Policy-oriented simulation methods can be a useful tool for informing policy makers about the basic characteristics of climate policy choices. These simulation methods can either involve informal linkages between policy choices, climate trajectories, and economic information, or be implemented in a formal integrated modeling framework. For example, the C-ROADS model6 divides the countries of the world into blocs
with common situations or common interests (such as the developed nations), takes as input the commitments to GHG emissions reductions each bloc might be willing to make, and generates projected emissions, atmospheric CO2 concentrations, temperature, and sea level rise over the next 100 years. The underlying model is simple enough to be used in real time by policy makers to ask "what if" questions that can inform negotiations. It can also be used in combination with gaming simulations in which individuals or teams take on the roles of blocs of countries and negotiate with each other to simulate not only the climate system but also the international negotiation process. When such simplified models are used, however, it is important to ensure that the simplified representations of complex processes are backed up, supported, and verified by more comprehensive models that can simulate the full range of critical processes in both the Earth system and human systems.
Heuristic models and exercises have also been developed that engage decision makers, scientists, and others in planning exercises and gaming to explore futures. Such tools are particularly well developed for military and business applications but have also been applied to climate change, including in processes that engage citizens (Pou-madère et al., 2008; Toth and Hizsnyik, 2008). Though not predictive, such models and exercises can provide unexpected insights into future possibilities, especially those that involve human interactions. They can also be powerful tools for helping decision makers understand and develop strategies to cope with uncertainty, especially if coupled with improved visualization techniques (Sheppard, 2005; Sheppard and Meitner, 2005).
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