Symposium at The International Society for History, Philosophy, and Social Studies of Biology (5-10 July 2015, Montreal)
Stuart Firestein: Explicit Failure
Isabella Sarto-Jackson: Intrinsic Hidden Constraints in Data-intensive Biology
Ann-Sophie Barwich: ‘Simply’ Failure or Delayed Success? Mapping Smells in the Brain
Symposium abstract: Science fails. And it fails on a daily basis. Experiments go wrong, measurements do not deliver anticipated results, probes are contaminated, models are considered too simplistic and not representative, and some inappropriately applied techniques lead to false positives. When philosophical debate has dealt with scientific failures, it predominantly focussed on justifying the success of the scientific enterprise in terms of its capacity to represent reality. An often-overlooked characteristic of science—not at least in the quest for more grants and media suited success stories—is that it inevitably must fail to do the job it sets out to do. For scientific research to exceed our initial modelling assumptions and to continuously trump our ever-adjusting experimental limits, things have to go wrong.
We want to investigate different aspects of failure as integral to science. Our interest in failure refers to more than the obscure ‘element of surprise’ or an incentive to do better next time. We think that failures in science are beneficial in their own right. Not only because failures might lead to accidental findings, or because they correct prior assumptions in an experimental set-up. Rather, we think failures guide scientific enquiry on a par with success stories. Failure complements success more than in some proverbial sense: While a success enforces a current research strategy, failure opens up many different alternatives routes broadening our inquiry. By asking why something appears to present a failure, different possibilities are conceived. Each of these possibilities can be investigated by designing constraints under which different features and behaviours of research materials are modelled and simulated. Failure requires creative and flexible reasoning that exceeds a given modelling outset. The importance of failure in science lies in its demand to rethink the constraints that underlie our models and, moreover, our current successes.