Modelling

Competencies for systems thinking practitioners. Part 2: Skills and behaviours

Edited by Gabriele Bammer

editors-addition_systems

What skills and behaviours are required by those seeking to provide expert systemic analysis, advice and facilitation to support decision-makers in understanding and addressing complex problems?

This is the second of two blog posts presenting the systems thinking competencies provided by the UK Institute for Apprenticeships and Technical Education (no date) and completes an overview of what decision makers should be able to expect from systems thinking practitioners, along with the knowledge, skills and behaviour competencies such systems thinking practitioners should have. The first blog post provided overall expectations of what systems thinking practitioners should be able to do and knowledge competencies.

Skills competencies

Skills in 11 areas are required for a systems thinking practitioner.

Read more

Competencies for systems thinking practitioners. Part 1: Overall expectations and knowledge

Edited by Gabriele Bammer

editors-addition_systems

What knowledge, skills and behaviours are required by those seeking to provide expert systemic analysis, advice and facilitation to support decision-makers in understanding and addressing complex problems, ie., problems that have no single ‘owner’ or cause, and no simple solution? What should decision makers be able to expect from the systems thinking practitioners they engage with?

The UK Institute for Apprenticeships and Technical Education (no date) provides competencies in knowledge, skills and behaviours and these are reproduced here (knowledge competencies), and in a companion blog post (skills and behaviour competencies). This list of competencies provides a very useful way of getting an overview of systems thinking and the skills required.

What should systems thinking practitioners be able to do?

Read more

Improving facilitated modelling

By Vincent de Gooyert

vincent-de-gooyert
Vincent de Gooyert (biography)

Here I explore two outcomes of facilitated modelling – cognitive change and consensus forming – and ask: how can achieving those outcomes be improved?

But first, what is facilitated modelling?

Facilitated modelling is an approach where operational researchers act as facilitators to model an issue collaboratively with stakeholders, usually in a workshop. Operational research, also known as operations research, seeks to improve decision-making by developing and applying analytical methods.

Two central aims of facilitated modelling are to achieve cognitive change and to form consensus.

Cognitive change is the idea that participants of facilitated modelling workshops come in with a certain worldview, and that the intervention leads them to learn about the issue and accordingly change their minds.

Read more

Five core competency areas for participatory modeling

By Sondoss Elsawah, Elena Bakhanova, Raimo P. Hämäläinen and Alexey Voinov

mosaic_authors_sondoss-elsawah_elena-bakhanova_raimo-hamalainen_alexey-voinov
1. Sondoss Elsawah (biography)
2. Elena Bakhanova (biography)
3. Raimo P. Hämäläinen (biography)
4. Alexey Voinov (biography)

What knowledge and skills do individuals and teams need to be effective at participatory modeling?

We suggest that five core competency areas are essential for participatory modeling:

  1. systems thinking
  2. modeling
  3. group facilitation
  4. project management and leadership
  5. operating in the virtual space.

These are illustrated in the figure below.

These competency areas have naturally overlapping elements and should therefore be seen as a holistic and interdependent set. Further, while certain competencies such as modeling skills can be addressed by individual members of a participatory modeling team, the entire process is a team effort and it is necessary to also consider the competencies as a group skill.

Read more

Participatory scenario planning

authors_maike-hamann_tanja-hichert_nadia-sitas
1. Maike Hamann (biography)
2. Tanja Hichert (biography)
3. Nadia Sitas (biography)

By Maike Hamann, Tanja Hichert and Nadia Sitas

Within the many different ways of developing scenarios, what are useful general procedures for participatory processes? What resources are required? What are the strengths and weaknesses of involving stakeholders?

Scenarios are vignettes or narratives of possible futures, and when used in a set, usually depict purposefully divergent visions of what the future may hold. The point of scenario planning is not to predict the future, but to explore its uncertainties. Scenario development has a long history in corporate and military strategic planning, and is also commonly used in global environmental assessments to link current decision-making to future impacts. Participatory scenario planning extends scenario development into the realm of stakeholder-engaged research.

In general, the process for participatory scenario planning broadly follows three phases.

1. Identifying stakeholders and setting the scene

Read more

Five lessons to improve how models serve society

By Andrea Saltelli

andrea-saltelli
Andrea Saltelli (biography)

Models are mathematical constructs better understood by their developers than by users. So should the public trust models? What insights can help society demand the quality it needs from modeling?

Mathematical modelling is a multiverse, where each scientific discipline adopts its own styles of modeling and quality control. Very little in the way of ‘user instructions’ is available to those affected by modeling practices.

This blog post presents five lessons to improve modelling that were developed as a manifesto by a cross-disciplinary group of natural and social scientists (Saltelli et al., 2020).

Lesson 1: Mind the assumptions

Read more

Leadership in participatory modelling

By Raimo P. Hämäläinen, Iwona Miliszewska and Alexey Voinov

moasaic_authors_raimo-hamalainen_iwona-miliszewska_alexey-voinov
1. Raimo P. Hämäläinen (biography)
2. Iwona Miliszewska (biography)
3. Alexey Voinov (biography)

What can leadership discourse in the business literature tell us for leadership in participatory modelling?

Here we explore:

  • the difference between leadership and management in participatory modelling
  • different leadership styles and participatory modelling
  • three key leadership issues in participatory modelling: responsibility for best practices and ethics, competences, and who in the participatory modelling team should lead.

How does leadership differ from management in participatory modelling?

Read more

Eight grand challenges in socio-environmental systems modeling

By Sondoss Elsawah and Anthony J. Jakeman

authors_sondoss-elsawah_anthony-jakeman
1. Sondoss Elsawah (biography)
2. Anthony Jakeman (biography)

As we enter a new decade with numerous looming social and environmental issues, what are the challenges and opportunities facing the scientific community to unlock the potential of socio-environmental systems modeling?

What is socio-environmental systems modelling?

Socio-environmental systems modelling:

  1. involves developing and/or applying models to investigate complex problems arising from interactions among human (ie. social, economic) and natural (ie. biophysical, ecological, environmental) systems.
  2. can be used to support multiple goals, such as informing decision making and actionable science, promoting learning, education and communication.
  3. is based on a diverse set of computational modeling approaches, including system dynamics, Bayesian networks, agent-based models, dynamic stochastic equilibrium models, statistical microsimulation models and hybrid approaches.

Read more

Why model?

By Steven Lade

Steven Lade
Steven Lade (biography)

What do you think about mathematical modelling of ‘wicked’ or complex problems? Formal modelling, such as mathematical modelling or computational modelling, is sometimes seen as reductionist, prescriptive and misleading. Whether it actually is depends on why and how modelling is used.

Here I explore four main reasons for modelling, drawing on the work of Brugnach et al. (2008):

  • Prediction
  • Understanding
  • Exploration
  • Communication.

I start with mental models – the informal representations of the world that we all use as we go about both our personal and professional lives – and then move on to formal models.

Read more

Four patterns of thought for effective group decisions

By George P. Richardson and David F. Andersen

authors_george-richardson_david-andersen
1. George P. Richardson (biography)
2. David F. Andersen (biography)

What can you do if you are in a group that is trying to deal with problems that are developing over time, where:

  • root causes of the dynamics aren’t clear;
  • different stakeholders have different perceptions;
  • past solutions haven’t worked;
  • solutions must take into account how the system will respond; and,
  • implementing change will require aligning powerful stakeholders around policies that they agree have the highest likelihood of long-term success?

The fields of systems thinking and system dynamics modelling bring four important patterns of thought to such a group decision and negotiation:

Read more

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.

Key goals and purposes of scenarios can be any of the following:

Read more

Agent-based modelling for knowledge synthesis and decision support

By Jen Badham

Jen Badham (biography)

The most familiar models are predictive, such as those used to forecast the weather or plan the economy. However, models have many different uses and different modelling techniques are more or less suitable for specific purposes.

Here I present an example of how a game and a computerised agent-based model have been used for knowledge synthesis and decision support.

The game and model were developed by a team from the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), a French agricultural research organisation with an international development focus. The issue of interest was land use conflict between crop and cattle farming in the Gnith community in Senegal (D’Aquino et al. 2003).

Read more