Tag Archives: complexity science

Flipping through my RSS feeds

Google ReaderAfter 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

Exploring the science of complexity

Lorenz AttractorThis blog post is about what I see as one of the most important papers linking the complexity sciences to development and humanitarian efforts – at least it is for me personally, but I think it also takes a very important position in the discussion in general.

The paper has the title ‘Exploring the science of complexity: Ideas and implications for development and humanitarian efforts’ and is authored by Ben Ramalingam (author of the blog Aid on the Edge of Chaos) and Harry Jones with Toussaint Reba and John Young. The paper can be downloaded here.

Why do I think is the paper so important? For me personally it was the first paper I read that explicitly linked the two domains (complexity science and international development) and it does that in a very comprehensive and systematic manner.

Ramalingam and colleagues go back to the origins of complexity sciences and put it into context by showing applications in the social, political and economic realms. They unpack the complexity sciences and present them in ten key concepts divided into three sets, i.e., complexity and systems, complexity and change, and complexity and agency. Here an overview taken from p 8. of their paper:

Complexity and systems: These first three concepts relate to the features of systems which can be described as complex:

  1. Systems characterised by interconnected and interdependent elements and dimensions are a key starting point for understanding complexity science.
  2. Feedback processes crucially shape how change happens within a complex system.
  3. Emergence describes how the behaviour of systems emerges – often unpredictably – from the interaction of the parts, such that the whole is different to the sum of the parts.

Complexity and change: The next four concepts relate to phenomena through which complexity manifests itself:

  1. Within complex systems, relationships between dimensions are frequently nonlinear, i.e., when change happens, it is frequently disproportionate and unpredictable.
  2. Sensitivity to initial conditions highlights how small differences in the initial state of a system can lead to massive differences later; butterfly effects and bifurcations are two ways in which complex systems can change drastically over time.
  3. Phase space helps to build a picture of the dimensions of a system, and how they change over time. This enables understanding of how systems move and evolve over time.
  4. Chaos and edge of chaos describe the order underlying the seemingly random behaviours exhibited by certain complex systems.

Complexity and agency: The final three concepts relate to the notion of adaptive agents, and how their behaviours are manifested in complex systems:

  1. Adaptive agents react to the system and to each other, leading to a number of phenomena.
  2. Self-organisation characterises a particular form of emergent property that can occur in systems of adaptive agents.
  3. Co-evolution describes how, within a system of adaptive agents, co-evolution occurs, such that the overall system and the agents within it evolve together, or co-evolve, over time.

In great detail they explain every concept, give examples and discuss the implications of the concepts for the development system.

I like the paper because it really brings together all those important concepts in an accessible way. Although the paper is pretty long (89 pages all in all), it is not at all a boring read. In the conclusion part of the paper, the authors also describe the difficulty of presenting such an intricate matter as complexity sciences, itself being not a unified scientific discipline:

[…] it is useful to note that scientific knowledge is usually characterised with reference to the metaphor of a building. The ease with which the terms ‘foundations’, ‘pillars’ and ‘structures’ of knowledge are used indicates the prevalence of this architectural metaphor. Our difficulty was in trying to represent complexity science concepts as though they were parts of a building. They are, in fact, more like a loose network of interconnected and interdependent ideas. A more detailed look highlights conceptual linkages and interconnections between the different ideas. The best way to see how they fit together in the development and humanitarian field would be to try to apply them to a specific challenge or problem. […] Based on our reading, however, a grand edifice may never be erected along the lines of, for example, neoclassical economics. If this is the case, it may be that we need to become better accustomed to a network-oriented model of how knowledge and ideas relate to each other.

For me, it is intriguing how the science of complexity not only defies scientific practices by diverting from the pure deductive and inductive approaches and combining them but also evaded characterizations in ‘traditional’ scientific schemes such as the building mentioned above. This reminds me of the book ‘Complexity and Postmodernism’ by Paul Cilliers, which I started reading but I got stuck somewhere in the middle, overwhelmed by his theory and language. I hope that I will finish it some day and report on that here.

The authors also try to answer a number of questions around the topic of the application of complexity to development and what it means for example for international donors. A few quotes from the concluding remarks:

In our view, the value of complexity concepts are at a meta-level, in that they suggest new ways to think about problems and new questions that should be posed and answered, rather than specific concrete steps that should be taken as a result.

[…]

As well as use by implementing agencies, an understanding of complexity must also be built into the frameworks of the donors and others who hold the power to determine the shape of development interventions. This may be easier said than done – complexity requires a shift in attitudes that would not necessarily be welcome to many working in Northern agencies. For example, such a shift may require adjusting away from the ‘mechanistic’ approach to policy, or being prepared to admit that most organisations are learning about development interventions as they go along, or being transparent about the fact that taxpayers’ money may be spent on a project that does not guarantee results. It may mean having smaller, but better programmes.

[…]

At the start of our exploration, our view was simply that complexity would be a very interesting place to visit. At the end, we are of the opinion that many of us in the aid world live with complexity daily. There is a real need to start to recognise this explicitly, and try and understand and deal with this better. The science of complexity provides some valuable ideas. While it may be impossible to apply the complexity concepts comprehensively throughout the aid system, it is certainly possible and potentially very valuable to start to explore and apply them in relevant situations.

To do this, agencies first need to work to develop collective intellectual openness to ask a new, potentially valuable, but challenging set of questions of their mission and their work. Second, they

need to work to develop collective intellectual and methodological restraint to accept the limitations of a new and potentially valuable set of ideas, and not misuse or abuse them or let them become part of the ever-swinging pendulum of aid approaches. Third, they need to be humble and honest about the scope of what can be achieved through ‘outsider’ interventions, about the kinds of mistakes that are so often made, and about the reasons why such mistakes are repeated. Fourth, and perhaps most importantly, they need to develop the individual, institutional and political courage to face up to the implications.

I’d recommend anyone who works in international development and is interested in complexity to read this paper. It is a perfect entry point also for people with no background in complexity science.

Melanie Mitchell: Complexity – A Guided Tour

The latest book I finished reading on complexity is Melanie Mitchell’s ‘Complexity – A Guided Tour’. The book is going through the very basics of what is colloquially known as complexity science, a mix of scientific disciplines on the search for a common theory that applies to all complex systems, from human genomes to artificial intelligence and from the evolution of species to the economy.

Mitchell starts off her journey by mentioning a number of complex systems, i.e. ant colonies, the brain, and the immune system, economies and the world wide web, directly putting forward the questions ‘Are there common properties in complex systems?‘ and ‘How can complexity be measured?

The first question she answers directly with three very generic properties that are inherent to all complex systems: complex collective behavior, signaling and information processing, and adaptation. For the second question she proposes a couple of measures, but when concluding the book, she makes it clear that there is no commonly agreed measurement for complexity.

In part one of the book, Mitchell comprehensively describes the background and history of complexity, including the fields of information, computation (herself being a computer scientist), evolution, and genetics. In part two she focuses on life and evolution in computers to further deepen the topic of computation in part three. Part four explores the realms of network thinking, leading to a more ‘complex’ view on evolution, before concluding the book in part five.

From this very interesting basis of ‘complexity science’ from physics, mathematics, computer sciences, biology, etc., for which to understand I had to dig deep into my knowledge from University, I distilled some takes from the book that I think are particularly relevant for my work:

– One of the basic properties of complex systems is that they are extremely dependent on the initial conditions. Even if we have a very ‘simple’, completely deterministic complex system (e.g. the logistic map), we are not able to predict its behavior without knowing the exact initial parameters (exact meaning that even changes in the tenth or more decimal place of a parameter can have a significant impact). Now, the systems in which we work in development are much more complex than the logistic map in the sense that they are hardly deterministic from the point we look at them (since we work with humans, it is impossible to model their decisions). Secondly, we are never able to gather all necessary data to determine the initial conditions for a model to run. This insight strengthens my belief that we should concentrate our use of tools to make sense of the systems to qualitative ones, since quantitative modeling can hardly predict the behavior of a system and, hence, the outcome of an intervention.

– To know how information flows through a system is crucial to determine how it works and to be able to influence it. The reason being that these processes are also energy intensive, i.e. they follow the laws of thermodynamics. I honestly never lost a thought on that before reading Mitchell’s chapter on information entropy or the so called ‘Shannon entropy’ (coined by Claude Shannon, whose work stood at the beginning of what is now called information theory). The take for me here is to focus our analysis more on information flows and how a system is managing these flows, not only focusing our analyses on flows of goods and money. To give a relatively simple example: in order to understand how an ant colony works and specifically how an ant colony takes decisions, we need to know how information is collected, communicated, and processed.

– Based on the insights of the question how systems compute information, Mitchell describes that most decisions that are taken by agents in complex systems are mostly based on feedback from the agent’s direct environment, based on samples and statistical probabilities. To go back to the example of ants: every individual ant makes decisions based on the frequency of feedback from ants it meets or the intensity of pheromones on a particular track towards a possible food source. Similar in the systems we work in in development: actors take decisions mainly based on information from their direct environment. Hence, if we analyze causal loops in a system, we should focus on the feedback that comes from the direct environment of our target group.

– At one point in the book, Mitchell talks about models to simulate reality. Specifically, she mentioned so called ‘idea models’ as being “relatively simple models meant to gain insight into a general concept without the necessity of making detailed predictions (…)”. The exploration of such idea models have been the major thrust of complex systems research. Mitchell describes idea models as ‘intuition pumps’: thought experiments to prime our intuitions about complex phenomena. Although Mitchell’s idea models are rather general concepts such as the prisoners’ dilemma, I think that qualitative causal loop models of specific systems we work in can also be seen as idea models and used as intuition pumps. Working in complex systems such as markets in developing countries, we also have to prime our intuition in how these systems work in order to understand them and be able to work with them to bring about change. This brings me back to my point on focusing on qualitative models, sense-making models. We are hardly able to gather enough data to be able to run satisfactory simulations of market systems, so we have to work more following ‘idea models’ of the systems and base our decisions on intuition and experience.

– Finally, Mitchell confirms in the conclusion of the book that the so called ‘complexity science’ is not one coherent science as the term would suggest. Many different disciplines are working with complex systems and thanks to places like the Santa Fe Institute the different scientists also work together and exchange their insights. Nevertheless, there is not yet one coherent vocabulary for this field, nor are there any general theories that can be applied in all fields. Furthermore, there is still a field of critiques on the field, mainly stating that nothing significant has come out of the field so far. To quote Mitchell on that: “As you can glean from the wide variety of topics I have covered in this book, what we might call modern complex systems science is, like its forebears [Mitchell mentions ‘cybernetics’ and the so called ‘General Systems Theory’], still not a unified whole but rather a collection of disparate parts with some overlapping concepts. What currently unifies different efforts under this rubrik are common questions, methods, and the desire to make rigorous mathematical and experimental contributions that go beyond the less rigorous analogies characteristic of these earlier fields.

The same is also true for people who work for the better use of insights of this fragmented ‘complexity theory’ in development projects. We lack the necessary vocabulary and not only that – we also lack a general understanding how to go about the challenge to better embrace complexity in what we do and avoid to fall back into a mode of coming up with ‘engineering solutions’ based on simple cause-and-effect models. There is now a bunch of people who want to take this challenge and do the work necessary to develop a common vocabulary and toolkits to better harvest the insights of the ‘complexity school’. Let’s keep the train moving!

I enjoyed reading Mitchell’s book very much. It is well written and gives a solid background of the scientific concept of complexity. I think, though, you need to be a person enjoying sciences and especially natural and computer sciences, to really enjoy the book. Mitchell writes about the logistic map, cellular automata, Gödel’s theorem, the Turing machine, fractals, etc., etc. If you are interested in complexity and have the nerve to go through theoretical scientific concepts like a self replicating computer program or genetic algorithms, then you really should read the book.

PS on a humorous note: One part that really caught my attention was when Mitchell wrote about the research on computation in natural systems and the work of Stephen Wolfram. He has done research with cellular automata and how they can compute information (cellular automata are simple lines or squares of cells that change their state [usually on or off] following very simple rules based on information from their neighboring cells). Wolfram’s thesis which he brought forward in his 2002 book ‘A New Kind of Science’ in very simple words and as I as a layman understood it is that when cellular automata can do universal computation (the term ‘Universal Computation’ refers to any method or process capable of computing anything able to be computed), presumably most natural systems are able to do universal computation, too. Where am I going with that? Well, the notion that presumably many natural systems can do universal computation really got me thinking about what Douglas Adams wrote in his book ‘The Hitch Hiker’s Guide to the Galaxy’ about the earth being a computer designed to find the question to which the answer was 42. We really should start asking questions to those white mice …