Unknown unknowns – Page 2 – Integration and Implementation Insights

How can we know unknown unknowns?

January 8, 2020September 10, 2019

By Michael Smithson

In a 1993 paper, philosopher Ann Kerwin elaborated a view on ignorance that has been summarized in a 2×2 table describing crucial components of metacognition (see figure below). One margin of the table consisted of “knowns” and “unknowns”. The other margin comprised the adjectives “known” and “unknown”. Crosstabulating these produced “known knowns”, “known unknowns”, “unknown knowns”, and unknown unknowns”. The latter two categories have caused some befuddlement. What does it mean to not know what is known, or to not know what is unknown? And how can we convert either of these into their known counterparts?

Accountability and adapting to surprises

May 3, 2021August 27, 2019

By Patricia Hirl Longstaff

Image of Patricia Hirl Longstaff — Patricia Hirl Longstaff (biography)

We have all been there: something bad happens and somebody (maybe an innocent somebody) has their career ruined in order to prove that the problem has been fixed. When is blame appropriate? When is the blame game not only the wrong response, but damaging for long-term decision making?

In a complex and adapting world, errors and failure are not avoidable. The challenges decision-makers and organizations face are sometimes predictable but sometimes brand new. Adapting to surprises requires more flexibility, fewer unbreakable rules, more improvisation and deductive tinkering, and a lot more information about what’s going right and going wrong. But getting there is not easy because this challenges some very closely held assumptions about how the world works and our desire to control things.

Designing scenarios to guide robust decisions

February 10, 2021July 2, 2019

By Bonnie McBain

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.

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

May 21, 2021August 15, 2017

By Jan Kwakkel

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

Intrinsic limits to predictability in complex systems.
A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
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.

Dealing with deep uncertainty: Scenarios

July 21, 2020January 5, 2017

By Laura Schmitt Olabisi

What is deep uncertainty? And how can scenarios help deal with it?

Deep uncertainty refers to ‘unknown unknowns’, which simulation models are fundamentally unsuited to address. Any model is a representation of a system, based on what we know about that system. We can’t model something that nobody knows about—so the capabilities of any model (even a participatory model) are bounded by our collective knowledge.

One of the ways we handle unknown unknowns is by using scenarios. Scenarios are stories about the future, meant to guide our decision-making in the present.

Uncertainty in participatory modeling – What can we learn from management research?

June 9, 2021November 10, 2016

By Antonie Jetter

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.