Efficient Random Assignment under a Combination of Ordinal and Cardinal Information on Preferences

Consider a collection of m indivisible objects to be allocated to n agents, where m = n. Each agent falls in one of two distinct categories: either he (a) has a complete ordinal ranking over the set of individual objects, or (b) has a set of “plausible” benchmark von Neumann-Morgenstern (vNM) utility functions in whose non-negative span his “true” utility is known to lie. An allocation is undominated if there does not exist a preference-compatible profile of vNM utilities at which it is Pareto dominated by another feasible allocation. Given an undominated allocation, we use the tools of linear duality theory to construct a profile of vNM utilities at which it is ex-ante welfare maximizing. A finite set of preference-compatible vNM utility profiles is exhibited such that every undominated allocation is ex-ante welfare maximizing with respect to at least one of them. Given an arbitrary allocation, we provide an interpretation of the constructed vNM utilities as subgradients of a function which measures worst-case domination.Random Assignment, Efficiency, Duality, Linear Programming

Ordinal efficiency under the lens of duality theory

An allocation's ordinal efficiency deficit (OED) is defined as the greatest ordinal efficiency loss that can result from its application. More precisely, an allocation's OED is the negative of the greatest total amount by which it may be stochastically dominated by another feasible allocation. Thus, an allocation is ordinally efficient if and only if its OED is zero. Using this insight, we set up a linear program whose optimal objective value corresponds to a given allocation's OED. Furthermore, we show that the OED is a piecewise-linear convex function on the set of allocations. We use the optimal dual variables of the linear program to construct a profile of von Neumann-Morgenstern (vNM) utilities that is compatible with the underlying ordinal preferences, and which is a subgradient of the OED at the given allocation. When the given allocation is ordinally efficient, our analysis implies that it is ex-ante welfare maximizing at the constructed vNM profile, and we recover the ordinal efficiency theorem due to McLennan (2002

Characterizing Pareto Optima: Sequential Utilitarian Welfare Maximization

We characterize Pareto optimality via sequential utilitarian welfare maximization: a utility vector u is Pareto optimal if and only if there exists a finite sequence of nonnegative (and eventually positive) welfare weights such that $u$ maximizes utilitarian welfare with each successive welfare weights among the previous set of maximizers. The characterization can be further related to maximization of a piecewise-linear concave social welfare function and sequential bargaining among agents a la generalized Nash bargaining. We provide conditions enabling simpler utilitarian characterizations and a version of the second welfar

Evaluating the quality of radiotherapy treatment plans with uncertainty using data envelopment analysis

External beam radiation therapy is a common treatment method for cancer. Radiotherapy is planned with the aim of achieving conflicting goals: while a sufficiently high dose of radiation is necessary for tumour control, a low dose of radiation is desirable to avoid complications in normal, healthy, tissue. This thesis aims to support the radiotherapy treatment planning process for prostate cancer by evaluating the quality of proposed treatment plans relative to previous plans. We develop a variable selection technique, autoPCA, to select the most relevant variables for use in our Data Envelopment Analysis (DEA) models. This allows us to evaluate how well plans perform in terms of achieving the conflicting goals of radiotherapy. We develop the uncertain DEA problem (uDEA) for the case of box uncertainty and show that for small problems this can be solved exactly. This study of uncertainty is motivated by the inherently uncertain nature of the treatment process. Robust DEA, uDEA and simulation are applied to prostate cancer treatment plans to investigate this uncertainty. We identify plans that have the potential to be improved, which clinicians then replan for us. Small improvements were seen and we discuss the potential difference this could make to planning cases that are more complex. To aid this, we develop a prototype software, EvaluatePlan, that assesses the efficiency of a plan compared to past treatment plans

Regularized algorithms for ranking, and manifold learning for related tasks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (leaves 119-127).This thesis describes an investigation of regularized algorithms for ranking problems for user preferences and information retrieval problems. We utilize regularized manifold algorithms to appropriately incorporate data from related tasks. This investigation was inspired by personalization challenges in both user preference and information retrieval ranking problems. We formulate the ranking problem of related tasks as a special case of semi-supervised learning. We examine how to incorporate instances from related tasks, with the appropriate penalty in the loss function to optimize performance on the hold out sets. We present a regularized manifold approach that allows us to learn a distance metric for the different instances directly from the data. This approach allows incorporation of information from related task examples, without prior estimation of cross-task coefficient covariances. We also present applications of ranking problems in two text analysis problems: a) Supervise content-word learning, and b) Company Entity matching for record linkage problems.by Giorgos Zacharia.Ph.D