Science - March 18, 2010

Holism 2.0: Towards the Defragmentation of Science

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Since prehistoric times human beings have tried to explain and control their overwhelmingly complex and threatening environment with tales, beliefs and rituals. Only relatively recent is the development of science, an approach that is somehow different from anything we did before, and so successful in solving problems that many people have gained a deep faith in our ability to 'carve the future'.

Dies Natalis lecture Wageningen Universiteit 2010, Marten Scheffer

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Not everyone, however, feels equally confident in this extrapolation of scientific victory. Most of the success comes from branches of science that study only tiny subsets of the complex world, and the disciplines that attempt to study larger chunks, such as ecology, sociology and climatology seem to make much less progress, although these deal with questions that really matter much to us in the long run.

Human impact on ecosystems and the climate system has increased sharply over the past century, and there is widespread concern about the long-term sustainability of our life-style. However, despite a good understanding of many mechanisms, our capacity to predict the behaviour of complex systems is rather limited. For instance, we know the effect of CO 2 on the radiation balance but are poorly equipped to predict the expected response of the climate system. Similarly, while we know much about the reproductive biology of corals, we do not understand what determines the resilience of entire reef systems to the effects of fishing or climate change. On a smaller scale we understand much of the operation of neurons, but have been unable to understand and cure complex brain disorders such as migraine that severely impair the functioning of a large fraction of even the wealthiest population.

Clearly there is a pattern: We understand small parts of the world increasingly well, but have lost track of the whole. We focus on well definable problems, but miss out on the big picture. In fact it may well be that the same focus that leads to excellent science for the details, blinds us to the numerous connections and feedbacks that may ultimately determine the resilience and stability of systems as a whole.

It is widely believed that connecting our different branches of science through interdisciplinary cooperation should help to find solutions to large complex problems. However, progress is notoriously slow. I think, overcoming our inability to understand the mechanisms that govern dynamics of complex systems such as the climate, ecosystems, society or the human body is a central challenge to science today. In my view, progress on this front will require a radically different way of doing science. Of course, I do not mean to say the science we did so far is wrong. Obviously it has been tremendously powerful. However, I would consider my plea a success if it leaves you convinced that we do need to add a fundamentally different approach if we want to tackle the huge issues ahead. An approach that helps us understand systems as a whole, without falling into the trap of becoming vague. The best thing I can hope for is that it will leave you inspired to think about the question how such a 'Holism 2.0' could be created.

First of all, I should stress that I do not claim to have the answer to that question. Nonetheless, I have given it some thought, and I would like to share some preliminary ideas with you. In brief, I will propose that two things might help a lot to gain a better understanding of complex systems as a whole. Firstly, I think it would help if we could connect the mathematical theory of dynamical systems to real world problems, and secondly I believe that we should aim for a radical multi-disciplinarity involving not only a wide range of sciences, but also thinkers from outside science. I will use the remainder of my time today to clarify those two points.


Unlashing the Power of Dynamical Systems Theory
It is well known that choosing the right problem is a key aspect of good progress in science. We teach our students that if they want to become a successful scientist they should focus on something that is neither too simple nor too complex. Indeed this attitude has produced much of scientific progress as we know it. However, the focus on tractable problems also implies that we systematically leave out feedbacks between our object of study and other parts of the larger complex system to which it invariably belongs. Yet, such feedbacks now appear crucial in explaining phenomena ranging from chaotic unpredictability of the weather and ecological communities to abrupt climate change, the crash of stock markets, or the onset of migraine or epileptic seizures.

Such effects of feedbacks on the behaviour of systems is also a central focus of dynamical systems theory. Rather than referring to any particular part of the world, this theory addresses what seems to be another world: a mathematical world of strange attractors, catastrophe folds and metastable states where torus destruction and homoclinic bifurcations are everyday events. So disparate is the language and notation in this discipline that it is hard to imagine that it has anything to do with reality as we know it. Indeed, it deals with a kind of mirror world, but in fact, underlying structures of the real world show up in this mirror world with a beautiful clarity that can never be seen in reality.

The generality of this type of theory makes it a potentially ideal bridge of communication between scientific disciplines that are usually not in touch. To illustrate this let me highlight an exciting recent finding: the existence of generic early warning for critical transitions. Critical transitions are shifts to a contrasting state that happen when a system reaches a tipping point. The existence of tipping points is intuitively straightforward in simple physical examples. For instance it is what can happen to a chair if one leans backwards too far. The idea that large complex systems would have the same properties may seem counter-intuitive. However, evidence is rapidly accumulating that tipping points occur in a wide range of ecosystems and also in the climate and societies. A central problem if it comes to managing critical transitions at tipping points is their unpredictability. Models of large and complex systems such as ecosystems or the climate are unlikely to become reliable enough over the coming decades to predict tipping points accurately.
As it is so hard to predict tipping points, it is of great applied interest that we found a class of generic indicators that work for all kinds of tipping points, and therefore may be used even in the common situation that we have little understanding of how the system actually works. This may sound strange, but the explanation for this universality is that on an abstract theoretical level, critical transitions in a wide range of complex systems are in fact quite related. A key property of systems approaching a tipping point is their recovery rate upon small perturbations tends to zero. This insight has put us on the track of novel ways to predict transitions in ecosystems, and the climate, but also opened up a new research line aimed at predicting the onset of migraine attacks.

Other examples of phenomena that can arise from feedbacks in a way that dynamical systems theory can reveal, are tipping points in the climate, ecosystems or the brain; self-organized patterns in the skin of animals, desert vegetation or evolution; cycles in menstruation, forest fires or corruption; and chaos in plankton, the weather or financial markets.


Catalyzing Radical Multi-Disciplinarity
While Dynamical Systems Theory may highlight fundamental laws that rule a wide range of systems, it also forces us to work with grossly simplified models of reality. It may broaden our view in some ways, but, at the same time, it provides yet another focus that can prevent us from seeing the big picture in other ways. It all boils down to a classical dilemma in scientific thinking. As soon as we have a theory, it inevitably tends to cause myopia. As Chamberlin phrased it more than a century ago, we should be alert to "the imminent danger of an unconscious selection and of a magnifying of phenomena that fall into harmony with the theory and support it and an unconscious neglect of phenomena that fail of coincidence".
I sometimes have the impression that scientists in those days were much more aware of this danger than we are now. Rereading Darwin, on the deck of a sailing ship that followed the famous voyage of the Beagle, I got impressed again by his very careful interpretation of facts, in view of different possible explanatory theories. What makes scientific style so different today? Darwin was probably under less stress to publish and perhaps therefore less tempted to 'stretch his results' in one particular direction than the current generation. However, he was certainly also much broader, having read all the main scientific works on climate, geology and biology, while also having a keen interest in human societies.

Science has grown exponentially since those days, and perhaps as a consequence we cannot cover such a wide range of disciplines in one person as Darwin did. However, I think we actually do not need that to develop broad views of complex systems. In fact, I will argue we can do it much better by joining forces between scientists from different disciplines, and equally importantly, by connecting to a broader group of thinkers, that is 'beyond science'.
You may find this suggestion radical. Indeed, it clinches with the idea that to solve the most complex problems, we need the best of the best scientists. However, let me confront you to a remarkable recent scientific result. In a nutshell the finding is that a group of randomly chosen people will be better at solving a complex problem than an equally large group of people that are individually selected as the best problem solvers. The reason is that those very good problem solvers tend to be similar in their views. Therefore adding a random person will add more to the combined expertise than adding another excellent solver. This eye-opener highlights a fundamental issue when it comes to broadening our science: if we wish to optimise our capacity to solve complex problems, we should be aware of a trade-off between individual excellence and diversity.

Giving this some more thought it may after all seem quite logical. But why then do we repeatedly turn to the same few experts for advice; why then is interdisciplinary cooperation the exception rather than the rule; and why then are we not inclined to take time to ponder the opinions and ideas suggested by the numerous laymen that claim to have something useful to contribute? Why does science remain dominated by a small group of the-best-of-the-best with relatively closed networks of cooperators?  The answer, most likely, is that there is simply a strong selection for excellence, combined with various self-reinforcing mechanisms. The bottom line is that science is rather narrowly focusing on work around a few dominant theories. Combined with the tendency to select well-defined problems dealing with small parts of complex systems this is obviously not a good precondition for developing novel visions of the functioning of complex systems as a whole.

So, how could we try to catalyze a change towards a science that is more open minded; a science that is less limited by dominant theories; a science that helps to see the bigger picture of the forces that regulate change and stability in complex systems ranging from the human body to society, ecosystems and the climate?

Certainly, Wageningen University has a tremendous potential if it comes to connecting the countless islands of knowledge, and in doing so mapping the sea of ignorance that may separate them and formulate novel broader visions. We already have a good track record when it comes to 'systems-thinking' and we like to do science that has an impact on society. Perhaps most importantly, we have people covering a wide range of disciplines, age-classes and cultures. For instance, our small university harbours over 100 nationalities.

Unfortunately, we are far from optimally using this fantastic potential though. Just as most Dutch universities we have an old-fashioned, strongly hierarchic system. This may seem an efficient way of making optimal use of the excellent leaders we have amongst us. However, studies reveal that notorious scientific breakthroughs (measured as Nobel prizes, Crawford prizes etc.) come significantly more from institutes with very flat hierarchies. Not only are we hierarchical, we also tend to have a rather homogeneous group of western males at the top of the hierarchy. Indeed, less than 10% of our full professors are female, and the colourful range of cultures we harbour is largely confined to our students. Most likely we could greatly enhance our overall quality by making our staff more diverse. However, a combination of mechanisms makes the process of improving the balance tantalizingly slow.

Nonetheless, I believe that we can create spectacular change independently of the slow turn-over of staff if we daringly break grounds in the way we do some things. Fortunately for you I am not in charge. My radicalism would certainly play havoc with essential systems we have in place. Nonetheless, I cannot resist sharing two wild ideas that might bring us closer to the Holism 2.0 I have in mind.    

Firstly, I suggest we stop attempting to promoting particular behaviour through a monetary stimulus. It may seem obvious that differentiated funding to reward cooperation and productivity would be a good way to create the incentives we want. However, it may well have an overall negative effect on the cooperation we need so urgently for our new science. Elegant experiments unequivocally demonstrate that conscious or unconscious preoccupation with money makes people more reluctant to offer help and also less inclined to ask for help. It may seem counter-intuitive, but incentive systems based on monetary rewards may thus well be counter-productive. The EU, NWO, but also Wageningen University have highly complex systems for targeted rewarding. Eliminating those systems may seem like a silly thing to do, but in addition to saving an impressive amount of time and money, it might well make scientists more cooperative, which is essential if we are serious about defragmenting science.

My second suggestion for defragmenting science is invoked by recent experiments in scientific institutions around the world to catalyze cooperation across a wider array of disciplines. The key to success appears to be social-engineering. While this may sound a bit manipulative, there can hardly be objections to the use food and drinks to lure people out of their offices and mix. In fact, it is not a very novel idea. Our word for scientific symposium, comes from the 'Symposion' that ancient Greeks had: A place to enjoy drinks and good food. If we build a revolutionary Symposion in the middle of our campus, I think we may create a powerful reactor where people across disciplines, cultures and ages get together around lectures by provocative thinkers, culture, food and drinks. Imagine a place different from anything we have at our university. Embarrassingly informal, where you can write erasable on the windows, tables and walls; discuss novel visions while eating your bread hot-from-the-oven with a back-to-basics delicious soup from locally grown products.

Could defragmenting science be as simple as that? I do not know the formula of the Holism 2.0 we need. However, I am quite confident that if the right catalyzing elements are there, it will be discovered in such a Symposion by the wonderfully diverse people we have in Wageningen.

Further reading:
Hong L, Page SE (2004) Groups of diverse problem solvers can outperform groups of high-ability problem solvers. PNAS 101: 16385-16389.
Scheffer, M. 2009. Critical Transitions in Nature and Society. Princeton University Press, Princeton and Oxford.
Vohs, K. D., Mead, N. L., & Goode, M. R. (2006). The psychological consequences of money. Science, 314, 1154-1156.
Whitfield, J (2008) An indifference to boundaries. Nature, Volume 451: 872-873.

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