49,259 research outputs found
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.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61670/1/yagil_1.pd
Directed expected utility networks
A variety of statistical graphical models have been defined to represent the conditional independences underlying a random vector of interest. Similarly, many different graphs embedding various types of preferential independences, such as, for example, conditional utility independence and generalized additive independence, have more recently started to appear. In this paper, we define a new graphical model, called a directed expected utility network, whose edges depict both probabilistic and utility conditional independences. These embed a very flexible class of utility models, much larger than those usually conceived in standard influence diagrams. Our graphical representation and various transformations of the original graph into a tree structure are then used to guide fast routines for the computation of a decision problem’s expected utilities. We show that our routines generalize those usually utilized in standard influence diagrams’ evaluations under much more restrictive conditions. We then proceed with the construction of a directed expected utility network to support decision makers in the domain of household food security
Decision making with decision event graphs
We introduce a new modelling representation, the Decision Event Graph (DEG), for asymmetric
multistage decision problems. The DEG explicitly encodes conditional independences
and has additional significant advantages over other representations of asymmetric decision
problems. The colouring of edges makes it possible to identify conditional independences on
decision trees, and these coloured trees serve as a basis for the construction of the DEG.
We provide an efficient backward-induction algorithm for finding optimal decision rules on
DEGs, and work through an example showing the efficacy of these graphs. Simplifications of
the topology of a DEG admit analogues to the sufficiency principle and barren node deletion
steps used with influence diagrams
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