Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

Abstract

Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent-based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss-reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low-probability/high-impact risks.

Learning to forecast, risk aversion, and microstructural aspects of financial stability

“…In particular, Silvestre (2017) adopts a model in which the probability of default of countries is explained by means of a modified version of Tirole (2015), in which the risk aversion is negatively correlated with both default probability and income, whereas Epstein (2002) describes different evolution of civil violence among citizens with different risk perceptions, and Haer et al (2017) show implications on flood risk analysis of different individual risk aversion of households. Focused on the description of financial markets, Fabretti and Herzel (2017) discuss the effects of convex incentives on trading behavior of agents in a naive rational-investors-vs-noise-traders model, by showing that the risk aversion (through the incentives) may affect market dynamics.…”

Integrating human behaviour dynamics into flood disaster risk assessment

“…76 Novel complex systems studies in flood-risk assessment that use agent-based models (ABM) are gaining traction and show that it is possible to better integrate scientific theories on human behavior and perception into risk assessment by relating behavior to adaptation actions. 51,77,78,79,80 An ABM simulates individual behavior, 79 whereby agents represent different models of choice while acting in their own interests, such as maximizing their welfare or minimizing adaptation costs, often using simple decision rules. Agents can learn, move, and influence (and are influenced by) the risk they face, resulting in differing adaptation actions.…”

Household migration in disaster impact analysis: incorporating behavioural responses to risk

“…Neglecting household responses in disaster impact analysis is therefore likely to lead to a biased estimate of the total disaster effect. One study suggests that neglecting human behaviour in flood risk assessment studies can result in a misestimation of future flood risk of a factor two (Haer et al 2016). Vigdor (2008) argues that the massive population displacements following Katrina in 2005 shifted the city to a new equilibrium of housing prices and employment: the rampant destruction of houses, mainly in poor neighbourhoods of the city, implied a substantial negative supply shock in the lower end of the housing market, forcing poor households to leave New Orleans.…”

“…This model shows how a relatively small change, i.e the move of one agent, sets off bandwagon effects leading to full segregation of neighbourhoods. ABMS have been applied to a wide range of research topics, including risk on land and housing markets (Geanakoplos et al 2012;Putra et al 2015;Filatova et al 2009), evaluation of flood protection measures (Haer et al 2016;O'Connell and O'Donnell 2013;Sobiech 2013;Brouwers and Boman 2011) and distributional effects of natural disasters (Naqvi and Rehm 2014). A number of authors have investigated household migration in the context of climate change using ABMs (Cai et al 2013;Smith 2014;Kniveton et al 2012).…”