Detecting non-linear change ‘inside-the-system’ and ‘out-of-the-blue’

Susan van ‘t Klooster and Marjolijn Haasnoot

Author - Susan van ‘t Klooster
Susan van ‘t Klooster (biography)

Change can be expected, envisioned and known, and even created, accelerated or stopped. But change does not always follow a linear and predictable path, nor is it always controllable. Novelty and surprise are inescapable features of life. Non-linear change can involve threats or opportunities.

Although it defines the world we live in, who we are, the outlooks we have and what we do, we often do not relate to non-linear change in a meaningful way. What is holding us back from engaging with it? How do we deal with non-linear change? And what are promising ways forward?

Author - marjolijn-haasnoot
Marjolijn Haasnoot (biography)

Why is thinking about and anticipating non-linear change difficult?

Generally speaking, non-linearity is difficult to define and conceptualize, because there are multiple interacting forces at the intersection of many domains, manifesting on different spatial and temporal scales and many different actors and (often conflicting) perspectives are involved. As a result, both the nature of change, its underlying causalities, potential chain reactions, and its potential effects are uncertain and at best partially knowable.

Non-linearity is also difficult to grasp because it is about processes and events that may or may not happen. Such processes and events are complex and uncertain and can lead to different perspectives and disagreement between stakeholders. When non-linearity occurs, it may come as a surprise or shock and may have a disruptive effect. It is no understatement to say that we are inexperienced with respect to non-linearity.

Instead, we are much more experienced in reasoning from some evolutionary development. As a result of this general tendency to focus on logical consequences of causal patterns in the past and the present, being confronted with non-linear change may generate insecurity, confusion and a general feeling of discomfort. These feelings can result in ignorance and paralysis of decision making. Thus, refraining from thinking about it makes us vulnerable.

We describe two different, but potentially synergetic, approaches in which detecting and monitoring seeds of change are key: ‘inside-the-system’ and ‘out-of-the-blue’, illustrated in the figure below. Both approaches share the idea that systems can become unstable beyond a critical value. Induced by fluctuations within a system and by external disturbances, a system can instantaneously change. In his book Earth in the Balance (2000), former US Vice President Al Gore uses the analogy of a pile of sand. Dropping one grain after another on a pile of grain does not budge the ‘grain system’. Instead, it slowly builds a stable cone. However, at some point, a critical value is reached, which causes the cone to collapse. Each approach discussed below has a different search light for finding system thresholds and changes.

vantklooster_detecting-non-linear-change_ inside-the-system-out-of-the-blue
Different search lights: ‘Inside-the-system’ and ‘Out-of-the-blue’ (Copyright: Susan van ‘t Klooster)

Finding seeds of non-linear change – ‘inside-the-system’

This approach searches primarily for system indicators that may be an early warning prelude to change and adaptation tipping points. Coherent and longitudinal monitoring of these indicators can help to:

  • create a deeper insight into the system’s dynamics;
  • signal changes that jeopardize (or provide opportunities) for achieving defined objectives in a timely manner;
  • change plans and strategies to continue to achieve the objectives under changed conditions;
  • implement actions not too early nor too late; and to avoid investing too much or too little (Haasnoot et al., 2018).

By focusing on those seeds of change we know relatively well because they are part of the current system – so-called ‘known unknowns’, – we become more conscious about potential new conditions and situations.

An example is adaptive planning in the context of Dutch delta management, where important change indicators include observed and projected sea-level rise along the coast, global mean sea-level rise, storm surge frequency and frequency of alarms to close storm surge barriers.

Finding seeds of non-linear change – ‘out-of-the-blue’

This approach searches for so-called ‘wild cards’: low-probability/high-impact events that may be a prelude to a major, sudden and disruptive break with the status-quo.

Signals of change are found beyond the dominant frames and outside the system. The focus here is on cross-cutting trends and events that may surprise us by coming ‘out-of-the-blue’. We do not know if they may happen, the rapidity with which they may unfold nor their potential effects.

It is, therefore, explicitly not the objective to predict and control such disruptive threats (or opportunities). Instead, this approach is aimed towards:

  • scaling down our blind spots towards our future;
  • becoming more literate in understanding the nature of potential disruptors;
  • being more prepared once our current systems are challenged;
  • avoiding a panic reaction and creating an information and strategic advantage.

These changes are considered ‘unknown unknowns’, but it is possible to imagine what some of these changes could be, such as energy becoming available in a limitless supply, the collapse of a major currency, average global life expectancy increasing to 120 years or a new pest that wipes out grain crops.

Strengths and weaknesses

Both approaches have their own strengths and weaknesses. A risk of the ‘inside-the-system’ approach is that signals that are outside ‘the system’ and that do not fit into a dominant mental frame, remain unnoticed. To avoid tunnel vision, it is important to actively search for signals that could change current paradigms (potential game changers) beyond the system.

A weakness related to the ‘out-of-the-blue’ approach is the difficulty of making a solid link with decision-making.

At the same time, both approaches can reinforce one another: The strength of the first lies in its solid systems knowledge and link with decision-making. The second approach has a strong imaginative potential, that can be used to avoid perceptive and interpretive biases.

Four questions we should ask ourselves to detect change

How can we better integrate both perspectives and create synergies between them? Synergy starts by asking the following four questions:

  • Descriptive: What emergent trends (‘inside-the-system’) and potentially disruptive events (‘out-of-the-blue’) do we see or can we imagine? This involves using both systems knowledge and imaginative power.
  • Estimative: How (un)certain are we? This involves embracing, explicating and using multivocality, instead of assuming consensus.
  • Generative: What is the relative impact? This involves focusing on both direct and indirect effects.
  • Responsive: What can we do in response? This involves linking analysis to action, eg., adapting a plan or strategy, preparatory action, installing/altering a monitoring system, communication about outcomes for awareness raising, and further research.

Monitoring both in and outside the system helps to detect and anticipate change and to prepare in a timely fashion if needed.

Final remarks

Do you have suggestions for other ways to deal with non-linearity? Do you have useful examples where ‘inside-the-system’ and ‘out-of the-blue’ approaches have been successfully used to deal with non-linearities?

References:
Gore, A. (2000). Earth in the balance. Ecology and the human spirit. Earthscan: New York, United States of America.

Haasnoot, M. van ’t Klooster, S. and van Alphen, J. (2018). Designing a monitoring system to detect signals to adapt to uncertain climate change. Global Environmental Change, 52: 273-285. (Online) (DOI): https://doi.org/10.1016/j.gloenvcha.2018.08.003

Biography: Susan van ‘t Klooster PhD researches and advises strategic policy and decision-making processes as a freelance consultant in the Netherlands. She specializes in foresight methodology, practice and processes. Her research interests include evaluative foresight, evaluative risk assessment and anticipatory monitoring. Her research covers a wide range of areas, including adaptive water management, spatial planning, environmental policy, population health, education, (aviation) security, social security and employment and migration management.

Biography: Marjolijn Haasnoot PhD is a senior researcher/advisor at Deltares (an independent institute for applied research in the field of water and subsurface) and Associate Professor at Utrecht University in the Netherlands. She specializes in water management, climate adaptation, integrated assessment modeling and decision-making under deep uncertainty. Over the past 20 years she worked on international and national research and consultancy projects assessing impacts of climate change, sea level rise, socio-economic developments and alternative management options to develop robust and adaptive plans. She developed the Dynamic Adaptive Policy Pathways (DAPP) method to support decision making under uncertain change. She was one of the founders of the Society for Decision Making under Deep Uncertainty.

This blog post is part of a series on unknown unknowns as part of a collaboration between the Australian National University and Defence Science and Technology.

For the eight other blog posts already published in this series, see: https://i2insights.org/tag/partner-defence-science-and-technology/

Scheduled blog posts in the series:
January 28, 2020: How can resilience benefit from planning? by Pedro Ferreira
February 11, 2020: Why do we protect ourselves from unknown unknowns? by Bem Le Hunte
February 25, 2020: Theory U: a promising journey to embracing unknown unknowns by Vanesa Weyrauch

 

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

By Christiane Prange and Alicia Hennig

author - christiane prange
Christiane Prange (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. Continue reading

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. Continue reading

Four strategies for improving knowledge exchange among scientists and decision-makers

By Chris Cvitanovic

Chris Cvitanovic (biography)

How can we improve knowledge exchange among scientists and decision-makers to facilitate evidence informed decision-making? Of course there is no one size fits all approach, but here I outline four strategies that could be adapted and implemented across different contexts: (i) knowledge co-production, (ii) embedding, (iii) knowledge brokers, and (iv) boundary organisations. These are illustrated in the figure below.

Knowledge co-production

Perhaps the most widely advocated approach to achieving improved knowledge exchange, knowledge co-production refers to the process whereby decision-makers actively participate in scientific research programs from the onset, collaborating with researchers throughout every aspect of the study including design, implementation and analysis. Continue reading

Keys to transformation: Interactions of values, rules and knowledge

By Russell Gorddard, Matthew Colloff, Russell Wise and Michael Dunlop

Adapting to climate change can require profound alterations in environmental management and policy. However the social context of a decision process limits options and resists change, often dooming attempts to adapt to climate change even before they begin. How can decision makers in policy and management more effectively see the institutional and social water they swim in, in order to better drive change?

Values, rules and knowledge (vrk) provide a useful heuristic to help decision makers analyze how the social system shapes their decision context. Put simply, decisions require:

  • knowledge of options and their implications
  • values to assess the options
  • rules that enable implementation.
gorddard_values-rules-knowledge
Figure adapted from original in Gordardd et al. (2016)

Viewing the decision context as an interconnected system of values, rules and knowledge can reveal limits to adaptation and suggest strategies for addressing them (Gorddard et al. 2016).

Values are the set of ethical precepts that determine the way people select actions and evaluate events.

Rules are both rules-in-use (norms, practices, habits, heuristics) and rules-in-form (regulations, laws, directives).

Knowledge is both evidence-based (scientific and technical) knowledge and experiential knowledge.

Decision context is the subset of interacting subsystems that are at play in a particular decision process. One core idea is that the decision context may exclude relevant values, knowledge or rules from being considered in decisions. Adaptation may therefore involve change in the decision context.

russell-gorddard
Russell Gorddard (biography)

matt-colloff
Matthew Colloff (biography)

russell-wise
Russell Wise (biography)

michael-dunlop
Michael Dunlop (biography)

Continue reading

The path perspective on modelling projects

By Tuomas J. Lahtinen, Joseph H. A. Guillaume, Raimo P. Hämäläinen

tuomas-lahtinen
Tuomas J. Lahtinen (biography)

How can we identify and evaluate decision forks in a modelling project; those points where a different decision might lead to a better model?

Although modellers often follow so called best practices, it is not uncommon that a project goes astray. Sometimes we become so embedded in the work that we do not take time to stop and think through options when decision points are reached.

Joseph H. A. Guillaume (biography)

One way of clarifying thinking about this phenomenon is to think of the path followed. The path is the sequence of steps actually taken in developing a model or in a problem solving case. A modelling process can typically be carried out in different ways, which generate different paths that can lead to different outcomes. That is, there can be path dependence in modelling.

raimo-hamalainen
Raimo P. Hämäläinen (biography)

Recently, we have come to understand the importance of human behaviour in modelling and the fact that modellers are subject to biases. Behavioural phenomena naturally affect the problem solving path. For example, the problem solving team can become anchored to one approach and only look for refinements in the model that was initially chosen. Due to confirmation bias, modelers may selectively gather and use evidence in a way that supports their initial beliefs and assumptions. The availability heuristic is at play when modellers focus on phenomena that are easily imaginable or recalled. Moreover particularly in high interest cases strategic behaviour of the project team members can impact the path of the process. Continue reading