Earlier today, in a feature on the science behind gun policies, I told you about how difficult it is to get reliable answers that pinpoint exactly what helps society and what hurts it. Models — computer algorithms that help us understand how complex systems work — play a role in this, but the ones used for gun research aren't very good yet. In fact, that's true about a lot of sociology fields, write the editors of the Get Stats blog. In general, our knowledge of how society works lags far behind our knowledge of the natural world. Can that ever be fixed? Some scientists think so.
This is a fascinating problem that affects a lot of scientific modeling (in fact, I'll be talking about this in the second part of my series on gun violence research) — the more specific and accurate your predictions, the less reliable they sometimes become. Think about climate science. When you read the IPCC reports, what you see are predictions about what is likely to happen on a global basis, and those predictions come in the form of a range of possible outcomes. Results like that are reliable — i.e, they've matched up with observed changes. But they aren't super accurate — i.e., they don't tell you exactly what will happen, and they generally don't tell you much about what might happen in your city or your state. We have tools that can increase the specificity and accuracy, but those same tools also seem to reduce the reliability of the outcomes. At The Curious Wavefunction, Ashutosh Jogalekar explains the problem in more detail and talks about how it affects scientist's ability to give politicians and the public the kind of absolute, detailed, specific answers they really want.