What else to do on an early Sunday morning than listening to some baroque music and catching up with some blogs. Here a couple of things I found interesting from a complexity perspective. Continue reading
Owen Barder, Senior Fellow and Director for Europe of the Center for Global Development last week posted a talk online, adapted from his Kapuściński Lecture of May 2012, in which he explores the implications of complexity theory for development policy (the talk is also available as audio-only version on the Development Drums podcast).
The talk tells a persuasive story of what has gone wrong in international development and in the various models of growth it used; that the adoption of the concepts of adaptation and co-evolution allow for much more accurate models; a brief description of complex adaptive systems and complexity theory; and what consequences these insights have for development policy. But these positive turns in development come for a price: we can no longer ignore that we – the developed nations – are also a part of the larger system and that our (policy) actions strongly influence the development potential of poor countries. It is no longer enough to ‘send money’ and experts and think that this will buy us out of our responsibilities towards those countries.
I want to quickly summarize what I think are the key points of Owen’s presentation, starting with what seems to me an obvious point:
Development is not an increase in output by an individual firm; it’s the emergence of a system of economic, financial, legal, social and political institutions, firms, products and technologies, which together provide the citizens with the capabilities to live happy, healthy and fulfilling lives.
Owen talks about various (economic) models and theories that have neglected this systemic perspective and, subsequently, failed to deliver successes in development. The focus of the economic models shifted over the years from providing capital and investment to technology.
Since this approach of ‘provision’ did not work out, the lack of favorable policies was blamed for hindering the market to achieve its theoretical potential. As a consequence, the Washington Consensus introduced which policies needed to be adopted by a country to be able to grow. As we know, this also did not work out, although the Washington Consensus did, according to Owen, have some positive impacts in developing countries.
After the Washington Consensus, development agencies focused on weak institutions and spent (and are still spending) huge amounts of money on institutional strengthening and capacity building initiatives. The results have been modest. Adding to the difficulties is the fact that it is still not clear which institutions are really important for development.
Most recently, a new book published by Daron Acemoglu and James A. Robinson (Why Nations Fail) promotes politics as culprit of failing development. According to them, the institutions are weak because it actually suits the elite that is in power to run them like this [what an insight …!!!]
All these models that were applied were actually based on traditional economic theory. After seeing all these approaches fail, Owen switches to a new way of describing economic development, based on adaptation and co-evolution in complex adaptive systems.
After making a compelling argument why complexity theory can actually better describe the real economy out there, Owen describes seven policy implications deducted from that insight.
- Resist engineering and avoid isomorphic mimicry. The first point mainly stems from the fact that solutions developed through evolution generally outperform design. The latter point mainly implicates that institutions that were mainly built after a blueprint following ‘best practices’ but do not connect to the local environment will have not much use.
- Resist fatalism. Development should not be seen as a pure Darwinian process. Smart interventions by us can accelerate and shape evolution.
- Promote innovation.
- Embrace creative destruction. Innovation without selection is no use. Feedback mechanisms to force performance in economic and social institutions are necessary.
- Shape development. The fitness function which the selective pressure enforces should represent the goals and values of a community.
- Embrace experimentation. Experimentation should become a part of a development process.
- Act global. We need to make a bigger effort to change processes that we can control, for example international trade, the selection of leadership in international organization, etc.
Owen is not telling any news in his presentation, but he succeeds to develop a compelling storyline on why complexity theory is relevant for development and why processes that are based on adaptation and co-evolution much better describe why some countries develop while other seem stuck in the poverty trap.
In my view this is an immensely important contribution to the discussion on how we can reform the international aid system to live up to our responsibility of enabling all people on this planet to live happy and fulfilled lives.
I would like to point your attention to an excellent guest post on Ben Ramalingam’s Aid on the Edge of Chaos Blog by Frauke de Weijer, policy and fragile states specialist at the ECDPM think tank on the use of complexity theory in state building and fragility.
There are two points I particularly want to point out. One is Ms. de Weijer’s comment on fragile states being wicked problems, when she says that
This is not to say that applying a different approach, i.e. a ‘complexity theory approach’, will fix the problem. Wicked problems are not particularly ‘fixable’, which is exactly why they are wicked in the first place!
This resonates well with the basic insight of the failure of a ‘problem-fix’ approach or engineering solution when working in complex systems. Systems cannot really be broken, they always work well for someone, otherwise there would not be forces that try to hold the system in place as it is.
The second thing Ms. de Weijer mentions is one of the starting points into working in fragile states she identifies:
Societal change is painful, takes time, is unpredictable and does not follow well-established paths. For external actors engaging in such settings, conflict-sensitivity is key, but the principle of doing no harm is naïve. It is a matter of mitigating these risks to the best of our ability.
I agree with Ms. de Weijer in as far as I don’t thing that in a complex system with its high number of interdependence, a so called ‘do no harm’ approach really works. As soon as we intervene in a system, we change it and since complex systems are inherently unpredictable, we will also not be able to predict whether we will do any harm or not. And as a link to the earlier posts on targeting vs. holism (here and here), sustainable and long-term change might first be painful to our ‘beneficiaries’, but in the long run be the better solution as a forced ‘do no harm’ intervention that circumvents the actual problem.
There are also some interesting comments of other readers added to the post.
After three weeks of more or less constant work, I’m finally having some time to have a look at my RSS feeds. After the first shock of seeing more than 3000 new entries, containing over 100 unread blog posts, I just started reading from the top. Here a couple of things I found interesting (not related to any specific topic):
SciDevNet: App to help rice farmers be more productive – I don’t know about the Philippines, but I haven’t seen many rice farmers in Bangladesh carrying a smartphone (nor any extension workers for that matter).
Owen abroad: What are result agenda? – An interesting post about the different meanings of following a ‘results agenda’ for different people, i.e., politicians, aid agency managers, practitioners, and (what I call) ‘complexity dudes’. I’m not very satisfied with Owen’s assessment, though, because I think he is not giving enough weight to the argument that results should be used to manage complexity. I think to manage complexity, we don’t need rigorous impact studies, but much more quality focused results regarding the change we can achieve in a system and the direction our intervention makes the system move.
xkcd: Backward in time – an all time favorite cartoon of mine, here describing how to make long waits pass quickly.
Aid on the Edge: on state fragility as wicked problem and Facebook, social media and the complexity of influence – Ben Ramalingam seems to be back in the bloggosphere with two posts on one of my favorite blogs on complexity science and international development. In the first post, he explores the notion of looking at fragile states as so called ‘wicked problems’, i.e., problems that are ill defined, highly interdependent and multi-causal, without any clear solution, etc. (see definition in the blog post). Ben concludes that the way aid agencies work in fragile states needs to undergo fundamental change. He presents some principles on how this change could look like from a paper he published together with SFI’s Bill Frej last year.
In the second piece Ben looks into the complex matter of how socioeconomic systems can be influenced, and how this can be measured, by giving an example of Facebook trying to calculate its influence on the European economy and why its calculations are flawed. The basic argument is that one’s decision to do something is extremely difficult to analyze and even more difficult to trace back to an individual influencer. Also our decisions and, indeed, our behavior, are complex systems. One of the interesting quotes from the post: “Influentials don’t govern person-to-person communication. We all do. If society is ready to embrace a trend, almost anyone can start one – and if it isn’t, then almost no one can.”
Now, to make the link back to Owen’s post mentioned above on rigorous impact analyses: how can we ever attribute impacts on a large scale to individual development programs or donors if we cannot measure the influentials’ impact on an individual’s behavior? I rather like to think of a development program as an agent poking into the right spots, the spots where the system is ready to embrace a – for us – favorable trend. But then to attribute all the change to the program would be preposterous.
Enough reading for today, even though there are still 86 unread blog posts in my RSS reader, not the least 45 from the power bloggers Duncan Green and Chris Blattman. I’ll go and watch some videos now of the new class I recently started on Model Thinking, a free online class by Scott E Page, Professor of Complex Systems, Political Science, and Economics at the University of Michigan. Check it out: http://www.modelthinking-class.org/
For people with less time, a couple of participants are tweeting using #modelthinkingcourse
I recently stumbled over a blog called Complexity Finance by a company called Rational Investment. A series of three posts which I liked was called ‘What ants can teach us about the market’. In part one, the author writes about a phenomenon that the number of ants would, given two identical and steadily replenished food sources not be divided 50/50, but rather 80/20:
Alan Kirman found some interesting behavior in the foraging activities of ants. He starts his account by citing the results of an experiment by Deneuboug et al. (1987a) and Pasteels et al. (1987) where two identical food sources were offered to ants. They were replenished so that they remained identical. Ants, after a period of time, were found not to be split 50/50 as common sense would conclude, but rather 80/20. Kirman further noted that this 80/20 split would often reverse inexplicably. This phenomenon is mirrored in studies by Becker where only one of two similar restaurants on opposite sides of the street tend to attract long lines of customers.
Apparently, this behavior is also mirrored by investors in a market.
In part three, another interesting concept is introduced: herding. Herding was identified as a common behavior in markets, responsible for creating trends.
Described as “History’s Hidden Engine”, socionomics posits that large trends in society and the market are driven by social mood. If the society at large is feeling positive, constructive behavior ensues, e.g. cooperation between governments, a rising stock market, expanding economy, box-shaped cars and brighter fashion tones. A negative mood will cause society to go to war, the stock market to decline, a recession/depression, rounder-shaped cars and darker fashion tones.
Socionomics is counter-intuitive in that most people believe events cause social mood. The stock market goes up and investors feel happy. Socionomics believes that a society that feels happy, for whatever prior cause, will cause them to buy stocks. It is the mood that causes the event. This mood is generated and reinforced through the herding mechanism.
Herding behavior is simply acting the way others do. It is a type of sampling heuristic and, like cognitive biases, is triggered in times of uncertainty. When uncertain about what to do, most will default to following the actions of others. The socionomic model of herding describes it as “a model of unconscious, prerational herding behavior that posits endogenous dynamics that have evolved in homogenous groups of humans in contexts of uncertainty, while eschewing the traditional economic assumptions of equilibrium and utility-maximization.”
I wonder how this herding behavior could be used in the work of developing markets for the poor in developing countries. I do recognize one type of herding in these contexts that I often don’t see as particularly helpful, but a very understandable behavior: all people in a region, market, village, etc. do the same thing, regardless whether it is particularly beneficial or profitable. In general, diversification would not only lead to higher profits by tapping new markets, but also to a higher degree of resilience by not depend on only one product. A negative instance of herding?
Maybe the increasing interest of companies (and investors?) in social business can be seen as a positive type of herding that needs to be better exploited.
I haven’t been very actively writing here recently. I think I got trapped in the question ‘what meaningful could I write that is not already out there?’. Well, anyway, just a short post today with some thoughts that I have carried around for a while. I just came across a post on the Aid on the Edge blog that was talking about South Africa and the uncertainty of its future:
We human beings do not like uncertainty. We seek to understand what events portend, taking comfort in coming up with an answer. (…) Yet sometimes there is more wisdom, and more comfort to be taken, in acknowledging a more humbling truth – that which of many alternative futures (including ones we cannot imagine) will come to pass is unknowable, is a product of decisions and actions that have not yet been made. This understanding of change as something ‘emergent’, evolving, which can unfold in far-reaching yet ex ante unpredictable directions, is the key insight of ‘complexity theory’ – an insight which can offer a useful dose of humility to governance prognosticators.
The question that comes to my mind when reading this is how to handle the tension of the uncertainty of the future and the deeply institutionalized need for planning in development institutions.
I have worked with a systems dynamics approach combining causal loop diagrams with a method called the sensitivity analysis. It helps us to determine the relative importance of impact factors in a system and characterize them as active, critical, passive, and buffering. Together, these two tools allow to select impact factors that could be targeted by development agencies in future projects.
Now, what is the value of causal loop diagrams? Some people say that they are not more than an improved version of linear causal chains, but still not able to reflect ‘real’ complexity, i.e., the unpredictability of complex systems. Loop diagrams still work with cause and effect relationships, the cause and effect between two factors that can be connected with other factors to eventually build a loop. Yet, complexity sciences say that in complex systems cause and effect are hard to determine, so why bother?
I think that the causal loop analysis and the sensitivity analysis allow us to evaluate the factors that are most relevent and focus on them. They further illustrate some of the more prominent feedback mechanisms of the system that could amplify or hamper our interventions or that we could even use to change some of the dynamics of the system in our favor. They also cater the need of planning or at least of establishing a rational base for planning.
But yeah, we have to avoid falling back into the ‘we can predict the future’ trap, trying to build a prefect model of a system (just remember, models of complex systems have to be as complex as the real thing to accurately simulate it). Complex systems remain inherently unpredictable and our actions need to be tuned to the reactions of the system to any intervention. The above mentioned tools help us to make sense of the dynamics of a system and to select the more promising interventions. They do, however, not release us from the need of an experimental (or may I call it evolutionary) approach to solving real problems in real systems.
I would appreciate any thoughts on that in the comments!
I had the privilege to participate in a part of an event organized by USAID on embracing complexity and what this means for the agency. I participated by webinar, which unfortunately only covered the first half of the day. However, Ben Ramalingam, one of the speakers at the event, posted a summary of the day on his blog. I highly recommend to read his post here.