As a modeller, I often get requests from research or policy colleagues along the lines of ‘we want a model of the health system’. It’s relatively easy to recognise that ‘health system’ is too vague and needs explicit discussion about the specific issue to be modelled. It is much less obvious that the term ‘model’ also needs to be refined. In practice, different modelling methods are more or less appropriate for different questions. So how is the modelling method chosen?
I approach this topic as an analytic philosopher rather than a specialist in co-creation. It’s clear that co-creation is thought to offer a promising response to real world problems and it connects in interesting ways with my own work on epistemic virtues and vices.
What is ‘co-creation’ and what are its benefits, real or imagined? To ‘create’ something is to bring it into existence. Co-creation, as I understand it, is the creation of a product by two or more people or agencies with particular characteristics working together in a particular way.
The key questions are: (a) what is the ‘product’ of co-creation? (b) What are the particular characteristics of those involved in co-creation? (c) What is the particular ‘way’ of working together that distinguishes co-creation from other collaborative activities?
How can we address social, environmental, political and health problems that are too big and too complex for any single person, organization or institution to solve, or even to budge? How can we pool our wisdom and work collaboratively toward purposes that are larger than ourselves?
In theory at least, co-creation generates innovative solutions that transcend what would otherwise be produced by the participants acting on their own. In other words, co-creation can foster synergy.
To maximize synergy, a co-creative group should include participants who understand the problem from all the relevant perspectives. The more complex the problem, the greater the number and diversity of stakeholders who should be included in the process. A broader range of perspectives and ways of thinking allows for a richer and more comprehensive analysis of the problem, as well as more innovative solutions that address more of the underlying factors.
Co-creation aims at genuine and meaningful interaction among researchers, service providers, policy makers, consumers, and other key stakeholders. It is also known as co-production, co-design and co-construction. Co-creation is often a buzzword with fuzzy meanings of who collaborates with whom, when and how (processes) and to what end (outcomes) in addressing sustainability and other complex problems. Yet there is emerging evidence on best practices of co-creation. Although this evidence is mostly based on individual case studies or comparisons of small sets of cases, the following eight strategies provide valuable guidance for researchers and practitioners.
The language of ‘co-processes’ is much in vogue in policy, practice and academic communities worldwide. In commerce, product design and politics, the power of the crowd has long been recognised, but can co-processes be harnessed for the public good? The answer, right now, appears to be ‘maybe’.
What are co-processes and what are they for?
The briefest survey of the literature on co-processes confirms there is substantial variation in how they are defined and what methods or techniques they include. A confusing multiplicity of related terms exists—co-construction, co-production, co-design, co-innovation, co-creation—all are in regular use, sometimes interchangeably, and often defined at an unhelpful level of abstraction (for more on this topic see the blog post by Allison Metz on Co-creation, co-design, co-production, co-construction: same or different?). Nevertheless, however we define co-processes, participatory methods, boundary-spanning and inclusivity to varying degrees are foundational principles that can be detected in most accounts. Beyond that, the stated purposes and proposed outcomes vary considerably.
Being responsive to stakeholder interests and suggestions is important for successful participatory modeling. We share lessons from an exciting, five year project in the UK entitled the Sustainable Uplands. The project sought to bring together a variety of groups ranging from academics, policy makers, residents, conservationists, and different ‘end user’ groups that all, in some way, held a stake in upland park areas in the UK.
Our process was iterative, tacking back and forth between field work, consultations among the research team, consultations with non-academic stakeholders, and modeling. Not only were our models heavily influenced by what stakeholders told us were important values and considerations regarding upland areas, but these also informed how we went about gathering the data.
Who can make systems change? The challenges of complexity are intensely felt by those who are trying to make strategic interventions in coupled human-environmental systems in order to fulfill personal, societal, or institutional goals. The activists, leaders, and decision-makers I work with often feel overwhelmed by trying to deal with multiple problems at once, with limited time, resources, and attention. We need tools to help leaders cut through the complexity so that they can identify the most effective strategies to make change.
This is where participatory system dynamics modelers like myself come in.
Efforts to improve the use of models to support policy and practice on water resources issues have increased awareness of the role of advocacy and public engagement in the modeling process. Hydrologists have much to learn from the recent experience of climate scientists who have discovered that scientific knowledge is not independent of the political context in which it is used but rather is co-produced by scientists and society.
Despite a strong consensus among climate scientists in the Intergovernmental Panel on Climate Change’s (IPCC) 2013 report that “warming of the climate system is unequivocal,” approximately one-third of the USA’s population still does not believe that global temperatures have risen over the past 100 years and does not trust the things that scientists say about the environment.
La modélisation participative cherche à impliquer un groupe de personnes dans la conception et la révision d’un modèle. L’objectif à terme consiste à mieux caractériser les problèmes actuels et imaginer collectivement comment tenter de les résoudre. Dans le domaine de l’environnement en particulier, il apparaît nécessaire que les acteurs concernés se sentent impliqués dans la démarche de modélisation, afin qu’ils puissent exprimer leurs propres points de vue, mais aussi pour mieux s’engager dans des décisions collectives. De ce fait, pour aborder la gestion intégrée des ressources, il est nécessaire de mettre les acteurs au centre des préoccupations de recherche, à la fois lors de la phase la conception du modèle mais aussi pour l’exploration de ces scénarios.
What are the results of participatory modeling efforts? What contextual factors, resources and processes contribute to these results? Answering such questions requires the systematic and ongoing evaluation of processes, outputs and outcomes. At present participatory modeling lacks a framework to guide such evaluation efforts. In this post I offer some initial thoughts on the features of this framework.
A first step in developing an evaluation framework for participatory modeling is to establish criteria for processes, outputs, and outcomes. Such criteria would answer a basic question about what it means when we say that a participatory modeling process, output, or outcome is good, worthy, or meritorious.
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.
Like many practices in life, there is an art and a science to facilitation. Certainly, best practices in facilitating processes within participatory modeling mirror many of those practices highlighted in guides to other participatory approaches. It is of critical importance that the expectations around the word “effective”, as taken from the definition above, are identified and negotiated. How can an individual or team of individuals help the process if expectations are unmatched?