@phdthesis{Weidenfeld2003,
  author    = {Weidenfeld, Andrea},
  title     = {Interpretation of and reasoning with conditionals : probabilities, mental models, and causality},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5207},
  school      = {Universit{\"a}t Potsdam},
  year      = {2003},
  abstract  = {In everyday conversation \"if\" is one of the most frequently used conjunctions. This dissertation investigates what meaning an everyday conditional transmits and what inferences it licenses. It is suggested that the nature of the relation between the two propositions in a conditional might play a major role for both questions. Thus, in the experiments reported here conditional statements that describe a causal relationship (e.g., \"If you touch that wire, you will receive an electric shock\") were compared to arbitrary conditional statements in which there is no meaningful relation between the antecedent and the consequent proposition (e.g., \"If Napoleon is dead, then Bristol is in England\"). Initially, central assumptions from several approaches to the meaning and the reasoning from causal conditionals will be integrated into a common model. In the model the availability of exceptional situations that have the power to generate exceptions to the rule described in the conditional (e.g., the electricity is turned off), reduces the subjective conditional probability of the consequent, given the antecedent (e.g., the probability of receiving an electric shock when touching the wire). This conditional probability determines people's degree of belief in the conditional, which in turn affects their willingness to accept valid inferences (e.g., \"Peter touches the wire, therefore he receives an electric shock\") in a reasoning task. Additionally to this indirect pathway, the model contains a direct pathway: Cognitive availability of exceptional situations directly reduces the readiness to accept valid conclusions. The first experimental series tested the integrated model for conditional statements embedded in pseudo-natural cover stories that either established a causal relation between the antecedent and the consequent event (causal conditionals) or did not connect the propositions in a meaningful way (arbitrary conditionals). The model was supported for the causal, but not for the arbitrary conditional statements. Furthermore, participants assigned lower degrees of belief to arbitrary than to causal conditionals. Is this effect due to the presence versus absence of a semantic link between antecedent and consequent in the conditionals? This question was one of the starting points for the second experimental series. Here, the credibility of the conditionals was manipulated by adding explicit frequency information about possible combinations of presence or absence of antecedent and consequent events to the problems (i.e., frequencies of cases of 1. true antecedent with true consequent, 2. true antecedent with false consequent, 3. false antecedent with true consequent, 4. false antecedent with false consequent). This paradigm allows testing different approaches to the meaning of conditionals (Experiment 4) as well as theories of conditional reasoning against each other (Experiment 5). The results of Experiment 4 supported mainly the conditional probability approach to the meaning of conditionals (Edgington, 1995) according to which the degree of belief a listener has in a conditional statement equals the conditional probability that the consequent is true given the antecedent (e.g., the probability of receiving an electric shock when touching the wire). Participants again assigned lower degrees of belief to the arbitrary than the causal conditionals, although the conditional probability of the consequent given the antecedent was held constant within every condition of explicit frequency information. This supports the hypothesis that the mere presence of a causal link enhances the believability of a conditional statement. In Experiment 5 participants solved conditional reasoning tasks from problems that contained explicit frequency information about possible relevant cases. The data favored the probabilistic approach to conditional reasoning advanced by Oaksford, Chater, and Larkin (2000). The two experimental series reported in this dissertation provide strong support for recent probabilistic theories: for the conditional probability approach to the meaning of conditionals by Edgington (1995) and the probabilistic approach to conditional reasoning by Oaksford et al. (2000). In the domain of conditional reasoning, there was additionally support for the modified mental model approaches by Markovits and Barrouillet (2002) and Schroyens and Schaeken (2003). Probabilistic and mental model approaches could be reconciled within a dual-process-model as suggested by Verschueren, Schaeken, and d\&\#39;Ydewalle (2003).},
  subject      = {Konditional},
  language  = {en}
}
@phdthesis{Roezer2018,
  author    = {R{\"o}zer, Viktor},
  title     = {Pluvial flood loss to private households},
  doi       = {10.25932/publishup-42991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429910},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XXII, 109},
  year      = {2018},
  abstract  = {Today, more than half of the world's population lives in urban areas. With a high density of population and assets, urban areas are not only the economic, cultural and social hubs of every society, they are also highly susceptible to natural disasters. As a consequence of rising sea levels and an expected increase in extreme weather events caused by a changing climate in combination with growing cities, flooding is an increasing threat to many urban agglomerations around the globe. To mitigate the destructive consequences of flooding, appropriate risk management and adaptation strategies are required. So far, flood risk management in urban areas is almost exclusively focused on managing river and coastal flooding. Often overlooked is the risk from small-scale rainfall-triggered flooding, where the rainfall intensity of rainstorms exceeds the capacity of urban drainage systems, leading to immediate flooding. Referred to as pluvial flooding, this flood type exclusive to urban areas has caused severe losses in cities around the world. Without further intervention, losses from pluvial flooding are expected to increase in many urban areas due to an increase of impervious surfaces compounded with an aging drainage infrastructure and a projected increase in heavy precipitation events. While this requires the integration of pluvial flood risk into risk management plans, so far little is known about the adverse consequences of pluvial flooding due to a lack of both detailed data sets and studies on pluvial flood impacts. As a consequence, methods for reliably estimating pluvial flood losses, needed for pluvial flood risk assessment, are still missing. Therefore, this thesis investigates how pluvial flood losses to private households can be reliably estimated, based on an improved understanding of the drivers of pluvial flood loss. For this purpose, detailed data from pluvial flood-affected households was collected through structured telephone- and web-surveys following pluvial flood events in Germany and the Netherlands. Pluvial flood losses to households are the result of complex interactions between impact characteristics such as the water depth and a household's resistance as determined by its risk awareness, preparedness, emergency response, building properties and other influencing factors. Both exploratory analysis and machine-learning approaches were used to analyze differences in resistance and impacts between households and their effects on the resulting losses. The comparison of case studies showed that the awareness around pluvial flooding among private households is quite low. Low awareness not only challenges the effective dissemination of early warnings, but was also found to influence the implementation of private precautionary measures. The latter were predominately implemented by households with previous experience of pluvial flooding. Even cases where previous flood events affected a different part of the same city did not lead to an increase in preparedness of the surveyed households, highlighting the need to account for small-scale variability in both impact and resistance parameters when assessing pluvial flood risk. While it was concluded that the combination of low awareness, ineffective early warning and the fact that only a minority of buildings were adapted to pluvial flooding impaired the coping capacities of private households, the often low water levels still enabled households to mitigate or even prevent losses through a timely and effective emergency response. These findings were confirmed by the detection of loss-influencing variables, showing that cases in which households were able to prevent any loss to the building structure are predominately explained by resistance variables such as the household's risk awareness, while the degree of loss is mainly explained by impact variables. Based on the important loss-influencing variables detected, different flood loss models were developed. Similar to flood loss models for river floods, the empirical data from the preceding data collection was used to train flood loss models describing the relationship between impact and resistance parameters and the resulting loss to building structures. Different approaches were adapted from river flood loss models using both models with the water depth as only predictor for building structure loss and models incorporating additional variables from the preceding variable detection routine. The high predictive errors of all compared models showed that point predictions are not suitable for estimating losses on the building level, as they severely impair the reliability of the estimates. For that reason, a new probabilistic framework based on Bayesian inference was introduced that is able to provide predictive distributions instead of single loss estimates. These distributions not only give a range of probable losses, they also provide information on how likely a specific loss value is, representing the uncertainty in the loss estimate. Using probabilistic loss models, it was found that the certainty and reliability of a loss estimate on the building level is not only determined by the use of additional predictors as shown in previous studies, but also by the choice of response distribution defining the shape of the predictive distribution. Here, a mix between a beta and a Bernoulli distribution to account for households that are able to prevent losses to their building's structure was found to provide significantly more certain and reliable estimates than previous approaches using Gaussian or non-parametric response distributions. The successful model transfer and post-event application to estimate building structure loss in Houston, TX, caused by pluvial flooding during Hurricane Harvey confirmed previous findings, and demonstrated the potential of the newly developed multi-variable beta model for future risk assessments. The highly detailed input data set constructed from openly available data sources containing over 304,000 affected buildings in Harris County further showed the potential of data-driven, building-level loss models for pluvial flood risk assessment. In conclusion, pluvial flood losses to private households are the result of complex interactions between impact and resistance variables, which should be represented in loss models. The local occurrence of pluvial floods requires loss estimates on high spatial resolutions, i.e. on the building level, where losses are variable and uncertainties are high. Therefore, probabilistic loss estimates describing the uncertainty of the estimate should be used instead of point predictions. While the performance of probabilistic models on the building level are mainly driven by the choice of response distribution, multi-variable models are recommended for two reasons: First, additional resistance variables improve the detection of cases in which households were able to prevent structural losses. Second, the added variability of additional predictors provides a better representation of the uncertainties when loss estimates from multiple buildings are aggregated. This leads to the conclusion that data-driven probabilistic loss models on the building level allow for a reliable loss estimation at an unprecedented level of detail, with a consistent quantification of uncertainties on all aggregation levels. This makes the presented approach suitable for a wide range of applications, from decision support in spatial planning to impact- based early warning systems.},
  language  = {en}
}