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

The strength of failing (or how I learned to love ugly babies)

Community member post by Randall J. Hunt

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Randall J. Hunt (biography)

How to give others your hard-won insights so that their work can be more informed, efficient, and effective? As I’ve gotten older, it is something that I think about more.

It is widely recognized that the environment is an integrated but also “open” system. As a result, when working with issues relating to the environment we are faced with the unsatisfying fact that we won’t know “truth”. We develop an understanding that is consistent with what we currently know and what we consider state-of-the-practice methods. But, we can never be sure that more observations or different methods would not result in different insights. Continue reading

The integrative role of landscape

Community member post by David Brunckhorst, Jamie Trammell and Ian Reeve

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David Brunckhorst (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. 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

Four things everyone should know about ignorance

Community member post by Michael Smithson

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Michael Smithson (biography)

“Ignorance” is a topic that sprawls across a grand variety of disciplines, professions and problem domains. Many of these domains have their own perspective on the unknown, but these are generally fragmentary and often unconnected from one another. The topic lacks a home. Until fairly recently, it was a neglected topic in the humanities and human sciences.

I first started writing about it in the 1980’s (e.g., my book-length treatment, Ignorance and Uncertainty: Emerging Paradigms), but it wasn’t until 2015 that the properly compiled interdisciplinary Routledge International Handbook on Ignorance Studies (Gross and McGoey 2015) finally appeared.

Given the wide-ranging nature of this topic, here are four things everyone should know about ignorance. 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