Robustness of Multiple Objective Decision Analysis Preference Functions

This research investigated value and utility functions in multiobjective decision analysis to examine the relationship between them in a military decision making context. The impact of these differences was examined to improve implementation efficiency. The robustness of the decision model was examined with respect to the preference functions to reduce the time burden imposed on the decision maker. Data for decision making in a military context supports the distinction between value and utility functions. Relationships between value and utility functions and risk attitudes were found to be complex. Elicitation error was significantly smaller than the difference between value and utility functions. Risk attitudes were generally neither constant across the domain of the evaluation measure nor consistent between evaluation measures. An improved measure of differences between preference functions, the weighted root means square, is introduced and a goodness of fit criterion established. An improved measure of risk attitudes employing utility functions is developed. Response Surface Methodology was applied to improve the efficiency of decision analysis utility model applications through establishing the robustness of decision models to the preference functions. An algorithm was developed and employs this information to provide a hybrid value-utility model that offers increased elicitation efficiency

Multi-criterion two-sided matching of Public-Private Partnership infrastructure projects: Criteria and methods

Two kinds of evaluative criteria are associated with Public-Private Partnership (PPP) infrastructure projects, i.e., private evaluative criteria and public evaluative criteria. These evaluative criteria are inversely related, that is, the higher the public benefits; the lower the private surplus. To balance evaluative criteria in the Two-Sided Matching (TSM) decision, this paper develops a quantitative matching decision model to select an optimal matching scheme for PPP infrastructure projects based on the Hesitant Fuzzy Set (HFS) under unknown evaluative criterion weights. In the model, HFS is introduced to describe values of the evaluative criteria and multi-criterion information is fully considered given by groups. The optimal model is built and solved by maximizing the whole deviation of each criterion so that the evaluative criterion weights are determined objectively. Then, the match-degree of the two sides is calculated and a multi-objective optimization model is introduced to select an optimal matching scheme v ia a min-max approach. The results provide new insights and implications of the influence on evaluative criteria in the TSM decision

Structured Preference Representation and Multiattribute Auctions

Handling preferences over multiple objectives (or attributes) poses serious challenges to the development of automated solutions to complex decision problems. The number of decision outcomes grows exponentially with the number of attributes, and that makes elicitation, maintenance, and reasoning with preferences particularly complex. This problem can potentially be alleviated by using a factored representation of preferences based on independencies among the attributes. This work has two main components. The first component focuses on development of graphical models for multiattribute preferences and utility functions. Graphical models take advantage of factored utility, and yield a compact representation for preferences. Specifically, I introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well studied utility independence concept. I show how conditional utility independence leads to an effective functional decomposition that can be exhibited graphically, and how local conditional utility functions, depending on each node and its parents, can be used to calculate joint utility. The second main component deals with the integration of preference structures and graphical models in trading mechanisms, and in particular in multiattribute auctions. I first develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. Previous multiattribute mechanisms generally either remain agnostic about traders’ preference structures, or presume highly restrictive forms, such as full additivity. I present an approximately efficient iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreasing elicitation and computation burden while allowing for expressive preference representation. Further, I apply preference structures and preference-based constraints to simplify the particularly complex, but practically useful domain of multi-unit multiattribute auctions and exchanges. I generalize the iterative multiattribute mechanism to a subset of this domain, and investigate the problem of finding an optimal set of trades in multiattribute call markets, given restrictions on preference expression. Finally, I apply preference structures to simplify the modeling of user utility in sponsored-search auctions, in order to facilitate ranking mechanisms that account for the user experience from advertisements. I provide short-term and long-term simulations showing the effect on search-engine revenues.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61670/1/yagil_1.pd

Decision making

This chapter reviews normative and descriptive aspects of decision making. Expected Utility Theory (EUT), the dominant normative theory of decision making, is often thought to provide a relatively poor description of how people actually make decisions. Prospect Theory has been proposed as a more descriptively valid alternative. The failure of EUT seems at least partly due to the fact that people’s preferences are often unstable and subject to various influences from the method of elicitation, decision context, and goals. In novel situations, people need to infer their preferences from various cues such as the context and their memories and emotions. Through repeated experience with particular decisions and their outcomes, these inferences can become more stable, resulting in behavior that is more consistent with EUT

Choosing Wearable Internet of Things Devices for Managing Safety in Construction Using Fuzzy Analytic Hierarchy Process as a Decision Support System

Many safety and health risks are faced daily by workers in the field of construction. There is unpredictability and risk embedded in the job and work environment. When compared with other industries, the construction industry has one of the highest numbers of worker injuries, illnesses, fatalities, and near-misses. To eliminate these risky events and make worker performance more predictable, new safety technologies such as the Internet of Things (IoT) and Wearable Sensing Devices (WSD) have been highlighted as effective safety systems. Some of these Wearable Internet of Things (WIoT) and sensory devices are already being used in other industries to observe and collect crucial data for worker safety in the field. However, due to limited information and implementation of these devices in the construction field, Wearable Sensing Devices (WSD) and Internet of Things (IoT) are still relatively underdeveloped and lacking. The main goal of the research is to develop a conceptual decision-making framework that managers and other appropriate personnel can use to select suitable Wearable Internet of Things (WIoT) devices for proper application/ implementation in the construction industry. The research involves a literature review on the aforementioned devices and the development and demonstration of a decision-making framework using the Fuzzy Analytic Hierarchy Process (FAHP)

Partner selection in virtual enterprises

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200

Clinical and economic evaluation of laparoscopic surgery compared with medical management for gastro-oesophageal reflux disease : 5-year follow-up of multicentre randomised trial (the REFLUX trial)

Peer reviewedPublisher PD

Essays on Consumer Search and A+B Auctions

This dissertation consists of two chapters. The first paper estimates demand for gasoline in the presence of two types of imperfect price information: ex-ante uncertainty about each station\u27s price and uncertainty about the distribution of all stations\u27 prices. Volatile wholesale cost causes retail gasoline prices to fluctuate regularly, making it difficult for consumers to remain aware of the overall price level in the market or the stations offering the lowest price. In this article, I develop a model in which consumers formulate their prior belief of the current price distribution using the prices observed during past driving trips, and then Bayesian update their beliefs with each new price observed, before deciding whether to purchase gasoline or continue searching for a cheaper price. I estimate this model by utilizing a unique data set of station-level daily gasoline sales and prices, combined with data on the empirical distribution of various traffic flows. My empirical results suggest that consumers are able to learn about the overall price increases or decreases resulting from the wholesale cost movements relatively quickly. In addition, I find that price distribution uncertainty is the primary component of imperfect price information, and if it were eliminated, consumers could achieve 70 percent of the total savings that could be realized by having perfect price information. Furthermore, by incorporating travel patterns, the estimation suggests that cross-price elasticity between two stations depends largely on the amount of common traffic they share. My second paper studies the effect of complexity in multi-dimensional bidding and competition in A+B (price + quality) auctions using a laboratory experiment. I examine whether the behaviors of human bidders are consistent with the predictions of two alternative models of auctions: the Bayes-Nash Equilibrium model and the Quantal Response Equilibrium (QRE) model. I extend the QRE framework to multi-dimensional A+B auctions. The results indicate that the QRE model, as a generalization of the rational models of behavior by allowing decision making errors, predicts bidder behaviors well across different treatments as the number of bidders and the dimensionality of the bid vary