possible) and learn through collecting data (see picture below) what conclusions are supported best by the evidence. They experiment, make mistakes, and test the variations on a theme. They learn what is dangerous; they learn what is tasty; they learn how to speak.
Kids doing science
Our responses to experiences are very similar to Bayesian reasoning. Take trust as an example. If some dudette off the street – let’s call her Margaret – were to recommend a movie, say Moon, we might not heed her words since we have no reason to think we’d have the same taste in movies as her, but if upon watching Moon we found that we quite enjoyed it – we’d be more likely to rely on Margaret’s next tip, say Wadjda. And if Wadjda was also to our liking, we’d probably trust Margaret’s advice when she suggests Fast & Furious 6 (oops). But that blunder would reduce our confidence in her next recommendation, etc. If we define our experience of the movie in binary terms such as “liked” and “disliked”, the situation resembles the classic coin-toss experiment in which one tries to determine if a coin is biased by flipping it many times.
A recent interview with George Ellis, from the University of Cape Town, had him confront the potential for his religious faith to affect his scientific views. (Part of) his response:
“Many key aspects of life (such as ethics: what is good and what is bad, and aesthetics: what is beautiful and what is ugly) lie outside the domain of scientific inquiry”
appears – at first glance – to concur with Stephen Jay Gould’s vision of non-overlapping magisteria (NOMA). NOMA demands that moral values lie in the domain of religion, a claim heavily criticised by Richard Dawkins in his book, The God Delusion. Not a huge fan of the book, but I’m definitely less of a fan of NOMA. For completeness, according to NOMA, art and beauty then belong to yet another “magisterium”. Ellis argues, rather, that ethics “what is good or bad” is “a philosophical or religious question”. [Edit: to clarify, my qualms are regarding whether this is a religious question, not whether it’s a philosophical question.]
“Scuola di Atene” or “The School of Athens”, fresco by Raphael 1509-1510
The under-representation of women and minorities within certain areas of academia, business and education, particularly in high-paying jobs and managerial roles is well documented. Anti-discrimination laws can only do so much when unconscious bias is rife. This is where affirmative action comes in, controversial as it is, in the form of any of the following:
- scholarships for minorities
- prioritising minorities given equal qualifications
- diversity quotas.
The general idea is to account for the “selection function” that is privilege. In science, data is collected from objects which satisfy certain criteria. Often, the data we really want is convolved with the selection function. An example from astronomy is when you include only galaxy clusters brighter than a certain threshold in your sample; you are more likely to include objects: of higher mass; at low-medium redshift; that have cool-cores. If one wants to know the properties of an otherwise “fair” sample, you must perform some kind of deconvolution or correction (easier said than done). That’s what affirmative action attempts to do (also easier said than done). For the rest of this blog-post, I look at recent attempts to improve the representation of women, in particular, as well as responses to these .
The protestor clashes with policemen. One sprays tear gas not 5 centimetres from his ear, while the other holds him in a tight grip. You have barely begun to acclimatize to the mood of the room — the undercurrent of rage and fear — when suddenly, you are confronted by bodies. Dead. Mostly children. On the street; in coffins; in ambulances; in pools of oil; and, in the World Press Photo of the Year by Swedish Paul Hansen, carried through the streets of Gaza in the arms of grieving uncles.