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Urban pluvial flood management requires detailed spatial and temporal information on flood characteristics and damaging consequences. There is lack of quantitative field data on pluvial flooding resulting in large uncertainties in urban flood model calculations and ensuing decisions for investments in flood protection. In this paper four different data sources are discussed, based on literature and expert consultation, that are believed to be of value for the acquisition of quantitative data on pluvial flooding. Data assembled by insurance agencies on flood damage, call databases held by water authorities and emergency services and remote sensing images cover years of observational data that can be mined to obtain data on flood characteristics and occurrence. Flood monitoring using sensor technology can be effective to collect additional pluvial flood data, that is not captured by existing data sources.

Agent-based modelling is a popular and suitable tool for exploring the possible states of so-called socio-technical systems. These systems consist of both technical artefacts (the physical infrastructure, e.g. pipelines), and many social artefacts (relevant actors and institutions, e.g. end-users and governments), which are intertwined with each other and strongly interact (De Bruijn & Herder, 2009). The quality of the model output strongly depends on the quality of the rules of an agent, i.e. the lines of code that describe how an agent behaves. Slightly different rules on agent-level, may lead to significantly different outcomes on system-level (Bousquet, Cambier, Mullon, Morand & Quensiere, 1994; Levine & Fitzgerald, 1992). Thus, valid rules for agents are crucial for a valid analysis of socio-technical systems as a whole. When modelling a socio-technical system with an agent-based model, some agents represent social artefacts, and thus must simulate real-life behaviour. However, in many cases, rules that describe social phenomena, are not based on empirically tested, theoretical models, and agents display unrealistically simplistic behaviour (Jager & Janssen, 2003). This restricts the analysis with respect to social behaviour, and may even lead to an invalid system analysis. In earlier research it is suggested that gaming simulations can be used to improve the realism and diversity of agent behaviour. However, this application of games has not been examined extensively. This research aims to acquire insight in whether this application of games is possible and feasible. The central research question thereby is: To what extent can gaming contribute to the definition of realistic behavioural rules for agents in an agent-based model, within the context of modelling socio-technical systems? An extensive literature research shows several problems and challenges with agent-based modelling. These include fundamental problems with the currently used methods used for gathering information about realistic behaviour (i.e. interviews and literature research). Several characteristics of gaming can help to reduce some of these these challenges, providing theoretical evidence that there is a potential for synergy between the two methods. Based on a structural comparison of the methodological processes of agent-based modelling and gaming, it appears that there are several possibilities in which gaming can contribute to the definition of realistic behavioural rules of agents. In this thesis we elaborate on using gaming as a data collection tool. The gap between global knowledge about how agents behave and the implementation of precise rules in models is large. In case games can function as a valid data collection tool, the collected data can function as a basis for rules, which helps to overcome, or at least to reduce, design and formalization problems. When a game is used as a data collection tool, there are three basic requirements that must be met: one must be able to generate valid data, to measure the desired data, and to analyse the data. Several aspects with regard to fulfilling these goals, affect the validity and the costs of the data collection tool. Furthermore, choices within one design (game design, design data collection, or design data analysis), may have implications, direct or indirect, on other design choices, both within that design and in the other two. This interconnectedness makes the design process of the data collection tool as a whole very complex. Neither the game, nor the data collection method, nor the data analysis method is per definition leading in the decision which is the most suitable design. Whether, and how much the data collection tool can contribute to the definition of realistic behavioural rules, is very context dependent. The proposal for using games as a data collection tool, and the proposed design framework have been done solely on a theoretical basis. The development of a useful and feasible tool, however, should also have a decent practical basis. Therefore, this work should be seen as the first of many research projects on this topic. The lessons learned from the application of this tool can and should be used to improve the proposed design framework.