Partner – SESYNC – Page 2 – 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.

The strength of failing (or how I learned to love ugly babies)

August 24, 2022December 1, 2016

By Randall J. Hunt

randall-hunt — Randall J. Hunt (biography)

How to give others your hard-won insights so that their work can be more informed, efficient, and effective? As I’ve gotten older, it is something that I think about more.

It is widely recognized that the environment is an integrated but also “open” system. As a result, when working with issues relating to the environment we are faced with the unsatisfying fact that we won’t know “truth”. We develop an understanding that is consistent with what we currently know and what we consider state-of-the-practice methods. But, we can never be sure that more observations or different methods would not result in different insights.

Learning through modeling

October 9, 2023November 17, 2016

By Kirsten Kainz

How can co-creation communities use models – simple visual representations and/or sophisticated computer simulations – in ways that promote learning and improvement? Modeling techniques can serve to generate insights and correct misunderstandings. Are they equally as useful for fostering new learning and adaptation? Sterman (2006) argues that if new learning is to occur in complex systems then models must be subjected to testing. Model testing must, in turn, yield evidence that not only guides decision-making within the current model, but also feeds back evidence to improve existing models so that subsequent decisions can be based on new learning.

Consider the real-world case I was involved in of a meeting in a school district that intends to roll-out a new mathematics curriculum and support teachers’ use of the new curriculum through professional development. The district has made a large monetary investment in the curriculum and professional development both through the purchase of materials and the dedication of human resources to the effort.

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

August 24, 2022November 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.

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.’

Problem framing and co-creation

August 24, 2022October 27, 2016

By Graeme Nicholas

How can people with quite different ways of ‘seeing’ and thinking about a problem discover and negotiate these differences?

A key element of co-creation is joint problem definition. However, problem definition is likely to be a matter of perspective, or a matter of how each person involved ‘frames’ the problem. Differing frames are inevitable when participants bring their differing expertise and experience to a problem. Methods and processes to support co-creation, then, need to manage the coming together of people with differing ways of framing the problem, so participants can contribute to joint problem definition.

Improving health care services through Experience-based Co-design

August 24, 2022October 20, 2016

By Glenn Robert and Annette Boaz

authors_glenn-robert_annette-boaz — 1. Glenn Robert (biography)
2. Annette Boaz (biography)

There is lots of talk about the potential of co-creation as an approach to improving public services, but what does it actually look like (and do) in practice?

We describe one specific approach that has been used extensively for improving the quality of health care services: Experience-based Co-design.

Key Features and Stages

Experience-based Co-design draws on elements of participatory action research, user-centred design, learning theory and narrative-based approaches to change.

The key features of Experience-based Co-design are that it:

places patients at the heart of a quality improvement effort working alongside staff to improve services
maintains a focus on designing experiences (not just systems or processes).

It has six stages.

Stage 1 involves establishing the governance and project management arrangements.

A co-creation challenge: Aligning research and policy processes

August 24, 2022October 13, 2016

By Katrin Prager

How does the mismatch between policy and research processes and timelines stymie co-creation? I describe an example from a project in Sachsen-Anhalt state in Germany, along with lessons learnt.

The project, initiated by researchers, aimed to use a more participatory approach to developing agri-environmental schemes, in order to improve their effectiveness. Officers from the Agricultural Payments department of the Sachsen-Anhalt Ministry for Agriculture were invited to participate in an action research project that was originally conceived to also involve officers from the Conservation department of the same ministry, farmer representatives and conservation groups.

Should I trust that model?

September 15, 2023October 6, 2016

By Val Snow

How do those building and using models decide whether a model should be trusted? While my thinking has evolved through modelling to predict the impacts of land use on losses of nutrients to the environment – such models are central to land use policy development – this under-discussed question applies to any model.

In principle, model development is a straightforward series of steps:

• Specification: what will be included in the model is determined conceptually and/or quantitatively by peers, experts and/or stakeholders and the underlying equations are decided

• Coding: the concepts and equations are translated into computer code and the code is tested using appropriate software development processes

Creating a pragmatic complexity culture / La creación de una cultura pragmática de la complejidad

August 24, 2022September 29, 2016

By Cristina Zurbriggen

An English version of this post is available

¿Cómo pueden los gobiernos, las comunidades y el sector privado efectivamente trabajar juntos para lograr un cambio social hacia el desarrollo sostenible?

En este blog describo los procesos claves que permitieron a Uruguay lograr uno de los regímenes más avanzados de protección del suelo de tierras de cultivo de secano en el mundo. Una explicación del proceso es la creación de una cultura pragmática de la complejidad, una cultura inclusiva, deliberativa que reconoce la naturaleza compleja del problema y abraza el potencial de lo posible.

ICTAM: Bringing mental models to numerical models

August 24, 2022September 22, 2016

By Sondoss Elsawah

How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?

This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.

More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey.

Participatory processes and participatory modelling: The sustainable procedure framework

August 24, 2022September 15, 2016

By Beatrice Hedelin

How can we resolve debates about participatory processes between proponents and skeptics? What role can participatory modelling play in improving participatory processes?

Proponents argue for the merits of participatory processes, which include learning; co-production of knowledge; development of shared understanding of a problem and shared goals; creation of trust; and local power and ownership of a problem.

Sceptics point to evidence of inefficient, time-consuming, participatory processes that escalate conflict and mistrust. They also highlight democratic problems; lack of transparency; and powerful actors that benefit in relation to weaker ones such as the unorganized, poor, and uneducated.