Methods for evaluating Decision Problems with Limited Information

LImited Memory Influence Diagrams (LIMIDs) are general models of decision problems for representing limited memory policies (Lauritzen and Nilsson (2001)). The evaluation of LIMIDs can be done by Single Policy Updating that produces a local maximum strategy in which no single policy modification can increase the expected utility. This paper examines the quality of the obtained local maximum strategy and proposes three different methods for evaluating LIMIDs. The first algorithm, Temporal Policy Updating, resembles Single Policy Updating. The second algorithm, Greedy Search, successively updates the policy that gives the highest expected utility improvement. The final algorithm, Simulating Annealing, differs from the two preceeding by allowing the search to take some downhill steps to escape a local maximum. A careful comparison of the algorithms is provided both in terms of the quality of the obtained strategies, and in terms of implementation of the algorithms including some considerations of the computational complexity

Unfair Utilities and First Steps Towards Improving Them

Many fairness criteria constrain the policy or choice of predictors. In this work, we propose a different framework for thinking about fairness: Instead of constraining the policy or choice of predictors, we consider which utility a policy is optimizing for. We define value of information fairness and propose to not use utilities that do not satisfy this criterion. We describe how to modify a utility to satisfy this fairness criterion and discuss the consequences this might have on the corresponding optimal policies.Comment: 20 page

Ecosystem management via interacting models of political and ecological processes

The decision to implement environmental protection options is a political one. Political realities may cause a country to not heed the most persuasive scientific analysis of an ecosystem's future health. A predictive understanding of the political processes that result in ecosystem management decisions may help guide ecosystem management policymaking. To this end, this article develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes. This model is realized in a system of interacting influence diagrams that model the decision making of a country's political bodies. These decisions interact with a model of the ecosystem enclosed by the country. As an example, a model for Cheetah (Acinonyx jubatus) management in Kenya is constructed and fitted to decision and ecological data

Gestión de ecosistemas mediante modelos interactivos de procesos políticos y ecológicos

The decision to implement environmental protection options is a political one. Political realities may cause a country to not heed the most persuasive scientific analysis of an ecosystem’s future health. A predictive understanding of the political processes that result in ecosystem management decisions may help guide ecosystem management policymaking. To this end, this article develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes. This model is realized in a system of interacting influence diagrams that model the decision making of a country’s political bodies. These decisions interact with a model of the ecosystem enclosed by the country. As an example, a model for Cheetah (Acinonyx jubatus) management in Kenya is constructed and fitted to decision and ecological data.La decisión de implementar opciones de protección medioambiental es de carácter político. Las realidades políticas de un país pueden permitir ignorar los análisis científicos más rotundos acerca de la futura salud de un ecosistema. Una comprensión predictiva de los procesos políticos que conducen a la toma de decisiones sobre la gestión de los ecosistemas puede contribuir a orientar las políticas relativas a dichas áreas. Con este objetivo, el presente artículo desarrolla un modelo estocástico temporal acerca de cómo los procesos políticos influyen y son influidos por los procesos de los ecosistemas. Dicho modelo se ha estructurado a partir de un sistema de diagramas de influencia interactivos que configuran la toma de decisiones de las instituciones políticas de un país. Dichas decisiones interactúan con un modelo del ecosistema presente en el país. Así, a modo de ejemplo, se elabora un modelo para la gestión del guepardo (Acinonyx jubatus) en Kenia, ajustándose a los datos ecológicos y de toma de decisiones

Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

Evaluating Influence Diagrams using LIMIDs

We present a new approach to the solution of decision problems formulated as in- uence diagrams. The approach converts the inuence diagram into a simpler structure, the LImited Memory Inuence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depicted. Because the requisite information is explicitly represented in the diagram, the evaluation procedure can take advantage of it. In this paper we show how to convert an inuence diagram to a LIMID and describe the procedure for nding an optimal strategy. Our approach can yield signicant savings of memory and computational time when compared to traditional methods. 1 INTRODUCTION Inuence Diagrams (IDs) were introduced by Howard and Matheson (1981) as a compact representation of decision problems. Since then, various authors have attempted to formalize their approach and develop algorithms for evaluating IDs. Olmsted (1983) and Shachter (1986) initiated research in this di..