Structured Preference Representation and Multiattribute Auctions

Preference Handling;Auctions;Multiattribute Auctions;Multiattribute Preferences;Computer Science;Engineering;Computer Science & Engineering

Engel, YagilMacKie-Mason, Jeffrey K. ;

University of Michigan

关键词: Preference Handling;    Auctions;    Multiattribute Auctions;    Multiattribute Preferences;    Computer Science;    Engineering;    Computer Science & Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/61670/yagil_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Handling preferences over multiple objectives (or attributes) poses serious challenges tothe development of automated solutions to complex decision problems. The number ofdecision 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 onindependencies among the attributes. This work has two main components.The first component focuses on development of graphical models for multiattributepreferences and utility functions. Graphical models take advantage of factored utility, andyield a compact representation for preferences. Specifically, I introduce CUI networks, acompact graphical representation of utility functions over multiple attributes. CUI networksmodel multiattribute utility functions using the well studied utility independence concept.I show how conditional utility independence leads to an effective functional decompositionthat can be exhibited graphically, and how local conditional utility functions, depending oneach node and its parents, can be used to calculate joint utility.The second main component deals with the integration of preference structures andgraphical models in trading mechanisms, and in particular in multiattribute auctions. I firstdevelop multiattribute auctions that accommodate generalized additive independent (GAI)preferences. Previous multiattribute mechanisms generally either remain agnostic abouttraders’ 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 theparticularly complex, but practically useful domain of multi-unit multiattribute auctionsand 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 callmarkets, 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.