Dynamic Learning Process for Selecting Storm Protection Investments

Increasingly aggressive weather events, such as hurricane-driven storm surges, threaten surface transportation systems and motivate defensive actions, including hardening. Decisions about the design and scale of hardening investments are informed by meteorological records. Historically based probabilities of severe storms are used in practice to define expected values of the intensity of weather assaults (e.g., the 100-year storm) and then to select defenses. The prospects of climate change and rising sea level suggest that assuming weather events are stationary may present added risks to surface transportation infrastructure, particularly in coastal environments. This paper proposes a dynamic, learning-based investment strategy, similar to the concept of real options, that updates estimates of storm surges on the basis of experience and recommends incremental hardening investments when observed trends indicate that additional defense is warranted. Monte Carlo simulation is used to compare and evaluate static (expected value-based) and dynamic investment strategies in the context of storm intensity patterns that are (a) known, (b) incorrectly estimated, and (c) nonstationary, with growing intensity. Results suggest that when the future is well described by past experience, the static, once-and-done decision strategy works well, but when the underlying storm generation process is unknown, or when it is changing (growing) in intensity, the learning-based dynamic strategy is especially advantageous. These results underscore the importance of flexibility in designing storm protection, of tracking weather events closely to detect emerging trends, and of data-driven decision strategies. This dynamic approach to decision making under uncertainty can be applied to other sources of uncertainty, for example, demand estimates.