Multiple Objectives Satisficing Under Uncertainty

Ph.DDOCTOR OF PHILOSOPH

Satisficing in multi-armed bandit problems

Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty. We propose two sets of satisficing objectives for the multi-armed bandit problem, where the objective is to achieve reward-based decision-making performance above a given threshold. We show that these new problems are equivalent to various standard multi-armed bandit problems with maximizing objectives and use the equivalence to find bounds on performance. The different objectives can result in qualitatively different behavior; for example, agents explore their options continually in one case and only a finite number of times in another. For the case of Gaussian rewards we show an additional equivalence between the two sets of satisficing objectives that allows algorithms developed for one set to be applied to the other. We then develop variants of the Upper Credible Limit (UCL) algorithm that solve the problems with satisficing objectives and show that these modified UCL algorithms achieve efficient satisficing performance.Comment: To appear in IEEE Transactions on Automatic Contro

Data-driven satisficing measure and ranking

We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of risk measures, we consider problems constrained by general convex risk measures and specifically by Conditional value-at-risk. Starting from offline optimization, we apply sample average approximation technique and argue the convergence rate and validation of optimal solutions. In online stochastic optimization case, we develop primal-dual stochastic approximation algorithms respectively for general risk constrained problems, and derive their regret bounds. For both offline and online cases, we illustrate the relationship between risk ranking accuracy with sample size (or iterations).Comment: 26 Pages, 6 Figure

Methods for incorporating ecological impacts with climate uncertainty to support robust flood management decision-making

Modern and historic flood risk management involves accommodating multiple sources of sources of uncertainty and potential impacts across a broad range of interrelated sectors. Sources of uncertainty that affect planning include internal climate variability, anthropogenic changes such as land use and system performance expectations, and more recently changes in climatology that affect the resources supporting the system. Flood management systems potentially impact human settlements within and beyond the systems’ scope of planning, local weather patterns, and associated ecological systems. Federal guidelines across nations have called for greater consideration of uncertainty and impacts of water resources planning projects, but methods for meeting these needs remain poorly established. At the same time, there is increased attention to the ecological impacts of water resources systems and growing expectations that negative impacts be mitigated. The confluence of climate change and increasing demand for environmental quality presents a challenging flood management decision context. This work presents several alternative methods for incorporating ecological impacts into flood risk management and evaluation procedures alongside climate uncertainty, which are illustrated through application to a flood management system on the Iowa River. First, to integrate climate change and uncertainty information into these decision models, the dissertation presents a decision-centric trend detection test in which the threshold for accepting or rejecting a trend in observed data is determined by the expected cost of drawing a false conclusion. Next, the dissertation presents a decision model to choose a portfolio of adaptation options based on portfolios’ expected economic and monetized ecological performance under uncertain future flood hazard. The dissertation also develops a robust optimization model with an alternate treatment of ecological performance to maximize the range of future conditions over which performance is acceptable in both economic and ecological impact sectors. Lastly, the dissertation presents a method for deriving a posterior distribution of changes in climate parameters based on a combination of a prior constructed based on climate model projections and likelihood based on the historic record. The goals of this work are to develop enhanced decision support tools that accommodate the unique context of flood risk management decisions and to improve the set of methods available to characterize future flood hazard and its associated uncertainty

A humanistic approach to organizations and to organizational decision-making

This paper attempts to take steps towards the formulation of a more human approach to the theory of the firm than the conventional economics-based models. Unbounded rationality, self-interest and the absence of learning are shown to be crucial assumptions of conventional economic theory. Then, the essential assumptions of an alternative approach are put forward and discussed. Next, I present an alternative view of organizations, which has its foundations in the concepts of mission, distinctive competence, identification and unity. Finally, the implications of such an approach for management decision-making are shown, emphasizing that three criteria have to be considered in any non-trivial decision in an organizational context.theory of the firm; bounded rationality; self-interest; distinctive competence; mission; identification;

Schedules, Calendars and Agendas

Time management instruments such as schedules, calendars and agendas are obvious tools to organise individual and collective action. Besides being of great practical significance in the western world and beyond, these tools are remarkable in that they are rarely questioned by those who are governed by them. Yet, they are tools and as such they can be used by management in organisations. This paper will explore: -why these time instruments are much legs visible than the task itself, -to what extent they are knowingly used by management, and -if their effectiveness is somehow limited to certain activities. It is argued that the unobtrusiveness oftime instruments is related to the natural distinction between content and context. Tasks, intellectual or practical, lead the actors to focus on content. Time management instruments appear to belong to context instead. Hence, they are normally taken for granted, framing the problem.Time; management

Introducing health gains in location-allocation models: A stochastic model for planning the delivery of long-term care

Although the maximization of health is a key objective in health care systems, location-allocation literature has not yet considered this dimension. This study proposes a multi-objective stochastic mathematical programming approach to support the planning of a multi-service network of long-term care (LTC), both in terms of services location and capacity planning. This approach is based on a mixed integer linear programming model with two objectives – the maximization of expected health gains and the minimization of expected costs – with satisficing levels in several dimensions of equity – namely, equity of access, equity of utilization, socioeconomic equity and geographical equity – being imposed as constraints. The augmented ε-constraint method is used to explore the trade-off between these conflicting objectives, with uncertainty in the demand and delivery of care being accounted for. The model is applied to analyze the (re)organization of the LTC network currently operating in the Great Lisbon region in Portugal for the 2014-2016 period. Results show that extending the network of LTC is a cost-effective investment

EVALUATING THE PREDICTIVE CAPABILITY OF NUMERICAL MODELS CONSIDERING ROBUSTNESS TO NON-PROBABILISTIC UNCERTIANTY IN THE INPUT PARAMETERS

The paradigm of model evaluation is challenged by compensations between various forms of errors and uncertainties that are inherent to the model development process due to, for instance, imprecise model input parameters, scarcity of experimental data and lack of knowledge regarding an accurate mathematical representation of the system. When calibrating model input parameters based on fidelity to experiments, such compensations lead to non-unique solutions. In turn, the existence of non-unique solutions makes the selection and use of one `best\u27 numerical model risky. Therefore, it becomes necessary to evaluate model performance based not only on the fidelity of the predictions to experiments but also the model\u27s ability to satisfy fidelity threshold requirements in the face of uncertainties. The level of inherent uncertainty need not be known a priori as the model\u27s predictions can be evaluated for increasing levels of uncertainty, and a model form can be sought that yields the highest probability of satisfying a given fidelity threshold. By implementing these concepts, this manuscript presents a probabilistic formulation of a robust-satisfying approach, along with its associated metric. This new formulation evaluates the performance of a model form based on the probability that the model predictions match experimental data within a predefined fidelity threshold when subject to uncertainty in their input parameters. This approach can be used to evaluate the robustness and fidelity of a numerical model as part of a model validation campaign, or to compare multiple candidate model forms as part of a model selection campaign. In this thesis, the conceptual framework and mathematical formulation of this new probabilistic treatment of robust-satisfying approach is presented. The feasibility and application of this new approach is demonstrated on a structural steel frame with uncertain connection parameters, which has undergone static loading conditions