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Climate change is sure to surprise us, both in its impacts and in the technological and behavioral changes that will affect society's ability to respond (NRC 2009). Any successful response to climate change-both the challenges of limiting the magnitude of future climate change and adapting to its impacts-will clearly involve policies that evolve over time in response to new information and that are robust over a wide range of difficult-to-predict future conditions. Recent years have seen expanding interest in decision frameworks and approaches to help identify and evaluate such policies. Funke and Paetz (2011) offer robust control theory as one means to evaluate such robust and adaptive policies for reducing greenhouse gas emissions. Taxonomy of approachesBefore explicitly addressing Funke and Paetz's work, it is useful to describe the broader context. The climate change policy community seeks analytic methods that can help identify and evaluate robust adaptive strategies for limiting greenhouse gas emissions. Any approach intended for such purposes must address at least three types of factors, a: 1) description of how policies evolve over time, 2) description of the uncertainty about the future, and 3) decision criteria for comparing alternative options. Table 1 presents a variety of options, which we describe in more detail below. In addition, any such analytic approaches can serve many purposes, including predicting the response of decision makers in order to improve scientific understanding about their behavior; prescribing the best policy option; or informing deliberations among decision makers as part of a process of decision support (NRC 2009). This commentary will focus on approaches intended for this third purpose.As shown in Table 1, the climate literature includes a variety of approaches for describing how policies evolve over time. The sequential decision approach, commonly used in the climate policy literature, represents policies as a sequence of choices over time,

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Abstract:A new approach for anticipating and shaping adversarial behavior is developed and demonstrated. The approach extends the notion of prediction, which is a forecast of the future from a third party point of view, to anticipation, which is a forecast from the perspective of an entity having partial control in a domain. Shaping utilizes the models developed for anticipation to determine actions that influence another influential entity (e.g., an enemy) and actions to direct the emergent phenomena of a domain according to an entity's objectives. The approach is developed using principles of control theory and demonstrated in the southeastern region of Afghanistan. A key capability demonstrated by this approach is its ability to handle proactive adversaries when actionable intelligence is nonexistent. In the demonstration, Taliban (Red) combat actions are anticipated from the perspective of the coalition forces (Blue) across time, across space, and by the current state of the region and then shaped to Blue's desires. Shaping identifies periods of time that simultaneous or alternating Blue combat actions in different regions help meet Blue's military and nonmilitary objectives.

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