Farming for ideas.
July 28th, 2009If I don’t get tempted back into finance, I’d like to look at mathematically modelling aspects of human behaviour. Â The point here isn’t to predict what individuals will do, or even necessarily to get accurate quantitative predictions about aggregate behaviour; more to develop models that capture qualitative features of human and political behaviour and to get some idea about the stability of those features. Â Are asset price bubbles a stable attractor? Â How well is the number of political parties in a system predicted by the electoral system?
This is kind of what economists do already, although I expect that one can probably throw a lot more mathematical sophistication at the problem. I’m not the first person to think about attempting this and, if I was a better researcher, I’d probably be reading the report from this conference, rather than randomly pontificating about my own ideas.  But I’m not, so here are some of them …
After listening to some public health people talking about BSE and CJD I began to think about whether one could get an idea for when cattle ranchers would comply with BSE reporting rules and when they would “shoot, shovel and shut up“?  What are the effects of the similar incentives for bureaucrats / political decision makers to cover things up?  Can one use these models to estimate the maximum possible levels of under-reporting of BSE and does that effect predictions about the level of a possible CJD epidemic in Canada?
Mathematically, we expect a “phase transition” – the first farmer who reports a mad cow in a province will probably never farm again. Â Regardless of the level of financial compensation available, this is a pretty high cost. Â But the thousandth farmer to report will probably not make the local newspaper, and so can cull, and rebuild, their herd with little stigma. Â So once reporting hits a certain level, one would expect a fairly sudden switch from a “tendency not to report” to a “generally content to report” state. Â So two regimes with the same levels of epidemic could, in fact, have wildly differing reported levels of epidemic depending on whether the phase transition had taken place. Â The point at which this happens will be parameterised by not only the level of an epidemic and the amount of compensation available (and level of punishment for non-reporting) but also by farmers’ perceptions of the future willingness of politicians and bureaucrats to tolerate non-reporting (and the interest of the media in exposing it) and their perception of the future likelihood of other farmers failing to report. Â These two last factors will be affected by the current decisions made by farmers, so we get an interesting interaction between current events and uncertain future events. Â We can capture this kind of thing using similar maths to that used to model derivative financial instruments (I know that’s not a great advertisement right at the moment, but they are still useful tools!)
Similar dynamics might be exhibited in the behaviour of politicians and regulators. When perceptions of the levels of an epidemic (or any nasty and growing event – housing bubble?) are low, the politician who advocates thorough monitoring and regulation will likely be looked on unfavourably.  But at higher perceived levels – as the interested parties expand beyond the farmers to the general population, thoroughness and action are likely to be rewarded.  The politician who acts too soon will likely be ruined before they are vindicated; the politician who acts too late will be blamed for not acting soon enough.  Politically, I guess that the right time to move is probably just before everyone else does, which, mathematically, is a pretty unstable thing to predict.  However, one might be able to say something about the statistical distribution of behaviours.  I wonder if one could validate this kind of prediction against the behaviours exhibited by different governments in response to swine flu?




