Preference Data Research Articles

Declarative Approaches to Outcome Determination in Judgment Aggregation

Judgment aggregation (JA) offers a generic formal framework for modeling various set- tings involving information aggregation by social choice mechanisms. For many judgment aggregation rules, computing collective judgments is computationally notoriously hard. The central outcome determination problem, in particular, is often complete for higher levels of the polynomial hierarchy. This complexity barrier makes it challenging to de- velop practical exact algorithms to outcome determination. Taking on this challenge, in this work we develop practical exact algorithms for outcome determination under a range of the most central JA rules—namely Kemeny, Slater, MaxHamming, Young, Dodgson, Reversal scoring, Condorcet, Ranked agenda, and LexiMax—by harnessing the declarative approach, in particular, Boolean satisfiability (SAT) and integer programming techniques. For the Kemeny, Slater, MaxHamming, Young, and Dodgson rules, we detail direct ap- proaches based on maximum satisfiability (MaxSAT) and integer programming. For the Reversal scoring, Condorcet, Ranked agenda, and LexiMax rules, we develop iterative al- gorithms, including algorithms based on the counterexample-guided abstraction refinement (CEGAR) paradigm, making use of recent advances in incremental MaxSAT solving and preferential SAT-based reasoning. We provide an open-source implementation of the algo- rithms, and empirically evaluate them using real-world preference data. We compare the performance of our implementation to a recent approach which makes use of declarative solver technology for answer set programming (ASP). The results demonstrate that our approaches scale significantly beyond the reach of the ASP-based algorithms for all of the judgment aggregation rules considered.

What changes the autonomous vehicle acceptance after COVID-19? Evidence from China

We propose a new theoretical autonomous vehicle (AV) acceptance model and use pre-post data to predict acceptance intention changes in the post-pandemic era. Based on the Technology Acceptance Model (TAM), this paper further incorporated trust, perceived risk, social influence, and perceived COVID-19 risk into the original TAM to explore the public acceptance of AVs. Moreover, combining the structural equation model and ordered logit model, this paper adopted a hybrid choice model including psychological attributes, socio-demographic attributes, travel attributes as well as ethics, legal responsibilities, and vehicle safety levels to clarify determinants of the public AV acceptance changes before and after the pandemic. This study solicited 466 residents in China, and used online surveys to collect stated preference data. The results show that the significant positive impact of COVID-19 on the changes in acceptance of AVs for people's mobility, especially for the higher automation levels. It makes AVs more appealing after COVID-19 outbreak. In addition, perceived usefulness, perceived ease of use, trust, and social influence also have a significant positive impact on the public AV acceptance changes. Gender, age, dedicated lanes, government subsidies, traffic accident experience, travel mode, travel expense, and other factors are related to AV acceptance changes as well. We provide insights for measuring the changes in acceptance of AVs across time due to public health emergencies.

Value of time and port choice: An approach regarding import companies in Brazil

The cargo release time in Brazilian ports is a critical factor that impacts the competitiveness of companies. This paper analyzed the port choice process from companies in two states in the Southeast region of Brazil: Rio de Janeiro and Minas Gerais. We used Stated Preference data. Multinomial logit and mixed logit with error components models were estimated. We analyzed the following attributes: taxation, road transport tariff, ship calls, port tariff, sea freight tariff and cargo release time. The experimental designs were structured using an efficient design for Rio de Janeiro, and a Bayesian efficient design for Minas Gerais. A comparison of the port choice behavior was carried out between the states. For each state, the Value of Time (VOT) was calculated referring to the willingness of companies to pay for the reduction of one unit (day) of cargo release at ports. The estimated VOT for companies in RJ was R$/t.day 387.45 (77.49 U$S/t.day) and for companies in MG, it was R$/t.day 364.93 (72.98 U$S/t.day). The results indicate that the estimated values may vary according to characteristics, such as company size and product type.

Let long-term interests talk: An disentangled learning model for recommendation based on short-term interests generation

In e-commerce recommendation systems, users’ long-term and short-term interests jointly influence product selection. However, the behavioral conformity phenomenon tends to be more prominent in short-term sequences, and the entanglement of true preference and popularity conformity data confuses the user’s real interest needs. To address this issue, we propose a sequential recommendation model called DFRec to disentangle short-term interests from popularity bias. By leveraging long-term interest trends, the model promotes the separation of short-term interests from popularity-driven deviations, thereby reducing the impact of popularity interference in short-term sequences. Firstly, we propose a Disentangled Frequency Attention Network(DFAN) to address the entanglement between real sequence features and conformity data in users’ short-term behavioral sequences. The approach clarify the non-entangled representation of the user’s short-term interest and conformity on the basis of long-term interest trends. Secondly, in order to capture the real long-term interest characteristics of users, this paper suggests using a Learnable Filter(LF) to filter the noise frequencies in long-term sequence. The method decouples the horizontal and vertical directions of the sequence and filters out the noise in both directions. Finally, consider the importance of the two interests characteristics is dynamic, we propose a joint learning framework with dual embeddings to balance and fusion these two features of users’ interests. Experimental results on three public datasets demonstrate that our model effectively captures dynamic user interests and outperforms six baseline models.

A genuine need or nice to have? Understanding HTA representatives' perspectives on the use of patient preference data.

The roles and potential value of patient preference (PP) data in health technology assessment (HTA) remain to be fully realized despite an expanding literature and various efforts to establish their utility. This article reports lessons learned through a series of collaborative workshops with HTA representatives, organized by the Health Technology Assessment International's Patient Preferences Project Subcommittee. Five online workshops were conducted between June 2022 and June 2023, seeking to facilitate collaborative learning and reflection on ways that PP data can be integrated into HTA. Participants included nine HTA representatives from the United States, Canada, Australia, England, and the Netherlands. Workshops were recorded, transcribed, and thematically analyzed. Despite appreciating the value of PP data, participants were ambivalent about their use in HTA. Some felt that they were already getting the information they needed from the cost-effectiveness analysis or existing patient involvement processes. Others thought that PP data would be very helpful at the initial and final stage of the decision-making process and, particularly, in the following cases: (a) when technology has important non-health benefits; (b) when the clinical and/or cost-effectiveness evidence is marginal; and (c) when treatment is indicated for a large and heterogeneous population. Issues related to the validity and reliability of PP studies were frequently raised, with preference heterogeneity at the core of these concerns. Collaborating with HTA representatives in the "co-creation" of PP research can help address their concerns and facilitate mutual learning about how PP data can be used in HTA.

Large language models (LLMs) as agents for augmented democracy.

We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens' preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil's 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject's individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a 'bundle rule', which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

The Development of IV-VIKOR Method Under Hesitant Fuzzy Sets in MCDM

Due to its solution correctness and ease of evaluation, the VIKOR approach has already established itself as a popular multi-criterion decision-making (MCDM) method. This strategy focuses on picking and ranking from a list of viable options in order to assist the decision-maker in determining a final course of action. Additionally, it looks for a middle ground to resolve a problem with competing demands. It produces a ranking list of compromises based on a precise measurement of distance from the ideal response. The hesitant fuzzy set (HFS) has furthermore come under increased study because of its potency and ability in capturing ambiguity and uncertainty. This paper introduces the inversion of VIKOR method (IV-VIKOR) to take uncertain fuzzy conditions into account. These novel concepts serve as the foundation for an IV-VIKOR method that we suggest, and a real-world application is given to demonstrate how well our method works when dealing with MCDM problem with hesitant fuzzy preference data.

Sea‐Level Rise, Drinking Water Quality and the Economic Value of Coastal Tourism in North Carolina

AbstractWe estimate the economic benefits of avoiding reductions in drinking water quality due to sea level rise accruing to North Carolina (NC) coastal tourists. Using stated preference methods and responses from recent coastal visitors, we find that tourists are 2%, 8%, and 11% less likely to take an overnight trip if drinking water tastes slightly, moderately, or very salty at their chosen destination. The majority of those who decline a trip would take a trip to another NC beach without water quality issues, others would take another type of recreational trip, with a minority opting to stay home. Willingness to pay for an overnight beach trip declines with the salty taste of drinking water. We find evidence of attribute non‐attendance in the stated preference data, which impacts the regression model and estimates of the willingness to pay for trips. Combining economic and hydrological models, annual aggregate benefit losses due to low drinking water quality could be as high as $232 million by 2040.

Urban Air Mobility Passengers’ Profiling: Evidence from Milan Airports, Italy

Urban air mobility (UAM) is expected to offer new travel options for passengers to and from airports in the near future, despite uncertainties associated with regulatory issues, environmental concerns, and societal impacts. This paper analyzes the socioeconomic and behavioral factors that could influence users’ modal choices (including UAM services) for accessing and egressing airports. Using revealed and stated preference data collected at Milan airports (Italy), mixed logit and hybrid choice modeling specifications are estimated and compared with profile potential UAM passengers. Our findings suggest that the level of service, socioeconomic factors, and trip-related variables explain passengers’ choices better than latent traits such as fear of flying, propensity for technological advances, and expectations about the safety of UAM services. In other words, the additional complexity of hybrid choice modeling is not justified by the slight gain in likelihood compared with the estimated mixed logit model. The results also indicate that high-income individuals traveling for business purposes are the most likely demand segment to use UAM services, at least initially. Moreover, highly educated individuals and employees who have their travel expenses reimbursed for work trips are less likely to choose UAM services for airport access or egress, preferring traditional ground taxis.

Parasitic plants show striking convergence in host preference across angiosperm lineages.

The host specificity of a parasite underpins its ecology, distribution, invasive potential and adaptability, yet for most parasitic plants host ranges are poorly understood. We examine host-parasite relationships across lineages to infer how host specificity may have influenced the evolution of parasitism in plants. Host preference data for all plant holoparasite species were manually collected from literature and herbarium specimens, then analysed to investigate and visualise host diversity and specificity. We reveal a disproportionality in host preference across host lineages: the Asteraceae contains 10% of angiosperm diversity but is infected by 31% of parasite species; meanwhile Monocots comprise 23% but are infected by just 3.2%. Furthermore, we observe striking convergence in host preference: Asteraceae, Euphorbiaceae and Fabaceae are infected by six, five and four independent parasite lineages, respectively. We also demonstrate considerable variation in the degree of host specificity among closely related parasite species; a result that does not reflect the expectation of holoparasites - especially endoparasites - as host specialists. The marked pattern of convergence in preference across disparate lineages points to a common pathway in the evolution of parasitism of eudicots in preference to monocots, which may have in turn have been driven by a divergence in host root and vascular architecture. The unexpected variation in host specificity among closely related species suggests that even apparent generalists may comprise cryptic host-specific taxa. This highlights the value of host preference as an additional consideration in parasitic plant taxonomy. Together, our data point to a complex interplay between ecological and physiological factors driving the evolution of host-parasite interactions. Moreover, they emphasize how little is known about the ecology of most holoparasitic plants, a group of organisms that are especially vulnerable at a time of unprecedented biodiversity loss and extinction.

Understanding the interactions that children and young people have with their natural and built environments: A survey to identify targets for active travel behaviour change in Wales.

Active travel offers many societal benefits, including improving people's mental and physical health and minimising our impacts on the environment. Increasing active travel is particularly important amongst children and young people (CYP), who are building habits which they will carry into adulthood. Studies on active travel amongst CYP are limited, however, with most research focusing on adult participants or on adult perceptions of children. This study sought to understand CYP's interactions with the built and natural environment-and therefore their access to active travel-through the Capability, Opportunity, Motivation, Behaviour (COM-B) model. With a stakeholder group representing local government, youth organisations and active travel organisations, we co-created two bilingual questionnaires-one for young people aged 12-16 years living in Wales and the other for parents of young people aged 12-16 years living in Wales. Both questionnaires collected information on behaviour and perceived capability, opportunity and motivation of CYP to engage with their natural and built environments. The questionnaires included a discrete choice experiment (DCE), which proposed a series of binary choice questions indicating preferences based on landscape, journey time and type of travel. A total of 124 questionnaires (38 young people and 86 parents) were returned for analysis. These data indicate that CYP's time spent outdoors is not dependent upon geography (rural/urban/suburban), season, or school holidays. There was a significant difference in capability, opportunity and motivation between parents and CYP, with parents over-estimating the psychological capability of CYP to engage outdoors. The preference data indicate that active travel is the favoured mode of transport, with both CYP and parents stating that they would increase travel time in order to travel actively. While this response is not consistent with respondent's day-to-day travel choices, it suggests that the limitations to active travel may be psychological capability and automatic motivation, rather than a lack of opportunity.

Impact of student backgrounds on the outcomes of maritime postgraduate professional education

Maritime taught postgraduate education plays a vital role in cultivating expertise within the ever-evolving global logistics industry. Notably, the diverse backgrounds of students are of great importance in this context, given the scarcity of undergraduate maritime programs and the industry's demand for professionals from various disciplines. However, this diversity also poses a challenge to educators in terms of effectively training and evaluating these students. This study focuses on a maritime taught postgraduate professional program and investigates how student background characteristics affect their learning outcomes and academic performance. By utilizing both stated preference data (SP) and revealed preference data (RP) collected from a postgraduate maritime professional program in Hong Kong, the study aims to mitigate hypothetical bias and gain a comprehensive understanding of the factors influencing student perceptions and experiences in maritime industry. The study measures five dimensions of student learning outcomes, including professionalism, strategic thinking, lifelong learning capability, career development, and academic performance. The findings suggest that student background characteristics have minimal impact on maritime learning outcomes, with only gender and undergraduate programs showing some influence. The study highlights the inclusive nature of maritime professional education, emphasizing that training quality, rather than personal background, is the key determinant of success.

Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation

Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established. This paper investigates the effectiveness of fine-grained feedback which captures nuanced distinctions in image quality and prompt-alignment, compared to traditional coarse-grained feedback (for example, thumbs up/down or ranking between a set of options). While fine-grained feedback holds promise, particularly for systems catering to diverse societal preferences, we show that demonstrating its superiority to coarse-grained feedback is not automatic. Through experiments on real and synthetic preference data, we surface the complexities of building effective models due to the interplay of model choice, feedback type, and the alignment between human judgment and computational interpretation. We identify key challenges in eliciting and utilizing fine-grained feedback, prompting a reassessment of its assumed benefits and practicality. Our findings -- e.g., that fine-grained feedback can lead to worse models for a fixed budget, in some settings; however, in controlled settings with known attributes, fine grained rewards can indeed be more helpful -- call for careful consideration of feedback attributes and potentially beckon novel modeling approaches to appropriately unlock the potential value of fine-grained feedback in-the-wild.

Investigating work commuters mode choice behavior in Dalian city during pandemic: a hybrid choice model approach

ABSTRACT This study examined commuters' mode choice behavior during the COVID-19 pandemic, focusing on the impact of both tangibles and intangibles factors. To do so, the authors developed a Hybrid Choice Model (HCM) which investigated the effects of sociodemographic characteristics, travel attributes, and intangible factors such as satisfaction, safety, and discomfort, on travel behavior. The model was tested using Stated Preference data collected from commuters in Dalian City (China) via wenjuanxing, an online survey platform. The results indicate that individual characteristics played a significant role in shaping people's perceptions and mode choice behavior was influenced by sociodemographic factors, travel characteristics, and the aforementioned intangible factors. More specifically, individuals, who perceived a higher level of safety, measured in terms of worries about robbery, accidents, and pandemic concerns, were less likely to choose cars. People with a stronger perception of discomfort favored metro over bus. Private car users reported higher satisfaction. The findings will be useful to policymakers for establishing more resilient transport solutions.

A systematic literature review of slow tourism

This systematic literature review focuses on slow tourism, which grew in popularity during COVID-19. It highlights the gaps in the literature for future researchers by compiling the papers published on slow tourism between 2010 and 2021. In addition, the managerial suggestions we make serve as lessons for practitioners. Developed as a systematic literature review, we used different selection criteria including papers published in English in Q1 or Q2 journals between 2010 and 2021 in the sample. This study identified the following parameters: the number of publications, the most preferred research methodology and data collection methods, and the geographical coverage of slow tourism papers. The relationship between slow tourism and sustainability is strengthened by our analysis. We identified qualitative studies, particularly in-depth interviews, as the most popular data collection method for slow tourism studies. Australia, the United Kingdom, and Poland were the most popular destinations in terms of the geographical context of these studies, followed by Italy. As the literature is missing a unified definition of slow tourism, we propose one based on the four-pillar model.

GraNDe: Efficient Near-Data Processing Architecture for Graph Neural Networks

Graph Neural Network (GNN) models have attracted attention, given their high accuracy in interpreting graph data. One of the primary building blocks of a GNN model is aggregation, which gathers and averages the feature vectors corresponding to the nodes adjacent to each node. Aggregation works by multiplying the adjacency and feature matrices. The size of both matrices exceeds the on-chip cache capacity for many realistic datasets, and the adjacency matrix is highly sparse. These characteristics lead to little data reuse, causing intensive main-memory accesses during the aggregation process. Thus, aggregation exhibits memory-intensive characteristics and dominates most of the total execution time. In this paper, we propose GraNDe, an NDP architecture that accelerates memory-intensive aggregation operations by locating NDP modules near DRAM datapath to exploit rank-level parallelism. GraNDe maximizes bandwidth utilization by separating the memory channel path with the buffer chip in between so that pre-/post-processing in the host processor and reduction in NDP modules operate simultaneously. By exploring the preferred data mappings of the operand matrices to DRAM ranks, we architect GraNDe to support adaptive matrix mapping that applies the optimal mapping for each layer depending on the dimension of the layer and the configuration of a memory system. We also propose adj-bundle broadcasting and re-tiling optimizations to reduce the transfer time for adjacency matrix data and to improve feature vector data reusability by exploiting tiling with consideration of adjacency between nodes. GraNDe achieves 3.01× and 1.69× on average, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.00\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.98\times$</tex-math></inline-formula> speedups of GCN aggregation over the baseline system and the state-of-the-art NDP architecture for GCN, respectively.

A Novel Class of Unfolding Models for Binary Preference Data

Abstract We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices.

Exploring the Causal Association between Morning Diurnal Preference and Psychiatric Disorders: A Bidirectional Two-Sample Mendelian Randomization Analysis.

The causal connection between morning diurnal preference and psychiatric disorders remains enigmatic. Using bidirectional two-sample Mendelian randomization (MR), we aim to explore the potential causal associations between morning diurnal preference and seven prominent psychiatric disorders. MR is a genetic epidemiological method that leverages genetic variants as instrumental variables to infer causal associations between exposures and outcomes. We obtained morning diurnal preference data from genome-wide association study (GWAS) datasets and identified 252,287 individuals as morning people. Psychiatric disorder data were sourced from the FinnGen consortium R9 dataset. Our primary analysis used the inverse-variance weighted (IVW) approach to evaluate the overall causal effect by combining the estimates from each genetic variant. Addition analyses, including weighted median, MR-Egger regression, weighted mode, and simple mode techniques were conducted to ensure robustness. Being a morning person is related to reduced odds of multiple psychiatric disorders, including depression or dysthymia (OR: 0.93, 95% CI: 0.88, 0.999), anxiety disorders (OR: 0.90, 95% CI: 0.84, 0.96), self-harming behaviors (OR: 0.87, 95% CI: 0.76, 0.99), substance-use disorders (OR: 0.81, 95% CI: 0.71, 0.93), alcohol dependence (OR: 0.82, 95% CI: 0.73, 0.92), alcohol use disorders (OR: 0.85, 95% CI: 0.76, 0.94), acute alcohol intoxication (OR: 0.86, 95% CI: 0.76, 0.96), schizophrenia (OR: 0.77, 95% CI: 0.65, 0.92), and schizophrenia or delusion (OR: 0.80, 95% CI: 0.70, 0.92). Alcohol dependence (OR: 0.97, 95% CI: 0.94, 0.999) and alcohol use disorders (OR: 0.96, 95% CI: 0.94, 0.99) were also related to a lower morning diurnal preference. Our study provides evidence that being a morning person is a protective factor for various psychiatric disorders from a genetic perspective. The results provide insights for potential targeted interventions to improve mental wellbeing.

ACCEPTABILITY RATINGS AND NUTRITIVE VALUE OF FOUR SELECTED FODDER SPECIES BY YANKASA SHEEP IN SUB-HUMID NIGERIA

A study was conducted to estimate the dry matter intake of four selected forage species by Yankasa sheep so as to assess their nutritive value and preference by the animals. Four fodder tree species, viz; Gliricidia sepium, Gmelina arborea, Leucaena leucocephala, and Spondis purpurea were offered to 12 Yankasa sheep in a free choice cafeteria feeding method that lasted for 14 days to determine their intake and coefficient of preference (CoP). Data obtained from the experiment were subjected to a two-way analysis of variance (ANOVA). Preference data were analysed as a complete randomized design using collection days as replicates and shrub species and animals as treatments. The fodder samples were chemically analysed. Results revealed that their chemical composition varied significantly (P&lt;0.05). The DM, CP, CF, Ash, EE, and NFE ranged between 33.80-40.50%, 12.80- 26.41%, 12.80-14.20%, 9.10-11.11%, 11.03-15.21% and 29.90-55.31%, respectively. Significantly (P&lt;0.05) higher mean daily fodder intake (2015.7 and 1820.4 gDM) were observed in L. Leucocephala and G. arborea respectively, while the least (697.2 gDM) was observed in S. purpurea. The CoP of the fodder was best in L. leucocephala (1.53) and G. arborea which were the preferred species by the animals. The preference ranking in descending order was L. leucocephala &gt; G. arborea &gt; G. sepium &gt; S. purpurea. It was concluded that L. leucocephala and G. arborea were the preferredamongst the selected fodder species, suggesting their potentials as feed supplements for sheep.