Enabling co-creation: From learning cycles to aligning values, rules and knowledge

Community member post by Lorrae van Kerkhoff

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Lorrae van Kerkhoff (biography)

How do we improve? In the context of sustainable development, we continually confront the question of how we can develop meaningful and positive actions towards a ‘better’ world (social, ecological, economic outcomes) despite inherent uncertainties about what the future holds.

Co-creation is one concept among several that seek to reorientate us from simplistic, largely linear ideas of progress towards more nuanced, subtle ideas that highlight that there are many different aspects of ‘progress’, and these can be deeply contested and challenging to reconcile. Enabling co-creation, then – or operationalizing it – means finding practical ways to work together, to deal with our different experiences, aspirations and expectations as well as the uncertainties of the future.

Co-creation sits within a learning paradigm that suggests engagement, social and mutual learning, adaptation and flexibility are key to enabling action in the face of uncertainty. But how do we think about learning? Continue reading

Dealing with deep uncertainty: Scenarios

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Laura Schmitt Olabisi (biography)

Community member post by Laura Schmitt Olabisi

What is deep uncertainty? And how can scenarios help deal with it?

Deep uncertainty refers to ‘unknown unknowns’, which simulation models are fundamentally unsuited to address. Any model is a representation of a system, based on what we know about that system. We can’t model something that nobody knows about—so the capabilities of any model (even a participatory model) are bounded by our collective knowledge.

One of the ways we handle unknown unknowns is by using scenarios. Scenarios are stories about the future, meant to guide our decision-making in the present. Continue reading

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

Community member post by Antonie Jetter

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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.

Two key lessons from the management literature deal with the nature of uncertainty and responding to four major types of uncertainty. Continue reading

Making predictions under uncertainty

Community member post by Joseph Guillaume

Joseph Guillaume (biography)

Prediction under uncertainty is typically seen as a daunting task. It conjures up images of clouded crystal balls and mysterious oracles in shadowy temples. In a modelling context, it might raise concerns about conclusions built on doubtful assumptions about the future, or about the difficulty in making sense of the many sources of uncertainty affecting highly complex models.

However, prediction under uncertainty can be made tractable depending on the type of prediction. Here I describe ways of making predictions under uncertainty for testing which conclusion is correct. Suppose, for example, that you want to predict whether objectives will be met. There are two possible conclusions – Yes and No, so prediction in this case involves testing which of these competing conclusions is plausible. Continue reading

In praise of multidisciplinarity

Community member post by Gabriele Bammer

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Gabriele Bammer (biography)

What characterizes multidisciplinary research? When is it most appropriate? What does it take to do it well? Multidisciplinarity often gets a bad rap, being seen as less sophisticated than interdisciplinarity and transdisciplinarity. But does it have its own important role in dealing with complex social and environmental problems?

Multidisciplinary research has two primary characteristics:

  1. different disciplines independently shine their light on a particular problem, and
  2. synthesis happens at the end and can be undertaken by anyone.

Unlike interdisciplinary and transdisciplinary research, there is no attempt to agree upfront on either a problem definition or on how the different perspectives will be brought together. Continue reading