A convergence research approach to resolving ‘wicked problems’: Lessons from an interdisciplinary research team in land use science

Abstract

Many contemporary social and environmental problems are increasingly ‘wicked.’ Convergence research offers an effective approach to tackle wicked problems by integrating diverse epistemologies, methodologies, and expertise. Yet, there exists little discussion of how to develop and employ a convergence research approach. This article describes our collaborative research efforts to achieve convergence research and team science. For over a decade, we have sought to understand how drug trafficking activities, and the counternarcotics efforts designed to thwart them, catalyze catastrophic changes in landscapes and communities. We first discuss how understanding our wicked problem called for epistemological convergence of diverse data through a team science approach. We then unpack the potential insights and challenges of methodological convergence by drawing upon examples from our land cover and land use change analysis. Third, we argue that the nature of complex, pressing problems requires convergence research to be politically engaged and accountable to the multiple communities affected. This article aims to provide research teams insight into how to pursue epistemological and methodological convergence while attending to the inherent politics of producing knowledge about wicked problems. • Convergence research and team science are effective approaches to study ‘wicked problems’. • The War on Drugs is a ‘wicked problem’ driving deforestation and environmental degradation. • Research convergence requires integrating diverse epistemologies and methodologies. • Data interoperability methods can support epistemological integration. • ‘Wicked problems’ require politically accountable and policy-oriented research. • Key aspects of convergence research and team science occur at each research life cycle step.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.