Stabilize or Adapt? Expanding the Role of Uncertainty in Collaborative Governance

• In Event: Collaborative and Polycentric Governance

Abstract

Governing turbulence and uncertainty is one of the key challenges of public management and collaborative governance (Ansell and Trondal 2018). Collaborative Governance (CG) is a mode of governance in which public, non-profit, and private actors work together to achieve goals that are not achievable separately. While the role of uncertainty is recognized as a driver of collaboration (Emerson, Nabatchi, Balogh 2012), its role is not fully developed and understood in the dynamics and evolution perspectives of collaborative governance. This is especially important since scholars have called for such empirical work to better understand institutional change in collaborative governance (Ulibarri et al. 2020; Baldwin et al. 2023). We define Collaborative Governance Regime as a “particular mode of, or system for, public decision making in which cross-boundary collaboration represents the prevailing pattern of behavior and activity” (Emerson, Nabatchi, and Balogh 2012, 6). Many CGR units have a dilemma whether they will stabilize or adapt/change their operation when facing uncertainty. CGR’s decision to stabilize or adapt is critical to the health of CGR and its outcomes. This study asks three questions: 1) what are the types and sources of uncertainty in collaborative governance? 2) how stakeholders respond to and handle uncertainty? 3) how uncertainty and uncertainty handling evolve over time and drive institutional change?
In this study, I develop and use three types of uncertainty framework in collaborative natural resource governance: scientific uncertainty, relational uncertainty, and policy uncertainty. Scientific uncertainty is defined as a lack of knowledge about the causes or consequences of an environmental problem or decision within collaborative governance (Abbott 2005; Ulibarri 2019). Relational uncertainty is unknown about how other actors would think and behave in collaborative governance when faced with challenges and opportunities (Brugnach et al. 221; Baggio et al. 2019). Policy uncertainty is about whether developed plans via collaboration would be actually implemented on the ground (USPP).
While collaboration is an almost ubiquitous feature of governance in natural resource management, systematic analysis about the role of uncertainty and its dynamic nature is rare. Thus, I take a case study approach to dig deeper and fully specify the nature of uncertainty and its evolution in collaborative dynamics. San Pedro river is important ecological resource for species in the North American continent. Upper San Pedro Partnership (https://uppersanpedropartnership.org/) was established in 1998 to better manage Sierra Vista watershed in southeast AZ, USA (Richter et al. 2014; Gungle et al. 2016).
This study uses data from 22 in-depth interviews and archival sources. Archival sources include local news articles, policy reports, peer-reviewed articles, and meeting minutes. Interview sample includes a range of participants including policy makers, scientists, non-profit personnel, and citizens. I also interviewed key stakeholders who participated in the past to ensure correct understanding about the history of the partnership and its institutional environment.
Results suggest that three types of uncertainty have varying salience in different phases of CGR life cycle. I specify each category with empirical evidence. From dynamics perspective, while scientific uncertainty is moderately mitigated by co-producing agreeable science over time, relational and policy uncertainty are still salient in later stages of CGR life cycle. These unresolved uncertainties drive major institutional changes – formalization of the partnership, restructuring rules and organizations, creation of new partnership – at various stages of developmental phases. We also show various ways individuals and groups respond and adapt to uncertainties at a micro-level. Finally, we underscore the importance of gathering data from many participants in different phases to fully capture uncertainty dynamics and resulting decision making by CGR.
This study offers empirical and conceptual development in collaborative governance by expanding the role of uncertainty from a dynamic perspective. Findings suggest that understanding and specifying three types of uncertainties in sufficiently longer period– scientific, relational, policy – has a major benefit to understand CGR decision making and institutional change. Future studies can further develop case study or experimental approach to understand the relationship between uncertainty, decision making, and institutional change.

Author

• Minwoo Ahn, University of Arizona