An Agent-Based Model for Simulating Flood Governance and Community Resilience.

The FRAMe (Flood Resilience Agent-Based Model) serves as a framework designed to simulate flood resilience dynamics at the community level, focusing on a rural settlement in the Mekong River basin. Integrating empirical data from extensive surveys, Bayesian networks, and hydrological simulations, the framework quantifies resilience as a trade-off between robustness (resistance to damage) and adaptability (capacity for dynamic response). Agents include households, governments, and other institutional actors, linked by social and governance networks that facilitate knowledge transfer, resource distribution, and risk communication. FRAMe incorporates mechanisms for policy interventions and individual and collective decision-making, grounded in Protection Motivation Theory and MoHuB frameworks. The framework utilizes geographic data to achieve a spatially explicit design. Sensitivity analyses across five key parameter groups covering network structure, knowledge dissemination, and policy intensity demonstrate the model’s robustness and allow systematic evaluation of intervention effects. Simulation results show that increasing the intensity of government assistance reduces average recovery time by around 50 % and improves system-level robustness by about 30 %. In contrast, enhancing emergency relief primarily improves adaptability (approximately 10 %) and shortens recovery time, with limited effect on robustness. Higher knowledge dissemination roughly doubles adaptability but also introduces greater variability in robustness. By examining policy scenarios and agent behavior, FRAMe aims to inform adaptive flood management strategies and foster the improvement of community resilience.•The agent-based model is described using the ODD+D protocol.•A brief model user's guide is provided.•The model validation and sensitivity analysis processes that have been conducted are explained.

Enhancing resilience and reducing disaster risks are major societal challenges. Hence, it is crucial to understand resilience at the community level, as the impact of disasters and the potential for resilient development are particularly high at this scale. Key for understanding community resilience is the systematic assessment and measurement of resilience. The Flood Resilience Measurement for Communities (FRMC) framework offers a holistic approach to measure community flood resilience, facilitating the identification of interventions to strengthen resilience. This paper discusses empirically measured flood resilience data from 292 communities across 20 developing countries worldwide using the FRMC framework. Furthermore, it will provide examples of how such data can serve as a valuable resource for understanding the typology of community flood resilience across the world, and for identifying targeted interventions that address the unique challenges faced by different communities.

There are numerous biological scenarios in which populations of cells migrate in crowded environments. Typical examples include wound healing, cancer growth, and embryo development. In these crowded environments cells are able to interact with each other in a variety of ways. These include excluded-volume interactions, adhesion, repulsion, cell signaling, pushing, and pulling. One popular way to understand the behavior of a group of interacting cells is through an agent-based mathematical model. A typical aim of modellers using such representations is to elucidate how the microscopic interactions at the cell-level impact on the macroscopic behavior of the population. At the very least, such models typically incorporate volume-exclusion. The more complex cell-cell interactions listed above have also been incorporated into such models; all apart from cell-cell pulling. In this paper we consider this under-represented cell-cell interaction, in which an active cell is able to "pull" a nearby neighbor as it moves. We incorporate a variety of potential cell-cell pulling mechanisms into on- and off-lattice agent-based volume exclusion models of cell movement. For each of these agent-based models we derive a continuum partial differential equation which describes the evolution of the cells at a population level. We study the agreement between the agent-based models and the continuum, population-based models and compare and contrast a range of agent-based models (accounting for the different pulling mechanisms) with each other. We find generally good agreement between the agent-based models and the corresponding continuum models that worsens as the agent-based models become more complex. Interestingly, we observe that the partial differential equations that we derive differ significantly, depending on whether they were derived from on- or off-lattice agent-based models of pulling. This hints that it is important to employ the appropriate agent-based model when representing pulling cell-cell interactions.

Flood resilience is increasingly discussed in academia and practice as a complement to existing flood risk management approaches (Fekete, Hartmann, & Jüpner 2020). It is seen as a promising concept to deal with increasingly severe consequences of climate change in general, and with increasing flood risk in particular. The debate on flood resilience is linked to the paradigm shift from flood protection to risk management, which started in Europe after the major river flood events in 1993 and 1995 along the river Rhine (Hartmann, 2012), and has developed over the past decades—pushed by further major fluvial and pluvial flood events (Begum, Stive, & Hall, 2007; Hartmann & Juepner, 2014; Klijn, Samuels, & van Os, 2008; Patt & Jüpner, 2020). However, in flood risk management, the academic debate on resilience is only in its infancy (Jüpner et al., 2018; Vis, Klijn, Bruijn, & Buuren, 2003). Many different definitions exist (Disse, Johnson, Leandro, & Hartmann, 2020). Especially when it comes to specific implementation of resilience, the vagueness of the concept—which is sometimes described as one of its strengths (Baggio, Brown, & Hellebrandt, 2015; Brand & Jax, 2007)—can present a problem. How to design a resilient hydraulic infrastructure? This question has not only technical but also concerned with financial and legal aspects. This becomes more complicated when individual protection measures are put in a context of a larger system, such as a city or a region. When and how is a system such as a city or a region resilient, and how does one measure resilience? These questions are important, not only for construction but also for legal (such as liabilities), financial (budgetary), and political reasons. In other words, resilience is not (yet) a ready-to-use concept in flood risk management. Resilience challenges presumptions of traditional flood risk management in many ways. Resilience implies that more and different stakeholders and actors will be involved in flood risk management than before, such as landowners or spatial planners. Hitherto, flood risk management has been and still is mainly the domain of water management (Hartmann & Driessen, 2017), although the prevalence of the traditional civil engineering approach has been questioned before (van den Brink, 2009). This change is not positive for all actors—affected homeowners, but also city planners or mayors now must deal with questions that were previously outside of their realm of competence and responsibility. From a flood risk management perspective, many questions arise, such as: How does resilience add to and change the existing flood risk management system? How can resilience contribute to a more effective and efficient flood risk management approach? What are the specific advantages of integrating resilience in flood risk management? How can resilience be measured and quantified? Which parameters are most relevant? This special collection brings together contributions from different disciplines that address the specific challenges of implementing resilience in flood risk management. The special collection takes the European debate as a starting point. The European Floods Directive from 2007 (2007/EC/60) pushed the debate on flood risk management and resilience in Europe. This Directive established a legal frame on how countries in the European Union have to deal with flood risk for the first time (Hartmann & Jüpner, 2014). At the same time, the shift toward a risk-based approach raised questions such as "what can happen?" "what must not happen?" and "which security level can be realized at which costs?" Ultimately, flood risk management replaces the paradigm of complete protection against floods with the idea of managing the risk (Grünewald, 2005). This makes approaches to resilience and the reduction of damage potential (vulnerabilities)—not only for river floods but also for pluvial rainfall—important. The first three papers in this collection highlight the necessity and consequences of defining the concept of resilience. In their opinion paper, Fekete, Hartmann, and Jüpner (2020) point out that resilience is indeed a trend that neither can nor should be ignored in flood risk management, rather than it should be embraced—with a caveat: the concept is still not exactly defined and cannot be understood as a panacea for all shortcomings and challenges of flood risk management. So, is resilience merely a new buzzword? Dewulf et al. (2019) discuss the power and impact of defining resilience and therewith unravel the political-normative notion of flood resilience as a concept. This illustrates that, with flood resilience, flood risk management turns much more strongly towards social and political science than ever before. Discussing resilience and its definition is thus a crucial step toward successfully implementing it in flood risk management. In her advanced review, Rodina (2019) reveals different notions of resilience in different disciplines when taking on a broader view of the concept (i.e., not solely focusing on flood risks). She concludes that participation and stakeholder involvement are important components of resilience—a theme that is picked up in the next section of this special collection as well. So, though this article had not been part of the original set-up of the special collection, the conclusions are highly relevant for implementing flood resilience in flood risk management. Resilience requires more action by homeowners and citizens. All papers in this section of this special collection leave no doubt about that. Snel, Witte, Hartmann, and Geertman (2020), however, start with a very critical point of departure, namely that many publications on homeowner and citizen involvement do not seem to question if this involvement is necessary. The paper unravels the arguments that are prevalent in the academic debate for more involvement of homeowners in flood resilience, which supposedly comes along with a shift towards flood governance. Four types of main arguments are identified and discussed in the paper. Kuhlicke and the multidisciplinary team of authors writing on the behavioral turn in flood risk management (Kuhlicke et al., 2020) discuss in detail three underlying assumptions of increased involvement of citizens in flood risk management: the motivations of citizens, the effectiveness of measures, and capacities of citizens. In this vein, Rufat et al. (2020) discuss in their opinion paper need for a more nuanced debate on the link between flood risk perception and risk-mitigating behavior. Attems, Thaler, Genovese, and Fuchs (2020) then provide an overview of the measures that homeowners can take to enhance resilience by using property-level flood risk adaptation (PLFRA) measures. The paper confirms that such measures can indeed be an appropriate addition or complement to flood risk management. The third group of papers in this special collection addresses the issue of measuring and communicating resilience. The need for this comes not only from hydraulic engineering but also from the need to communicate about flood risk with homeowners and citizens, as they have to become more involved (as the earlier papers in this special collection clearly show). Pohl (2020) views resilience from a hydrological as well as a hydraulic engineering perspective and outlines what it means to include the concept in flood risk management. In particular, the contribution points out the difficulties of embedding resilience as a design criterion in hydraulic structures. Pohl concludes that—though the fuzziness of resilience is appreciated by some social scientists as a "boundary concept" (Baggio et al., 2015)—hydraulic engineering and hydrology inevitably require some quantifiable measurement of resilience. Fekete (2019) elaborates in his paper how cascading effects and critical infrastructure matter for flood resilience. It thereby shows how flood resilience with this perspective extends existing flood risk management approaches. Cascading effects of critical infrastructure can substantially affect the measurement of flood resilience. Schmitt and Scheid (2020) discuss the need for better risk communication and discuss approaches by focusing on pluvial floods. In particular, they show how a rainstorm severity index may supersede the communicative capacity of statistical rainfall parameters in the communication with nonexperts especially. In conclusion, the contributions in this special collection show how the concept of resilience is advancing flood risk management but also point out some crucial changes and challenges that flood resilience as a concept brings to the practical implementation of flood risk management. It seems inevitable that homeowners especially will play a much more fundamental role in flood resilience, which raises needs for measuring and communication of flood resilience. Along with this shift in flood risk management, the papers illustrate that flood resilience puts flood risk management in a hitherto unknown domain for many water managers, as flood resilience means embracing social and political science. Embedding flood resilience into flood risk management becomes a balancing act—on the one hand, resilience is a political and normative concept, but on the other hand, it needs to be implemented with concrete steps designed via civil engineering and hydrology. This tension is still unsolved and remains a dilemma in implementing flood resilience.

Immune system processes can be simulated using both system dynamics (SD) models based on differential equations and Agent-based (AB) models. The two approaches are intrinsically different but some methodologies have been developed to convert SD models into AB models with a variable degree of success. However, until now none of such methods have considered the use of scale factors in SD to AB model conversion. In this work, we revisited a well know SD model describing the interaction between effector T cells and tumor cells that was previously shown unsuitable for AB modeling. We introduced non-dimensional scaling factors in AB modeling and compared AB and AD simulations through a sensitivity analysis. Under this scenario, we obtained AB models that could successfully reproduce SD simulations with a reasonable number of agents and a stochastic behavior that did not compromise computer resources. In general, our results justify the introduction of non-dimensional scaling factors to reproduce SD simulations with AB models.

First insights from the Flood Resilience Measurement Tool: A large-scale community flood resilience analysis

Understanding and strengthening community-level resilience to natural hazard-induced disasters is critical for the management of adverse impacts of such events and the growth of community well-being. A key gap in achieving this is limited standardized and validated disaster resilience measurement frameworks that operate at local levels and are universally applicable. The Flood Resilience Measurement for Communities (FRMC) is a foremost tool for community flood resilience assessment. It follows a structured approach to comprehensively assess community flood resilience across five classes of capacities (capitals) to support strategic investment in resilience strengthening initiatives. The FRMC is a further development of an earlier version (the FRMT, the Flood Resilience Measurement Tool). The FRMT has been developed and applied between 2015 and 2017 in 118 flood prone communities across nine countries. It has been validated in terms of content and face validity as well as in terms of reliability. To reduce redundancy and survey effort, the FRMC holds a lesser number of indicators (44 versus 88) and has now been applied in over 320 communities across 20 countries. We examine the validation for the revised resilience construct and the new community applications and present a comprehensive overview of the statistical and user validation process and outcomes in both practical and scientific terms. The results confirm the validity, reliability as well as usefulness of the FRMC framework and tool. Furthermore, our approach and results provide insights for other resilience measurement approaches and their validation efforts. We also present a comprehensive discussion about the dynamic aspects of flood resilience at community level, and the many validation aspects that need to be incorporated both in terms of quantification efforts as well as usability on the ground.

We extend the ‘Wedding Ring’ agent-based model of marriage formation to include some empirical information on the natural population change for the United Kingdom together with behavioural explanations that drive the observed demographic trends. We propose a method to explore statistical properties of agent-based demographic models. By coupling rule-based explanations driving the agent-based model with observed data we wish to bring agent-based modelling and demographic analysis closer together. We present a Semi-Artificial Model of Population, which aims to bridge demographic micro-simulation and agent-based traditions. We then utilise a Gaussian process emulator – a statistical model of the base model – to analyse the impact of selected model parameters on two key model outputs: population size and share of married agents. A sensitivity analysis is attempted, aiming to assess the relative importance of different inputs. The resulting multi-state model of population dynamics has enhanced predictive capacity as compared to the original specification of the Wedding Ring, but there are some trade-offs between the outputs considered. The sensitivity analysis allows the identification of the most important parameters in the modelled marriage formation process. The proposed methods allow for generating coherent, multi-level agent-based scenarios aligned with some aspects of empirical demographic reality. Emulators permit a statistical analysis of their properties and help select plausible parameter values. Given non-linearities in agent-based models such as the Wedding Ring, and the presence of feedback loops, the uncertainty of the model cannot be assessed directly using traditional statistical methods. The use of statistical emulators offers a way forward

<p>Given the increased attention put on strengthening disaster resilience, there is a growing need to invest in its measurement and the overall accountability of resilience strengthening initiatives. There is a major gap in evidence about what actually makes communities more resilient when an event occurs, because there are no empirically validated measures of disaster resilience. Similarly, an effort to identify operational indicators has gained some traction only more recently. The Flood Resilience Measurement for Communities (FRMC) framework and associated, fully operational, integrated tool takes a systems-thinking, holistic approach to serve the dual goals of generating data on the determinants of community flood resilience, and providing decision-support for on-the-ground investment. The FRMC framework measures “sources of resilience” before a flood happens and looks at the post-flood impacts afterwards. It is built around the notion of five types of capital (the 5Cs: human, social, physical, natural, and financial) and the 4Rs of a resilient system (robustness, redundancy, resourcefulness, and rapidity). The sources of resilience are graded based on Zurich’s Risk Engineering Technical Grading Standard. Results are displayed according to the 5Cs and 4Rs, the disaster risk management (DRM) cycle, themes and context level, to give the approach further flexibility and accessibility.</p><p>The Zurich Flood Resilience Alliance (ZFRA) has identified the measurement of resilience as a valuable ingredient in building community flood resilience. In the first application phase (2013-2018), we measured flood resilience in 118 communities across nine countries, building on responses at household and community levels. Continuing this endeavor in the second phase (2018 – 2023) will allow us to enrich the understanding of community flood resilience and to extend this unique data set.</p><p>We find that at the community level, the FRMC enables users to track community progress on resilience over time in a standardized way. It thus provides vital information for the decision-making process in terms of prioritizing the resilience-building measures most needed by the community. At community and higher decision-making levels, measuring resilience also provides a basis for improving the design of innovative investment programs to strengthen disaster resilience.</p><p>By exploring data across multiple communities (facing different flood types and with very different socioeconomic and political contexts), we can generate evidence with respect to which characteristics contribute most to community disaster resilience before an event strikes. This contributes to meeting the challenge of demonstrating that the work we do has the desired impact – that it actually builds resilience. Our findings suggest that stronger interactions between community functions induce co-benefits for community development.</p>

This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative” is suitable. In defending this terminology, features distinguishing the approach from both “inductive” and “deductive” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. ! 1999 John Wiley & Sons, Inc.

In recent years, the acceleration of urbanization processes coupled with more frequent extreme weather including more severe flood events, have led to an increase in the complexity of managing community flood resilience. This research presents an empirical study to explore the factors influencing community flood resilience in six communities located in the Hubei Province of China. The study presents the development of a flood resilience evaluation index system, comprising the use of the decision-making trial and evaluation laboratory (DEMATEL) and interpretative structural modeling method (ISM) methods. The results show that the three most important factors affecting the flood resilience capacity of the community are (i) the investment in disaster prevention, (ii) disaster relief capacity and (iii) flood control and drainage capacity. The differences between the six communities were analyzed across four dimensions to reveal the strengths and weaknesses of the communities across these dimensions and in terms of their overall resilience. By analyzing the causal hierarchical relationship that affects community flood resilience, this study helps to enhance community resilience to flood disasters and reduce disaster risk. These findings are conducive to enhancing the sustainable development of urban communities and are expected to provide scientific guidance for community risk management and strategic decision-making.

European mountain regions are becoming more vulnerable to natural hazards due to global change, climate change, and land-use change. Therefore, it is essential to understand their resilience. Currently, quantitative and dynamic models of coupled human-landscape interactions are in their infancy. However, agent-based modelling (ABM) approaches have high potential to advance the analysis of the interplay of natural and social factors affecting socio-ecological resilience in European mountain communities. The Socio-Ecological Land Agent-Based Model (SECLAND) integrates information from qualitative interviews and spatial data into a quantitative modelling environment. This enriches the diversity of scenario modelling beyond economic rationales by incorporating individual agent's motivations for land-use decisions. The outputs from this model have been used as input to hydrological or ecological models on multiple occasions.SECLAND has been used to model the potential success of various adaptation strategies for coping with climate-induced natural hazards. In a study conducted in the department of Ariège, France, we analysed the potential impacts of intensified livestock grazing on mountain pastures under scenarios with strong climate change effects and increased extreme events. In this scenario, farmers use mountain pastures to seek additional forage resources in specific years. However, these grazing areas require considerate management in years when they are not needed for food provision. Our study also found that the utilization patterns of mountain pastures are strongly influenced by farm succession, vegetation regrowth on unused mountain pastures, and the search for cost-efficient forage resources. In a case study conducted in Eastern Austria, we found that adaptive learning moderates the decline in the number of active farms and farmland, regardless of the scenario conditions, compared to scenarios without adaptive learning. However, the results also indicate that adaptation increases the workload of farmers. This highlights the importance of considering more than just simplistic economic rationales when making land-use decisions. Agent-based models can be used to model socio-ecological responses and help cope with adaptation in complex socio-ecological systems.Both studies emphasise that in the context of risk management and socio-ecological resilience, learning and managing additional workload are key factors for achieving adaptive success. To further improve, it is necessary to couple agent-based models with climatic and landscape models, allowing for bi-directional feedback between social and natural systems. SECLAND has been adapted to integrate adaptive learning processes, demonstrating the possibility of capturing mutual system dynamics and feedback loops. This allows the full capacity of agent-based models to be used to assess the resilience of mountain communities to cope with natural hazards, using a scenario approach that includes heterogeneous agents, different trajectories of socio-economic conditions, as well as global and climate change dynamics. This presentation outlines a conceptual framework for operationalizing an interdisciplinary effort within a modelling environment that integrates human decision-making, socio-economic conditions, and climatic and landscape dynamics.

In this paper, we describe what we believe to be the simplest agent-based model. More specifically we define the class of the simplest agent-based simulation model formulations, referred to as SABM. We describe the nine essential elements of agent-based simulation models in the class SABM and give an example of such a model. We find that the simplest ABM formulation provides a basis for a transparent, compact and elegant description of agent-based modeling. The design illustrates the essential characteristics of agent-based modelling as a field of research and application and facilitates the explanation of agent-based modeling. We hope that the simplest formulation demystifies agent-based modeling for those within the simulation community, as well as those new to the field such as researchers from other disciplines and application communities.

In response to the inadequacy of flood control infrastructures under uncertainties, building community resilience has become a vital concern in modern flood risk management for flood mitigation and recovery options. Relatedly, there has been growing recognition of the importance of community flood resilience measurement, and several tools to measure resilience have been introduced since the turn of this century. However, overall yield from resilience works can be compromised if the measurements do not conform to theoretical basis. By identifying evaluation methodology which takes the multifaceted nature of resilience into account, this study analyzed existing community flood resilience measurement tools. The results show that the importance of assessing and enhancing community competence in flood resilience building is unnoticed by the majority of the analyzed frameworks. Adopting a participatory approach and measurement under uncertainties are overlooked by a significant proportion of the frameworks. Moreover, issues of spatial and temporal interdependencies have received less attention. Consequently, the multi faceted nature of resilience appears inadequately addressed in existing frameworks, and measurements of community flood resilience tend to be inconsistent. The study would support efforts being made to improve consistency and effectiveness community flood resilience measurements and to operationalize the concept of resilience in flood disaster management.

Sensitivity analysis is an important step in agent-based modeling of complex adaptive spatial systems to evaluate the contribution of influential variables to model response. Sensitivity analysis of agent-based models is computationally demanding, however, and this analysis tends to be intractable for large agent-based modeling. This computational challenge greatly limits our ability to investigate complex spatial dynamics using large agent-based models. The objective of this study is to gain insight into this computational issue by focusing on the sensitivity analysis of large agent-based modeling of spatial opinion exchange, accelerated using multiple graphics processing units (GPUs). We present a heterogeneous parallel computing approach based on nested parallelism for the global sensitivity analysis of the model. The agent-based opinion model is parallelized using many-core GPUs for the simulation of a large number of spatially aware and interacting agents. These agents exchange opinions for developing consensus on topics through processes of spatial neighborhood search and opinion update. Global sensitivity analysis of the opinion model is conducted using a variance-based approach, requiring numerous model runs for Monte Carlo integration. Intermodel parallelization is introduced to enable Monte Carlo runs of sensitivity analysis. We conduct global sensitivity analysis on a multi-GPU cluster. Experimental results indicate GPU-accelerated general-purpose computation provides an efficacious and feasible solution for the sensitivity analysis of large agent-based models. The heterogeneous parallel computing approach provides valuable insight into large-scale spatiotemporal problem solving by leveraging cyberinfrastructure-enabled computational capabilities.