Description
For decision-making in the context of adaptation to climate change in the long-term under uncertainty, one way is to use a Robust Decision-Making framework. When we are talking about adaptation and vulnerability it is not just changes in climate that will have an effect but also future socio-economic, political, cultural and technological developments (for example population growth, market prices, communication technologies etc), which in many cases will have a greater effect on vulnerability than climatic factors. Robust decision making needs to consider as many as possible factors and scenarios and identify the most acceptable situations (Lempert, Nakicenovic et al. 2004). In the face of this deep uncertainty, decision-makers systematically examine the performance of their adaptation strategies/policies/activities over a wide range of plausible futures driven by uncertainty about the future state of climate and many other economic, political and cultural factors.
The robust decision making is consistent with traditional optimum expected utility analysis, but the order is the other way around (Groves, Lempert 2007 p.76). While conventional analysis characterize uncertainties before ranking options, the robust decision approach starts from selecting decision options and then estimates utilities of options to identify potential vulnerabilities of potential strategies. In other words, the robust decision making is different from conventional sensitivity analysis. The conventional approach studies the variability of outcomes against many input variables. Instead, the robust decision making is to find strategies, which perform well insensitively to the most significant uncertainties.
There are four key elements for a robust decision approach:
- Assembling a large number of scenarios. Such ensembles contain a set of plausible futures as diverse as possible.
- Seeking robust, rather than optimal, strategies that perform "well enough" by meeting or exceeding selected criteria across a broad range of plausible futures and alternative ways of ranking the desirability of alternative scenarios. Robustness provides a useful criterion for long-term policy analysis because it reflects the approach many decision makers actually use under conditions of deep uncertainty.
- Employing adaptive strategies to achieve robustness. Adaptive strategies evolve over time in response to new information. Near-term adaptive strategies seek to influence the long-term future by shaping the options available to future decision makers. The near-term strategies are explicitly designed with the expectation that they will be revised in the future.
- Designing the analysis for interactive exploration of the multiplicity of plausible futures. Humans cannot track all the relevant details of the long-term. Working interactively with computers can discover and test hypothesis that prove to be true over a vast range of possibilities. Computer-aided exploration of scenario and decision spaces can help humans discover adaptive near-term strategies that are robust over large ensembles of plausible futures.
For element one, the robust decision approach assembles futures as a challenge set against which to test the robustness of alternative strategies. It profits from deriving scenario ensembles that provide the greatest possible futures consistent with available information. Information about the future might be in the form of quantifiable physical or economic laws -e.g. matter is conserved, or the average annual rate of economic growth over the entire twenty-century is unlikely to exceed four percent. For example, the IPCC created 4 SRES to identify key driving forces and characterize the range of uncertainty in future greenhouse gas emissions.
For element two (seeking robust strategies), a strategy is considered robust if it performs reasonably well compared to the alternatives across a wide range of plausible futures, while traditional decision analysis seeks the optimal strategy, that is, the one that performs best for a fixed set of assumptions about the future. Concept of robustness provides a computationally convenient basis for identifying policy arguments that are true over an ensemble of plausible futures. It offers a normative description of good choices under deep uncertainty. A robust approach can be quantitatively used by using the so- called regret measure. Regret is defined as the difference between the performance of a future strategy, given value function, and that of what would have been the best performing strategy in that same future scenario. Computer searches across the ensemble can help identify robust strategies- that is, ones with consistently small regret across many futures. In practice, long-term decision-making becomes an exercise in juggling difficult trade-offs and judging which values and scenarios should weigh more heavily, and which should downplayed. The choice rests on a complicated amalgam of moral, political and goal-defined judgments.
For element three (employing adaptive strategies), it compares the performance of alternative adaptive decision strategies, looking for those that are robust across a large ensemble of plausible future. These systematic explorations help decision makers assess alternative algorithms and choose those near-term actions that can best shape the choices available to future generations.
Finally, for the machine and human interaction, modern information technology makes possible a new and more powerful form of human-machine collaboration to find robust adaptive strategies over time (Lempert et al., 2003).
