Monthly archives for October, 2013

When is precaution the best design solution?

A series of interesting articles came up recently in the Guardian and presented differing viewpoints on the precautionary principle, which puts many policymakers in a conundrum especially while planning for the long-term. In one of the articles Andy Stirling1 highlights as to why the precautionary principle matters. Stirling suggests that it is important to consider various future policy options instead of using precaution as an excuse for not taking any action. He argues that doing the same requires “understanding, rather than denial, of the real nature of uncertainty”. He highlights that taking precaution suggests that we are not only considering risk but also uncertainty, whether it is owing to lack of empirical evidence, inherent complexity of an issue or system, differing scientific views, element of surprise etc. Stirling argues that the imminent pressure from policymakers about ‘justifying a decision’ makes scientists continue to ‘micro-correct’ their results and offering “risk-based prescriptions” by overlooking the precautionary principle that provides room to address uncertainty.

Tracey Brown2 on the other hand suggests that the precautionary principle “stops innovation in its tracks”. She argues that the principle makes us stop or ban something supposedly harmful and subsequent believe that we are safe from harm. She argues that the precautionary principle is built on our present knowledge of the world, which includes our present doubts, fears and biases. So in resisting change for the fear of the unknown we are resisting deviations from the status quo even if that itself is a huge problem that needs urgent attention, Brown argues. She says that we need big changes and some risks for pressing problems such as food security and energy and resisting change is not the solution. Steve Fuller3 also supports the above view in saying that the precautionary principle is based on current knowledge and does not acknowledge likely changes in knowledge in the future, owing to scientific advancements and data reinterpretations. Fuller advocates calculated risk-taking over taking precaution. He argues that by restricting risk-taking we are not allowing radical experimentation to happen, which in the past has many times helped us to “take major leaps in knowledge and overcome our natural limits”.

Indeed a critical challenge that faces decision-makers and planners is with respect to responding under uncertainty. Policymaking in the 21st century can be considered to be similar to gardening; it is “muddy, attentive and experiential, because we really do not know what growing conditions will prevail”4. The World Resources Report (2010) also highlights that decision making under uncertainty should be flexible to accommodate conditions of change, robust to withstand multiple scenarios in the future, and/or enable decisions to withstand long-term change. In terms of uncertainty also, there exists a range that moves from total ignorance of reality, to the deepest layer of uncertainty i.e. ‘unknown unknowns’5. Policy risk factors can also be completely known, or uncertain wherein the nature of their change in the future is doubtful (also referred to as ‘known unknowns’, for example changes in demography). An assumption of ‘no-harm’ or ‘no-regret’ nature of certain policy choices in the short-term can mask their adverse (sometimes irreversible) effects in the long-run and thus delay timely preventive action. For example, Chloro Fluoro Carbons were understood to be ‘safe and chemically inert’, masking their long-term detrimental impacts on the ozone layer (owing to high stability). Though research on atmospheric sciences was still evolving during that time, there were evidences pointing to a ‘possibility’ of damage to the ozone layer. However evidences pointing to a ‘possibility’ may not in itself, be adequate to initiate serious policy reforms. Policy-makers must thus learn to recognize early warnings or changes, especially as new knowledge emerges6. Whether to take a precautionary approach in policy design would eventually depend on a variety of factors such as the risk-taking behavior of the decision-making group, socio-economic and political dimensions of the issue, current level of knowledge about the risk of action versus inaction etc.

  1. Accessible at http://www.guardian.co.uk/science/political-science/2013/jul/08/precautionary-principle-science-policy []
  2. Accessible at http://www.guardian.co.uk/science/political-science/2013/jul/09/precautionary-principle-blunt-instrument []
  3. Accessible at http://www.guardian.co.uk/science/political-science/2013/jul/10/beyond-precautionary-principle []
  4. Swanson D, Barg S, Tyler S, et al. 2010. Seven tools for creating adaptive policies. Journal of Technological Forecasting and Social Change, 77(6), 924–939 []
  5. Walker, W.E., V.A.W.J. Marchau and D. Swanson, 2010. Addressing deep uncertainty using adaptive policies: Introduction to section 2. Journal of Technological Forecasting & Social Change, 77 (6), 917-923 []
  6. European Environment Agency (EEA), 2001. Late lessons from early warnings: the precautionary principle 1896–2000. Environment Issue Report no. 22. Copenhagen []

Can an ‘open data revolution’ lead to innovation in policy design?

imgres-4The lack of access to detailed public data can often limit the best of research across the world. Is providing open access to governmental data the answer to fuel innovation in the public sector and policy design? A recent blog article by Casey Coleman1, Chief Information Officer of United States (US)’s General Services Administrator discusses about US Administration’s new Open data policy which suggests that open data is “publicly available data structured in a way that enables the data to be fully discoverable and usable by end users”. Coleman refers to apps on smartphones as a way that products are being created using government data for the public benefit. Much of this data is available on government websites. In Europe as well, the idea of making public data accessible has been rapidly gaining momentum. In her speech early this year2, European Commission Vice President Neelie Kroes said, that “The open data revolution is all about individuals and entrepreneurs and that includes the giving them a role in policy design”. Kroes refers to data as “the new oil” as it is a “fuel for innovation, powering and energizing” Europe’s economy. She argues that open public data can enable transparency and improve public services. She does caution though that this data revolution would come at a cost and would need a thoughtful framework within which to operate. A framework that can ensure that data is openly available for multiple uses over time with similar rules of operation across datasets and users and that respects privacy, confidentiality and security concerns. The European Commission has already started making headway in this direction by gradually opening up more and more government data to the public with the objective of gathering all-Europe data within a one-stop-shop portal. Additionally the Commission is striving to make the results of all European Union-funded research as open access. The efforts of the Queensland government, Australia towards ensuring an open data revolution is also worth highlighting. The Queensland Premier is quoted3 saying that “Sharing data will promote new thinking and inventive solutions to benefit us all”.

Given these developments, it is important for policy practitioners and researchers to consider few critical issues that can affect the realization of innovative policy design in practice. For example, can a standard for data protection and usage be developed especially for sensitive-data such as that related to health? Will there be winners and losers in this arrangement? For example while open data access could encourage creative and innovative policy applications and solutions, can it also highlight policy gaps consequently leading to public distrust and unrest with no suggestion of possible policy solutions? Will an open data sharing practice hamper the data collection process itself and will it affect the functioning of ethical clearance boards? Data being generated by models in the form of future projections for socio-economic, demographic, biophysical scenarios etc. deserves a special mention here. These projections are simply estimates of the likely futures using a series of assumptions and specific methods. In the case that these projections become publicly available, it should be the obligation of the relevant scientists to share the details of their assumptions and limitations while giving out these projections to ensure that the users are equipped with the right information while choosing to use any of these projections.

  1. Accessible at http://gsablogs.gsa.gov/innovation/2013/08/27/open-data-the-next-phase-in-the-technology-revolution/ []
  2. Accessible at http://europa.eu/rapid/press-release_SPEECH-13-261_en.htm []
  3. Accessible at https://data.qld.gov.au/about []

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