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Exploring the Science of Complexity in Aid Policy and Practice

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A presentation given by Ben Ramalingam of the ODI on applying the concept of complexity to aid policy and practice. Part of an all-day seminar of the same name. See http://www.odi.org.uk/RAPID/events/Complexity for more information.

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Exploring the Science of Complexity Exploring the Science of Complexity in Aid Policy and Practice ODI, London 9 th July 2008
Agenda ,[object Object],[object Object],[object Object]
“ Exploring the science of complexity: ideas and implications...” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
There are two commonly contrasted ways of conceptualizing and mapping change ,[object Object],[object Object],Michael Quinn Patton May, 2008
The linear approach dominates the planning and learning frameworks used in much of business and policy ,[object Object],[object Object],[object Object],[object Object],[object Object]
Key implications of the linear approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The operational realities of aid work are dealt with by particular models...
Models that look like this...
Or even this...
[object Object],[object Object]
And the aid system itself is not altogether straightforward...
We asked: what does complexity science have to say about aid problems?
First, what is complexity science?
Complexity science is a collection of ideas focused on understanding the dynamics of change in different natural systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Complexity is not a building of knowledge (cf basis, foundations, pillars...) Source: ADB Knowledge Management for Evaluation
It is a network of connected ideas and concepts…. 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
Complexity concepts provide ways of thinking about and understanding… ,[object Object],[object Object],[object Object]
Think back to key implications of the machine metaphor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agenda ,[object Object],[object Object]
1/ 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
Interconnected and interdependent elements and dimensions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The extent of interconnectedness and interdependence becomes most visible during crises (e.g. when it is too late...) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Biases towards simple ,[object Object],[object Object],[object Object]
Following a Recipe A Rocket to the Moon Raising a Child ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Complicated (Problem) Complex (Mess) Simple (Puzzle) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Implications: analyse and deal with the complex realities and dynamics of the aid system ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2 / 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
Emergence from “minimum rules” ,[object Object],[object Object],[object Object],[object Object]
 
[object Object]
Implications ,[object Object],[object Object],[object Object],[object Object],[object Object]
8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation 3 / 5 key concepts
Many planning frameworks model change as linear, based on a cause-effect relationship X Y Linear, Predictable Focused on the end result The program (X) gets the credit!
A number of commonly held assumptions are based on linear thinking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Implications: Challenge linearity, wherever it may lie ,[object Object],[object Object],[object Object]
4 / 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
Tiny differences in initial conditions can lead to very different outcomes ,[object Object]
There are inevitable degrees of unpredictability within and non-comparability across complex systems, and these can arise from the most miniscule of differences
Implications: re-think the purpose and focus of planning and learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
5 / 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
Complex adaptive systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social, political and economic life are characterised by complex systems made up of self-organising adaptive agents ,[object Object],[object Object],[object Object]
[object Object],[object Object],Examples of non-linear social and economic change created by adaptive agents and the self-organising capacities of those around them
[object Object],[object Object]
Implications: people, networks and relationships matter ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agenda ,[object Object],[object Object],[object Object]
1. There are different perspectives on the value of complexity science ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Perspective generally depends on… ,[object Object],[object Object],[object Object]
2. There are a number of common criticisms... ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
3. Complexity scientists use a range of ideas and concepts - familiar, challenging and baffling - to make distinctions between simple, complicated and complex systems and phenomena
Following a Recipe A Rocket to the Moon Raising a Child ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Complicated Complex Simple ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
4. Complexity concepts have the potential to support the intuition, innovation and navigation of leaders and practitioners
5. Complexity concepts are not about “ what you dos” , but about “ how you dos” ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],...and interpretive frameworks are more important than is commonly believed
6. Complexity science gives additional weight to calls for re-thinking five key areas ,[object Object],[object Object],[object Object],[object Object],[object Object]
Given the resistance to change in the power dynamics of aid, real world applications may continue to be “innovative”, “under the radar” and “outside the mainstream” of aid policy and practice...
7. Four suggestions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank you! ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Exploring the Science of Complexity in Aid Policy and Practice

  • 1. Exploring the Science of Complexity Exploring the Science of Complexity in Aid Policy and Practice ODI, London 9 th July 2008
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. The operational realities of aid work are dealt with by particular models...
  • 8. Models that look like this...
  • 10.
  • 11. And the aid system itself is not altogether straightforward...
  • 12. We asked: what does complexity science have to say about aid problems?
  • 13. First, what is complexity science?
  • 14.
  • 15. Complexity is not a building of knowledge (cf basis, foundations, pillars...) Source: ADB Knowledge Management for Evaluation
  • 16. It is a network of connected ideas and concepts…. 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
  • 17.
  • 18.
  • 19.
  • 20. 1/ 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27. 2 / 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
  • 28.
  • 29.  
  • 30.
  • 31.
  • 32. 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation 3 / 5 key concepts
  • 33. Many planning frameworks model change as linear, based on a cause-effect relationship X Y Linear, Predictable Focused on the end result The program (X) gets the credit!
  • 34.
  • 35.
  • 36.
  • 37. 4 / 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
  • 38.
  • 39. There are inevitable degrees of unpredictability within and non-comparability across complex systems, and these can arise from the most miniscule of differences
  • 40.
  • 41. 5 / 5 key concepts 8 Adaptive Agents 10 Co-Evolution 4 Non-Linearity 3 Emergence from Simple Rules 6 Phase space and attractors 5 Sensitivity to initial conditions 1 Interconnected and interdependent elements and dimensions 2 Feedback processes 7 Strange attractors and the edge of chaos 9 Self organisation
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51. 3. Complexity scientists use a range of ideas and concepts - familiar, challenging and baffling - to make distinctions between simple, complicated and complex systems and phenomena
  • 52.
  • 53. 4. Complexity concepts have the potential to support the intuition, innovation and navigation of leaders and practitioners
  • 54.
  • 55.
  • 56.
  • 57. Given the resistance to change in the power dynamics of aid, real world applications may continue to be “innovative”, “under the radar” and “outside the mainstream” of aid policy and practice...
  • 58.
  • 59.

Notes de l'éditeur

  1. Thanks Eric. It’s a real honour to be asked to address the LINGOS annual conference on the topic of field level learning, a topic which is very close to my heart. I have to apologise for not being there in person – we decided on this occasion that the carbon footprint was just too big...