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Robert Ricigliano: Making Peace Last

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I just finished reading an excellent new book by Robert Ricigliano titled ‘Making Peace Last. A Toolbox for Sustainable Peacebuilding’. Though it might come as a surprise to some of you that I am interested in peacebuilding, I have to say that I was mainly reading the book because it describes an approach to peacebuilding based on complexity theory and systems thinking. One of my big goals is to have such an approach for a more generalized audience and therefore Ricigliano’s book proved to be a big treasure trove for me. I have used systemic analysis methodologies before and Ricigliano’s book added some important insights and gave me some tips on how to further improve my methodology. Here’s a link to the homepage of the book: http://www.makingpeacelast.com/

I want to write a brief review on Ricigliano’s book here and might later pick out some of the interesting aspects he touches upon.

Ricigliano’s book is intended as a guide for people working in peacebuilding to make their interventions more effective and sustainable. In the first part of the book, Ricigliano introduces the basic principles of systemic approaches and complexity and elaborates on why they are better able to achieve real and sustainable changes. Based on his work and with a rich background of peacebuilding and system/complexity literature, Ricigliano develops a framework for systemic peacebuilding called the SAT model.

Part 1 starts out with the basic question of why we are not doing better in peacebuilding nowadays. For Ricigliano, one of the main reasons is the fact that the energy of peacebuilding work is disbursed in hundreds of different directions while the myriads of activities are not guided by an underlying grounded theory or overall strategy of change. To change this, Ricigliano sees as the most urgent task of the peacebuilding community to confront the micro-macro paradox. He describes this paradox as failure of the many programs, diverse in nature and particulars, which are successful measured by their own ability to achieve immediate program objectives at local (micro) level, to lead to systemic (macro-level) change.

Indeed, this realization is not only true for the peacebuilding community, but in my eyes for most of the development community.

As a remedy to the micro-macro gap, Ricigliano proposes a holistic approach, that goes beyond peacebuilding in the narrow sense and includes the whole development and humanitarian fields, which need to be combined under one grand strategy. Development, according to Ricigliano, is still to sector-focused with separate goals, approaches and jargon. I very much agree with this point in Ricigliano’s analysis as I have pointed this out again and again in my work. Unfortunately, however, Ricigliano in my view fails to deliver very much on this particular point in his book by again focusing it specifically on peacebuilding without explicitly including many points of contact with other disciplines.

An interesting question Ricigliano poses in this regard, though, is whether peace can serve as a supraordinate goal. He identifies the need for this question in the fact that development workers from different fields fail to agree what to call their common concern. Ricigliano identifies, however, a general consensus that various practitioners are striving for something more than economic growth, rule of law, poverty reduction, or war crimes prosecutions and labels this, following two researchers that did some extensive work in Afghanistan, Peace Writ Large (PWL). He goes on:

For peace to serve as the supraordinate goal of diverse practitioners, it must be redefined so as to avoid utopian critiques or a trade-off between peace and justice. In this regard, consider the following definition:

“Peace is a state of human existence characterized by sustainable levels of human development and healthy processes of societal change.”

I kind of understand the search for a supraordinate goal for all development practitioners. But then again I am not sure if one single supraordinate goal would bring us any further in working in complex adaptive systems. It is a bit like the question for the meaning of life. The supraordinate goal implies that there is an ideal state of a system where it is in complete equilibrium and fulfills the vision formulated in our goal. In my view, this goes against the fundamental basics of complexity theory itself, which basically says that the only systems in a stable equilibrium are dead systems. But I guess that’s rather a detail.

Ricigliano advocates for a shift of mind towards systemic peacebuilding. He introduces the basic aspects of complex systems, i.e., the interaction and relationships among parts, the interconnectedness of parts, the feedback and dynamics, and emerging patterns. He differentiates between ‘stepping in’, i.e. the analysis of the parts, their interaction, relationships and interconnectedness, feedback and dynamics, and ‘stepping back’.

The practice of stepping back from the parts far enough to see patterns or wholes is a way of incorporating lots of complexity yet still yielding a manageable and useful narrative.

With his considerations on systemic approaches, Ricigliano develops a ‘systemic theory of peacebuilding’ based on the three-part model of general system change developed by Daniel Katz and Robert Kahn. Katz and Kahn identified three major components of complex social systems: norms, values, and roles. By using other references from the literature on systemic changes and his experiences in peacebuilding, Ricigliano redefines the three elements to use them as a framework for systemic change in the peacebuilding context as follows:

  • Structural: This refers to systems and institutions designed to meet people’s basic human needs.
  • Attitudinal: This refers to shared norms, beliefs, social capital, and intergroup relationships that affect the level of cooperation between groups or people.
  • Transactional: This refers to processes and skills used by key people to peacefully manage conflict, build interpersonal relationships, solve problems collaboratively and turn ideas into action.

He calls this the structural, attitudinal, and transactional (SAT) model. All three levels are interconnected and for systemic and lasting change to happen, change must take place at all the three levels. Ricigliano sees the transactional domain as a lever for driving systemic change since it is seen as the most accessible place to start a systemic change process.

I am wondering if these three domains hold also true as framework for systemic change in other fields than peacebuilding. According to Ricigliano’s logic they should, since he defines peace or ‘Peace Writ Large’ as ultimate goal of all development initiatives. I would be interested to test this on a real world example, such as in economic development.

At the end of part 1 of the book, Ricigliano asks what would be needed to promote the SAT model. Here, he points out one aspect that I find profoundly important:

The means used to promote a certain goal must be consistent with the goal itself.

This means to make the peacebuilding system more systemic, we have to look at the peacebuilding system as a complex system and adapt our strategy accordingly. Ricigliano identifies two ‘Systems Shifts’ that need to be considered for the peacebuilding system to be more systemic:

Systems Shift 1: From Solutions Focused to Learning Focused. The most important realization for me is that we should not see a system as ‘broken’ and look for a solution (an engineering approach) but we have to realize that a system is what it is. A system cannot be broken, it always works. Maybe not as we would like it to, but it works. Seeking solutions to fix problems, as Ricigliano points out, gives the appearance that situations can be controlled and that we can thus impose our will on them. But complex systems cannot be controlled and hence, there is no way we can ‘fix’ them. The key thing is to work with, not against, the energy and motion in the system.

The response of the SAT model to this insight is to propose a new type of project cycle based on ‘planning, acting, and learning’ (PAL). Hence, Ricigliano promotes the fact that we need to analyze the system and try to learn from it as we go to find the best-adapted means to bring about change in the system. As a consequence, our projects need to be transformed from problem solving projects into learning projects.

Instead of monitoring and evaluation processes that force agencies into pursuing predetermined outcomes and punishing ‘errors’, learning requires peacebuilders to be ‘error embracing’.

Systems Shift 2: Linear Change (Adding Up) versus Nonlinear Change (Interacting Out). Ricigliano points out that many programs are still operating under the erroneous notion that change happens through a linear process and program impacts will add up to long-term systemic change. Alas, reality teaches us otherwise.

In systems, change to an element in the system or to a relationship between two elements causes a chain reaction that spirals out from the initial intervention in the system. (…) So, rather than adding up, changes in a system ‘interact out’, meaning that they cause multiple and often unpredictable ripple effects throughout the system.

The response of the SAT model in this case is to build what Ricigliano calls ‘Networks of Effective Action’, realizing that no one organization can affect an entire system on its own.

Here again, Ricigliano also gives practitioners outside the peacebuilding field rich material and insights that can help us to make our work more systemic and in the long run more effective.

Having introduced the basic understanding of systemic change models and his SAT Model, Ricigliano introduces in the second part of his book a very elaborate methodology for systemic peacebuilding assessment and planning that is largely based on the systems thinking school. In Ricigliano’s words, part 2 of the book takes up the challenge of how to listen to a system to plan interventions meant to nurture systemic change. This part also contains vast resources not only for people working in peacebuilding but also for practitioners in other fields. The methodology introduced by Ricigliano has a big similarity to a methodology I have used for assessing systems and potentials for change, lately in an assessment that I did in Kosovo. Nevertheless, with his rich background of experiences from all over the world, Ricigliano makes the methodology very tangible and I could still add a lot to my understanding of how to use it.

In part 3, after having introduced us to how to listen to the system, Ricigliano maps out methods to catalyze systemic change. Compared with the two other parts, this third part is most specifically tailored for peacebuilders and in my view less accessible and less directly useful for other practitioners. You can feel that Ricigliano is on his thematic home-turf here. The introduced methodology mainly focuses on negotiations between conflict parties and how to best organize, design, facilitate or support these negotiations in a way that is compatible with the SAT model. Nevertheless, I could gain more insights in this part that can be applied to other actors, not only combatants in certain African or Asian countries, such as businessmen.

All in all, a really well written book that describes in many details a methodology to approach peacebuilding in a more systemic way, based on a rich backpack of experiences that Ricigliano brings along from his work in the peacebuilding field. The methodology can easily be adapted to other areas of intervention, such as economic development or the development of social services, keeping in mind that the goals that all development people share are not that far apart: to create a better world for all people.

Melanie Mitchell: Complexity – A Guided Tour

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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 …