Albert Faber The policies of complexity. Beinhocker the Origin of Wealth 1 26 May 2011 Response by Albert Faber: The policies of complexity 2

Transcription

1 Beinhocker the Origin of Wealth 1 26 May 2011 Response by Albert Faber: The policies of complexity 2 1. Introduction Every once in a while, you come across a book that fully captures your attention, that enchants and challenges you to re-think your perspectives on the world. The Origin of Wealth by Eric Beinhocker is such a book, and I am very grateful that Eric Beinhocker is here today to present this work (and more!) in less than one hour. I already have had the pleasure to see earlier versions of this presentation before, and I continue to be impressed by the clarity and eloquence with which he presents the difficult topic of complexity economics. This is not to be underestimated: it takes great scholarship to convince that complexity thinking opens up new perspectives for academics and policy makers alike. Eric Beinhocker manages to make complexity thinking less complex. This is a great achievement, that will surely help to bring complexity thinking closer to many scholars from other disciplines. For me personally, works like The Origin of Wealth have helped significantly to structure my own, otherwise intuitive, ideas about social and economic dynamics. I am glad to have the opportunity here today to publicly thank you for contribution to my thinking, and undoubtely to that of many others. In this reference talk, I will touch upon two issues. First, I will explore some pathways for application in evolutionary policy-making. Thereafter, I will try to identify some of the research and policy challenges ahead, especially those that are of interest to the PBL s future research programmes. 1 2 Beinhocker, E. (2006). The origin of wealth. Evolution, complexity and the radical remaking of economics, Random House. Many thanks to Annemieke Righart for editing, and to Ton Manders, Johan Brons, Hotze Lont and Sander van den Burg for discussing various previous versions. 1 of 6

2 2. Policy and complexity First I would like to explore how the ideas from complexity thinking can be used to draw lessons for policy-making. Complexity thinking offers some strong arguments to re-think command & control governance. In doing so, it is important to sum up again the most significant notions of complex adaptive social systems. The great complexity thinker Stuart Kauffman refers to systems on the edge of order and chaos. 3 This implies that a system has just enough coherence to maintain some level of structure, but is also flexible enough to be able to adapt to changing circumstances. Uncertainty is an inherent feature to the dynamics in such systems. Knowledge and resources are distributed among a great variety of relevant agents and institutions. Clearly, policy interference in such systems would be extremely difficult: interactions lead to the emergence of properties and structures that cannot be predicted; tipping points lead to unexpected cascade effects; and complex policy problems cannot be solved by reducing them to any number of partial problems. It is then difficult to perform directive, command & control styles of governance. In other words: the characterisation of society as a complex adaptive system changes the locus of governance, from top-down directions to what can be referred to as an enabling state. This argument will be elaborated further in a forthcoming PBL report, De Energieke Samenleving ( The Energetic Society ). This afternoon, the main question is: how could policy (particularly environmental policy) be framed in this new perspective of complexity, emergence and adaptation? This question is of crucial importance: in my view, the ability to deal with this question is a key criterion for the policy acceptance of complexity thinking. So, the challenge here is to find leverage for policy-making in complex adaptive systems. In order to find this leverage, it is instructive to repeat that, in complexity economics, the main 3 Kauffman, S.A. (1995). At Home in the Universe. The Search for Laws of Self-organization and Complexity. Oxford, Oxford University Press 2 of 6

3 economic driver is described in terms of evolutionary dynamics. Evolution is characterised by the three steps of the general evolutionary algorithm: 1. recombination, innovation and mutation leads to variety; 2. environmental fitness determines selection; 3. Retention is the storage and further amplification of knowledge on what is successful. This algorithm presents a general, computational view on the process of evolution in all complex adaptive systems. It thus accounts for both economic processes and biological processes. 4 Policy leverage can be found in each of the three key processes of the evolutionary algorithm. 5 With respect to the creation of variety, the main strategic policy lesson would be that it is unwise to put all eggs in one basket when looking for a solution to a policy problem. A policy focus on one or only a small number of technological top areas essentially would exclude a great number of other opportunities. Given the high level of inherent uncertainty, option costs would become very high. This could prove to be particularly harmful when new economic or environmental circumstances arise. With respect to selection, it is important to join Eric Beinhocker to reconsider the role of the market: the market is not the price setting mechanism for the most efficient allocation of supply and demand, but rather an evolutionary search mechanism, that provides the most effective selection process in the search for needs and solutions. 6 An evolutionary policy would therefore focus on the framing of more generic selection criteria, that ensures the proper working of this search mechanism in the direction of social preference. In environmental policy, for example, this could be done by setting CO 2 -standards. Then a note on the concept of retention. From a complexity perspective, I think it is important to realize that environmental policies are likely to have very different effects in 4 For an excellent overview on the application of the Darwinian conjecture in the social sciences, see Geoffrey Hodgson and Thorbjørn Knudsen (2010) Darwin s Conjecture: The Search for General Principles of Social and Economic Evolution. University of Chicago Press, Chicago. 5 This perspective is in itself not new, check for an early application e.g. Herman R. van Gunsteren (1976). The Quest for Control. A Critique of the Rational-Central-Rule Approach in Public Affairs. London, Wiley. 6 Gintis, H. (2006). Review of The Origin of Wealth. Journal of Economic Literature XLIV: Gintis quotes Beinhocker (p. 294) in that Markets win over command and control, not because of their efficiency at resource allocation in equilibrium, but because of their effectiveness at innovation in disequilibrium. 3 of 6

4 different technological regimes. Such regimes differ, for example, in their state of development, in the nature of co-evolutionary mechanisms, or in the power relations among agents. This requires a dynamic perspective on policy and policy instruments. An evolutionary policy strategy would need the inclusion of some level of adaptive capacity (or flexibility), in order to be able to learn to accommodate new perspectives on policy solutions, for example when the nature of the regime changes. Flexibility again refers to the maintenance of a variety of options: a portfolio of opportunities to tackle yet uncertain situations. Of course, flexibility comes at a cost, and an evolutionary policy would need to strike a balance between efficiency and flexibility. So how could we translate these policy strategies into concrete policy action? In order to be able to answer that question, we should first ask ourselves: what is it that is innovated, selected and reproduced? In other words: what is that evolves? 7 On this issue, I think it is important to adhere to rules of interaction. Actions of social agents are not arbitrary, but are determined (and restricted) by rules of interaction. Examples are: prices, knowledge, routines, social reflection to others, resource availability, etcetera. Rules of interaction are largely based on improvisation, experiences and inductive learning. 8 It is precisely these rules of interaction that evolve to new rules and new heuristics. A key challenge for environmental policy is to connect with such rules of interaction. Policy makers have three main options for interference in the rules of interaction 9. First, the introduction of new rules can provide direction that fits a sustainable pathway. For example: turn off the lights in rooms when no-one is there. Many technical solutions are available, such as lights that turn of when no movement is registered. Policy options may be to promote such innovations or to apply stricter rules for their application. 7 This question is of course not new in evolutionary social studies. Often, the object of evolution is coined in terms of either institutions or ideas. 8 On the nature of structurally bounded, improvised action, see Boutellier, H. (2011). De improvisatiemaatschappij. Over de sociale ordening van een onbegrensde wereld. Den Haag, Boom Lemma uitgevers. 9 The interference with rules of interaction has been framed in a different way by Thaler and Sunstein in terms of nudging, i.e. to affect the choice architecture of humans by setting defaults (people often choose the first available option), by providing feedback (allow people to be reflective on the consequences of their actions) and by expecting error (i.e. expect people to take easy options). See an elaborate exposé in Richard H. Thaler and Cas R. Sunstein (2009). Nudge: Improving Decisions about Health, Wealth and Happiness. Penguin paperback. Some of the examples are from Thaler s WRR-Lecture. 4 of 6

5 Second, the new rule needs to be reproduced, for example by affecting its social acceptance. Price mechanism are often useful, as they shift the underlying economic sense-making. Third, it is useful to provide some reflexive mechanism, that helps to show the effect of the application of the new rule. Better information on energy costs could be helpful, for example with more than once a year energy bills. A novel example is with energy bills that compare your energy use with the average of the neighbourhood, thus providing an incentive to try to be better than your peers. A very nice technological example is the Ambient Orb, which will glow red when you start using a lot of energy. In summary, I would like to underline Eric Beinhockers general lesson for policy: We may not be able to predict or direct economic evolution, but we can design our institutions and societies to be better or worse evolvers (p. 324). But in addition, I would like to invite Mr. Beinhocker to go one step further and share some of his thoughts on policy strategies and perspective for policy action in a context of complexity. 4. Lessons for PBL: future challenges and research Finally, I would very shortly like to outline two main challenges to future research, specifically for the Netherlands Environmental Assessment Agency. First, a note on modelling. PBL is a leading institute on modelling and system analysis. We host a range of global assessment models like IMAGE, TIMER and GISMO. These models are well-geared to system analysis, but have limited scope for the inclusion of policy, other than at a very aggregate level (such as global or regional CO 2 -taxes). However, what is lacking, is a tool that accounts for the middle range, meso-level, which is, I would argue, the level on which policy leverage is to be found and which aligns best with the notions from complexity economics. Unfortunately, formal economic complexity modelling is presently still poor, but I have a strong intuition that a complexity approach in intermediate, middlerange modelling could provide considerable perspective to improve policy relevance. I hope that Mr. Beinhocker can extent these thoughts on modelling perspectives a little further. 5 of 6

6 Second, I would suggest that there is still considerable scope for improvements in the understanding of social dynamics and structuration in complexity economics. Many complexity studies focus on the issue of emergence, i.e. the macro outcome of micro dynamics, but still ignore how structures (institutions, norms, infrastructures, etc) feed back to determine micro behaviours. I would argue that there is still scope to improve conceptual and analytical rigour, for example by the inclusion of social theories of agency and structuration. 10 Clearly, this could also help to improve the understanding of policy effects on rules of interaction. Further advancements to improve the understanding of social dynamics in complexity thinking would involve a systematic interdisciplinary approach (not only to transcend the physical and social science divide, 11 but also to transcend paradigmatic divides within social sciences). I think that PBL is particularly well geared to take up the challenge to further integrate social sciences in its research programmes. I am curious whether Mr. Beinhocker has some further ideas to progress along this line of thinking. 5. Outro The excellent introduction by Eric Beinhocker will surely provide the inspiration to discuss further the policy consequences of the complexity economic perspective. Albert Faber PBL Netherlands Environmental Assessment Agency, Bilthoven, The Netherlands 10 There should be considerable scope to do so. While the (neoclassical) economic sciences often still remain divided in micro and macro approaches, sociology has already been thinking for about three decades on the connection between agency and structure. See specifically Giddens, A. (1986). The Constitution of Society: Outline of the Theory of Structuration, Polity Press, Cambridge. 11 Urry, J. (2002). Global Complexity. Polity Press, Cambridge. 6 of 6