Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

By Jan Kwakkel

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Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are.

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Toolkits for transdisciplinary research

By Gabriele Bammer

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Gabriele Bammer (biography)

If you want to undertake transdisciplinary research, where can you find relevant concepts and methods? Are there compilations or toolkits that are helpful?

I’ve identified eight relevant toolkits, which are described briefly below and in more detail in the journal GAIA’s Toolkits for Transdisciplinarity series.

One toolkit provides concepts and methods relevant to the full range of transdisciplinary research, while the others cover four key aspects: (i) collaboration, (ii) synthesis of knowledge from relevant disciplines and stakeholders, (iii) thinking systemically, and (iv) making change happen.

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Good practices in system dynamics modelling

By Sondoss Elsawah and Serena Hamilton

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Sondoss Elsawah (biography)

Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.

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Serena Hamilton (biography)

We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems.

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Scaling up amidst complexity

By Ann Larson

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Ann Larson (biography)

How can new or under-utilized healthcare practices be expanded and institutionalized to achieve audacious and diverse global health outcomes, ranging from eliminating polio to reversing the rise in non-communicable diseases? How can complex adaptive systems with diverse components and actors interacting in multiple ways with each other and the external environment best be dealt with? What makes for an effective scale-up effort?

Four in-depth case studies of scale-up efforts were used to explore if there were different pathways to positively change a complex adaptive system.

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Learning to tackle wicked problems through games / Aprendiendo a hacer frente a problemas perversos a través de los juegos/ Apprendre à affronter les problèmes sournois à travers les jeux

By Claude Garcia, Anne Dray and Patrick Waeber

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1. Claude Garcia (biography)
2. Anne Dray (biography)
3. Patrick Waeber (biography)

A Spanish version and a French version of this post are available

Can we help the next generation of policy makers, business leaders and citizens to become creative, critical and independent thinkers? Can we make them aware of the nature of the problems they will be confronted with? Can we strengthen their capacity to foster and lead stakeholder processes to address these problems?

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Scoping: Lessons from environmental impact assessment

By Peter R. Mulvihill

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Peter R. Mulvihill (biography)

What can we learn about the role and importance of scoping in the context of environmental impact assessment?

“Closed” versus “open” scoping

I am intrigued by the highly variable approaches to scoping practice in environmental impact assessment and the considerable range between “closed” approaches and more ambitious and open exercises. Closed approaches to scoping tend to narrow the range of questions, possibilities and alternatives that may be considered in environmental impact assessment, while limiting or precluding meaningful public input. Of course, the possibility of more open scoping is sometimes precluded beforehand by narrow terms of reference determined by regulators.

When scoping is not done well, it inevitably compromises subsequent steps in the process.

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Complexity and agent-based modelling

By Richard Taylor and John Forrester

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Richard Taylor (biography)

Policy problems are complex and – while sometimes simple solutions can work – complexity tools and complexity thinking have a major part to play in planning effective policy responses. What is ‘complexity’ and what does ‘complexity science’ do? How can agent-based modelling help address the complexity of environment and development policy issues?

Complexity

At the most obvious level, one can take complexity to mean all systems that are not simple, by which we mean that they can be influenced but not controlled. Complexity can be examined through complexity science and complex system models.

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Critical Back-Casting

By Gerald Midgley

gerald-midgley
Gerald Midgley (biography)

How can we design new services or strategies when the participation of marginalized stakeholders is vital to ethicality? How can we liberate people’s creativity so we can move from incremental improvements to more fundamental change?

To answer these questions, I have brought together insights from Russ Ackoff and Werner Ulrich to develop a new method that I call Critical Back-Casting.

Russ Ackoff, writing in the 1980s, is critical of organizations that focus on incremental improvements without ever asking whether they are doing the right thing in the first place. Thus, they are at risk of continually ‘improving’ the wrong thing, when they would be better off going for a more radical redesign. Ackoff makes two far-reaching prescriptions to tackle this problem.

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Six actions to mobilise knowledge in complex systems

By Bev J. Holmes and Allan Best

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Bev J. Holmes (biography)

What are the practical implications of mobilising knowledge in complex systems? How can the rules, regulations, incentives and long-entrenched power structures of a system be changed so that knowledge mobilisation is maximized?

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Allan Best (biography)

We propose six interdependent actions, briefly described below, undertaken at two levels, by those who: (1) are managing specific knowledge mobilization initiatives (initiative managers), and (2) are in a position to make the environment more receptive to change (key influencers). These people may not necessarily be involved in specific initiatives.

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The integrative role of landscape

By David Brunckhorst, Jamie Trammell and Ian Reeve

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1. David Brunckhorst (biography)
2. Jamie Trammell (biography)
3. Ian Reeve (biography)

Landscapes are the stage for the theatre of human-nature interactions. What does ‘landscape’ mean and what integrative function does it perform?

What is landscape?

Consider a painting of a landscape or look out a window. We imagine, interpret and construct an image of the ‘landscape’ that we see. It’s not surprising that landscapes (like the paintings of them) are valued through human perceptions, and evolve through closely interdependent human-nature relationships. Landscapes are co-constructed by society and the biophysical environment. Landscape change is, therefore, a continuous reflection of the evolving coupled responses of environment and institutions. Landscapes are especially meaningful to those who live in them.

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Uncertainty in participatory modeling – What can we learn from management research?

By Antonie Jetter

antonie-jetter
Antonie Jetter (biography)

I frequently struggle to explain how participatory modeling deals with uncertainty. I found useful guidance in the management literature.

After all, participatory modeling projects and strategic business planning have one commonality – a group of stakeholders and decision-makers aims to understand and ultimately influence a complex system. They do so in the face of great uncertainty that frequently cannot be resolved – at least not within the required time frame. Businesses, for example, have precise data on customer behavior when their accountants report on annual sales. However, by this time, the very precise data is irrelevant because the opportunity to influence the system has passed.

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Model complexity – What is the right amount?

By Pete Loucks

p-loucks
Pete Loucks (biography)

How does a modeler know the ’optimal’ level of complexity needed in a model when those desiring to gain insights from the use of such a model aren’t sure what information they will eventually need? In other words, what level of model complexity is needed to do a job when the information needs of that job are uncertain and changing?

Simplification is why we model. We wish to abstract the essence of a system we are studying, and estimate its likely performance, without having to deal with all its detail. We know that our simplified models will be wrong. But, we develop them because they can be useful. The simpler and hence the more understandable models are the more likely they will be useful, and used, ‘as long as they do the job.’

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