Agent-based modeling of incentives to encourage pre-disaster relocation in anticipation of coastal flooding

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Extreme storms such as Hurricanes Katrina and Sandy have made clear that forced relocation due to floods is a major and growing problem. By contrast to short-term evacuation, relatively little is known about how to manage long-term relocation. Extensive social-science research has documented the advantages of voluntary vs. forced relocation. Strategies for encouraging voluntary retreat from at-risk coastal areas prior to catastrophic flooding can reduce the number of people affected, the resulting disruption and property loss, and the costs for emergency relief. Moreover, the relatively predictable nature of sea-level rise makes it possible to know well in advance which geographic areas are most likely to face increasing flooding hazards. This predictability facilitates proactive planning and anticipatory relocation as one way of reducing projected increases in flood damage, as recommended by the Federal Emergency Management Agency. However, many responses to sea-level rise can be problematic. Anticipatory relocation of entire communities is extremely costly (roughly $1 million per person). Furthermore, the effectiveness of many community and household-scale flood-protection efforts can be limited. For example, buying out flood-damaged properties after a flood does not prevent disruption and loss of personal property; similarly, protective barriers, such as seawalls, frequently encourage development in at-risk areas. Taken together, these observations suggest that it is worth exploring methods to encourage voluntary relocation before severe flooding. We study possible ways to incentivize relocation prior to flooding, focusing on two areas vulnerable to coastal flooding (Brooklyn, NY, and Galveston, TX) with different housing costs and with socioeconomic/demographic characteristics. Many studies examining mitigation of risks in vulnerable coastal areas adopt the viewpoint of a single decision maker (either a prototypical resident, or a local government), without considering different stakeholder goals, values, or preferences. In this work, game theory is used to model interactions between government vs. private citizens, to identify solutions whereby government can incentivize residents to relocate before they otherwise might. Agent-based modeling is used to enhance the realism of the game, address decision-maker heterogeneity, and explore behavior that may be difficult to model analytically (such as emergent network effects influenced by the behavior of other residents). Few researchers to date have approached the issue of relocation as a problem that can be addressed using formal methods such as game theory. The proposed project explicitly considers the different discount rates of stakeholders involved in relocation decisions (government vs. private individuals), making it possible to frame the problem as a multiplayer game rather than merely a decision problem for a single decision maker. A game-theoretic perspective facilitates the identification of “win-win” solutions whereby government can encourage relocation sooner than would otherwise occur. Sensitivity analyses will address uncertainty about flooding risks, network effects (such as cascading or tipping), free-rider effects (e.g., residents who take advantage of government subsidies but would have relocated even without subsidies), and differing behavioral models of resident decision-making. The project contributes to improved societal understanding of mechanisms for reducing vulnerability to coastal flooding from sea-level rise.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.