Informed Decision Making During Pregnancy: Risk Consideration in Clinical Practice and Research

This thesis addressed a cluster of issues related to risk and the pregnant woman’s decision making in clinical practice and research. In terms of clinical practice, the minimal risk concept - a low risk standard codified in research ethics regulations - was applied to clinicians’ information provision to the patient. As clinicians must discuss a variety of health risks, minimal risk standards may be useful as a threshold to demarcate risks that clinicians should discuss with the patient. Application of minimal risk standards to risk factors in pregnancy showed the usefulness and limitations of these standards. In terms of pregnant women’s clinical research participation, analyses of national and international research ethics regulations suggested that regulations could potentially be overprotective. A grounded theory study revealed that pregnant women were protective of themselves and their fetus in considering clinical research participation. In determining whether a clinical research project involving pregnant women would be acceptable, obstetric healthcare providers emphasized the adherence to regulatory requirements while researchers in reproduction areas focused on scrutinizing the scientific quality and interpretation of prerequisite studies. These three populations shared safety concerns. While minimal risk standards may be useful in identifying risks to be discussed with the patient, determining permissible risk during pregnancy may be more complex, including consideration on the risk benefit ratio in the pregnant woman’s context. This thesis has implications on risk communication in clinical practice and research, policies on clinical research with pregnant women, and education for healthcare providers, clinical investigators, and the general public

Flood Management in a Complex River Basin with a Real-Time Decision Support System Based on Hydrological Forecasts

During the last decades, the Upper Rhone River basin has been hit by several flood events causing significant damages in excess of 500 million Swiss Francs. From this situation, the 3rd Rhône river training project was planned in order to improve the flood protection in the Upper Rhone River basin in Vaud and Valais Cantons. In this framework, the MINERVE forecast system aims to contribute to a better flow control during flood events in this catchment area, taking advantage of the existing hydropower multi-reservoir network. This system also fits into the OWARNA national project of the Swiss Federal Office of Environment by establishing a national platform on natural hazards alarms. The Upper Rhone River basin has a catchment area with high mountains and large glaciers. The surface of the basin is 5521 km2 and its elevation varies between 400 and 4634 m a.s.l. Numerous hydropower schemes with large dams and reservoirs are located in the catchment area, influencing the hydrological regime. Their impact during floods can be significant as appropriate preventive operations can decrease the peak discharges in the Rhone River and its main tributaries, thus reducing the damages. The MINERVE forecast system exploits flow measurements, data from reservoirs and hydropower plants as well as probabilistic (COSMO-LEPS) and deterministic (COSMO-2 and COSMO-7) numerical weather predictions from MeteoSwiss. The MINERVE hydrological model of the catchment area follows a semi-distributed approach. The basin is split into 239 sub-catchments which are further sub-divided into 500 m elevation bands, for a total of 1050 bands. For each elevation band, precipitation, temperature and potential evapotranspiration are calculated. They are considered in order to describe the temperature-driven processes accurately, such as snow and glaciers melt. The hydrological model was implemented in the Routing System software. The object oriented programming environment allows a user-friendly modelling of the hydrological, hydraulic and operating processes. Numerical meteorological data (observed or predicted) are introduced as input in the model. Over the calibration and validation periods of the model, only observed data (precipitation, temperature and flows) was used. For operational flood forecast, the observed measurements are used to update the initial conditions of the hydrological model and the weather forecasts for the hydrological simulations. Routing System provides then hydrological predictions in the whole catchment area. Subsequently, a warning system was developed especially for the basin to provide a flood warning report. The warning system predicts the evolution of the hydrological situation at selected main check points in the catchment area. It displays three warning levels during a flood event depending on respective critical discharge thresholds. Furthermore, the multi-reservoir system is managed in an optimal way in order to limit or avoid damages during floods. A decision support tool called MINDS (MINERVE Interactive Decision Support System) has been developed for real-time decision making based on the hydrological forecasts. This tool defines preventive operation measures for the hydropower plants such as turbine and bottom outlet releases able to provide an optimal water storage during the flood peak. The overall goal of MINDS is then to retain the inflowing floods in reservoirs and to avoid spillway and turbine operations during the peak flow, taking into account all restrictions and current conditions of the network. Such a reservoir management system can therefore significantly decrease flood damages in the catchment area. The reservoir management optimisation during floods is achieved with deterministic and probabilistic forecasts. The definition of the objective function to optimise is realised with a multi-attribute decision making approach. Then, the optimisation is performed with an iterative Greedy algorithm or a SCE-UA (Shuffled Complex Evolution – University of Arizona) algorithm. The developed decision support system combines the high-quality optimisation system with its user-friendly interface. The purpose is to help decision makers by being directly involve in main steps of the decision making process as well as by understanding the measures undertaken and their consequences

Constrained Rationality: Formal Value-Driven Enterprise Knowledge Management Modelling and Analysis Framework for Strategic Business, Technology and Public Policy Decision Making & Conflict Resolution

The complexity of the strategic decision making environments, in which busi- nesses and governments live in, makes such decisions more and more difficult to make. People and organizations with access to the best known decision support modelling and analysis tools and methods cannot seem to benefit from such re- sources. We argue that the reason behind the failure of most current decision and game theoretic methods is that these methods are made to deal with operational and tactical decisions, not strategic decisions. While operational and tactical decisions are clear and concise with limited scope and short-term implications, allowing them to be easily formalized and reasoned about, strategic decisions tend to be more gen- eral, ill-structured, complex, with broader scope and long-term implications. This research work starts with a review of the current dominant modelling and analysis approaches, their strengths and shortcomings, and a look at how pioneers in the field criticize these approaches as restrictive and unpractical. Then, the work goes on to propose a new paradigm shift in how strategic decisions and conflicts should be modelled and analyzed. Constrained Rationality is a formal qualitative framework, with a robust method- ological approach, to model and analyze ill-structured strategic single and multi- agent decision making situations and conflicts. The framework brings back the strategic decision making problem to its roots, from being an optimization/efficiency problem about evaluating predetermined alternatives to satisfy predetermined pref- erences or utility functions, as most current decision and game theoretic approaches treats it, to being an effectiveness problem of: 1) identifying and modelling explic- itly the strategic and conflicting goals of the involved agents (also called players and decision makers in our work), and the decision making context (the external and internal constraints including the agents priorities, emotions and attitudes); 2) finding, uncovering and/or creating the right set of alternatives to consider; and then 3) reasoning about the ability of each of these alternatives to satisfy the stated strategic goals the agents have, given their constraints. Instead of assuming that the agents’ alternatives and preferences are well-known, as most current decision and game theoretic approaches do, the Constrained Rationality framework start by capturing and modelling clearly the context of the strategic decision making situation, and then use this contextual knowledge to guide the process of finding the agents’ alternatives, analyzing them, and choosing the most effective one. The Constrained Rationality framework, at its heart, provides a novel set of modelling facilities to capture the contextual knowledge of the decision making sit- uations. These modelling facilities are based on the Viewpoint-based Value-Driven - Enterprise Knowledge Management (ViVD-EKM) conceptual modelling frame- work proposed by Al-Shawa (2006b), and include facilities: to capture and model the goals and constraints of the different involved agents, in the decision making situation, in complex graphs within viewpoint models; and to model the complex cause-effect interrelationships among theses goals and constraints. The framework provides a set of robust, extensible and formal Goal-to-Goal and Constraint-to Goal relationships, through which qualitative linguistic value labels about the goals’ op- erationalization, achievement and prevention propagate these relationships until they are finalized to reflect the state of the goals’ achievement at any single point of time during the situation. The framework provides also sufficient, but extensible, representation facilities to model the agents’ priorities, emotional valences and attitudes as value properties with qualitative linguistic value labels. All of these goals and constraints, and the value labels of their respective value properties (operationalization, achievement, prevention, importance, emotional valence, etc.) are used to evaluate the different alternatives (options, plans, products, product/design features, etc.) agents have, and generate cardinal and ordinal preferences for the agents over their respective alternatives. For analysts, and decision makers alike, these preferences can easily be verified, validates and traced back to how much each of these alternatives con- tribute to each agent’s strategic goals, given his constraints, priorities, emotions and attitudes. The Constrained Rationality framework offers a detailed process to model and analyze decision making situations, with special paths and steps to satisfy the spe- cific needs of: 1) single-agent decision making situations, or multi-agent situations in which agents act in an individualistic manner with no regard to others’ current or future options and decisions; 2) collaborative multi-agent decision making situ- ations, where agents disclose their goals and constraints, and choose from a set of shared alternatives one that best satisfy the collective goals of the group; and 3) adversarial competitive multi-agent decision making situations (called Games, in gamete theory literature, or Conflicts, in the broader management science litera- ture). The framework’s modelling and analysis process covers also three types of con- flicts/games: a) non-cooperative games, where agents can take unilateral moves among the game’s states; b) cooperative games, with no coalitions allowed, where agents still act individually (not as groups/coalitions) taking both unilateral moves and cooperative single-step moves when it benefit them; and c) cooperative games, with coalitions allowed, where the games include, in addition to individual agents, agents who are grouped in formal alliances/coalitions, giving themselves the ability to take multi-step group moves to advance their collective position in the game. ...