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.

Read more

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.

Read more

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.

Read more

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:

Read more

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:

Read more

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).

Read more

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?

Read more

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!

Read more

You are biased!

By Matthew Welsh

matthew-welsh
Matthew Welsh (biography)

Complex, real-world problems require cooperation or agreement amongst people of diverse backgrounds and, often, opinions. Our ability to trust in the goodwill of other stakeholders, however, is being eroded by constant accusations of ‘bias’. These are made by commentators about scientists, politicians about media outlets and people of differing political viewpoints about one another. Against this cacophony of accusation, it is worthwhile stepping back and asking “what do we mean when we say ‘bias’ and what does it say about us and about others?”.

Read more

Using the concept of risk for transdisciplinary assessment

By Greg Schreiner

greg-schreiner
Greg Schreiner (biography)

Global development aspirations, such as those endorsed within the Sustainable Development Goals, are complex. Sometimes the science is contested, the values are divergent, and the solutions are unclear. How can researchers help stakeholders and policy-makers use credible knowledge for decision-making, which accounts for the full range of trade-off implications?

‘Assessments’ are now commonly used.

Read more

A new boundary object to promote researcher engagement with policy makers / Un nuevo objeto frontera para promover la colaboración de los investigadores con los tomadores de decisiones

By María D. López Rodríguez

maria-lopez-rodriguez.jpg
María D. López Rodríguez (biography)

A Spanish version of this post is available

Can boundary objects be designed to help researchers and decision makers to interact more effectively? How can the socio-political setting – which will affect decisions made – be reflected in the boundary objects?

Here I describe a new context-specific boundary object to promote decision making based on scientific evidence. But first I provide a brief introduction to boundary objects.

What is a ‘boundary object’?

Read more

Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

By Jan Kwakkel

jan-kwakkel
Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are.

Read more