If you didn’t make it to this year’s BSPS annual conference in Oxford, we’ve teamed up with Philosophy Streaming to record the Presidential Address and the plenary discussions for your listening pleasure!
Endowed by the Latsis Foundation, the Lakatos Award is given to an outstanding contribution to the philosophy of science. Winners are presented with a medal and given the chance to deliver a lecture based on the winning work. To celebrate the 2015 and the 2016 award winners—Thomas Pradeu and Brian Epstein, respectively—they each delivered a lecture at the LSE last week. Introduced by Hasok Chang, Pradeu’s lecture is entitled ‘Why Philosophy in Science? Re-Visiting Immunology and Biological Individuality’ and Epstein’s is ‘Rebuilding the Foundations of the Social Sciences’.
There are many good reasons to want social policy to be based, where possible, on numerical evidence and indicators. If the data clearly shows that placing babies on their back reduces the risk of cot death, this information should guide the advice which midwives give to new parents. On the other hand, not everything that matters can be measured, and not everything that can be measured matters. The care a midwife offers may be better or worse in ways that cannot be captured by statistical indicators. Furthermore, even when we are measuring something that matters, numbers require interpretation and explanation before they can be used to guide action. It is important to know if neo-natal mortality rates are rising or falling, but the proper interpretation of this data may require subtle analysis. To make matters worse, many actors aren’t interested in proper interpretation, but in using the numbers to achieve some other end; as a stick with which to beat the midwifery profession, say.
Models and modelling practices in science were once ignored in philosophy of science; however, in the past fifty years they have been anything but. From Mary Hesse’s pioneering work in the 1960s, to the writing of Ron Giere, Uskali Maki, Nancy Cartwright, Mary Morgan, and Margaret Morrison in the 80s and 90s, to today’s contributions from Michael Weisberg, Mauricio Suarez, Wendy Parker, and too many others to mention, scientific models are now studied left and right. This work is no longer quirky or marginal, and it spans many scientific fields. There are detailed and intricate accounts of what models are, the variety of different models, and the epistemic and social roles played by models. But we would like to suggest that in one respect, more should be done.
In the last few decades, economists have puzzled over the curious phenomenon of so-called ambiguity-averse preferences. You are indifferent between (A) receiving a cash prize if a coin lands heads, and (B) receiving the prize if a coin lands tails. You are also indifferent between (A*) receiving the prize if the Nikkei stock index goes up and (B*) receiving the prize if it goes down; for you are totally ignorant about the Japanese stock market. But you prefer (A) to (A*), and you prefer (B) to (B*). Thus, intuitively, you prefer gambling on the more familiar toss of a coin than on the less familiar stock market.