Scenarios – Integration and Implementation Insights

Participatory scenario planning

December 13, 2022December 6, 2022

authors_maike-hamann_tanja-hichert_nadia-sitas — 1. Maike Hamann (biography)
2. Tanja Hichert (biography)
3. Nadia Sitas (biography)

By Maike Hamann, Tanja Hichert and Nadia Sitas

Within the many different ways of developing scenarios, what are useful general procedures for participatory processes? What resources are required? What are the strengths and weaknesses of involving stakeholders?

Scenarios are vignettes or narratives of possible futures, and when used in a set, usually depict purposefully divergent visions of what the future may hold. The point of scenario planning is not to predict the future, but to explore its uncertainties. Scenario development has a long history in corporate and military strategic planning, and is also commonly used in global environmental assessments to link current decision-making to future impacts. Participatory scenario planning extends scenario development into the realm of stakeholder-engaged research.

Ten dialogue methods for integrating judgments

November 10, 2022August 2, 2022

By David McDonald, Gabriele Bammer and Peter Deane

authors_david-mcdonald_gabriele-bammer_peter-deane — 1. David McDonald (biography)
2. Gabriele Bammer (biography)
3. Peter Deane (biography)

What formal dialogue methods can assist researchers in synthesising judgments about a complex societal or environmental issue when a range of parties with different perspectives are involved? How can researchers decide which methods will be most suitable for their purposes?

We review ten dialogue methods. Our purpose is not to describe the dialogue methods in detail, but instead to review the circumstances in which each method is likely to be most useful in a research context, bearing in mind that most methods a) were not developed for research, b) can be applied flexibly and c) have evolved into different variations.

Designing scenarios to guide robust decisions

April 4, 2023July 2, 2019

By Bonnie McBain

What makes scenarios useful to decision makers in effectively planning for the future? Here I discuss three aspects of scenarios:

goals;
design; and,
use and defensibility.

Goals of scenarios

Since predicting the future is not possible, it’s important to know that scenarios are not predictions. Instead, scenarios stimulate thinking and conversations about possible futures.

Key goals and purposes of scenarios can be any of the following:

Scoping: Lessons from environmental impact assessment

April 15, 2022April 4, 2017

By Peter R. Mulvihill

peter-mulvihill — 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.

Unintended consequences of honouring what communities value and aspire to

April 15, 2022January 26, 2017

By Melissa Robson

It seems simple enough to say that community values and aspirations should be central to informing government decisions that affect them. But simple things can turn out to be complex.

In particular, when research to inform land and water policy was guided by what the community valued and aspired to rather than solely technical considerations, a much broader array of desirable outcomes was considered and the limitations of what science can measure and predict were usefully exposed.

Dealing with deep uncertainty: Scenarios

October 25, 2022January 5, 2017

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.

The integrative role of landscape

March 23, 2022November 15, 2016

By David Brunckhorst, Jamie Trammell and Ian Reeve

authors_mosaic_david-brunckhorst_jamie-trammell_ian-reeve — 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.

Making predictions under uncertainty

August 24, 2022August 25, 2016

By Joseph Guillaume

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.