Knowledge integration is essential in inter- and transdisciplinary (ITD) education and research. However, integration looks different wherever it emerges (Klein 2012), which makes it difficult to learn about integration from multiple cases. ITD theorists and practitioners long to answer questions such as these: What conditions make integration more likely to succeed or fail? How can we describe integration in ways that others can understand? What are the minimal markers of successful integration? We sense that somehow integration is both one thing and myriad things at once, and this paradox stymies our attempts to understand and reliably facilitate integration.

In this presentation, we contribute to what we call the "philosophy of integration" by characterizing a "general use of the concept" of integration (O'Malley 2013) that does justice both to the fact that the term ‘integration’ is broadly used in very similar ways across the crossdisciplinary literature and the fact that this literature describes a rich variety of integrative cases in inter-, trans- and other crossdisciplinary work. We argue that, for certain purposes, this general use of ‘integration’ can be characterized in terms of a simple, customizable model that is a development of the Input-Process-Output (IPO) approach to crossdisciplinary integration introduced by O'Rourke and colleagues (2016).

After describing the simple model using example cases, we demonstrate how it can be combined with structures imported from other frameworks (e.g., levels of organization in biology, timescales in sociology of science). We show how such combinations enable new research questions about integration across cases and within cases over time and space. Like the original IPO model, inputs, process, and outputs remain essential features of the simple model. However, unlike the IPO model, which includes all possible ways integration can vary, the simple model is customized to include only the variables needed to describe the integrative case(s) under consideration. Modeling integration in this way preserves enough commonalities (inputs, processes, and outputs) to be able to recognize integration in vastly different cases while also being able to capture the sources of their variation–certain features about the inputs, processes, and outputs.

We demonstrate how the model can be customized and applied using nine accounts of integration in the life sciences that were published in 2013 as part of a special collection of Studies in the History and Philosophy of Biological and Biomedical Sciences. These accounts summarize a diverse array of life sciences research activity, and the model distills these accounts to highlight the main sources of variability in the set. In this example application, the sources are mainly features of the inputs and outputs. We show how the model is therefore a concise tool for describing and analyzing key components of integration within and across cases. We conclude by indicating some ways the model could be further customized to support future work, especially deeper examination of integrative processes that include “microintegrations”, or local events of integration that ground larger integrative achievements.

One of our most important technologies is the research discipline, i.e., the “quasi-institutional” structure that produces knowledge in a limited range through organized, social interaction (O’Rourke et al. 2019). Research disciplines and their problems grow up together: on the one hand, a discipline’s problems are described in its language and are amenable to its methods; on the other, a discipline grows and changes as it grapples with the problems it takes on. Thus, the problems that occupy a discipline tend to be discipline-sized problems. Unfortunately, the problems that confront our communities, our countries, and our planet are not discipline-sized problems. While they overlap with disciplinary problems, they have no respect for disciplinary boundaries.

Complex problems like these require complex responses, where that involves assembling different epistemic perspectives on a problem and combining them to address its dynamically interrelated characteristics. A number of research modalities have been described going back to the 1920s that are motivated by the need to meet complex problems with complex responses, such as multidisciplinarity, interdisciplinarity, and transdisciplinarity. As a research modality, convergence is a relatively recent entry. Over the past two decades, there has been an increasing amount of work on convergence, spurred on by interest from the US National Academies, the National Research Council, and National Science Foundation (e.g., NASEM 2019). Despite increased attention to convergence, there has been no consensus about the concept, although not for lack of trying (see Frechtling et al. 2021). This lack of consensus means that no standardized conception of convergence grounds the systematic development of convergence programs and projects or the consistent evaluation of convergence proposals or products. Of course, convergence could simply be a banner that agencies and investigators wave to indicate their support for more expansive, boundary-spanning research, in which case standardization would be unnecessary; in this presentation, though, we assume that it is a technical concept for classifying a specific type of scientific activity, and as such it should be developed with enough precision to be rigorously applied.

In this presentation we will briefly summarize the literature on convergence to reveal different senses and streams of thought that underscore the lack of conceptual consensus. We will also address two theoretical research questions: what is convergence? and how does convergence compare with interdisciplinarity and transdisciplinarity? In doing so, we present a perspective on the nature of convergence and then describe four models of convergence as a process, drawn from the literature, that we relate to the standard spectrum of crossdisciplinary research modalities (e.g., multi-, inter-, and transdisciplinarity). We close by considering the theorist’s challenge when it comes to convergence and the implications that has for the empirical investigation of convergence research.

National Academies of Sciences, Engineering, and Medicine (NASEM). (2019). Fostering the culture of convergence in research: Proceedings of a workshop. Washington, DC: The National Academies Press.

O’Rourke, M., Crowley, S., Laursen, B. K., Robinson, B., Vasko, S. E. (2019). Disciplinary diversity in teams, integrative approaches from unidisciplinarity to transdisciplinarity. In K. L. Hall, A. L. Vogel, and R. T. Croyle (Eds.), Advancing Social and Behavioral Health Research through Cross-Disciplinary Team Science: Principles for Success. Berlin/Heidelberg: Springer. pp. 21–46.

Historically, the design discipline has been acknowledged for its integrative capacity in addressing complexity by crafting cohesive and aesthetically appealing perspectives, thereby informing planning, decision-making, and policy (Ovink & Boeijenga, 2018). However, despite its recognized integrative nature, design has not received adequate acknowledgment within the discourse of knowledge integration, particularly within the Interdisciplinary and Transdisciplinary (ITD) community. With an increasing demand for integrative approaches to address complex spatial development questions, the Resilient Delta initiative, led by a Gluon researcher, developed and applied a design-based interdisciplinary knowledge integration methodology to transform expert knowledge into action-oriented insights to support the complex development ambitions of Maasterras in Doredrecht.

This presentation will illustrate agency of design in the shaping of integrated perspectives, while also addressing the constraints that impede its effectiveness.. Through a detailed examination of a case study, it unveils a series of enabling and disabling factors that influenced the agency of design within the integrative process. These factors encompass disciplinary biases, power dynamics, conventional business paradigms, and siloed administrative structures. These factors underscore thats there are real barriers and bottlenecks in inter and transdisciplinary integration and collaboration.

By shedding light on these challenges, this research emphasizes the necessity of both recongizing the limitations and influences of a design based knowledge integration framework. Moreover it unveils realities of inter- and trandiscplinarity collaboration, and the potential gap from theory to practice. Ultimately this presentation aims to provide critical insights into to the inherent value of a design-based knowledge integration approach in tackling complex development questions, while also fostering awareness of potential disbaling factors that many hinder integration efforts within inter- and trandsciplinary collaboration.

Henk Ovink, & Jelte Boeijenga. (2018). Too big : Rebuild by Design : a transformative approach to climate change. Nai010 Publishers.