Decision making – Page 5 – Integration and Implementation Insights

Dealing with deep uncertainty: Scenarios

October 25, 2022January 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.

Model complexity – What is the right amount?

August 24, 2022November 3, 2016

By Pete Loucks

How does a modeler know the ’optimal’ level of complexity needed in a model when those desiring to gain insights from the use of such a model aren’t sure what information they will eventually need? In other words, what level of model complexity is needed to do a job when the information needs of that job are uncertain and changing?

Simplification is why we model. We wish to abstract the essence of a system we are studying, and estimate its likely performance, without having to deal with all its detail. We know that our simplified models will be wrong. But, we develop them because they can be useful. The simpler and hence the more understandable models are the more likely they will be useful, and used, ‘as long as they do the job.’

The interplay between knowledge and power / La interacción entre el conocimiento y el poder

September 5, 2024July 14, 2016

By Cristina Zurbriggen

An English version of this post is available

La mayoría de los recientes enfoques para abordar problemas complejos no incluyen la dimensión política. Por otra parte, la ciencia política, así como los estudios de política pública y de gobierno contemporáneo han realizado escasas contribuciones al tratamiento de los procesos de toma de decisiones desde dinámicas complejas.

¿Cómo podemos desarrollar marcos innovadores que incorporen la dimensión política? ¿Cómo podemos articular la producción conocimiento considerando también la forma en que pensamos acerca de la política, la rendición de cuentas y la responsabilidad social? En concreto, ¿cuál es la dimensión política del proceso de co-creación de conocimiento y cuáles son las implicaciones de la participación política, la experimentación y el aprendizaje colectivo?

Tool users old and new: Why we need models

February 5, 2025June 23, 2016

By Suzanne A. Pierce

Ask most 21st century citizens whether they like technology and they will respond with a resounding, “Yes!” Ask them why and you’ll get answers like, “Because it’s cool and technology is fun!” or “Technology systems help us learn and understand things.” Or “Technology helps us communicate with one another, keep up with current events, or share what we are doing.” Look at the day-to-day activities of most people on the planet and you’ll find that they use some form of technology to complete almost every activity that they undertake.

When you think about it, technologies are really just tools. And we humans are tool users of old.

Models as narratives

September 5, 2024June 16, 2016

By Alison Singer

I don’t see the world in pictures. I mean, I see the world in all its beautiful shapes and colors and shadings, but I don’t interpret the world that way. I interpret the world through the stories I create. My interpretations of these stories are my own mental models of how I view the world. What I can do then, to share this mental model, is create a more formalized model, whether it is a simple picture (in my case a very badly drawn one), or a system dynamics model, or an agent-based model. People think of models as images, as representations, as visualizations, as simulations. As tools to represent, to simplify, to teach, and to share. And they are all these things, and we need them to function as these things, but they are also stories, and can be interpreted and shared as such.

Facilitating multidisciplinary decision making

April 15, 2022May 17, 2016

By Bob Dick

Imagine this scenario. You are confronted by a wicked problem, such as the obesity epidemic. You know it’s a wicked problem – many previous attempts to resolve it have failed.

Suppose that you wish to develop a plan to remedy obesity. You have identified as many relevant areas of expertise and experience as you can and approached appropriate people – researchers, health practitioners, people with political influence, and so on.

You bring them together to pool their expertise—only to find that you now have another problem. Encouraging them to work collaboratively is more difficult than you expected. They talk in jargon. Their understanding is narrow. Their commitment is to their own discipline. Some of their understanding is tacit. Some of them are argumentative. And more. What are you to do?

Should researchers be honest brokers or advocates?

September 5, 2024April 5, 2016

By John Callewaert

When to advocate and when to be an honest broker is a question that deserves serious attention by those working on collaborative and engaged research initiatives. In my role as the Integrated Assessment director at the University of Michigan’s Graham Sustainability Institute I facilitate a wide array of collaborative research efforts. For most of our initiatives we strive to work within an honest broker frame. Following the work of Pielke (2007), the honest broker engages in decision-making by clarifying and sometimes expanding the scope of choice to decision-makers. Our recent analysis of options for High Volume Hydraulic Fracturing in Michigan^[1] (fracking) and outlining sustainability goals for our Ann Arbor campus^[2] are two examples which involved teams of faculty, students, practitioners and decision-makers.

The honest broker approach was particularly important for the project on fracking given the polarized views that can sometimes be associated with this topic.

Moving from models that synthesize to models that innovate

September 11, 2024March 24, 2016

By Pete Loucks

When computer technology became available for developing and using graphics interfaces for interactive decision support systems, some of us got excited about the potential of directly involving stakeholders in the modeling and analyses of various water resource systems. Many of us believed that generating pictures that could show the impact of various design and management decisions or assumptions any user might want to make would give them a better understanding of the system being modeled and how they might improve its performance.

We even got fancy with respect performing sensitivity analyses and displaying uncertainty. Our displays were clear, understandable, and colorful. Sometimes we witnessed users even believing what they were seeing. We occasionally had to remind users that our models were, and would continue to be, approximations of reality, at best. It was fun developing and using such tools, and indeed today most models that are used to analyze river basins, groundwater, and coastal zones incorporate interactive, graphics-based, decision support frameworks.