Preference Uncertainty Research Articles

Value-Driven Design Process: A Systematic Decision-Making Framework Considering Different Attribute Preferences From Multiple Stakeholders

In general, architectural design is a loosely structured, open-ended activity that includes problem definition, representation, performance evaluation, and decision making. A number of approaches have been proposed in the literature to organize, guide, and facilitate the design process. The main objective of this paper is to seek a logical and rigorous means to aid in developing an optimized design that is acceptable to the customer or user of the product. The convention design approaches heavily involve decision making, which is integral to the architectural design process and is an important element in nearly all phases of design. There is a need to reframe the decision-making process to transform and improve the design process in order for finial building to achieve the performance goals. The first step in making an effective design decision is to understand the stakeholders' and team players' (architect, engineer, client, and consultant) different preferences based on their needs, experiences, and expectations of the project. In this paper, we first provide an overview about conventional decision-making method and process, identify the existing attributes that contribute to decision making in design, and outline the obstacles present in making optimized sustainable design decisions due to the uncertainty of different stakeholders' preferences. Then, we present one case study to identify and compare different preferences among engineering students, practicing architects, and the general public, and we analyze how the three groups attribute different weight to the major design attributes. This paper provides some novel insights into a value-driven sustainable design process, and it will be one of the building blocks for creating a framework to integrate game theory into the design decision-making process, considering multiple stakeholders' perspectives and preferences for building attributes as future research tasks.

Sin licenses revisited

We analyze personalized regulation in the form of sin licenses to correct the distortion in the consumption of a harmful good when consumers suffer from varying degrees of self-control problems. We take into account preference uncertainty, which generates a trade-off between flexibility and commitment provided by sin licenses. We also account for the possibility that consumers may trade the sin good in a secondary market, which partially erodes the commitment power of sin licenses. We show that if sophisticated consumers are allowed to choose any general, individualized pricing scheme for sin goods, they will choose a system of sin licenses. Nevertheless, sin licenses do not implement the social optimum in our general setting. We derive a simple criterion for assessing whether switching to a system of sin licenses improves welfare over linear sin taxes.

Alternative approaches to treat respondent uncertainty in contingent willingness to pay estimation

Purpose The purpose of this paper is to theoretically using two new models to analyze the effect of respondents’ uncertainty about their stated willingness to pay (WTP) on welfare estimates in the contingent valuation method (CVM) theoretically using two new models, and empirically to reveal consumers’ WTP to improve drinking water supply safety (WSS) in China. Design/methodology/approach In this paper, two alternative preference uncertainty treatment approaches are proposed to estimate consumers’ WTP theoretically and they are applied to China’s WSS improvement program from a payment card method, which depends on how consumers’ certainty level about their valuation is. Furthermore, four regression models are presented to investigate the determinants of consumers’ WTP. Findings Theoretically, the alternative approaches that proposed in this research can remove overestimation bias from traditional CVM method but with lower estimation efficiency. In addition, the empirical results of the uncertainty adjusted models show that the expected WTP to improve drinking WSS is from 0.55 to 0.56 Renminbi yuan/ton, which are lower than the estimates from the conventional standard CVM models. Consumers’ preferences for their concerns about WSS, attitudes toward WSS improvement programs, trusts in implement authorities and their knowledge of WSS have significant effects on the WTP for improving drinking WSS and on respondents’ uncertainty too. Originality/value Theoretically to the authors’ knowledge, it is the first attempt to compare alternative approaches to treat respondent uncertainty using numerical certainty scale combined with payment card format valuation questions in CVM. Empirically it is the first study at this large scale that investigates consumers’ WTP for improving drinking WSS incorporating with respondent uncertainty in China. In addition, to assess consumer preferences for improved drinking water safety and the sources of uncertainty, information on consumers’ attitudes toward WSS are considered at the first time.

Convergence to consensus in heterogeneous groups and the emergence of informal leadership.

When group cohesion is essential, groups must have efficient strategies in place for consensus decision-making. Recent theoretical work suggests that shared decision-making is often the most efficient way for dealing with both information uncertainty and individual variation in preferences. However, some animal and most human groups make collective decisions through particular individuals, leaders, that have a disproportionate influence on group decision-making. To address this discrepancy between theory and data, we study a simple, but general, model that explicitly focuses on the dynamics of consensus building in groups composed by individuals who are heterogeneous in preferences, certain personality traits (agreeability and persuasiveness), reputation, and social networks. We show that within-group heterogeneity can significantly delay democratic consensus building as well as give rise to the emergence of informal leaders, i.e. individuals with a disproportionately large impact on group decisions. Our results thus imply strong benefits of leadership particularly when groups experience time pressure and significant conflict of interest between members (due to various between-individual differences). Overall, our models shed light on why leadership and decision-making hierarchies are widespread, especially in human groups.

Cold-Start Collaborative Filtering

Collaborative Filtering (CF) is a technique to generate personalised recommendations for a user from a collection of correlated preferences in the past. In general, the effectiveness of CF greatly depends on the amount of available information about the target user and the target item. The cold-start problem, which describes the difficulty of making recommendations when the users or the items are new, remains a great challenge for CF. Traditionally, this problem is tackled by resorting to an additional interview process to establish the user (item) profile before making any recommendations. During this process the user's information need is not addressed. In this thesis, however, we argue that recommendations would be preferably provided right from the beginning. And the goal of solving the cold-start problem should be maximising the overall recommendation utility during all interactions with the recommender system. In other words, we should not distinguish between the informationgathering and recommendation-making phases, but seamlessly integrate them together. This mechanism naturally addresses the cold-start problem as any user (item) can immediately receive sequential recommendations without providing extra information beforehand. This thesis solves the cold-start problem in an interactive setting by focusing on four interconnected aspects. First, we consider a continuous sequential recommendation process with CF and relate it to the exploitation-exploration (EE) trade-off. By employing probabilistic matrix factorization, we obtain a structured decision space and are thus able to leverage several EE algorithms, such as Thompson sampling and upper confidence bounds, to select items. Second, we extend the sequential recommendation process to a batch mode where multiple recommendations are made at each interaction stage. We specifically discuss the case of two consecutive interaction stages, and model it with the partially observable Markov decision process (POMDP) to obtain its exact theoretical solution. Through an indepth analysis of the POMDP value iteration solution, we identify that an exact solution can be abstracted as selecting users (items) that are not only highly relevant to the target according to the initial-stage information, but also highly correlated with other potential users (items) for the next stage. Third, we consider the intra-stage recommendation optimisation and focus on the problem of personalised item diversification. We reformulate the latent factor models using the mean-variance analysis from the portfolio theory in economics. The resulting portfolio ranking algorithm naturally captures the user's interest range and the uncertainty of the user preference by employing the variance of the learned user latent factors, leading to a diversified item list adapted to the individual user. And, finally, we relate the diversification algorithm back to the interactive process by considering inter-stage joint portfolio diversification, where the recommendations are optimised jointly with the user's past preference records. The pdf version of my thesis is available for download at http://discovery.ucl.ac.uk/1474118/.

Validity of Willingness to Pay Measures under Preference Uncertainty.

Recent studies in the marketing literature developed a new method for eliciting willingness to pay (WTP) with an open-ended elicitation format: the Range-WTP method. In contrast to the traditional approach of eliciting WTP as a single value (Point-WTP), Range-WTP explicitly allows for preference uncertainty in responses. The aim of this paper is to apply Range-WTP to the domain of contingent valuation and to test for its theoretical validity and robustness in comparison to the Point-WTP. Using data from two novel large-scale surveys on the perception of solar radiation management (SRM), a little-known technique for counteracting climate change, we compare the performance of both methods in the field. In addition to the theoretical validity (i.e. the degree to which WTP values are consistent with theoretical expectations), we analyse the test-retest reliability and stability of our results over time. Our evidence suggests that the Range-WTP method clearly outperforms the Point-WTP method.

Market Structure and Advance Selling

When products are sold in advance, i.e. prior to consumption, consumers trade off an early, uninformed purchase at a low price against a late, informed purchase at a high price. This paper considers the effect of market structure on the prevalence of advance selling. We show that in an oligopolistic market with multi-product firms, advance selling (with its associated allocative inefficiency) is decreasing in market concentration when the consumers’ preference uncertainty is high but can be increasing when uncertainty is low.

Comparing welfare estimates across stated preference and uncertainty elicitation formats for air quality improvements in Nairobi, Kenya

AbstractThe effect of preference uncertainty on estimated willingness to pay (WTP) is examined using identical payment cards and alternative uncertainty elicitation procedures in three split samples, focusing on air quality improvement in Nairobi. The effect of the stochastic payment card (SPC) and polychotomous payment card (PPC) are compared with a conventional payment card (PC). Substantial financial support is found for improved air quality in Nairobi, with approximately 85 per cent of the whole sample stating a positive WTP. The way WTP values are elicited, with and without ability to express preference uncertainty, has significant effect on WTP welfare estimate. Allowing respondents to express experienced uncertainty when stating WTP value yields more conservative but less accurate WTP values for inclusion in policy analysis. The PPC seems to hold more promise since it is easier to understand and imposes less cognitive burden on survey participants than the SPC in a developing country context.

Analysing remanufacturing decisions of supply chain members in uncertainty of consumer preferences

ABSTRACTThis article formulates a gaming model of the closed-loop supply chain with manufacturers (as the leader), sellers and consumers where consumers may or may not be willing to pay remunerative price for remanufactured products vis-à-vis new products. In the model, manufacturers produce new as well as remanufactured products, whereas sellers distribute them. In stable circumstances, this article presents the functional formula of the optimal manufacturing pricing decisions. The results show that when the cost of new products is significantly lower than that of remanufactured ones, manufacturers choose to produce new products only. When the difference between cost of new and remanufactured products is moderate, manufacturers tend to produce both new and remanufactured products, and in some regions, production of new and remanufactured products is proportional to each other. When the difference between cost of new and remanufactured products is enormous, their production and sale tend to stop. Also, this article analyses the effects of varying cost of new and remanufactured products and the recycling rate on pricing decisions of supply chain members. This article contributes to the management of manufacturers’ and sellers’ remanufacturing decisions and also provides advice on how governments can guide consumer preferences.

Competition, preference uncertainty, and jamming: A strategic communication experiment

We conduct a game-theoretic laboratory experiment to investigate the nature of information transmission in a communication environment featuring competition and information asymmetry. Two senders have private information about their preferences and simultaneously send messages to a receiver in a one-dimensional space with a large number of states, actions, and messages. We find that equilibrium predictions fare poorly and that senders overcommunicate by consistently exaggerating their messages. Over time, exaggeration increases and communication unravels. Our analysis suggests that exaggeration and unraveling can only be partially explained by bounded rationality models of iterated reasoning or belief learning. Instead, behavior is consistent with a naive form of exaggeration in which senders know they must exaggerate, but they do so in an understated way that is less responsive to their private information or to opponents' past behavior than would be fully optimal.

Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis.

BackgroundEstimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly.MethodsA probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions.ResultsThe patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95 % CI: 0.48 to 0.54), 0.51 (95 % CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95 % CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27 %. This probability decreased to 18 % when uncertainty in performances was not included and increased to 41 % when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61 %. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95 % CI: 0.65 to 0.68), 0.57 (95 % CI: 0.56 to 0.59), 0.67 (95 % CI: 0.61 to 0.71), and 0.19 (95 % CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49 %.ConclusionsPreferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.

A fuzzy model for managing natural noise in recommender systems

E-commerce customers demand quick and easy access to products in large search spaces according to their needs and preferences. To support and facilitate this process, recommender systems (RS) based on user preferences have recently played a key role. However the elicitation of customers preferences is not always precise either correct, because of external factors such as human errors, uncertainty and vagueness proper of human beings and so on. Such a problem in RS is known as natural noise and can bias customers recommendations. Despite different proposals have been presented to deal with natural noise in RS none of them is able to manage properly the inherent uncertainty and vagueness of customers preferences. Hence, this paper is devoted to a new fuzzy method for managing in a flexible and adaptable way such uncertainty of natural noise in order to improve recommendation accuracy. Eventually a case study is performed to show the improvements produced by this fuzzy method regarding previous proposals.

Studying the Phenomenon of Competitive Advantage and Differentiation: Market and Entrepreneurial Orientation Perspective

The purpose of this paper is to identify and explain how market orientation and entrepreneurial orientation helps a firm in effectively differentiating its product or service offerings in relation to its industry rivals. The discussion in the paper has been done from various perspectives in the light of relevant past and modern researches while keeping the focus towards explaining how market orientation and entrepreneurial orientation practices of a firm allow it to achieve a competitive advantage in the industry by effectively differentiating its products. Market orientation has been discussed as an important antecedent to product differentiation and innovation which lead to greater competitiveness and superior performance in the industry. The findings suggest that market orientation and entrepreneurial orientation have a direct positive relationship with a firm’s ability to properly differentiate its offerings against its competitors. They help a firm in analyzing its customers’ needs, competitors’ strategies, and changing market conditions; reducing preference uncertainty, and understanding the consequences and associated risks with the late strategic moves and product differentiation strategies.

Grey-Based Preference in a Graph Model for Conflict Resolution With Multiple Decision Makers

To capture uncertainty in preferences, definitions based on grey numbers are incorporated into the graph model for conflict resolution (GMCR), a realistic and flexible methodology to model and analyze strategic conflicts. A general grey number is a real number that may be a member of a discrete set of real numbers, or may fall within one or several intervals. It can represent uncertain preference of decision makers in a meaningful way. Here, a grey-based preference structure is developed and integrated with GMCR. Utilizing a number of grey-based ideas, solution concepts describing human behavior under conflict in the face of uncertain preference are defined for a conflict model. This grey-based GMCR is then applied to a generic sustainable development conflict with uncertain preferences in order to demonstrate how it can be conveniently utilized in practice.

Option prioritization for unknown preference

An option prioritization technique is developed to efficiently elicit the preferences, both unknown and crisp, of decision makers (DMs) in strategic conflicts. In the Graph Model for Conflict Resolution, each DM has one or more options, each of which may be selected or not. A state, or possible scenario, is formed when all DMs make an option selection. The software GMCR II contains an option prioritization procedure that makes it easy for a modeller to enter a DM’s crisp preference ordering over the states using prioritized statements describing the DM’s preferred option combinations. This procedure is extended by adding two new logical connectives that describe uncertainty of preference. For each DM, a range of possible scores for each feasible state can then be calculated, facilitating the determination of a preference ordering containing uncertainty by comparing and ranking scores. To demonstrate how this new methodology can be used to represent unknown preferences in a real-world decision problem, it is applied to a Canadian dispute over proposed water exports.

Advocacy and political convergence under preference uncertainty

We study the formation of advocacy groups and how they can impact policy outcomes by revealing information about voters׳ preferences to uninformed political candidates. We conduct a laboratory experiment based on a two-candidate spatial electoral competition setting where the policy preferences of voters are (initially) unknown and change over time. In the control treatment candidates learn about the preferred policy of the median voter through the voting outcome of elections. In the advocacy treatments, voters can organize themselves into advocacy groups in order to reveal their policy preferences. We find that voters often overcome the collective action problem of forming an advocacy group. In fact, we observe the formation of both informative advocacy groups, which convey new information, and uninformative advocacy groups, which do not. Overall, advocacy groups significantly speed up the convergence to the preferred policy of the median voter. However, advocacy does not lead to higher earnings as the gains from faster convergence are offset by the costs of group formation.

Inventory Planning for a Modular Product Family

This paper is motivated by observing that an increasing number of firms are offering modular products assembled with multiple option choices for the consumer. Starting with the PC offerings by Dell which allowed (and still allows) users to configure their product by choosing among multiple choices for each option, the current market place seems to have evolved to a make‐to‐stock scenario where Apple offers its IPAD series with multiple models each with a unique storage size, color, and wireless chip technology. The focus of our work is on determining the optimal stocking level of modular end‐products. Our analysis is based on a benchmark model with the aim of maximizing expected profit subject to an aggregate fill rate constraint as well as variant‐specific individual fill rates under a make‐to‐stock setting. To further assess the robustness of our finding, we consider the extensions of correlated market preferences over options, price‐dependent demand, and alternative probability distributions for characterizing uncertainty in market preferences or aggregate demand. Finally we also show how to extend the single period model into a multiple‐period setting. Through extensive computational analysis, we find that more precise estimates of market preferences for various modular options constitute extremely valuable information that goes beyond the usefulness of forecasts of aggregate market demand. From a practical perspective, this might be indicative of another classic marketing‐operations trade‐off. Offering more options for consumers would be preferred by marketing managers since this would reach more consumers and hence, enhance product sales. On the other hand, the ability to obtaining greater forecast accuracy would decline when the number of options increase. Hence, from an operational perspective, it would be preferred to limit option choices (so that better forecasts can be obtained) since this would lead to lower stocking costs and hence, higher profits.

Port management and multiple-criteria decision making under uncertainty

The methodology for port management in this study takes into account the stochastic nature of climate as well as of use and exploitation agents. It provides a probabilistic characterization of the performance of different alternatives in terms of a set of indicators that reflect the potential benefits for different interest groups. The joint distribution functions thus obtained for a set of pre-defined management alternatives are used to compare strategies and to make decisions, based on stochastic multi-criteria analysis. This method, which also addresses the uncertainty of decision-maker preferences, ranked the strategies, and at the same time, specified the typical decision-maker preferences for each strategy. In the analysis of a hypothetical port, four alternatives were designed as solutions for port congestion. The criteria selected were the service quality provided to vessels, the total goods handled and the profits accrued by the Port Authority. The criteria measuring the performance of each strategy were statistically analyzed first. Then, assuming the uniform distribution of decision-maker preferences, the alternatives were ranked, and the relative importance of each criterion in the decision-making process was obtained.

The Role of Discount Vouchers in Market with Customer Valuation Uncertainty

Online discount voucher market In the discount voucher market, customers usually face two types of valuation uncertainty, namely, preference uncertainty and consumption state uncertainty. Preference uncertainty is related to the customer's lack of relevant experience with the merchant, whereas consumption state uncertainty is related to the advance selling nature of the discount voucher mechanism. By taking a comprehensive perspective (i.e., considering revenue management and promotion effect at the same time), we find (i) no show of voucher buyers may not be a good thing for the merchant, especially for those large or start‐up ones; (ii) offering refund may always hurt the merchant's profit and the PayPal model may not be optimal in terms of maximizing social welfare; and (iii) market segmentation is not necessary for the profitability of promotion.

Consumer Privacy and Targeted Pricing with Stochastic Valuations

I study the market for consumer information. Firms cannot commit to privacy, so buyers anticipate disclosure of purchase history for targeted pricing. In the literature, strategic rejections of informative offers make purchase data worthless, which is paradoxical given the large investments in data analytics. I show that buyers' uncertainty regarding future preferences allows full separation of types, leading to valuable purchase data. Buyers of any given type are assumed to share similar expectations about the evolution of future valuations. Preference uncertainty generates consumer-to-consumer externalities, which is why strategic consumers are sometimes better off when they remain unaware of targeted pricing. When preferences are transitory, firms have an incentive to raise consumer awareness about targeted pricing.