In the past I was surprised that there seemed to be no biography of Henri Poincaré. I recently noticed that a biography of him had appeared in 2013. The title is ‘Henri Poincaré. A scientific biography’ and the author is Jeremy Gray. At the moment I have read 390 of the 590 pages. I have learned interesting things from the book but in general I found it rather disappointing. One of the reasons is hinted at by the subtitle ‘A scientific biography’. Compared to what I might have hoped for the book concentrates too much on the science and too little on the man. Perhaps Poincaré kept his private life very much to himself and thus it was not possible to discuss these aspects more but if this is so then I would have found it natural that the book should emphasize this point. I have not noticed anything like that. I also found the discussion of the scientific topics of Poincaré’s work too technical in many places. I would have preferred a presentation of the essential ideas and their significance on a higher level. There are other biographies of great mathematicians which made a better impression on me. I am thinking of the biography of Hilbert by Constance Reid and even of the slim volumes (100 pages each) on Gauss and Klein written in East Germany.

On important discovery of Poincaré was chaos. He discovered it in the context of his work on celestial mechanics and indeed that work was closely connected to his founding the subject of dynamical systems as a new way of approaching ordinary differential equations, emphasizing qualitative and geometric properties in contrast to the combination of complex analysis and algebra which had dominated the subject up to that point. The existence of chaos places limits on predictability and it is remarkable that these do not affect our ability to do science more than they do. For instance it is known that there are chaotic phenomena in the motion of objects belonging to the solar system. This nevertheless does not prevent us from computing the trajectories of the planets and those of space probes sent to the other end of the solar system with high accuracy. These space probes do have control systems which can make small corrections but I nevertheless find it remarkable how much can be computed a priori, although the system as a whole includes chaos.

This issue is part of a bigger question. When we try to obtain a scientific understanding of certain phenomena we are forced to neglect many effects. This is in particular true when setting up a mathematical model. If I model something using ODE then I am, in particular, neglecting spatial effects (which would require partial differential equations) and the fact that often the aim is not to model one particular object but a population of similar objects and I neglect the variation between these objects which I do not have under control and for whose description a stochastic model would be necessary. And of course quantum phenomena are very often neglected. Here I will not try to address these wider issues but I will concentrate on the following more specific question. Suppose I have a system of ODE which is a good description of the real-world situation I want to describe. The evolution of solutions of this system is uniquely determined by initial data. There remains the problem of sensitive dependence on initial data. To be able to make a prediction I would like to know that if I make a small change in the initial data the change in some predicted quantity should be small. What ‘small’ means in practice is fixed by the application. A concrete example is the weather forecast whose essential limits are illustrated mathematically by the Lorenz system, which is one of the icons of chaos. Here the effective limit is a quantitative one: we can get a reasonable weather forecast for a couple of days but not more. More importantly, this time limit is not set by our technology (amount of observational data collected, size of the computer used, sophistication of the numerical programs used) but by the system itself. This time limit will not be relaxed at any time in the future. Thus one way of getting around the effects of chaos is just to restrict the questions we ask by limits on the time scales involved.

Another aspect of this question is that even when we are in a regime where a system of ODE is fully chaotic there will be some aspects of its behaviour which will be predictable. This is why is is possible to talk of ‘chaos theory’- I know too little about this subject to say more about it here. One thing I find intriguing is the question of model reduction. Often it is the case that starting from a system of ODE describing something we can reduce it to an effective model with less variables which still includes essential aspects of the behaviour. If the dimension of the reduced model is one or two then chaos is lost. If there was chaos in the original model how can this be? Has there been some kind of effective averaging? Or have we restricted to a regime (subset of phase space) where chaos is absent? Are the questions we tend to study somehow restricted to chaos-free regions? If the systems being modelled are biological is the prevalence of chaos influenced by the fact that biological systems have evolved? I have seen statements to the effect that biological systems are often ‘on the edge of chaos’, whatever that means.

This post contains many questions and few answers. I just felt the need to bring them up.

March 7, 2017 at 5:05 pm |

Don’t the limits of predictabilty coincide with the limits of constructive mathematics?

March 7, 2017 at 7:05 pm |

This is not what I was thinking of. In fact I do not know what ‘constructive mathematics’ means.