Turning Contention into Collaboration: Engaging Power, Trust, and Learning in Collaborative Networks

Given the complexity and multiplicity of goals in natural resource governance, it is not surprising that policy debates are often characterized by contention and competition. Yet at times adversaries join together to collaborate to find creative solutions not easily achieved in polarizing forums. We employed qualitative interviews and a quantitative network analysis to investigate a collaborative network that formed to develop a resolution to a challenging natural resource management problem, the conservation of vernal pools. We found that power had become distributed among members, trust had formed across core interests, and social learning had resulted in shared understanding and joint solutions. Furthermore, institutions such as who and when new members joined, norms of inclusion and openness, and the use of small working groups helped create the observed patterns of power, trust, and learning

Governing science as a complex adaptive system

Research policy is a complex matter. Copying best practices in research policy, as identified by benchmarking studies, is popular amongst policy makers but fails because of ‘knowledge asymmetries’. Research fields exhibit distinct knowledge dynamics that respond differently to governance interventions. Extending the idea of search regimes, this paper aims at providing a policy model for different knowledge dynamics by elaborating the notion of knowledge production as a complex adaptive system. Complex regimes emerge from three interacting sources of variance. In our conceptualisation, researchers are the nodes that carry the science system. Research can be considered as geographically situated practices with site specific skills, equipments and tools. The emergent science level refers to the formal communication activities of the knowledge published in journals and books, and announced in conferences. The contextual dynamics refer to the ways in which knowledge production provides resources for social and economic development. This conceptualization allows us to disaggregate knowledge dynamics both in horizontal (field related) and vertical (level related) dimensions by articulating the three different dynamics and their path dependencies (in research, science and society) in co-evolution with each other to produce distinct search regimes in each field. The implication for research governance is that generic measures can sometimes be helpful but there is clear need for disaggregated measures targeting field specific search regimes. Governing knowledge production through disaggregated measures means targeting in a distinct way not only different fields, but also, and more importantly, the interactions between local research practices, emergent scientific landscapes, and the field’s relationship to its societal context. If all three “levels” are aligned, there is a stable regime.search regime, research and innovation governance, complex adaptive system

How Dutch Institutions Enhance the Adaptive Capacity of Society

This report examines the adaptive capacity of the institutional framework of the Netherlands to cope with the impacts of climate change. Historically, institutions have evolved incrementally to deal with existing social problems. They provide norms and rules for collective action and create continuity rather than change. However, the nature of societal problems is changing as a result of the processes of globalization and development. With the progress made in the natural sciences, we are able to predict in advance, to a certain extent, the potential environmental impacts of various human actions on society, for example, climate change. This raises some key questions: Are our institutions capable of dealing with this new knowledge about future impacts and, more importantly, with the impacts themselves? Are our institutions capable of dealing with the inherent uncertainty of the predictions

Evaluation of stakeholder participation in monitoring regional sustainable development

This paper presents a theoretical framework that can be used to discuss the question of how context, time and different participatory process designs influence the results of participatory monitoring projects in terms of concrete outputs (such as sustainability indicators) and the more intangible social outcomes (such as learning and stakeholder relations). We will discuss and compare four different cases of participatory monitoring of provincial sustainable development in the Netherlands. The results show sustainability issues selected by the stakeholders reflect the socio-economic and ecological structural characteristics of their region. In a different context, stakeholders not only assign different weights to the same set of issues, but more importantly they select a completely different set of regional aims altogether. Since these regional structural characteristics only change slowly over time, the influence of time on stakeholder preferences is shown to be only of minor importance. However, the dissipation of learning effects is shown to be a fundamental challenge for the cyclical nature of participatory monitoring, especially when its goal is shared agenda building. Another important conclusion is that, in the design of participatory processes, more attention should be devoted to providing stakeholders with the opportunity to comment on an ‘intermediate’ product

Knowledge management for self-organised resource allocation

Many open systems, such as networks, distributed computing and socio-technical systems address a common problem of how to define knowledge management processes to structure and guide decision-making, coordination and learning. While participation is an essential and desirable feature of such systems, the amount of information produced by its individual agents can often be overwhelming and intractable. The challenge, thus, is how to organise and process such information, so it is transformed into productive knowledge used for the resolution of collective action problems. To address this problem, we consider a study of classical Athenian democracy which investigates how the governance model of the city-state flourished. The work suggests that exceptional knowledge management, i.e. making information available for socially productive purposes, played a crucial role in sustaining its democracy for nearly 200 years, by creating processes for aggregation, alignment and codification of knowledge. We therefore examine the proposition that some properties of this historical experience can be generalised and applied to computational systems, so we establish a set of design principles intended to make knowledge management processes open, inclusive, transparent and effective in self-governed social technical systems. We operationalise three of these principles in the context of a collective action situation, namely self-organised common-pool resource allocation, exploring four governance problems: (a) how fairness can be perceived; (b) how resources can be distributed; (c) how policies should be enforced and (d) how tyranny can be opposed. By applying this operationalisation of the design principles for knowledge management processes as a complement to institutional approaches to governance, we demonstrate empirically how it can guide solutions that satisfice shared values, distribute power fairly, apply "common sense" in dealing with rule violations, and protect agents against abuse of power. We conclude by arguing that this approach to the design of open systems can provide the foundations for sustainable and democratic self-governance in socio-technical systems.Open Acces

What Might Democratic Self-Governance in a Complex Social World Look Like?

The crisis facing democratic self-government is first and foremost a crisis of self-governance, not of democracy. Section II reviews the nature of complex systems and why our contemporary social and economic order qualifies as technically complexindeed, increasingly so—and why explicit overall, directed reform of our social world is hopeless. But hope is not easily abandoned: Section III critically looks at two continuing sources of hope. Section IV then turns to a critical issue: If not by central direction, how do such complex systems achieve orderliness and functionality? Section V turns to the heart of the matter: is democratic self-governance viable in our increasingly complex systems—or, more subtly, what form of self-governance seems the most viable? Section VI argues that effective self-governance is not a freestanding exercise of a general will but must be embedded in the deontic principles of a liberal order