Key Terms
Regime shift = when the key drivers in a system fundamentally change
Regime shift = when the key drivers in a system fundamentally change
"problem of fit" = interplay between institutional arrangements and ecological dynamics (Folke, Pritchard, Berkes, Colding & Svedin, 2007; Young 2002)
What are the main objective(s) of the paper?
- key contribution of this article: prove ideas, language and tools to move from conception of the policy process that links policy change to evolving policy context through the dynamic biophysical system
- " There are two key questions that arise in this regard:
(i) Can the policy process, which often plays out on the order of decades, possibly function when the policy context changes rapidly?
(ii)When the policy process and policy context involving a dynamic biophysical system co-evolve as a coupled feedback system depicted in Figure 1B, under what circumstances does infrastructure (socio-technical structures) emerge that makes the system flexible and robust versus rigid and vulnerable in the face of novel change?" (p. 517)
- will discuss approaches to navigate trade-offs between performance and robustness and choices between different vulnerabilities
- " explore how particular models of the policy process perform when coupled with particular classes of biophysical dynamics and uncertainties" (p. 522)
- "not to explain the policy process but, rather, to explore how different possible policy processes might function in a dynamic policy context."
- discuss two elements of SESs that are critical to governing changing, deeply uncertain systems:
(i) the notion of “fit”; and
(ii) fundamental properties of feedback systems. Following that we reflect on the associated set of design principles for such systems.
What are the important results and conclusions?
- " Studies of SES, and feedback systems more generally, suggest that multilevel, polycentric
governance regimes are essential to match institutions to challenges at the right temporal and organizational scale."
- "The long-term goal for scholars of sustainability science is to recognize which combination of variables tends to lead to relatively sustainable and productive use of particular resource systems operating at specific spatial and temporal scales and which combination tends to lead to resource collapses and high costs for humanity.... The key is assessing which variables at multiple tiers across the biophysical and social domains affect human behavior and social–ecological outcomes over time." (Ostrom 2007)
- "The long-term goal for scholars of sustainability science is to recognize which combination of variables tends to lead to relatively sustainable and productive use of particular resource systems operating at specific spatial and temporal scales and which combination tends to lead to resource collapses and high costs for humanity.... The key is assessing which variables at multiple tiers across the biophysical and social domains affect human behavior and social–ecological outcomes over time." (Ostrom 2007)
- What distinguishes the SES Framework from the Robustness Framework is that it provides a more systematic articulation of framework elements with relationships 1–8 in the Robustness Framework.(p. 522)
- how to operationalize" governance, a.k.a. translate de jure rules into rules-in-use
- elements that allow translation: monitoring, sanctioning and managing conflict (maintaining fairness)
- " linkages between the governance system and the resource being governed, will not be predicated on measuring harvest amounts—typically because this is too costly to monitor" (p. 525)
- "Rules on who, where, when, and how to harvest are easier to monitor and enforce than a quota, and if there is more confidence that people are following the rules, others will follow them too."
- " Policies with “good fits,” rather, tend to rely on more practical principles where measurement equates to common-sense, easily observable attributes of the biophysical context that are simple enough to not be debatable" (p. 525)
- "A governance regime that begins to rely on complex models and precise data that is costly to obtain is likely a “poor fit” in almost any context." (p. 525)
- we need to learn to govern systems we can never fully understand (p. 526)
- " navigating such performance-robustness, robustness-fragility trade-offs through policy design and policy processes involving learning and exploration are an essential element of public policy." (p. 528)
- System characteristics that enhance robustness and adaptive capacity:
- Diversity (a multiplicity of different types of regulatory feedback mechanisms), diversity of agents and connections important for the creation of a diverse portfolio of knowledge or shared organizational mental models (Staber & Sydow 2002)
- Redundancy (many regulatory mechanisms perform similar functions), ability to function when some modules fail, concurrent use of informal and formal rules for resource mngmt
- Modularity (some regulatory feedback mechanisms are allowed only limited connectivity with others) (p. 529)
- "Developing policies to increase robustness of SESs requires an explicit decision about robustness of what system properties and aspects of performance to what types of exogenous shocks. Once the choice about which vulnerabilities are to be addressed, building robustness requires navigating trade-offs between short-term efficiency and long-term robustness."
- " we need to shift thinking away from coalitions advocating for the “right” policy to policy processes that stimulate experimentation, adaptation, and learning" (p. 532)
Experimental design, statistical analyses or analytical approaches? Flaws?
- "Policies should therefore be seen as experiments that require systematic, ongoing monitoring and evaluation as elements of regulatory feedback networks. Decentralized experimentation would allow for innovation and increase the probability of achieving a fit between policies and local conditions (modularity and diversity). Governance at higher levels may stimulate a process of information exchange to facilitate learning from local-level experimentation." (p. 532)
- the "main lesson from studies of robustness is that successes from the past do not guarantee success in the future' (p. 532)
Assumptions made with models? Reasonable?
- '"fixed policy context' may involve considerable variation as long as that variation exhibits a stable structure." Public policy has done so through defining "risk."
- public policy has been a code or constraint that limits types of contracts allowed for spreading risk, reducing conflict and promoting social stability
- "discourse on societal collapse tends to focus on interaction of the inner policy feedback (decadal time scale) and the outer biophysical context feedback (centennial time scale)" (p. 516)
- complexity of system collapses under its own weight, possibly triggered by decisions occurring in the inner feedback loop
- must think seven generations ahead
- " it is not possible to design public policy for a given ecological (environmental) context—i.e., achieve a fit between policies and the biophysical context so that the SES is robust to all possible shocks. Thus, one aspect of the public policy process is effectively navigating trade-offs between performance and robustness and choices between different vulnerabilities." (p. 517)
- "Ostrom’s argument against (what C.S. Holling's defined as :command and control pahyology of natural resource management (Holling & Metcalfe 1996)) was very powerful: Small groups of people can effectively manage complex resource systems without top-down governance structures." (p. 518)
- "What distinguishes the SES Framework from the Robustness Framework is that it provides a more systematic articulation of framework elements the relationships 1–8." (p. 522)
Main conclusions supported by data? Why or why not?
Good References?
Meet stated objectives?
Number of times cited?
Impact on field?
Opinion