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20
Least expected cost query optimization: An exercise in utility
 In Proceedings of the ACM SIGMOD/SIGACT Conference on Principle of Database System (PODS
, 1999
"... We identify two unreasonable, though standard, assumptions made by database query optimizers that can adversely affect the quality of the chosen evaluation plans. One assumption is that it is enough to optimize for the expected case—that is, the case where various parameters (like available memory) ..."
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Cited by 36 (1 self)
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We identify two unreasonable, though standard, assumptions made by database query optimizers that can adversely affect the quality of the chosen evaluation plans. One assumption is that it is enough to optimize for the expected case—that is, the case where various parameters (like available memory) take on their expected value. The other assumption is that the parameters are constant throughout the execution of the query. We present an algorithm based on the “System R”style query optimization algorithm that does not rely on these assumptions. The algorithm we present chooses the plan of the least expected cost instead of the plan of least cost given some fixed value of the parameters. In execution environments that exhibit a high degree of variability, our techniques should result in better performance. 1
Aggregating disparate estimates of chance
, 2004
"... We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We ad ..."
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Cited by 19 (4 self)
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We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group’s expertise.
Great expectations. Part I: On the customizability of generalized expected utility
 In IJCAI
, 2003
"... We propose a generalization of expected utility that we call generalized EU (GEU), where a decision maker’s beliefs are represented by plausibility measures and the decision maker’s tastes are represented by general (i.e., not necessarily realvalued) utility functions. We show that every agent, “ra ..."
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Cited by 18 (3 self)
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We propose a generalization of expected utility that we call generalized EU (GEU), where a decision maker’s beliefs are represented by plausibility measures and the decision maker’s tastes are represented by general (i.e., not necessarily realvalued) utility functions. We show that every agent, “rational ” or not, can be modeled as a GEU maximizer. We then show that we can customize GEU by selectively imposing just the constraints we want. In particular, we show how each of Savage’s postulates corresponds to constraints on GEU. 1
Great Expectations. Part II: Generalized Expected Utility as a Universal Decision Rule
 In Proc. of the 18th International Joint Conference on Artificial Intelligence (IJCAI03
"... Abstract Many different rules for decision making have been introduced in the literature. We showthat a notion of generalized expected utility proposed in [Chu and Halpern 2003] is a universal ..."
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Cited by 12 (3 self)
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Abstract Many different rules for decision making have been introduced in the literature. We showthat a notion of generalized expected utility proposed in [Chu and Halpern 2003] is a universal
On recommending
 Journal of the American Society for Information Science and Technology
, 2002
"... The core of any document retrieval system is a mechanism that ranks the documents in a large collection in order of the likelihood with which they match the preferences of any person who interacts with the system. Given a broader interpretation of “recommending” than is commonly accepted, such a pre ..."
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Cited by 11 (0 self)
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The core of any document retrieval system is a mechanism that ranks the documents in a large collection in order of the likelihood with which they match the preferences of any person who interacts with the system. Given a broader interpretation of “recommending” than is commonly accepted, such a preference ordering may be viewed as a recommendation, made by the system to the informationseeker, that is itself typically derived through synthesis of multiple preference orderings expressed as recommendations by indexers, informationseekers, and document authors. The ERIn (EvaluationRecommendationInformation) model, a decisiontheoretic framework for understanding informationrelated activity, highlights the centrality of recommending in the document retrieval process, and may be used to clarify the respects in which indexing, rating, and citation may be considered analogous, as well as to make explicit the points at which contentbased, collaborationbased, and contextbased flavors of document retrieval systems vary.
A general framework for expressing preferences in causal reasoning and planning
 PROCEEDINGS OF THE SEVENTH INTERNATIONAL SYMPOSIUM ON LOGICAL FORMALIZATIONS OF COMMONSENSE REASONING
, 2005
"... We consider the problem of representing arbitrary preferences in causal reasoning and planning systems. In planning, a preference may be seen as a goal or constraint that is desirable, but not necessary, to satisfy. To begin, we define a very general query language for histories, or interleaved sequ ..."
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Cited by 7 (1 self)
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We consider the problem of representing arbitrary preferences in causal reasoning and planning systems. In planning, a preference may be seen as a goal or constraint that is desirable, but not necessary, to satisfy. To begin, we define a very general query language for histories, or interleaved sequences of world states and actions. Based on this, we specify a second language in which preferences are defined. A single preference defines a binary relation on histories, indicating that one history is preferred to the other. ¿From this, one can define global preference orderings on the set of histories, the maximal elements of which are the preferred histories. The approach is very general and flexible; thus it constitutes a “base” language in terms of which higherlevel preferences may be defined. To this end, we investigate two fundamental types of preferences that we call choice and temporal preferences. We consider concrete strategies for these types of preferences and encode them in terms of our framework. We suggest how to express aggregates in the approach, allowing, for example, the expression of a preference for histories with lowest total action costs. Last, our approach can be used to express other approaches, and so serves as a common framework in which such
Coherent probability from incoherent judgment
 Journal of Experimental Psychology: Applied
, 2001
"... People often have knowledge about the chances of events but are unable to express their knowledge in the form of coherent probabilities. This study proposed to correct incoherent judgment via an optimization procedure that seeks the (coherent) probability distribution nearest to a judge's estimates ..."
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Cited by 5 (3 self)
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People often have knowledge about the chances of events but are unable to express their knowledge in the form of coherent probabilities. This study proposed to correct incoherent judgment via an optimization procedure that seeks the (coherent) probability distribution nearest to a judge's estimates of chance. This method was applied to the chances of simple and complex meteorological events, as estimated by college undergraduates. No judge responded coherently, but the optimization method found close (coherent) approximations to their estimates. Moreover, the approximations were reliably more accurate than the original estimates, as measured by the quadratic scoring rule. Methods for correcting incoherence facilitate the analysis of expected utility and allow human judgment to be more easily exploited in the construction of expert systems. Suppose you think the probability that the Internet will expand next year is.90. Suppose you also think the probability that the Internet will expand and PC makers will be profitable is.91. Then you have assigned a greater chance to a conjunction rather than to one of its conjuncts; hence, your judgments are incoherent. You may, nonetheless, prove to be more insightful than someone with
Uncertainty classification for the design and development of complex systems
 Proceedings of the 3 rd Annual Predictive Methods Conference, Veros Software
, 2003
"... Uncertainty plays a critical role in the analysis for a wide and diverse set of fields from economics to engineering. The term ‘uncertainty ’ has come to encompass a multiplicity of concepts. This paper begins with a literature survey of uncertainty definitions and classifications from various field ..."
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Cited by 5 (2 self)
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Uncertainty plays a critical role in the analysis for a wide and diverse set of fields from economics to engineering. The term ‘uncertainty ’ has come to encompass a multiplicity of concepts. This paper begins with a literature survey of uncertainty definitions and classifications from various fields. A classification of uncertainty for the design and development of complex systems follows. The various classifications are more practical than theoretical: to make distinct the techniques used to address each type of uncertainty and to demonstrate the effects of each type of uncertainty in each field. The classification for the design and development of complex systems delineates ambiguity, epistemic, aleatory, and interaction uncertainty. Epistemic uncertainty is further subdivided into modelform, phenomenological, and behavioral uncertainty, each of which is described in detail. The uncertainty taxonomy presented is an integral part of ongoing research into propagating and mitigating the effect of all types of uncertainty in the design and development of complex multidisciplinary engineering systems.
The Capabilities of Chaos and Complexity
, 2009
"... To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic h ..."
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Cited by 3 (1 self)
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To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, selfordered states, and organization must all be carefully defined and distinguished. In addition their causeandeffect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, selfordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating selfordered phenomena. But can spontaneous chaos and complexity steer events and processes toward pragmatic benefit, select function over non function, optimize algorithms, integrate circuits, produce computational halting, organize processes into formal systems, control and regulate existing systems toward greater efficiency? The question is pursued of whether there might be some yettobe discovered new law of biology that will elucidate the derivation of prescriptive information and control. “System” will be rigorously defined. Can a lowinformational rapid succession of Prigogine’s dissipative structures selforder into bona fide organization?
Propagating and Mitigating Uncertainty in the Design of Complex Multidisciplinary Systems
, 2005
"... iii Frederick Douglass once said “If there is no struggle, there is no progress. ” I think this statement describes my PhD experience here at Caltech well. In ways the past four and half years have been more challenging than I expected yet also more rewarding than I anticipated. This journey would n ..."
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Cited by 3 (0 self)
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iii Frederick Douglass once said “If there is no struggle, there is no progress. ” I think this statement describes my PhD experience here at Caltech well. In ways the past four and half years have been more challenging than I expected yet also more rewarding than I anticipated. This journey would not have been possible without the financial and emotional support of my family and the guidance of my advisors present and past: Professors Fred Culick (Caltech), Victoria Coverstone (University of Illinois at Urbana–Champaign), and Alec Gallimore (University of Michigan). Their support of my education transformed a possibility of a PhD from a dream, to a hope, and now to a reality. I want to thank my Ph. D. advising committee: Professors Erik Antonsson, Jim Beck, and John Ledyard. All three provided invaluable insight and knowledge, especially Professor Beck who assisted with the subset simulation work. Along with Professor Culick, my advising committee gave me the liberty to pursue research ideas and shielded me from many bureaucratic and financial issues a PhD requires. I also thank Dr. Joel Sercel who provided the initial impetus and research ideas that this thesis became and Melinda Kirk for administrative support. This thesis builds upon work by a variety of researchers including Professor Ivan Au