Accountability and adapting to surprises

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.

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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.

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Using the arts and design to build student creative collaboration capacity

By Edgar Cardenas

Edgar Cardenas (biography)

How can undergraduate and graduate students be helped to build their interdisciplinary collaboration capacity? In particular, how do they build capacity between the arts and other disciplines?

In 2018, I co-facilitated the annual, 3-day Emerging Creatives Student Summit, an event for approximately 100 undergraduate and graduate students from 26 universities organized by the Alliance for the Arts in Research Universities. Students’ majors ranged from the sciences, engineering, music, arts, and design.

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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.

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Dealing with deep uncertainty: Scenarios

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Laura Schmitt Olabisi (biography)

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.

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

By Antonie Jetter

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Antonie Jetter (biography)

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.

Two key lessons from the management literature deal with the nature of uncertainty and responding to four major types of uncertainty.

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