Paper Session: Cognition and Collaboration as Knowledge Integration and Transfer

Tuesday, August 2, 2022
11:30 PM - 12:45 PM ET

Towards a Body of Knowledge of Transdisciplinary Research

Christian Pohl

Abstract: Teams starting a transdisciplinary research (TDR) project often risk to reinvent the wheel. This is because in this field, there is no systematic learning and storing of knowledge and experiences. Also, it is unclear what kind of knowledge gained in such projects should be systematized at all and can be transferred to a next project. A main aim of transdisciplinary knowledge production is to be relevant for addressing a problem in its particular political, cultural, historical, ecological and economic context. In other words, TDR produces situated knowledge (Haraway, 1988). TDR aims, however, also at supporting societal problem solving in general and therefore‚ creating context-specific solutions that can (at least partly) be transferred to other contexts‚Äù (Nagy et al., 2020, 149). We studied 12 Swiss-based TDR projects in the field of sustainable development to find out what knowledge researchers and practitioners considered to be transferable between cases. We found the following knowledge to be transferable: (1) Transdisciplinary principles, (2) transdisciplinary approaches, (3) systematic procedures, (4) product formats, (5) experiential know-how, (6) framings and (7) insights, data and information. As far as we oversee the current discussion of TDR, systematic compilations of knowledge have been focused on transdisciplinary principles and approaches. One reason might be that knowledge about principles and approaches is helpful to group transdisciplinary activities and describe them as a field of research in its own rights. For the future we think transferable knowledge of the other classes should be further developed, too, foremost knowledge on systematic procedures, product formats and framings.



Haraway, D. (1988). Situated Knowledges - the Science Question in Feminism and the Privilege of Partial Perspective. Feminist Studies 14(3): 575-599.

Nagy, E., Ransiek, A., Schäfer, M., Lux, A., Bergmann, M., Jahn, T., Marg, O. & Theiler, L. (2020). Transfer as a reciprocal process: How to foster receptivity to results of transdisciplinary research. Environmental Science & Policy 104: 148-160.

Research Development Professionals as Catalysts for Statewide Interdisciplinary Teams

Melanie Bauer

Abstract: Florida faces many pressing regional issues such as hurricane and flood mitigation, harmful algal blooms, among others. These societal challenges are particularly relevant to our state but widespread around the U.S. and world. Solving these intractable problems requires team-based solutions that cross disciplinary boundaries and likely require collaborations between academia, government, industry, and others.Through a collaborative $300K NSF grant, a group of Research Development (RD) staff from Florida universities will perform researcher‚ matchmaking‚ and help develop interdisciplinary teams with members from across the state. Each team will focus on a Florida coastal challenge, and receive professional development and support in their idea development and grant seeking. The goal of this project is to create and sustain inter-institutional teams, with RD staff serving as the ‚glue‚Äù and acting as connective boundary spanners with institutional knowledge and awareness of opportunities for strategic growth, community partnerships, and statewide policy and funding initiatives. RD facilitators will nurture newly formed research teams into stable groups with aligned goals and defined member roles, leveraging Team Science (TS) and related activities to enhance knowledge integration and create a shared transdisciplinary framework. Successful completion of this project will advance the fields of TS and RD as well as inform how convergence research can be spawned, supported, sustained, and scaled. First, for TS it will identify gaps in approaches/tools to support geographically dispersed groups. It will elucidate how a motivating societal challenge coupled with knowledge integration activities, engagement with external stakeholders, and dedicated project management impact faculty mindset and team success. Additionally, it will test whether in-person team formation followed by virtual interactions is a viable model for inter-institutional collaboration. Second, for RD (still a young professional field) it will chart a way for expanding how it supports TS, including when collaborations span multiple institutions and diverse partner organizations. It will also elevate the role RD professionals play in engaging at the institutional, state, and national level to further research, translation, and economic goals. Finally, this project will serve as a model for driving faculty to consider, embrace, seek out, and accomplish convergent research objectives, of great interest to NSF and the U.S.The leaders of this project come from five Florida institutions and represent expertise in TS, technology-supported collaboration, faculty training/mentorship, research project ideation/proposal development, large-scale networking events, and education/social science research. This initiative was born out of a statewide network of research development professionals, the Florida Research Development Alliance, with current members from 22 Florida institutions.

What Knowledge Should Be Shared and Remain Unique to Individuals in Interdisciplinary Collaboration?

Susan Mohammed

Abstract: What knowledge should be shared and what knowledge should remain unique to individual members for interdisciplinary teams to be effective? Across 33 interviews, participants agreed that multidisciplinary respect, research goals, and the research process should be shared by PI/co-PI teams. Interviewees concurred that operational details of methodologies could remain divergent.

Toward a science of interdependence for autonomous human-machine teams and systems

William Lawless

Recently, computational autonomy has begun to receive significant attention, but neither the theory nor science is sufficiently developed to design and operate autonomous human-machine teams or systems (HMS). We review the reason for shifting from laboratory studies, which have failed to advance the science of autonomy, to open and uncertain environments where autonomous human-machine systems must operate (Endsley, 2021), along with supporting evidence from the field for autonomous human teams (Lawless, 2022). We attribute the need for this shift to the social sciences, including economics (Rudd, 2021), being overly focused on a science of individual agents (Leach, 2021), whether for humans or machines, a focus that has long been unable to generalize to new situations, applications and theory, let alone to make the leap to a science of human-machine teams, or, even more difficult, to a science of autonomy. The failure of traditional systems, predicated on the individual, to replicate what it means to even be the social event being observed is at the very heart of the impediment to be overcome as prelude to the mathematics of interdependence necessary for machines. We discuss case studies with a focus on how an autonomous human system investigated the first self-driving car fatality (NTSB, 2019); why a human-machine team of independent agents failed to prevent that fatality; and how an autonomous human-machine system might solve the same problem in the future. To advance the science, we reject the science of individuals as simplistic, especially the aggregation of independent effects among teammates as a viable scientific approach for teams, which we replace with what we know about the physics of interdependence for an HMS. As an early sign of success, while traditional social science is unable to generalize to autonomous human-machine teams and systems, the obverse appears to be true, that the science of autonomous human-machine systems generalizes to the social science of individual humans, human teams, and human systems, explaining, for example, why reduced interdependence from command governance (e.g., authoritarians, communists, gangs) disadvantages these systems compared to those that maximize competitive choices (viz., resources, teammates), critical to marshaling information to govern an HMS. We review the gaps in theory and future plans to address them.


Endsley, M. R. (2021). Human-AI Teaming;

Lawless, W. F. (2022), Risk Determination versus Risk Perception: A New Model of Reality for Human–Machine Autonomy. Informatics,  9(2), 30,

Leach, C. W. (2021). Editorial. Journal of Personality and Social Psychology, 120(1), 30–32.

NTSB (2019, 11/19), Collision Between Vehicle Controlled by Developmental Automated Driving System and Pedestrian,

Rudd, J. B. (2021). Why Do We Think That Inflation Expectations Matter for Inflation? (And Should We?). Federal Reserve,