60,102 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
tradersâ 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
von Neumann-Morgenstern and Savage Theorems for Causal Decision Making
Causal thinking and decision making under uncertainty are fundamental aspects
of intelligent reasoning. Decision making under uncertainty has been well
studied when information is considered at the associative (probabilistic)
level. The classical Theorems of von Neumann-Morgenstern and Savage provide a
formal criterion for rational choice using purely associative information.
Causal inference often yields uncertainty about the exact causal structure, so
we consider what kinds of decisions are possible in those conditions. In this
work, we consider decision problems in which available actions and consequences
are causally connected. After recalling a previous causal decision making
result, which relies on a known causal model, we consider the case in which the
causal mechanism that controls some environment is unknown to a rational
decision maker. In this setting we state and prove a causal version of Savage's
Theorem, which we then use to develop a notion of causal games with its
respective causal Nash equilibrium. These results highlight the importance of
causal models in decision making and the variety of potential applications.Comment: Submitted to Journal of Causal Inferenc
Preference for Skew in Lotteries: Evidence from the Laboratory
Using a laboratory experiment we investigate how skew inuences choices under risk. We find that subjects make significantly riskier choices when the distribution of payoffs is positively skewed, these choices being driven in part by the shape of the utility function but also by subjective distortion of probabilities. A utility model with probability distortion calibrated on laboratory data is able to explain why most gamblers in public lotteries buy only a small number of tickets.risk; skew; gambling; lab experiment
Preference for Skew in Lotteries: Evidence from the Laboratory
Using a laboratory experiment we investigate how skew in uences choices under risk. We find that subjects make significantly riskier choices when the distribution of payoffs is positively skewed, these choices being driven in part by the shape of the utility function but also by subjective distortion of probabilities. A utility model with probability distortion calibrated on laboratory data is able to explain why most gamblers in public lotteries buy only a small number of tickets.Risk; Skew; Gambling; Lab Experiment
Learning to Address Health Inequality in the United States with a Bayesian Decision Network
Life-expectancy is a complex outcome driven by genetic, socio-demographic,
environmental and geographic factors. Increasing socio-economic and health
disparities in the United States are propagating the longevity-gap, making it a
cause for concern. Earlier studies have probed individual factors but an
integrated picture to reveal quantifiable actions has been missing. There is a
growing concern about a further widening of healthcare inequality caused by
Artificial Intelligence (AI) due to differential access to AI-driven services.
Hence, it is imperative to explore and exploit the potential of AI for
illuminating biases and enabling transparent policy decisions for positive
social and health impact. In this work, we reveal actionable interventions for
decreasing the longevity-gap in the United States by analyzing a County-level
data resource containing healthcare, socio-economic, behavioral, education and
demographic features. We learn an ensemble-averaged structure, draw inferences
using the joint probability distribution and extend it to a Bayesian Decision
Network for identifying policy actions. We draw quantitative estimates for the
impact of diversity, preventive-care quality and stable-families within the
unified framework of our decision network. Finally, we make this analysis and
dashboard available as an interactive web-application for enabling users and
policy-makers to validate our reported findings and to explore the impact of
ones beyond reported in this work.Comment: 8 pages, 4 figures, 1 table (excluding the supplementary material),
accepted for publication in AAAI 201
Consumer Attitudes towards Sustainability Attributes on Food Labels
Concerns about climate change and the general status of the environment have increased expectation that food products have sustainability credentials, and that these can be verified. There are significant and increasing pressures in key export markets for information on Greenhouse gas (GHG) intensity of products throughout its life-cycle. How this information is conveyed to consumers is a key issue. Labelling is a common method of communicating certain product attributes to consumers that may influence their choices. In a choice experiment concerning fruit purchase decisions, this study estimates willingness to pay for sustainability attributes by consumers in Japan and the UK. The role of label presentation format is investigated: text only, text and graphical, and graphical only. Results indicate that sustainability attributes influence consumersâ fruit purchase decisions. Reduction of carbon in fruit production is shown to be the least valued out of sustainability attributes considered. Differences are evident between presentation formats and between countries, with increased nutrient content being the most sensitive to format and country while carbon reduction is the most insensitive and almost always valued the least.Willingness to pay, Choice experiment, Food labelling, Sustainability, Cross-country comparison, Agricultural and Food Policy, Consumer/Household Economics, Environmental Economics and Policy, Food Consumption/Nutrition/Food Safety, Q18, Q51, Q56,
KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development
Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top- recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.Comment: In Proc. of 6th Australasian Data Mining Conference (AusDM), Gold
Coast, Australia, pages 195--202, 200
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