The Strategic Choice Approach in shaping public policies

By Catherine Hobbs

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Catherine Hobbs (biography)

How can we be inspired, rather than overwhelmed, by differing perspectives in the inter-organisational planning required to more effectively address cross-cutting issues, or interacting areas of policy? How can we learn from the achievements of public policy action research, in the light of the local and global uncertainties of the 2020s?

Strategic Choice Approach was developed by John Friend with Allen Hickling, originating during the 1960s and 1970s. It emerged through a series of collaborative action research projects applied to public policy challenges in a number of countries, so that its origins are empirical rather than theoretical.

Friend described Strategic Choice Approach as being helpful as a practical approach to planning under pressure where “people of different outlooks and allegiances are working together with a shared concern to move rapidly towards commitments to action or to changes of policy on difficult issues of shared concern” (Friend, no date).

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Lessons for strengthening community-university partnerships

By David D. Hart, Bridie McGreavy, Anthony Sutton, Gabrielle V. Hillyer and Darren J. Ranco

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1. David D. Hart; 2. Bridie McGreavy; 3. Anthony Sutton; 4. Gabrielle V. Hillyer; 5. Darren J. Ranco (biographies)

In an increasingly polarized world, how can partnerships between communities and universities strengthen the kinds of deliberative and democratic practices that might help address many local and global challenges? How can such partnerships improve practices that involve listening and responding across differences (the deliberative part)? How can they help find ways to make shared decisions and take joint actions, knowing that complete agreement or mutual understanding may never be possible (the democratic part)?

We have reflected on our partnerships with people from Maine communities and Wabanaki (“People of the Dawnland”) Tribal Nations in North America, especially regarding challenges faced by communities that harvest clams and other bivalve mollusks from the intertidal mudflats along the length of this region’s enormous coastline (Hart et al., 2022). Here we present some of the key lessons from that work.

Common ground?

Some challenges facing local communities are less about competing ideologies and more about pragmatic concerns such as reducing water pollution, which can make it easier for people to listen to and learn about each other in the context of community planning.

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Participatory scenario planning

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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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Multidisciplinary perspectives on unknown unknowns

By Gabriele Bammer

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Gabriele Bammer (biography)

This is part of a series of occasional “synthesis blog posts” drawing together perspectives on related topics across i2Insights contributions.

How can different disciplines and practitioners enhance the ability to understand and manage unknown unknowns, also referred to as deep uncertainty?

Seventeen blog posts have addressed these issues, covering:

  • how unknown unknowns can be understood
  • exploiting unknown unknowns
  • accepting unknown unknowns
  • reducing unknown unknowns.

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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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Five lessons to improve how models serve society

By Andrea Saltelli

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Andrea Saltelli (biography)

Models are mathematical constructs better understood by their developers than by users. So should the public trust models? What insights can help society demand the quality it needs from modeling?

Mathematical modelling is a multiverse, where each scientific discipline adopts its own styles of modeling and quality control. Very little in the way of ‘user instructions’ is available to those affected by modeling practices.

This blog post presents five lessons to improve modelling that were developed as a manifesto by a cross-disciplinary group of natural and social scientists (Saltelli et al., 2020).

Lesson 1: Mind the assumptions

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Judgment and decision making with unknown states and outcomes

By Michael Smithson

Michael Smithson
Michael Smithson (biography)

What issues arise for effective judgments, predictions, and decisions when decision makers do not know all the potential starting positions, available alternatives and possible outcomes?

A shorthand term for this collection of possible starting points (also known as prior states), alternatives, and outcomes is “sample space.” Here I elucidate why sample space is important and how judgments and decisions can be influenced when it is incomplete.

Why is sample space important?

When it comes to dealing with unknowns, economists and others traditionally distinguish between “risk” (where probabilities can be assigned to every possible outcome) and “uncertainty” (where the probabilities are vague or unknown). Both of those versions of unknowns assume that decision makers know everything about the sample space.

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A quick guide to post-normal science

By Silvio Funtowicz

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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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Can foresight and complexity play together?

By James E. Burke

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James E. Burke (biography)

What is foresight and how does it differ from prediction? What role can complexity play in foresight? Does Cynefin® offer a possible framework to begin integrating foresight and complexity?

In this blog post, I describe how:

  • Foresight identifies clues for the future and integrates them into forecasts
  • Complexity theory offers ways to understand how the future emerges
  • Cynefin® gives us a framework of domains that allows us to better understand trends and forecasts.

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Can examining cross-disciplinary interactions illuminate unknown unknowns?

By Rick Szostak

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Rick Szostak (biography)

Unknown unknowns are challenges that we will face in future that we do not foresee today.

Here I argue that an important subgroup of unknown unknowns occurs when some phenomenon that we know a lot about has an unexpected effect on another phenomenon that we know a lot about, especially when there are few links between the two silos of knowledge. An example is unanticipated “interactions” between medications prescribed by medical practitioners from different specialities. Here I explore such disciplinary interactions more generally.

Disciplinary scholars focus on interactions among the phenomena that their discipline studies, but usually ignore interactions with phenomena studied in other disciplines. The academy as a whole thus devotes little attention to interactions among phenomena studied in different disciplines.

I have explored major historical transformations, which were generally surprises at the time and found they always involve interactions among the phenomena studied by multiple disciplines.

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

By Fabio Boschetti

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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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Choosing a suitable transdisciplinary research framework

By Gabriele Bammer

Author - Gabriele Bammer
Gabriele Bammer (biography)

What are some of the key frameworks that can be used for transdisciplinary research? What are their particular strengths? How can you choose one that’s most suitable for your transdisciplinary project?

The nine frameworks described here were highlighted in a series for which I was the commissioning editor. The series was published in the scientific journal GAIA: Ecological Perspectives in Science and Society between mid-2017 and end-2019.

Choosing among them is not a matter of right or wrong, but of each being more or less helpful for a particular problem in a particular context. And, of course, different frameworks can also be used in combination.

The brief descriptions and figures that follow aim to encapsulate each framework’s key strengths.

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