Preference Elicitation

As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of non-relevant content according to the information stored in a user profile, recommendation algorithms process information about user interests -acquired in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way -and exploit these data to generate a list of recommended items. Although each type of filtering method has its own weaknesses and strengths, preference handling is one of the core issues in the design of every recommender system: since these systems aim to guide users in a personalized way to interesting or useful objects in a large space of possible options, it is important for them to accurately capture and model user preferences. The goal of this chapter is to provide a general overview of the approaches to learning preference models in the context of recommender systems. In the first part we will introduce general concepts and terminology of recommender systems, giving a brief analysis of advantages and drawbacks for each filtering approach. Then we will deal with the issue of learning preference models, show the most popular techniques for profile learning and preference elicitation and analyze methods for feedback gathering in recommender systems.

A prerequisite for implementing collaborative filtering recommender systems is the availability of users' preferences data. This data, typically in the form of ratings, is exploited to learn the tastes of the users and to serve them with personalized recommendations. However, there may be a lack of preference data, especially at the initial stage of the operations of a recommender system, i.e., in the Cold Start phase. In particular, when a new user has not yet rated any item, the system would be incapable of generating relevant recommendations for this user. Or, when a new item is added to the system catalogue and no user has rated it, the system cannot recommend this item to any user. This chapter discusses the cold start problem and provides a comprehensive description of techniques that have been proposed to address this problem. It surveys algorithmic solutions and provides a summary of their performance comparison. Moreover, it lists publicly available resources (e.g., libraries and datasets) and offers a set of practical guidelines that can be adopted by researchers and practitioners.

This paper provides a review of research contributions on forest management and planning using multicriteria decision making (MCDM) based on an exhaustive literature survey. The review primarily focuses on the application aspects highlighting theoretical underpinnings and controversies. It also examines the nature of the problems addressed and incorporation of risk into forest management and planning decision making. The MCDM techniques covered in this review belong to several schools of thought. For each technique, a variety of empirical applications including recent studies has been reviewed. More than 60 individual studies were reviewed and classified by the method used, country of origin, number and type of criteria and options evaluated. The review serves as a guide to those interested in how to use a particular MCDM approach. Based on the review, some recent trends and future research directions are also highlighted.

This paper introduces the use of best-worst scaling to elicit taste-based preferences and presents a comparison of this scaling methodology with monadic preference ratings elicited on an unstructured line scale. Best-worst scaling (BWS) is a discrete choice task that forces respondents to make a discriminating choice among the samples under investigation by requiring them to select both the best and the worst option in an available (sub)set of samples. In an empirical case study that concerns consumer preferences for minced pork patties, it is found that the results from the two methods are highly correlated. However, there is some evidence to suggest that preference data elicited using best-worst scaling may better enable discovery of differences in sample preferences without being a more difficult test for consumers to take part in. Some of the advantages and disadvantages associated with the application of best-worst scaling in hedonic measurement are noted and discussed.

We advocate a more formal structural approach for comparing WTP for non-market or pre-test-market goods conveyed by fundamentally different preference elicitation mechanisms. Seven independent samples of respondents were asked to value the identical good. Elicitation methods include one actual purchase and six widely used hypothetical choice Ž . formats. Using a common underlying indirect utility function and stochastic structure allows Ž data for different elicitation methods to be used independently, compared pair-wise as in . much of the earlier literature or pooled across all samples in one unified model with heteroscedasticity across elicitation methods. Our differences in estimated WTP for the individual models are typical of earlier findings. However, pooled-data models that allow for heteroscedasticity reveal that while there are substantial differences in the amount of noise in the different samples, a common underlying systematic component of the preference struc-Ž . ture cannot be rejected for at least four and possibly five of these seven elicitation methods. Ž . ᮊ 2002 Elsevier Science USA

There is wide-ranging evidence, much of it deriving from economics experiments, of 'anomalies' in behaviour that challenge standard preference theories. This paper explores the implications of these anomalies for preference elicitation methods. Because methods that are used to inform public policy, such as contingent valuation, are based on standard preference theories, their validity may be called into question by the anomaly data. However, on a new interpretation, these anomalies do not contradict standard theory but are errors in stated preference that can be expected to disappear as people become more experienced in relevant decision environments. We explore the evidence for this interpretation and what implications follow for preference elicitation methodology.

Individual preferences must be evaluated to take into account the citizens’ interests for public decisions. Usually, economists assess individual preferences through market decisions. But more often, public decisions impact goods without market prices. Without a market to refer to, evaluating the consequences of a public decision on individual welfare is a challenging methodological problem. In spite of major improvements, preference

This paper reviews the main theories and methods used for multiple attribute decision making in a fuzzy environment. Fuzzy multiple attribute decisions involve two processes, the rating and the ranking of alternatives. If the rating results are crisp then the ranking procedure becomes straightforward, hence, the emphasis of this paper is on obtaining crisp ratings for alternatives. In order to aid the decision maker to express his/her attribute preferences, new elicitation techniques to determine attributes importance are proposed.

This paper presents the preference ratios in multiattribute evaluation (PRIME) method which supports the analysis of incomplete information in multiattribute weighting models. In PRIME, preference elicitation and synthesis is based on 1) the conversion of possibly imprecise ratio judgments into an imprecisely specified preference model, 2) the use of dominance structures and decision rules in deriving decision recommendations, and 3) the sequencing of the elicitation process into a series of elicitation tasks. This process may be continued until the most preferred alternative is identified or, alternatively, stopped with a decision recommendation if the decision maker is prepared to accept the possibility that the value of some other alternative is higher. An extensive simulation study on the computational properties of PRIME is presented. The method is illustrated with a re-analysis of an earlier case study on international oil tanker negotiations.

The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, contentbased preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates recommendations and visualizes a user's musical preferences, given her/his accounts on popular online music services. Using these services, the system retrieves a set of tracks preferred by a user, and further computes a semantic description of musical preferences based on raw audio information. For the audio analysis we used the capabilities of the Canoris API. Thereafter, the system generates music recommendations, using a semantic music similarity measure, and a user's preference visualization, mapping semantic descriptors to visual elements.

Objectives: To describe the views of health care decision-makers and providers operating in the UK National Health Service (NHS) concerning the concepts of cost-effectiveness, equity and access through a series of attitudinal questions; to evaluate the preferences of health care providers in relation to each of these concepts using a discrete choice experiment (DCE); to assess the impact of prior completion of an attitude questionnaire on preferences elicited through a DCE. Method: Three versions of a DCE questionnaire were developed with and without a series of attitudinal questions and randomly distributed to 1456 health care decision-makers and providers. The questionnaire sought to elicit their preferences between the competing objectives of cost-effectiveness, equity and access within the context of different hypothetical, specialist treatment programmes for cardiovascular disease. Results: The response rate was 26%. Female respondents exhibited a stronger preference than males for reducing health inequalities by targeting the worst off (Wald test, P < 0.001). Primary Care Trusts (PCTs), Strategic Health Authorities (SHA) or Department of Health (DoH) staff were also more likely than hospital managers to favour programmes that targeted the worst off (Wald test, P < 0.001 in each case). Those who were clinically trained and currently in a clinical post had a stronger preference for programmes with shorter waiting times compared to those in a managerial or non-clinical posts, who exhibited stronger preferences for equity. Completion of a series of attitudinal questions prior to completing the DCE task resulted in a lower proportion of dominant responses and an increased willingness to make trade-offs between attributes.

Recent research suggests that, in some situations, numeric information may lack the 'evaluability' of visual representations of the same data. In such cases, reliance upon numeric information exacerbates tendencies for survey respondents to rely upon heuristics rather than their underlying preferences in formulating responses. Adapting such insights to the field of non-market valuation, for certain environmental goods, information on increases or decreases in the numeric levels of an attributes may trigger reliance upon the loss aversion heuristic, leading to an exacerbation of the well know gains/loss asymmetry problem .

We compared a direct way to measure the relative importance of packaging and other extrinsic cues like brand name, origin, and price with the relative importance of these variables in an indirect discrete choice experiment. We used best-worst scaling (BWS) with visual and verbal presentation of the attribute descriptions as a way to directly ask consumers about wine packaging relevance. Both direct methods gave low packaging importance scores contrary to anecdotal industry evidence and beliefs. BWS results indicated all visual extrinsic cues were less important than verbal cues, with small variance among respondents, suggesting strong agreement about non-importance. We compared those results with a multi-media-based discrete choice experiment (DCE) that varied label and packaging attributes to produce shelf-like choice scenarios. The DCE results revealed much higher impacts due to packaging-related attributes, as well as significant preference heterogeneity. Our results suggest considerable caution in using direct importance measures with visual packaging attributes.

Non-market techniques are widely used for valuing environmental goods and services. Recent articles obtain results showing respondents to the right of the political spectrum are significantly less likely to vote in favour of environmental programs that provide public goods through public means. In consequence, their WTP is lower than that of individuals on the political left. We examine whether WTP differs systematically in accordance with political affiliation by using data from three stated preference surveys. We obtain results similar to the previous literature from only one survey. Our other two surveys employ different contexts that change the nature of the benefits from the good and/or its provision mechanism. The first of these finds no significant differences in WTP by respondent political affiliation and the second finds that respondents on the right of the political spectrum have statistically higher WTPs for a good when it is privately provided than under collective provision. Our results provide further support that context matters and that preferences elicited from surveys for environmental goods are not necessarily independent of the means by which the good is provided. (D.P. Dupont).

Individuals exhibit substantial heterogeneity in financial risk aversion. Recent work on twins demonstrated that some variation is influenced by individual heritable differences. Despite this, there has been no study investigating possible genetic loci associated with financial risk taking in healthy individuals. Here, we examined whether there is an association between financial risk preferences, elicited experimentally in a game with real monetary payoffs, and the presence of the 7-repeat allele (7R+) in the dopamine receptor D4 gene as well as the presence of the A1 allele (A1+) in the dopamine receptor D2 gene in 94 young men. Although we found no association between the A1 allele and risk preferences, we did find that 7R+ men are significantly more risk loving than 7R− men. This polymorphism accounts for roughly 20% of the heritable variation in financial risk taking. We suggest that selection for the 7R allele may be for a behavioral phenotype associated with risk taking. This is consistent with previous evolutionary explanations suggesting that selection for this allele was for behaviors associated with migration and male competition, both of which entail an element of risk.

We compared a direct way to measure the relative importance of packaging and other extrinsic cues like brand name, origin, and price with the relative importance of these variables in an indirect discrete choice experiment. We used best-worst scaling (BWS) with visual and verbal presentation of the attribute descriptions as a way to directly ask consumers about wine packaging relevance. Both direct methods gave low packaging importance scores contrary to anecdotal industry evidence and beliefs. BWS results indicated all visual extrinsic cues were less important than verbal cues, with small variance among respondents, suggesting strong agreement about non-importance. We compared those results with a multi-media-based discrete choice experiment (DCE) that varied label and packaging attributes to produce shelf-like choice scenarios. The DCE results revealed much higher impacts due to packaging-related attributes, as well as significant preference heterogeneity. Our results suggest considerable caution in using direct importance measures with visual packaging attributes.

Ecosystem services from tropical forestry draw increasing attention from the public and private sectors. However, the decision making of market actors involved in transactions concerning ecosystem services is not well known and described. The goal of this paper is to describe ...

One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust.

Most evolutionary multi-objective algorithms perform poorly in many objective problems. They normally do not make selective pressure towards the Region of Interest (RoI), the privileged zone in the Pareto frontier that contains solutions important to a DM. Several works have proved that a priori incorporation of preferences improves convergence towards the RoI. The work of (Fernandez, E. Lopez, F. Lopez, & Coello Coello, 2011) uses a binary fuzzy outranking relational system to map many-objective problems into a tri-objective optimization problem that searches the RoI; however, it requires the elicitation of many preference parameters, a very hard task. The use of an indirect elicitation approach overcomes such situation by allowing the parameter inference from a battery of examples. Even though the relational system of Fernandez et al. (2011) is based on binary relations, it is more convenient to elicit its parameters from assignment examples. In this sense, this paper proposes an evolutionary-based indirect parameter elicitation method that uses preference information embedded in assignment examples, and it offers an analysis of their impact in a priori incorporation of DM's preferences. Results show, through an extensive computer experiment over random test sets, that the method estimates properly the model parameter's values.

In this paper, we test whether preferences and willingness-to-pay estimates obtained in a Choice Experiment study are susceptible to starting point bias as is often the case in Dichotomous Choice Contingent Valuation studies. On the basis of a multinomial probit model, we find that preferences are indeed susceptible to starting point bias. In a splitsample design our results suggest that varying the price levels displayed in a so-called Instructional Choice Set presented prior to the actual preference eliciting choice sets, significantly impacts respondents' preferences and willingness-to-pay for protecting Danish nature areas from new motorway development. In particular, our results show that the bias is gender-specific. Only female respondents are significantly affected. Results further reveal that the impact of the starting point bias decays as respondents evaluate more and more choice sets. This supports the Discovered Preference Hypothesis, and on the basis of this we suggest a number of ways to potentially mitigate the starting point bias. (J. Ladenburg), sobo@life.ku.dk (S.B. Olsen). 1 In the literature, the term 'anchoring' is often used instead of SPB to describe more or less the same effect. For simplicity, only the term SPB will be used in the remainder of this paper.

The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, contentbased preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates recommendations and visualizes a user's musical preferences, given her/his accounts on popular online music services. Using these services, the system retrieves a set of tracks preferred by a user, and further computes a semantic description of musical preferences based on raw audio information. For the audio analysis we used the capabilities of the Canoris API. Thereafter, the system generates music recommendations, using a semantic music similarity measure, and a user's preference visualization, mapping semantic descriptors to visual elements.

We compare the inferences about consumer preferences that result from two different measurement techniques, a simplified hedonic rating system and a demand-revealing auction, for three separate groups of products. We find that product rankings are identical under the two measures if overall average ratings and bid prices are considered. At the level of the individual consumer, there are some differences in rankings obtained under the two measurement techniques.

Fifty-four percent of Benin&#39;s population in rural areas keep indigenous chickens for subsistence livelihoods. Despite the potential to alleviate poverty by improving indigenous chicken breeds, smallholders&#39; participation in the implementation of breeding programmes is weak. Participation could be improved with greater understanding of the many functions of chickens to smallholders. The objectives of this study are (1) to evaluate chicken

We describe a web-based system to support groups in elaborating participatory budgets. Rather than using physical meetings with voting mechanisms, we promote virtual meetings with explicit preference elicitation, guided negotiations and, only if consensus is not reached, voting.

In decision analysis, difficulties of obtaining complete information about model parameters make it advisable to seek robust solutions that perform reasonably well across the full range of feasible parameter values. In this paper, we develop the Robust Portfolio Modeling (RPM) methodology which extends Preference Programming methods into portfolio problems where a subset of project proposals are funded in view of multiple evaluation criteria. We also develop an algorithm for computing all non-dominated portfolios, subject to incomplete information about criterion weights and project-specific performance levels. Based on these portfolios, we propose a project-level index to convey (i) which projects are robust choices (in the sense that they would be recommended even if further information were to be obtained) and (ii) how continued activities in preference elicitation should be focused. The RPM methodology is illustrated with an application using real data on road pavement projects. (J. Liesiö ), pekka.mild@tkk.fi (P. Mild), ahti.salo@tkk.fi (A. Salo).

Destination recommendation systems not only recommend but also persuade. The goal of this study was to investigate the potential influences of the relevance, transparency of and effort required by the preference elicitation process on the perceived fit of a destination recommendation, while simultaneously considering user perceptions of the elicitation process (perceived enjoyment and personalization). The findings indicate that the relevance, transparency and length of the preference elicitation process serve as important cues for personalization, which in turn, influences enjoyment with the process and the perceived fit of the recommendation with one's preferences.

The world-wide-web (WWW) today consists of distinct, isolated islands of data and metadata. In the near future we expect the availability of a critical mass of data and metadata for use by intelligent agents that act on behalf of human users. These agents would identify, propose and capture new opportunities to assist human users in satisfying their goals, by traversing and acting on this semantically rich and abundant information. We envision a new class of agents, their networks and their communities that exist for the sole purpose of serving as their human "master's" Advocates -Advocate Agents. Advocate Agents learn a human's goals and preferences, collaborate with other agents, mine semantic content, identify new opportunities for action, propose them and finally transact them, while always keeping the human "in-the-loop." This paper discusses this class of distributed, intelligent, Advocate Agents, their potential uses, and proposed architectures and techniques that provide a conceptual framework for these networked agent societies to collaborate in the achievement of their human user's goals.

Research from behavioral psychology and experimental economics asserts that individuals construct preferences on a case-by-case basis when called to make a decision. A common, implicit assumption in engineering design is that user preferences exist a priori. Thus, preference elicitation methods used in design decision making can lead to preference inconsistencies across elicitation scenarios. This paper offers a framework for understanding preference inconsistencies, within and across individual users. We give examples of three components of this new framework: comparative, internal, and external inconsistencies across users.

A crucial aspect for the success of systems that provide decision or negotiation support is a good model of their user's preferences. Psychology research has shown that people often do not have well-defined preferences. Instead they construct them during the elicitation process. This implies that the interaction between the system and a user can greatly influence the quality of the preference information and the user's acceptance of the results provided by the system. In this paper we describe a user-centered approach to design preference elicitation interfaces. First, we extracted a number of criteria for successful design of preferences elicitation interfaces from literature and current systems designs. Second we constructed four new intermediate designs that are compositional with respect to different criteria and, furthermore correspond to different thinking styles of the user. Last, we offer first insights from an initial formative evaluation of our designs.

Investigation of the 'rationality' of responses to discrete choice experiments (DCEs) has been a theme of research in health economics. Responses have been deleted from DCEs where they have been deemed by researchers to (a) be 'irrational', defined by such studies as failing tests for non-satiation, or (b) represent lexicographic preferences. This paper outlines a number of reasons why deleting responses from DCEs may be inappropriate after first reviewing the theory underpinning rationality, highlighting that the importance placed on rationality depends on the approach to consumer theory to which one ascribes. The aim of this paper is not to suggest that all preferences elicited via DCEs are rational. Instead, it is to suggest a number of reasons why it may not be the case that all preferences labelled as 'irrational' are indeed so. Hence, deleting responses may result in the removal of valid preferences; induce sample selection bias; and reduce the statistical efficiency and power of the estimated choice models. Further, evidence suggests random utility theory may be able to cope with such preferences. Finally, we discuss a number of implications for the design, implementation and interpretation of DCEs and recommend caution regarding the deletion of preferences from stated preference experiments.

This paper shows that, when modelling freight demand, taking into consideration the presence of attribute cutoffs is important and has relevant repercussions on the estimates of service attributes coefficients. In this paper we focus on mode choice models for freight transport demand in the Marche region in Italy. Specific reference is paid to furniture and metallurgic productive sectors given their relevance for the region and their potential vocation for intermodal transport. Preference elicitation is done using choice based ...

Cost-effectiveness analysis (CEA) is increasingly important in international public health decision making, as reflected in several large-scale prioritization initiatives focussed on low-and middle-income countries (LMICs). The decision makers' willingness-to-pay for a unit of health gain, or ceiling ratio ((), is particularly crucial in CEA as the relative value against which efficiency is measured. Cost-effectiveness acceptability curves offer a solution to restrictive assumptions about (; however, reference case estimates are useful to promote consistency, facilitate new developments in decision analysis, compare estimates to benefit-cost ratios from other economic sectors, and explicitly inform decisions about equity in national and global budgets.

• This study illustrates key issues that are important in choosing between profile-case best-worst scaling and discrete choice experiment studies • Empirical research on the value of outcomes of social care reveals similar patterns in the preference weights obtained from the two approaches • In the majority of cases examined, preference weights are not significantly different once the weights have been appropriately normalised/rescaled Abstract This paper presents empirical findings from the comparison between two principal preference elicitation techniques: discrete choice experiments and profile-based best-worst scaling. Best-worst scaling involves less cognitive burden for respondents and provides more information than traditional "pick-one" tasks asked in discrete choice experiments. However, there is lack of empirical evidence on how best-worst scaling compares to discrete choice experiments. This empirical comparison between discrete choice experiments and best-worst scaling was undertaken as part of the Outcomes of Social Care for Adults project, which aims to develop a weighted measure of social care outcomes. The findings show that preference weights from best-worst scaling and discrete choice experiments do reveal similar patterns in preferences and in the majority of cases preference weights -when normalised/rescaled -are not significantly different.

If the goal of eliciting accurate user preferences over a complex value space is to be realized, the elicitation process must be through while at the same time not overly burdensome to the user. An ideal preference elicitation tool would maximize the information about preferences acquired from the user while minimizing the number of queries required. To this end, we present POET: the Online Preference Elicitation Tool. POET is a graphical Java applet designed to elicit complex preference structures to form a utility function. Once the elicitation process is complete POET outputs the resulting utility function as an XML document. Internally, POET represents utility for independent and dependent values as a set of dependency trees. Though POET's output and internal representation involves real numbers, from the user's point-of-view, the experience is completely symbolic.

We present a classification method for learning an opponent's preferences during a bilateral multi-issue negotiation. Similar candidate preference relations over the set of offers are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent's true preference relation lies in that class. Evidence used for classification decision-making is obtained by observing the opponent's sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponent's true preferences. Crown

Previous research indicates that decision makers are often reluctant to use potentially beneficial multi-criteria decision support systems (MCDSS). Prior research has not examined the specific impact of preference elicitation techniques on user acceptance of MCDSS. The present research begins to fill this gap by examining the effect on usersÕ MCDSS evaluations of two commonly used preference elicitation techniques, absolute measurement and pairwise comparisons, while holding constant all other aspects of the MCDSS and decision making task. Experimental results (N = 153) indicate that users consider MCDSS with pairwise comparisons to be higher in decisional conflict, more effortful, less accurate, and overall less desirable to use than MCDSS with absolute measurements. Thus, any potential normative superiority of a preference elicitation technique must be balanced against its potentially adverse effects on user acceptance of the MCDSS within which it is employed. We present a research agenda for exploring the tradeoffs between objective validity and user acceptance in the design of decision analysis tools.

Auctioning multi-dimensional items is a key challenge, which requires rigorous tools. This study proposes a multi-round, first-score, semi-sealed multi-attribute reverse auction system. A fundamental concern in multi-attribute auctions is acquiring a useful description of the buyers’ individuated requirements: hard constraints and qualitative preferences. To consider real requirements, we express dependencies among attributes. Indeed, our system enables buyers eliciting conditional constraints as well as conditional preferences. However, determining the winner with diverse criteria may be very time consuming. Therefore, it is more useful for our auction to process quantitative data. A challenge here is to satisfy buyers with more facilities, and at the same time keep the auctions efficient. To meet this challenge, our system maps the qualitative preferences into a multi-criteria decision rule. It also completely automates the winner determination since it is a very difficult task for buyers to estimate quantitatively the attribute weights and define attributes value functions. Our procurement auction looks for the outcome that satisfies all the constraints and best matches the preferences. We demonstrate the feasibility and measure the time performance of the proposed system through a 10-attribute auction. Finally, we assess the user acceptance of our requirements specification and winner selection tool.

This paper discusses the results of a conjoint analysis study developed to assess alternative land uses for an important part of the city of Venice: its Arsenal. Aim of the study is to illustrate the potential of stated preferences techniques for placing a value on redevelopment and reuse alternatives for an underutilized site with high historical, cultural and architectural significance. Very few studies have used conjoint choice to assess public preferences for alternative land uses in an ex-ante framework, i.e. masterplans. In this paper we present the results of a conjoint choice study conducted on the Arsenal of Venice.

Combinatorial exchanges (CEs) facilitate trade between multiple buyers and sellers that need to express complex preferences on bundles of items. We present the first design for an iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language that is concise and expressive for CEs. Winner-determination is directly formulated in terms of the structure of this language. The main innovation is that bidders interaction with proxy agents, and specify lower and upper valuations for trades by annotating the tree with value intervals. The design is entirely symmetric, handling buyers, sellers and mixed buyers and sellers. The proxy approach facilitates early price discovery and ensures useful preference elicitation even in early rounds. Constraint generation is used to generate linear prices without enumerating all possible trades. A proxied interpretation of a revealed-preference activity rule ensures progress. At termination, a VCG-based payment scheme tha...

There are few clearly described utility studies in advanced ovarian cancer, despite the public health importance of condition and the need for preference based measures of quality of life in economic evaluation of the new treatments. We used data clustering techniques to develop health state descriptions based on data from 66 women who completed the EORTC QLQ-C30 over a six month period while receiving chemotherapy for ovarian cancer. The health state descriptions were presented to a group of members of the general public (n = 38), via the internet, and preferences elicited using the standard gamble technique. Mean utility values ranged from 0.685 to 0.977, although the range of individual preferences was wider, including values as low as 0.125. This is the first study to use data clustering methods combined with internet preference elicitation in oncology. The resulting health state model is parsimonious, data driven, and incorporates quality of life items tailored to cancer. The estimates therefore meet the needs of policy makers while reflecting more accurately the experience of disease than those based on generic preference measures.

There are few clearly described utility studies in advanced ovarian cancer, despite the public health importance of condition and the need for preference based measures of quality of life in economic evaluation of the new treatments. We used data clustering techniques to develop health state descriptions based on data from 66 women who completed the EORTC QLQ-C30 over a six month period while receiving chemotherapy for ovarian cancer. The health state descriptions were presented to a group of members of the general public (n = 38), via the internet, and preferences elicited using the standard gamble technique. Mean utility values ranged from 0.685 to 0.977, although the range of individual preferences was wider, including values as low as 0.125. This is the first study to use data clustering methods combined with internet preference elicitation in oncology. The resulting health state model is parsimonious, data driven, and incorporates quality of life items tailored to cancer. The estimates therefore meet the needs of policy makers while reflecting more accurately the experience of disease than those based on generic preference measures.

In the context of hierarchical weighting, this paper operationalizes interval judgments which allow the decision maker to enter ambiguous preference statements by indicating the relative importance of factors as intervals of values on a ratio scale. Through such judgments the decision maker can capture the subjective uncertainty in his preferences and thus avoid the often cumbersome elicitation of exact ratio estimates. After each new statement the interval judgments are synthesized into dominance relations on the alternatives by solving a series of linear programming problems. This leads to an interactive process of preference programming which provides more detailed results as the decision maker gradually enters a more specific preference description. Moreover, the overall effort of preference elicitation is smaller than in the analytic hierarchy process because the most preferred alternative can usually be identified before all possible comparisons between pairs of factors have been completed.

Fifty-four percent of Benin's population in rural areas keep indigenous chickens for subsistence livelihoods. Maintaining diversity in chicken gene pools can provide several benefits for farmers, including risk pooling, diversification of income and maintaining availability of chickens suitable for cultural ceremonies. Despite the potential to alleviate poverty by improving indigenous chicken breeds, smallholders' participation in the implementation of breeding programmes is weak. Participation can be improved with greater understanding of the many functions of chickens to smallholders, particularly their economic contribution. The objectives of this study are (1) to evaluate chicken traits including market and non-market values, and (2) to assess factors that influence the conservation of indigenous breeds. Choice modelling, a multi-attribute preference elicitation technique, was applied across 300 households in two districts in Benin (Dassa and Toffo). The results revealed that adaptive and performance traits in chicken breeds are highly valued by farmers and that preferences differed greatly between farmers in the two districts. Many of the preferred traits are expressed in indigenous chickens, whose conservation should be supported through village chicken breeding programmes. However, a preference for white plumage, most common among exotic breeds, could hinder conservation of indigenous breeds, which are mostly brown or black. From an economic point of view, the aim of conserving culturally significant and disease resistant indigenous breeds is contrary to the objective of increasing chicken productivity. The lack of knowledge about chicken characterization and a lack of flock management were identified as further severe constraints to village breeding and conservation programmes.

Abstract: Throughout the world, water management and planning issues are becoming increasingly difficult to handle, and there have been calls for more adapted approaches to aid the decision-making processes required for water planning and management. Participatory ...