1. The idea of “catching the rhythm” of the “patterns of movement” in our constantly changing world.
2. More effectively taking context into account.
3. “We cannot know the systems, but we can know more. We cannot perfect the systems, but we can do better.”
The challenge is to develop methods and processes to better achieve these goals. (Reblogged by Gabriele Bammer)
Science is getting increasingly bureaucratized, more and more driven by metrics and indices, which have very little to do with the actual scientific content and recognition among peers. This is actively supported by the still dominant for-profit publication mechanism, which harvests products of scientific research for free, processes, reviews and edits them using voluntary work of scientists themselves and then sells the resulting papers back to the scientific community at obscene costs. The original ideals of scientific pursuit of truth for the sake of the betterment of humanity are diluted and forfeited in the exhausting race for grants, tenure, patents, citations and nominations. Something has to change, especially in the era of post-normal science when so much is at stake, and so little is actually done to address the mounting problems of the environment and society.
In computer programming open source emerged in the 1980s largely in opposition to attempts at licensing code and the growing dominance of Windows with the annoyingly secretive policies of Microsoft. It came as the idealistic philosophy of software development that stems from the so-called “gift culture” and “gift economy” based on this culture.
A key topic across disciplines is the authentic engagement and participation of key stakeholders in developing and guiding innovations to solve problems. Complex systems consist of dense webs of relationships where individual stakeholders self-organize through interactions. Research demonstrates that successful uptake of innovations requires genuine and meaningful interaction among researchers, service providers, policy makers, consumers, and other key stakeholders. Implementation efforts must address the various needs of these stakeholders. However, these efforts are described differently across disciplines and contexts – co-design, co-production, co-creation, and co-construction.
Developing consensus on terminology and meanings will facilitate future research and application of “co” concepts. For example, co-design is often used in health to describe processes that put users and communities at the heart of service design. Co-production is often discussed in socio-environmental science to allow users to participate in administration and delivery. Co-creation is often used in business to describe the involvement of customers in developing products and processes.
Modeling is the language of scientific discovery and has significant implications for how scientists communicate within and across disciplines. Whether modeling the social interactions of individuals within a community in anthropology, the trade-offs of foraging behaviors in ecology, or the influence of warming ocean temperatures on circulation patterns in oceanography, the ability to represent empirical or theoretical understanding through modeling provides scientists with a semi-standardized language to explain how we think the world works. In fact, modeling is such a basic part of human reasoning and communication that the formal practice of scientific modeling has been recently extended to include non-scientists, especially as a way to understand complex and poorly understood socio-environmental dynamics and to improve collaborative research. Although the field of participatory modeling has grown in recent years, there are still considerable questions about how different software tools common to participatory modeling can be used to facilitate communication and learning among diverse groups, which approaches are more or less suitable (given the nature of a community or environmental issue), and whether these approaches effectively lead to action-oriented outcomes.
In a recent special issue of the journal Nature on interdisciplinarity (17 September 2015, p313-315), Rick Rylance criticised “arcane debates about whether research is inter-, multi-, trans-, cross- or post-disciplinary”, opining “I find this faintly theological hair-splitting unhelpful.” Does he have a point?
Rylance was discussing these distinctions in the context of research funding, especially relating to effective funding and evaluation of… well, what are we talking about and what are we going to call it? That’s the nub of the problem. For now, let’s stick with the term used by Rylance, namely “interdisciplinarity”.
Rylance also introduced a current project of the Global Research Council, which is comprised of the heads of science and engineering funding agencies from around the world. The Global Research Council has selected interdisciplinarity as one of its two annual themes for an in-depth report, debate and statement between now and mid-2016.
The aim of this site is to host a global conversation about… well one of the challenges is that we don’t yet have an agreed name for our topic.
This is a conversation for you if your research does some of the following:
Gets people from different disciplines working together
Builds models of complex social and environmental problems
Helps policy makers use research evidence
Figures out ways to manage value conflicts
Finds ways to identify unknown unknowns
Maps interconnections between problem elements
Works with business to build better products
Involves community groups in defining the problem
Worries about adverse unintended consequences
Realises that context matters.
I think about these practices as integration and implementation sciences. You might call them systems thinking, action research, interdisciplinarity or transdisciplinarity, implementation science, post-normal science, mode 2 research, project management, complex systems science or a host of other terms.