Problem-structuring dynamics and meta-governance

2018 ◽  
pp. 145-166
2013 ◽  
Vol 229 (1) ◽  
pp. 143-154 ◽  
Author(s):  
Gerald Midgley ◽  
Robert Y. Cavana ◽  
John Brocklesby ◽  
Jeff L. Foote ◽  
David R.R. Wood ◽  
...  

2021 ◽  
Author(s):  
Schuyler Houser ◽  
Reza Pramana ◽  
Maurits Ertsen

<p>Recognizing the interrelatedness of water management and conceptual value of IWRM, many water resource governance systems are shifting from hierarchical arrangements towards more collaborative and participative networks. Increasing calls for participation recognize the value of drawing on social, political-administrative, and other kinds of knowledge in addition to technical water expertise. Participatory mandates, coordination bodies, and science-policy networks have emerged to facilitate knowledge integration, promote adaptive capacity, and align organizations in poly-centric systems.</p><p>Since the maintenance and effectiveness of such arrangements are contingent on trust and alignment rather than command and control, and since diverse stakeholders are engaged to co-produce knowledge, collaborators must grapple with identifying shared goals, developing knowledge management strategies to organize inputs, and attaining early progress to promote ongoing cooperation. But guidance is limited with respect to how such integrative aims are to be accomplished.</p><p>This research explores how systematic (but not necessarily convergent) problem structuring can support the forming, reordering, and cohering of water resource networks, especially when a complex issue – in this case, water quality management – rises to prominence on the policy agenda. In the early stages of a water quality project in the Brantas River Basin, Indonesia, stakeholder discussions suggested divergent conceptualizations of water quality and ideas about what conditions ‘matter’. Thus, instead of taking hydrological data as the starting point, this research first asks: What Brantas River(s) are we talking about, and why? Q-methodology is used to identify alternative perspectives on water quality held by a diverse set of stakeholders, including hydrologists. The analysis explores which aspects of the policy problem are consistent, which are contested, and whether problems indicated by hydrological science overlap, conflict, or cohere with those perceived by other stakeholders.</p><p>The research posits that, if scientists, engineers, decision-makers, community leaders, and other participants can appreciate areas of convergence and divergence regarding the water quality problem itself, they can lay groundwork for knowledge co-production; recognize opportunities for cooperation; better locate science in the problem space; and identify potential early wins to secure commitment. The research also asks to what extent consensus in problem structuring is necessary, or whether it is sufficient to identify strategies that are acceptable to different ontological viewpoints.</p>


2018 ◽  
Vol 27 (5) ◽  
pp. 853-884 ◽  
Author(s):  
Jorge Velez-Castiblanco ◽  
Diana Londono-Correa ◽  
Olandy Naranjo-Rivera
Keyword(s):  

2015 ◽  
Vol 16 (2) ◽  
pp. 189-235 ◽  
Author(s):  
DANIELA INCLEZAN ◽  
MICHAEL GELFOND

AbstractThe paper introduces a new modular action language,${\mathcal ALM}$, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993,Journal of Logic Programming 17, 2–4, 301–321; 1998,Electronic Transactions on AI 3, 16, 193–210) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that requirestructuring of knowledge.$\mathcal{ALM}$is meant to remedy this problem. Structuring of knowledge in${\mathcal ALM}$is supported by the concepts ofmodule(a formal description of a specific piece of knowledge packaged as a unit),module hierarchy, andlibrary, and by the division of a system description of${\mathcal ALM}$into two parts:theoryandstructure. Atheoryconsists of one or more modules with a common theme, possibly organized into a module hierarchy based on adependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them.Structuresare used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.


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