Utility Elicitation as a Classification Problem

2010

Abstract Most frameworks for utility elicitation assume a predefined set of features over which user preferences are expressed. We consider utility elicitation in the presence of subjective or user-defined features, whose definitions are not known in advance. We treat the problem of learning a user's feature definition as one of concept learning, but whose goal is to learn only enough about the concept definition to enable a good decision to be made. This is complicated by the fact that user utility is unknown.

2004

We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We demonstrate that when domain knowledge is available, even in the form of weak and somewhat inaccurate assumptions, significantly less data is required to build an accurate model of user preferences than when no domain knowledge is provided. This approach is based on the KBANN (Knowledge-Based Artificial Neural Network) algorithm pioneered by . We demonstrate this approach through two examples, one involves preferences under certainty, and the other involves preferences under uncertainty. In the case of certainty, we show how to encode assumptions concerning preferential independence and monotonicity in a KBANN network, which can be trained using a variety of preferential information including simple binary classification. In the case of uncertainty, we show how to construct a KBANN network that encodes certain types of dominance relations and attitude toward risk. The resulting network can be trained using answers to standard gamble questions and can be used as an approximate representation of a person's preferences. We empirically evaluate our claims by comparing the KBANN networks with simple backpropagation artificial neural networks in terms of learning rate and accuracy. For the case of uncertainty, the answers to standard gamble questions used in the experiment are taken from an actual medical data set first used by . In the case of certainty, we define a measure to which a set of prefer-ences violate a domain theory, and examine the robustness of the KBANN network as this measure of domain theory violation varies.

Department of Computer Science, University of Toronto, 2006

Journal of Biomedical Informatics, 2005

Complex decision models in expert systems often depend upon a number of utilities and subjective probabilities for an individual. Although these values can be estimated for entire populations or demographic subgroups, a model should be customized to the indi-vidualÕs specific parameter values. This process can be onerous and inefficient for practical decisions. We propose an interactive approach for incrementally improving our knowledge about a specific individualÕs parameter values, including utilities and probabilities, given a decision model and a prior joint probability distribution over the parameter values. We define the concept of value of elicitation and use it to determine dynamically the next most informative elicitation for a given individual. We evaluated the approach using an example model and demonstrate that we can improve the decision quality by focusing on those parameter values most material to the decision.

2012

In this thesis, we present a decision-theoretic framework for building decision support systems that incrementally elicit preferences of individual users over multiattribute outcomes and then provide recommendations based on the acquired preference information. By combining decision-theoretically sound modeling with effective computational techniques and certain user-centric considerations, we demonstrate the feasibility and potential of practical autonomous preference elicitation and recommendation systems.

Methods of Information in Medicine

Background In shared decision-making, a key step is quantifying the patient's preferences in relation to all the possible outcomes of the compared clinical options. According to utility theory, this can be done by eliciting utility coefficients (UCs) from the patient. The obtained UCs are then used in decision models (e.g., decision trees). The elicitation process involves the choice of one or more elicitation methods, which is not easy for decision-makers who are unfamiliar with the theoretical framework. Moreover, to our knowledge there are no tools that integrate functionalities for UC elicitation with functionalities to run decision models that include the elicited values. Objectives The first aim of this work is to provide decision support to the clinicians for the selection of the elicitation method. The second aim is to bridge the gap between UC elicitation and the exploitation of those UCs in shared decision-making. Methods Based on evidence from the utility theory liter...

EURO Journal on Decision Processes, 2015

Preferences are fundamental to decision processes, because decision analysts must account for the preferences of the stakeholders who participate in these processes and are impacted by the decision outcomes. To support the elicitation of stakeholder preferences, many models, procedures and methodologies have been proposed. These approaches to preference elicitation and learning will become more and more important with the proliferation of semi-automated computerized interfaces and the adoption of decision support systems which build on increasingly large datasets. One of the major central tasks of the decision analyst is to elicit the judgements and value systems of the decision makers (DMs), including their views on the problem, and to integrate the resulting information into a preference model from which recommendations can be derived. This preference elicitation activity can be tricky: the preferences expressed by the DMs can be imprecise, conflicting, unstable, time-dependent, yet they should be structured and synthesized into numerical values (or intervals of numerical values) concerning the parameters that characterize preferences in the decision model. For the domain Preference Elicitation and Learning, models, procedures and methodologies have been developed not only by researchers working in the field of Multiple Criteria Decision Aid but also in that of Artificial Intelligence. Their research has focused on the modeling, representation, elicitation, learning,

aaai.org

The development of automated preference elicitation tools has seen increased interest among researchers in recent years due to a growing interest in such diverse problems as development of user-adaptive software and greater involvement of patients in medical decision making. These tools not only must facilitate the elicitation of reliable information without overly fatiguing the interviewee but must also take into account changes in preferences. In this paper, we introduce two complementary indicators for detecting change in preference which can be used depending on the granularity of observed information. The first indicator exploits conflicts between the current model and the observed preference by using intervals mapped to gamble questions as guides in observing changes in risk attitudes. The second indicator relies on answers to gamble questions, and uses Chebyshev's inequality to infer the user's risk attitude. The model adapts to the change in preference by relearning whenever an indicator exceeds some preset threshold. We implemented our utility model using knowledge-based artificial neural networks that encode assumptions about a decision maker's preferences. This allows us to learn a decision maker's utility function from a relatively small set of answers to gamble questions thereby minimizing elicitation cost. Results of our experiments on a simulated change of real patient preference data suggest significant gain in performance when the utility model adapts to change in preference.

Acta Psychologica, 1988

In this paper multiattribute decision making is discussed in terms of decision-making knowledge. Special emphasis is on identification (measurement) and verification of utility functions, and their use for evaluation of alternatives and explanation of evaluation results. Axiomatic and direct approach in utility theory are compared to the approach based on inductive learning techniques which are known from the field of artificial intelligence. Alternatives or their parts with the known utility are taken as learning examples in order to construct utility (function) knowledge. This approach is supported by a special expert system shell for utility knowledge modelling. It is implemented on a personal computer as a part of DECMAK system.

Medical Decision Making

We present a novel way to codify medical expertise and to make it available to support medical decision making. Our approach is based on econometric techniques (known as conjoint analysis or discrete choice theory) developed to analyze and forecast consumer or patient behavior; we reconceptualize these techniques and put them to use to generate an explainable, tractable decision support system for medical experts. The approach works as follows: using choice experiments containing systematically composed hypothetical choice scenarios, we collect a set of expert decisions. Then we use those decisions to estimate the weights that experts implicitly assign to various decision factors. The resulting choice model is able to generate a probabilistic assessment for real-life decision situations, in combination with an explanation of which factors led to the assessment. The approach has several advantages, but also potential limitations, compared to rule-based methods and machine learning te...