Only in the section on crowding/traffic models is it at all convincing. Here the models do seem to match individual as well as subsequent aggregate behaviour (for example where static traffic will form on motorways). Here, where human behaviour is constrained to extremely simple behaviours and actions, it is credible that a simple model may suffice to capture the consequences of the actors’ interaction. This chapter actually presents some evidence of validation that goes beyond ‘data fitting’ or analogical credibility � evidence that is lacking elsewhere in the book.
If sociophysics makes the same mistake of lack of relevance that economics did, it will be similarly unsuccessful at illuminating social phenomena. Physics-type models may be impressive in terms of their formal content and analysis, but vague social interpretations are insufficient to ensure that they are about social phenomena. Assumptions and modelling styles that physicists use will tend to exacerbate this problem if they are not adapted to the different demands of social phenomena. The result could be yet another retreat away from the target phenomena into the exploration of the formal properties of models � the ‘inward turn’ that has occurred in many fields where empirical and/or practical success has not materialised (e.g. AI or Economics).
This book is by no means unique in making these kinds of conflation � they are rife within the world of social simulation. The culture of physics is a complex of different attitudes, norms, procedures, tools, bodies of knowledge and social structures that are extremely effective at producing useful knowledge in some domains � it is not for nothing that physists have gained status within our society. However when this culture is transported into new domains, such as that of modelling social phenomena, the culture does not travel uniformly. Thus we have seen (and Critical Mass documents) an influx of simple, physics-style simulation models into sociology but they have arrived without the usual physists’ insistence that models predict unseen data. It is part of the culture of physics to aspire to the simplest possible model of phenomena but a model which only acted as a sort of vague analogy with respect to its phenomena would get short shrift in traditional physics domains. Yet frequently one reads social simulation work which takes the form of physics-style models and yet uses only vague, hand-waving justifications to justify its relevance (and, at best, a rough fitting of known, aggregate data). Models need to be constrained by the subject matter they are supposed to be about � there are two main ways of doing this: by ensuring the model is designed to behave as we know it should do (typically the parts of the model); and by checking the resulting behaviour against corresponding observed behaviour (often in aggregate). Sociophysics models tend to avoid either: they impose over-simple behaviour onto the design and don’t validate strongly against unseen data. Thus whilst such models may have interesting behaviour there is little reason to suppose that they do in fact represent observed social behaviour.
1 For the sake of brevity I will lump all the models described in this book as sociophysics, since they share most of the characteristics of physics models. However, it must be said that most of the participants mentioned in the book would not see themselves or their models in this regard, and indeed be hostile to such a clustering. I do not mean to insult them but are simply using this review to point out and discuss certain approaches and styles of modelling that have been converged upon from many directions. Having said this, I have no doubt that this kind of modelling has been inspired and influenced by the success and methods of physics.
In Isaac Asimov’s Foundation science fiction trilogy there is a genius called Harry Seldon who has invented a science called “Psycho-history” . As it says (Asimov 1962, page 7):
“Psycho-history dealt not with man, but with man-masses. It was the science of mobs; mobs in their billions . The reaction of one man could be forecast by no known mathematics; the reaction of a billion is something else again.”
In a sense the whole book is an elaboration of the argument summarised on page 568: “Society is complex but that does not place it beyond our ken. As we have seen complexity of form and organisation can arise from simple underlying principles if they are followed simultaneously by a great number of individuals.” This is an argument that has been repeatedly made in the sciences of complexity (and particularly in ALife): that complex behaviour can result from the interaction of lots of simple parts. This is now well established, but the implied corollary that the complexity we observe is a result of lots of simple interactions (or that it is useful to model this in this way) does not, of course, follow. Grounds for hope does not make it a reality.
These are in sharp contrast with the situation in the social sciences which is ‘top-heavy’ in terms of abstract paradigms/frameworks and theories and relatively lacking in data collection and descriptive modelling. Sociophysics consists of a movement from traditional theoretical physics into the domain of the social sciences, what would be more productive is the import of some of the more mundane, but fundamental, aspects of physics.
This sort of idea has stalked social science for a long time � that, when looked at en masse a “physics of social phenomena” (or sociophysics  ) is possible. It holds out the prospect of the social sciences becoming a “real” science: avoiding being mired in the context-dependent detail of complex human behaviour, and aspiring to generality and predictive power. Critical Mass narrates the story of this approach.
ASIMOV I (1962) Foundation and Empire. London: Panther Books.
Critical mass reviews Critical Mass: How One Thing Leads to Another Reviewed by Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University In Isaac Asimov’s