Uncertainty

Participatory scenario planning

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.

In general, the process for participatory scenario planning broadly follows three phases.

1. Identifying stakeholders and setting the scene

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Ignorance: Vocabulary and taxonomy

By Michael Smithson

Michael Smithson
Michael Smithson (biography)

How can we better understand ignorance? In the 1980s I proposed the view that ignorance is not simply the absence of knowledge, but is socially constructed and comes in different kinds (Smithson, 1989). Here I present a brief overview of that work, along with some key subsequent developments.

Defining ignorance

Let’s begin with a workable definition of ignorance and then work from there to a taxonomy of types of ignorance. Our definition will have to deal both with simple lack of knowledge but also incorrect ideas. It will also have to deal with the fact that if one is attributing ignorance to someone, the ignoramus may be a different person or oneself.

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A quick guide to post-normal science

By Silvio Funtowicz

silvio-funtowicz
Silvio Funtowicz (biography)

Post-normal science comes into play for decision-making on policy issues where facts are uncertain, values in dispute, stakes high and decisions urgent.

A good example of a problem requiring post-normal science is the actions that need to be taken to mitigate the effects of sea level rise consequent on global climate change. All the causal elements are uncertain in the extreme, at stake is much of the built environment and the settlement patterns of people, what to save and what to sacrifice is in dispute, and the window for decision-making is shrinking. The COVID-19 pandemic is another instance of a post-normal science problem. The behaviour of the current and emerging variants of the virus is uncertain, the values of socially intrusive remedies are in dispute, and obviously stakes are high and decisions urgent.

In such contexts of policy making, normal science (in the Kuhnian sense, see Kuhn 1962) is still necessary, but no longer sufficient.

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Ten insights on the interplay between evidence and policy

By Kat Smith and Paul Cairney

authors_kat-smith_paul-cairney
1. Kat Smith (biography)
2. Paul Cairney (biography)

How can we improve the way we think about the relationship between evidence and policy? What are the key insights that existing research provides?

1. Evidence does not tell us what to do

It helps reduce uncertainty, but does not tell us how we should interpret problems or what to do about them.

2. There is no such thing as ‘the evidence’

Instead, there is a large number of researchers with different backgrounds, making different assumptions, asking different questions, using different methods, and addressing different problems.

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Considering uncertainty, awareness and ambiguity as a three-dimensional space

By Fabio Boschetti

author-fabio-boschetti
Fabio Boschetti (biography)

The concept of unknown unknowns highlights the importance of introspection in assessing knowledge. It suggests that finding our way in the set of known-knowns, known-unknowns, unknown-knowns and unknown-unknowns, reduces to asking:

  1. how uncertain are we? and
  2. how aware are we of uncertainty?

When a problem involves a decision-making team, rather than a single individual, we also need to ask:

  1. how do context and perception affect what we know?

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How can resilience benefit from planning?

By Pedro Ferreira

author - pedro-ferreira
Pedro Ferreira (biography)

Improved resilience can contribute to the ability to deal with unknown unknowns. Dealing with uncertainty is also at the core of every planning activity. The argument put forward here is that planning processes should be considered a cornerstone for any given resilience approach. An outline of planning and resilience is given, before presenting fundamental aspects of planning that should be strengthened within a resilience strategy.

Planning

From attempting to do as much as possible within a day’s work, to launching rockets into space or managing a nation, everything requires planning. The backbone of planning is widely recognised to be a more or less complex and distributed decision-making process that establishes priorities for the allocation of resources and attempts to reduce (down to levels deemed acceptable) uncertainty associated with the pursuit of such priorities.

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Yin-yang thinking – A solution to dealing with unknown unknowns?

By Christiane Prange and Alicia Hennig

authors_christiane-prange_alicia-hennig
1. Christiane Prange (biography)
2. Alicia Hennig (biography)

Sometimes, we wonder why decisions in Asia are being made at gargantuan speed. How do Asians deal with uncertainty arising from unknown unknowns? Can yin-yang thinking that is typical for several Asian cultures provide a useful answer?

Let’s look at differences between Asian and Western thinking first. Western people tend to prefer strategic planning with linear extrapolation of things past. The underlying mantra is risk management to buffer the organization and to protect it from harmful consequences for the business. But juxtaposing risk and uncertainty is critical. Under conditions of uncertainty, linearity is at stake and risk management limited.

In several Asian cultures, like China, dealing with high uncertainty and volatility is day-to-day business. The country overall scores comparatively low on the uncertainty avoidance index as illustrated by culture researcher Geert Hofstede (2001).

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Looking in the right places to identify “unknown unknowns” in projects

Author - Tyson R. Browning
Tyson R. Browning (biography)

By Tyson R. Browning

Unknown unknowns pose a tremendous challenge as they are essentially to blame for many of the unwelcome surprises that pop up to derail projects. However, many, perhaps even most, of these so-called unknown unknowns were actually knowable in advance, if project managers had merely looked in the right places.

For example, investigations following major catastrophes (such as space shuttle disasters, train derailments, and terrorist attacks), and project cost and schedule overruns, commonly identify instances where a key bit of knowledge was in fact known by someone working on that project—but failed to be communicated to the project’s top decision makers. In other cases, unknown unknowns emerge from unforeseen interactions among known elements of complex systems, such as product components, process activities, or software systems.

With the right mindset and toolset, we can shine a light into the right holes to uncover the uncertainties that could affect a project’s success.

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Designing scenarios to guide robust decisions

By Bonnie McBain

Bonnie McBain (biography)

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:

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Managing uncertainty in decision making: What can we learn from economics?

By Siobhan Bourke and Emily Lancsar

authors_siobhan-bourke_emily-lancsar
1. Siobhan Bourke (biography)
2. Emily Lancsar (biography)

How can researchers interested in complex societal and environmental problems best understand and deal with uncertainty, which is an inherent part of the world in which we live? Accidents happen, governments change, technological innovation occurs making some products and services obsolete, markets boom and inevitably go bust. How can uncertainty be managed when all possible outcomes of an action or decision cannot be known? In particular, are there lessons from the discipline of economics which have broader applicability?

While uncertainty is often discussed alongside risk, a fundamental difference between uncertainty and risk is that risk involves events with known probabilities (or probabilities based on reliable empirical evidence), whereas under uncertainty probabilities are unknown and reflect an individual’s subjective belief concerning the likelihood of a given outcome. Given the subjectivity, that likelihood can differ from person to person. It can also involve a perceived zero probability in the case of unforeseen events (or ‘unknown unknowns’).

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What every interdisciplinarian should know about p values

By Alice Richardson

Alice Richardson (biography)

In interdisciplinary research it’s common for at least some data to be analysed using statistical techniques. Have you been taught to look for ‘p < 0.05’ meaning that there is a less than 5% probability that the finding occurred by chance? Do you look askance at your statistician colleagues when they tell you it’s not so simple? Here’s why you need to believe them.

The whole focus on p < 0.05 to the exclusion of all else is a historical hiccup, based on a throwaway line in a manual for research workers. That manual was produced by none other than R.A. Fisher, giant of statistical inference and inventor of statistical methods ranging from the randomised block design to the analysis of variance. But all he said was that “[p = 0.05] is convenient to take … as a limit in judging whether a deviation is to be considered significant or not.” Convenient, nothing more!

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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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