The science of uncertainty

What to do with the complexity sciences? On the one hand they seem to offer a good resource for thinking about organisations, since our experience of daily life at work is that things rarely go in a straight line, are unlikely to turn out as we predicted, are disrupted by things coming out of left field. Rather than rely on models which demonstrate if-then causality, or schemata which disaggregate into parts and whole and assume an unproblematic relationship between them, tales of slime mould, ant colonies and computer models seem initially to offer more reality convergent explanations. On the other hand, what sense can we really make of, say, computer models given that they themselves are designed by humans and still abstract from a very complex background and rely on very strong assumptions? As we have discussed in previous posts, examples in the natural world, or from mathematics are often taken up simplistically by organisational theorists, who put forward ideas that managers should set simple rules for others to follow, or like the computer programmer, should somehow ‘create the conditions for emergence to happen’.

There is a pronounced queasiness on the part of scholars who would like management to be a science about turning to the social sciences for insight for fear of being thought relativistic or insufficiently rigorous. In defining themselves in opposition to what they see as less certain and precise methods they can take up quite extreme positions and claim that they are completely separate from the social situations they seek to research. They inhabit a purely Platonic world where phenomena are either true or false, they are either substantiated by evidence or they are not: if the methods are not replicable and generalisable then they are not scientific methods. The way that time, context, relationships of power and values condition what it is possible to say and do are inadmissible variables. Such arguments are cloaked in the rhetoric of high and noble science, where scientific method is derived mostly from the discipline of physics, which seeks timeless, unifying rules. Equally, social scientists can sometimes take up a polarised view against what they see as the shortcomings of postivism, such as the eminent sociologist Bent Flyvbjorg who has argued that natural sciences can have nothing to say about the social, precisely for the reasons set out above, that they exclude themes of time, power, values and interdependence.

It was part of the pragmatic philosophical project to work to reconcile the sciences of the natural and social worlds, to find a more scientific way of describing social phenomena, but also of socialising the scientific. In John Dewey’s terms, it required bringing together the object with the experience of the object. So, too, Norbert Elias was concerned to find systematic and rigorous methods which more adequately described social phenomena of which we are part, and which are never at rest. He argued that natural science methods, simplistically understood, reduce process and try to describe phenomena statically – this is an inadequate method for having anything meaningful to say about dynamic, interdependent, adaptive social processes, he claimed.

What is striking about some scholars from either natural scientific or social scientific backgrounds is how much they can find in common to say about complex social phenomena, but from very different premises. So some researchers, who would strongly consider themselves natural scientists are nonetheless developing their thinking in ways that challenges some of the usual certainties of their own tradition: that the truth is ‘out there’ to be discovered and that it is possible to be unchanged by the phenomenon one is observing, or that the phenomenon itself is unchanging.

Take for example, two social scientists, Peter Allen from Cranfield and Peter Hedstrom from Oxford University, who make no secret of the fact that they cleave to a natural scientific position. If a phenomenon cannot be described mathematically, they claim, then science can have nothing substantive to say about it. And yet, in modelling complex social processes mathematically they come to some very similar conclusions to scholars working in a more clearly social scientific tradition which is more sceptical of categorical truth claims. What they begin to converge on is a science of uncertainty, where time, diversity and interpretation are key to understanding complex social phenomena.

In this post we will discuss Peter Allen’s ideas; in the next we will take up Peter Hedstrom and thereafter, in a third post we will compare and contrast what we have explored in these two posts with the findings of scholars critical of a more purist natural scientific position.

Peter Allen has developed a variety of different computer models to pose questions about optimal strategy development for a fishing fleet wishing to maximise fish catches while not unsustainably depleting global fish stocks. In order to demonstrate his initial premise, that the conceptual framework of traditional science based on mechanical and equilibrium models are inadequate for describing human processes, he gradually makes his models more and more complex. Most modelling, he argues, is based on three assumptions: that microscopic events occur at an average rate, that individuals are of a given type and have a normal distribution around the average type, and that the system moves towards equilibrium. He develops his models through four stages of complexification from the initial equilibrium model, which contains the three assumptions above. Next at stage two he develops non-linear dynamical models, which contain the first two assumptions, but which are not assumed to move towards equilibrium. At stage three, self-organising systems, the model assumes average individuals but does not average microscopic events, nor does it assume the system will move towards equilibrium. Finally, in a model he terms evolutionary complex systems, neither the individuals nor the micro-interactions are assumed to be average, nor does the system move towards equilibrium.

Allen reminds us that these models are based on very strong assumptions, and still abstract from a much more complex reality, but nonetheless makes the case that his fourth and most complex model is able to tell us things that we could not know about current reality and future possibilities than we could learn from more reductive models. For example, he begins to problematise our current understandings about the need for an ‘optimal’ strategy. Indeed he suggests that there is no single optimal strategy for long. Rather, what the fourth model demonstrates is a series of particular moments in an unending imperfect learning process as different diverse players adapt to each other, and to the environment in which they find themselves acting. Time becomes an important factor in reading from the model, since what looks like a good strategy now may not become so after a number of iterations where everyone is adapting to what everyone else is doing. The patterning of interaction between agents, and the agents and their environment, is key to understanding what it is that is emerging:

… the landscape of possible advantage itself is produced by the actors in interaction, and that the detailed history of the exploration process itself affects the outcome. Paradoxically, uncertainty is therefore inevitable, and we must face this. Long term success is not just about improving performance with respect to the external of a complex society. The “payoff” of any action for an individual cannot be stated in absolute terms because it depends on what other individuals are doing. Strategies are interdependent…Innovation and change occur because of diversity, non-average individuals with their bizarre initiatives, and whenever this leads to an exploration into an area where positive feedback outweighs negative, then growth will occur. Value is assigned afterwards.’

The future, Allen argues, is not contained in the present, and strategy is not a ‘problem’ to be solved: there are a variety of possible choices and each choice will lead to its own consequences, its own strengths and weaknesses and it own successes and failures. Very little of this can be known beforehand and we will never be in a position to have enough information for a perfect understanding of what we are doing and what we need to be doing.

The threads of Allen’s argument that I find most interesting are that he has introduced paradox, uncertainty, time, and value as integral to our understanding of complex human experience. These would not usually be considered conventional natural science territory.


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