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

Multiattribute Call Markets.

Multiattribute auctions support automated negotiation in settings where buyers and sellers have valuations for alternate configurations of a good, as defined by configuration attributes. Bidders express offers to buy or sell alternate configurations by specifying configuration-dependent reserve prices, and the auction determines both the traded goods and transaction prices based on these offers. While multiattribute auctions have been deployed in single-buyer procurement settings, the development of double-sided multiattribute auctions-allowing the free participation of both buyers and sellers-has received little attention from academia or industry. In this work I develop a multiattribute call market, a specific type of double auction in which bids accumulate over an extended period of time, before the auction determines trades based on the aggregate collection of bids. Building on a polynomial-time clearing algorithm, I contribute an efficient algorithm for information feedback. Supporting the implementation of market-based algorithms, information feedback support extends the range of settings for which multiattribute call markets achieve efficiency. Multiattribute auctions are only one of many auction variants introduced in recent years. The rapidly growing space of alternative auctions and trading scenarios calls for both a standardized language with which to specify auctions, as well as a computational test environment in which to evaluate alternate designs. I present a novel auction description language and deployment environment that supports the specification of a broad class of auctions, improving on prior approaches through a scripting language that employs both static parameter settings and rule-based behavior invocation. The market game platform, AB3D, can execute these auction scripts to implement multi-auction and multi-agent trading scenarios. The efficiency of multiattribute call markets depends crucially on the underlying valuations of participants. I analyze the expected performance limitations of multiattribute call markets, using existing analytical results where applicable. Addressing a lack of theoretical guidance in many natural settings, I introduce a computational metric on bidder valuations, and show a correlation between this metric and the expected efficiency of multiattribute call markets. As further validation, I integrate multiattribute markets into an existing supply chain simulation, demonstrating efficiency gains over a more conventional negotiation procedure.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60822/1/klochner_1.pd

Do risk and time preferences have biological roots?

We revisit the claims about the biological underpinnings of economic behavior by specifically exploring if observed gender differences in risk/time preferences can be explained by natural fluctuations in progesterone/estradiol levels during the menstrual cycle and by prenatal exposure to testosterone levels. Results suggest that natural fluctuations in progesterone levels have a direct effect on discount rates and that estradiol/progesterone levels can indirectly affect time preferences by changing the curvature of the utility function. Using measured D2:D4 digit ratio, results imply that subjects with low digit ratio exhibit higher discount rates and risk loving preferences

Design and implementation of a web-based auction system

Electronic auction has been a popular means of goods distribution. The number of items sold through the internet auction sites have grown in the past few years. Evidently, this has become the medium of choice for customers. This project entails the design and implementation of a web-based auction system for users to trade in goods. The system was implemented in the Django framework. On account that the trade over the Internet lacks any means of ascertaining the quality of goods, there is a need to implement a feedback system to rate the seller’s credibility in order to increase customer confidence in a given business. The feedback system is based on the history of the customer’s rating of the previous seller’s transactions. As a result, the auction system has a built-in feedback system to enhance the credibility of the auction system. The project was designed by using a modular approach to ensure maintainability. There is a number of engines that were implemented in order to enhance the functionality of the auction system. They include the following: commenting engine, search engine, business intelligence (user analytic and statistics), graph engine, advertisement engine and recommendation engine. As a result of this thesis undertaking, a full-fledged system robust enough to handle small or medium-sized traffic has been developed to specification

Do risk and time preferences have biological roots?

We revisit the claims about the biological underpinnings of economic behavior by specifically exploring if observed gender differences in risk/time preferences can be explained by natural fluctuations in progesterone/estradiol levels during the menstrual cycle and by prenatal exposure to testosterone levels. Results suggest that natural fluctuations in progesterone levels have a direct effect on discount rates and that estradiol/progesterone levels can indirectly affect time preferences by changing the curvature of the utility function. Using measured D2:D4 digit ratio, results imply that subjects with low digit ratio exhibit higher discount rates and risk loving preferences

Preference Elicitation in Proxied Multiattribute Auctions

We consider the problem of minimizing preference elicitation in efficient multiattribute auctions, that support dynamic negotiation over non-price based attributes such as quality, time-of-delivery, and processor speed. We introduce asynchronous price-based mulfiattribute auctions, with proxy bidding agents that sit between the auctioneer and the participants. Empirical results demonstrate the preference elicitation savings that are provided with minimal price spaces, asynchronous updates, and proxy agents