Would it be helpful to identify two distinct forms of interdisciplinary scholarship ― 1) individual interdisciplinarity and 2) interdisciplinary dialogue and team science ― and to make this distinction explicit in the literature? What are the benefits and challenges of each? Are a different set of resources and methods required to achieve effective interdisciplinary scholarship?
As integration scientists are aware, there are many analyses of appropriate methods for conducting interdisciplinary work. Each has its own benefits and challenges, and each requires a different set of resources and methods for achieving effective interdisciplinary scholarship.
By Russell Gorddard, Matthew Colloff, Russell Wise and Michael Dunlop
Adapting to climate change can require profound alterations in environmental management and policy. However the social context of a decision process limits options and resists change, often dooming attempts to adapt to climate change even before they begin. How can decision makers in policy and management more effectively see the institutional and social water they swim in, in order to better drive change?
Values, rules and knowledge (vrk) provide a useful heuristic to help decision makers analyze how the social system shapes their decision context. Put simply, decisions require:
knowledge of options and their implications
values to assess the options
rules that enable implementation.
Viewing the decision context as an interconnected system of values, rules and knowledge can reveal limits to adaptation and suggest strategies for addressing them (Gorddard et al. 2016).
Values are the set of ethical precepts that determine the way people select actions and evaluate events.
Rules are both rules-in-use (norms, practices, habits, heuristics) and rules-in-form (regulations, laws, directives).
Knowledge is both evidence-based (scientific and technical) knowledge and experiential knowledge.
Decision context is the subset of interacting subsystems that are at play in a particular decision process. One core idea is that the decision context may exclude relevant values, knowledge or rules from being considered in decisions. Adaptation may therefore involve change in the decision context.
The role and importance of context in the interaction between research and policy is widely recognized. It features in general literature on the subject, in case studies on how research has successfully influenced policy (or not), and in practitioners´ reflections on the results of their work. But how does context specifically matter? Can we move beyond generic statements?
By Tuomas J. Lahtinen, Joseph H. A. Guillaume, Raimo P. Hämäläinen
How can we identify and evaluate decision forks in a modelling project; those points where a different decision might lead to a better model?
Although modellers often follow so called best practices, it is not uncommon that a project goes astray. Sometimes we become so embedded in the work that we do not take time to stop and think through options when decision points are reached.
One way of clarifying thinking about this phenomenon is to think of the path followed. The path is the sequence of steps actually taken in developing a model or in a problem solving case. A modelling process can typically be carried out in different ways, which generate different paths that can lead to different outcomes. That is, there can be path dependence in modelling.
Recently, we have come to understand the importance of human behaviour in modelling and the fact that modellers are subject to biases. Behavioural phenomena naturally affect the problem solving path. For example, the problem solving team can become anchored to one approach and only look for refinements in the model that was initially chosen. Due to confirmation bias, modelers may selectively gather and use evidence in a way that supports their initial beliefs and assumptions. The availability heuristic is at play when modellers focus on phenomena that are easily imaginable or recalled. Moreover particularly in high interest cases strategic behaviour of the project team members can impact the path of the process.
Starting with richly articulated pictures of where we would like to be at some defined point in the future has powerful consequences for any human endeavour. How can we use such “Outcome Spaces” to guide the conception, design, implementation, and evaluation of transdisciplinary research?
Our Outcome Spaces Framework (Mitchell et al., 2017) considers three essential impacts:
(1) improving the situation,
(2) generating relevant stocks and flows of knowledge, and
(3) mutual and transformational learning by the researcher/s and involved participants.
How do we improve? In the context of sustainable development, we continually confront the question of how we can develop meaningful and positive actions towards a ‘better’ world (social, ecological, economic outcomes) despite inherent uncertainties about what the future holds.
Co-creation is one concept among several that seek to reorientate us from simplistic, largely linear ideas of progress towards more nuanced, subtle ideas that highlight that there are many different aspects of ‘progress’, and these can be deeply contested and challenging to reconcile. Enabling co-creation, then – or operationalizing it – means finding practical ways to work together, to deal with our different experiences, aspirations and expectations as well as the uncertainties of the future.
Co-creation sits within a learning paradigm that suggests engagement, social and mutual learning, adaptation and flexibility are key to enabling action in the face of uncertainty. But how do we think about learning?
How can knowledge mobilisers – people who move knowledge into action – make sense of diverse definitions, navigate through the fragmented literature and better describe their work? It all starts with a few simple questions…
Over the past 15-20 years, research and practical activity focusing on how knowledge can be better shared and used has grown at what sometimes seems like an alarming rate. For many, the diverse range of literature, terminology, models and tools can seem overwhelming and bewildering. In 2010, for example, McKibbon and colleagues identified 100 different terms used to describe the activities and processes involved in linking knowledge and practice (McKibbon et al., 2010). And in 2014 Huw Davies and colleagues found 71 substantial reviews of research literature on this topic across health, social care and education (Davies et al., 2015).
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.
Would a focus on ‘knowledge ecology’ provide a useful alternative to ‘knowledge integration’ in inter- and trans-disciplinary research?
My experience in bringing perspectives from the humanities, arts and social sciences (HASS) to projects led by researchers from science, technology, engineering and mathematics (STEM) has led me to agree with Sharp and colleagues (2011) that ‘knowledge integration’ is essentially a positivist concept, dependent on the idealist model of a unified field of scientific knowledge to which every bit of science contributed.
What key actions can help research have impact? Interviews with 32 researchers and stakeholders across 13 environmental management research projects lead to the five principles and key issues described below (Reed et al., 2014).
How can we better understand governance when dealing with complex social and environmental issues? Here I describe a set of concepts that I have found useful — a governance compass. The aim is to provide guidance for organizations to align partnerships, accountability, equity, ownership and value at the ‘point of service’. The ‘point of service’ varies. For human health, it is the patient. In life-long learning, it is the professional. In agriculture sustainability, it is the landscape.
The governance compass identifies governance actors and their roles; governance styles and how they combine into a footprint; and finally how these combine with tasks into a governance framework. Although the compass has been developed for agricultural issues, it has broader relevance.