Decision support

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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Stakeholder engagement primer: 8. Generating ideas and reaching agreement

By Gabriele Bammer

primer_stakeholder-engagement_8

What skills for generating ideas and reaching agreement should every researcher involved in stakeholder engagement seek to cultivate? What key methods and concepts should they be familiar with?

The focus in this blog post is on generating ideas and reaching agreement, as well as recognising the “groan zone” between these two phases in a group process. Researchers will have diverse attributes and not everyone will be well-placed to cultivate the skills described here. Having an understanding of the skills can help in choosing the researchers best placed to undertake the stakeholder engagement.

Generating ideas: Brainstorming

For brainstorming to work well, it requires rapid-fire contributions, no holding back or self-censoring of ideas, and no discussion or criticism of the ideas proposed. It often involves a group of stakeholders (or stakeholders and researchers) sitting around a flipchart or whiteboard, with one person writing down the ideas as members of the group say them.

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A tool for transforming resistance to insights in decision-making

By Gemma Jiang

author_gemma-jiang
Gemma Jiang (biography)

Do you encounter resistance from your team members, especially in regard to difficult decisions? How might decision-making processes be better facilitated to generate insights instead of resistance?

I describe a conceptual framework and an accompanying practical tool from Lewis Deep Democracy (2021) that can transform resistance to insights in decision-making processes.

The conceptual framework: Understanding how decision making generates resistance

It is important first to understand the consciousness of a team. If you think of a team’s consciousness as an iceberg, the ideas and opinions that are expressed are the conscious part above the waterline, while those that are not expressed are the unconscious part below the waterline. If decisions are made based only on the team’s expressed ideas and opinions, those below the waterline will likely form resistance. This is often what happens with “majority rules” democracy.

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Gradients of agreement for democratic decision-making

By Hannah Love

hannah-love
Hannah Love (biography)

How does your team make decisions? Do you vote? Does the loudest voice usually win? Does everyone on the team generally feel heard? Does your team have a charter to provide guidance? Or maybe there is often just silence and the team assumes agreement?

The next time your team makes a decision, here is something new you can try! Kaner (2014) proposes using a gradients of agreement scale. The gradients of agreement, also known as the consensus spectrum, provides an alternative to yes/no decision-making by allowing everyone to mark their response along a continuum, as shown in the figure below.

What are the gradients of agreement and the benefits of using them?

This is a tool to support democratic decision-making. The gradients of agreement has a scale with numbers (1-8) and short descriptions.

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Eight grand challenges in socio-environmental systems modeling

By Sondoss Elsawah and Anthony J. Jakeman

authors_sondoss-elsawah_anthony-jakeman
1. Sondoss Elsawah (biography)
2. Anthony Jakeman (biography)

As we enter a new decade with numerous looming social and environmental issues, what are the challenges and opportunities facing the scientific community to unlock the potential of socio-environmental systems modeling?

What is socio-environmental systems modelling?

Socio-environmental systems modelling:

  1. involves developing and/or applying models to investigate complex problems arising from interactions among human (ie. social, economic) and natural (ie. biophysical, ecological, environmental) systems.
  2. can be used to support multiple goals, such as informing decision making and actionable science, promoting learning, education and communication.
  3. is based on a diverse set of computational modeling approaches, including system dynamics, Bayesian networks, agent-based models, dynamic stochastic equilibrium models, statistical microsimulation models and hybrid approaches.

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Blackboxing unknown unknowns through vulnerability analysis

By Joseph Guillaume

Author - Joseph Guillaume
Joseph Guillaume (biography)

What’s a productive way to think about undesirable outcomes and how to avoid them, especially in an unpredictable future full of unknown unknowns? Here I describe the technique of vulnerability analysis, which essentially has three steps:

  • Step 1: Identify undesirable outcomes, to be avoided
  • Step 2: Look for conditions that can lead to such outcomes, ie. vulnerabilities
  • Step 3: Manage the system to mitigate or adapt to vulnerable conditions.

The power of vulnerability analysis is that, by starting from outcomes, it avoids making assumptions about what led to the vulnerabilities. The causes of the vulnerabilities are effectively a ‘black box’, in other words, they do not need to be understood in order to take effective action. The vulnerability itself is either a known known or a known unknown. The causes of the vulnerability, on the other hand, can be unknown unknowns.

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Managing innovation dilemmas: Info-gap theory

By Yakov Ben-Haim

Author - Yakov Ben-Haim
Yakov Ben-Haim (biography)

To use or not to use a new and promising but unfamiliar and hence uncertain innovation? That is the dilemma facing policy makers, engineers, social planners, entrepreneurs, physicians, parents, teachers, and just about everybody in their daily lives. There are new drugs, new energy sources, new foods, new manufacturing technologies, new toys, new pedagogical methods, new weapon systems, new home appliances and many other discoveries and inventions.

Furthermore, the innovation dilemma occurs even when a new technology is not actually involved. The dilemma arises from new attitudes, like individual responsibility for the global environment, or new social conceptions, like global allegiance and self-identity transcending all nation-states. Even the enthusiastic belief in innovation itself as the source of all that is good and worthy entails a dilemma of innovation.

An innovation’s newness and the uncertainty of its promise for improvement is the source of the dilemma. Tomorrow we will understand the innovation better, its dangers and its benefits, but today we must decide.

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Why model?

By Steven Lade

Steven Lade
Steven Lade (biography)

What do you think about mathematical modelling of ‘wicked’ or complex problems? Formal modelling, such as mathematical modelling or computational modelling, is sometimes seen as reductionist, prescriptive and misleading. Whether it actually is depends on why and how modelling is used.

Here I explore four main reasons for modelling, drawing on the work of Brugnach et al. (2008):

  • Prediction
  • Understanding
  • Exploration
  • Communication.

I start with mental models – the informal representations of the world that we all use as we go about both our personal and professional lives – and then move on to formal models.

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Four patterns of thought for effective group decisions

By George P. Richardson and David F. Andersen

authors_george-richardson_david-andersen
1. George P. Richardson (biography)
2. David F. Andersen (biography)

What can you do if you are in a group that is trying to deal with problems that are developing over time, where:

  • root causes of the dynamics aren’t clear;
  • different stakeholders have different perceptions;
  • past solutions haven’t worked;
  • solutions must take into account how the system will respond; and,
  • implementing change will require aligning powerful stakeholders around policies that they agree have the highest likelihood of long-term success?

The fields of systems thinking and system dynamics modelling bring four important patterns of thought to such a group decision and negotiation:

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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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Agent-based modelling for knowledge synthesis and decision support

By Jen Badham

Jen Badham (biography)

The most familiar models are predictive, such as those used to forecast the weather or plan the economy. However, models have many different uses and different modelling techniques are more or less suitable for specific purposes.

Here I present an example of how a game and a computerised agent-based model have been used for knowledge synthesis and decision support.

The game and model were developed by a team from the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), a French agricultural research organisation with an international development focus. The issue of interest was land use conflict between crop and cattle farming in the Gnith community in Senegal (D’Aquino et al. 2003).

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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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