Preference Profiles Research Articles

Развитие аквакультуры северных и арктических территорий: проблемы и пути решения (на примере Архангельской области)

The relevance of this study is due to the need to develop and implement in regional practice effective management decisions that promote the development of fish farms and increase aquaculture pro-duction. Since fish farming (aquaculture) is one of the structural components of the fishery complex, the authors pay attention to both the peculiarities of its development in the conditions of the North and the Arctic, and the specifics of economic activity in the field of aquaculture projects. The article presents the results of a survey of the population of the Arctic territories of the Arkhangelsk Oblast on the consumption of fish and fish products, which allowed establishing various consumer profiles of preferences depending on their age category, as well as to identify the main reasons constraining the consumption of fish and fish products (among which: high price, availability of low-quality products in retail outlets, the lack of ability and desire to cook among the younger generation). Based on the results of expert interviews with producers of fish and fish products, as well as with representatives of the scientific community and authorities of the Arkhangelsk Oblast, a problem matrix was compiled and the necessary activities were identified to address key issues of the development of aquaculture projects in the region.

Entrepreneurial Habits and Professional Preferences in Students of Business Administration

This article presents the description and comparison of entrepreneurial habits and professional preferences in 213 students in the last year of the Business Administration career at three universities in the city of Arequipa. Entrepreneurial Habits Questionnaire and Profile of Professional Preferences were applied and it was found that 30.5% have professional preferences for the administration career and only 14% have a current business. With respect to professional preferences, it was found that UCSM students have a greater vocation for their career (40.5%), compared to students from UNSA (29%) and UCSP (20%). Likewise, students who have started a profitable business have more entrepreneurial habits and there are positive and low relationships between entrepreneurial habits, age, and Realism.

A topological proof of the Gibbard–Satterthwaite theorem

We give a new proof of the Gibbard–Satterthwaite Theorem. We construct two topological spaces: one for the space of preference profiles and another for the space of outcomes. We show that social choice functions induce continuous mappings between the two spaces. By studying the properties of this mapping, we prove the theorem.

Maximal domains for strategy-proof pairwise exchange

We analyze centralized markets for indivisible objects without money through pairwise exchange when each agent initially owns a single object. We consider rules that for each profile of agents preferences select an assignment of the objects to the agents. We present a family of domains of preferences (minimal reversal domains) that are maximal rich domains for the existence of rules that satisfy individual rationality, efficiency, and strategy-proofness. Each minimal reversal domain is defined by a common ranking of the set of objects, and agents’ preferences over admissible objects coincide with such common ranking but for a specific pair of objects.

Structure of single-peaked preferences

The paper studies a variety of domains of preference orders that are closely related to single-peaked preferences. We develop recursive formulas for the number of single-peaked preference profiles and the number of preference profiles that are single-peaked on a circle. The number of Arrow’s single-peaked preference profiles is found for three, four, and five alternatives. Random sampling applications are discussed. For restricted tier preference profiles, a forbidden subprofiles characterization and an exact enumeration formula are obtained. It is also shown that each Fishburn’s preference profile is single-peaked on a circle preference profile, and Fishburn’s preference profiles cannot be characterized by forbidden subprofiles.

Influence of Pomegranate Appearance Attributes on Consumer Choice, and Identification of Barriers and Drivers for Consumption.

In a prepurchase situation, consumers base their choice decision on external fruit characteristics, from which they infer internal characteristics. This study investigates consumer preference for pomegranate appearance using a choice-based conjoint analysis with 320 participants. We created 27 images of pomegranates that differed in varietal characteristics: colour (yellow, bicoloured, and red), shape (round, oval, and flattened), and calyx shape (open, semi-open, and closed). Colour was by far the most important factor for consumers, followed by fruit shape and calyx shape. Two preference profiles were identified. Most consumers liked bicolour and red pomegranates equally, and rejected yellow ones, while a smaller group concentrated their choice on bicolour pomegranates. In terms of fruit and calyx shape, oval and flattened fruit and open calyx were the most preferred by both consumer groups. Barriers and drivers for consumption were also investigated. There is still plenty of room to increase pomegranate consumption. Greater availability of pomegranates and ready-to-eat arils in grocery shops, obtaining new unseeded/easier-to-peel varieties, and providing a sensory label would help to overcome current barriers. Marketing campaigns should focus on a pomegranate's health benefits and its versatility in consumption.

Digitally nudging users to explore off-profile recommendations: here be dragons

In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging, i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study (N=1064\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=1064$$\\end{document}) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.

Decision Trade-Offs in Ecological Momentary Assessments and Digital Wearables Uptake: Protocol for a Discrete Choice Experiment.

Ecological momentary assessments (EMAs) and digital wearables (DW) are commonly used remote monitoring technologies that capture real-time data in people's natural environments. Real-time data are core to personalized medical care and intensively adaptive health interventions. The utility of such personalized care is contingent on user uptake and continued use of EMA and DW. Consequently, it is critical to understand user preferences that may increase the uptake of EMA and DW. The study aims to quantify users' preferences of EMA and DW, examine variations in users' preferences across demographic and behavioral subgroups, and assess the association between users' preferences and intentions to use EMA and DW. We will administer 2 discrete choice experiments (DCEs) paired with self-report surveys on the internet to a total of 3260 US adults through Qualtrics. The first DCE will assess participants' EMA preferences using a choice-based conjoint design that will ask participants to compare the relative importance of prompt frequency, number of questions per prompt, prompt type, health topic, and assessment duration. The second DCE will measure participants' DW preferences using a maximum difference scaling design that will quantify the relative importance of device characteristics, effort expectancy, social influence, and facilitating technical, health care, and market factors. Hierarchical Bayesian multinomial logistic regression models will be used to generate subject-specific preference utilities. Preference utilities will be compared across demographic (ie, sex, age, race, and ethnicity) and behavioral (ie, substance use, physical activity, dietary behavior, and sleep duration) subgroups. Regression models will determine whether specific utilities are associated with attitudes toward or intentions to use EMA and DW. Mixture models will determine the associations of attitudes toward and intentions to use EMA and DW with latent profiles of user preferences. The institutional review board approved the study on December 19, 2022. Data collection started on January 20, 2023, and concluded on May 4, 2023. Data analysis is currently underway. The study will provide evidence on users' preferences of EMA and DW features that can improve initial uptake and potentially continued use of these remote monitoring tools. The sample size and composition allow for subgroup analysis by demographics and health behaviors and will provide evidence on associations between users' preferences and intentions to uptake EMA and DW. Limitations include the cross-sectional nature of the study, which limits our ability to measure direct behavior. Rather, we capture behavioral intentions for EMA and DW uptake. The nonprobability sample limits the generalizability of the results and introduces self-selection bias related to the demographic and behavioral characteristics of participants who belong to web-based survey panels. DERR1-10.2196/47567.

Identification of Student Learning Style Preference Profiles in Hybrid Learning Settings: An Essential Consideration for Developing Learning Strategies

After Covid-19, several educational institutions have adopted the hybrid learning system. The transition of learning systems from face-to-face to hybrid settings presents obstacles and challenges that require adaptive solutions. The compatibility between hybrid learning systems and student learning style preferences is an issue that needs to be studied in more depth. Students have their preferred way when it comes to learning. They prefer to learn through visual (V), aural (A), read-write (R), or kinesthetic (K). Depending on their learning styles, student adaptation may have been challenging. In this regard, a study was designed to identify the learning style preferences among students. The VARK questionnaire is one of the most widely used diagnostic tools, allowing teachers to understand students' learning preferences and develop innovative learning practices. A quantitative cross-sectional design was conducted among students (n=486) in Univeristy X using the VARK questionnaire. Student learning style preferences were identified using latent profile analysis. Our findings suggest that most students were identified as having multiple learning modalities, while the rest had a single learning modality. Prevalence of kinesthetic and aural learning styles preference highest among students, followed by visual and read-write learning styles. Understanding how students retain the knowledge imparted to them is essential for lecturers to develop innovative and adaptive strategies.

Belief-independence and (robust) strategy-proofness

AbstractAn important appeal of strategy-proofness is the robustness that it implies. Under a strategy-proof voting rule, every individual has an optimal strategy independently of the behavior of all other voters, namely truth-telling. In particular, optimal play is robust with respect to the beliefs voters may have about the type and the behavior of the other voters. Following Blin and Satterthwaite (Economet J Economet Soc 45(4):881–888, 1977), we call this logically weaker property “belief-independence.” In this paper, we give a number of examples of voting rules that are belief-independent but not strategy-proof. However, we also show that belief-independence implies strategy-proofness under a few natural additional conditions. The notion of belief-independence naturally leads to a the strengthening of strategy-proofness to “robust” strategy-proofness which requires that no voter whose true preference may come from a restricted domain can benefit by submitting any unrestricted preference ordering given any unrestricted preference profile for all other voters. There are examples of strategy-proof voting rules (on a restricted domain) that are not robustly strategy-proof, but under natural additional conditions the two properties are shown to be equivalent.

Assessing reward preference using operant behavior in male and female mice.

Many different solid food pellets are available as reinforcers for rodents in operant behavior tests. Different reward formulations have not been compared, so it is unclear whether mice show strong preferences for different rewards and whether such preferences are consistent within or across sex and background strain. Here we show that mice have strong preferences for two balanced diet food rewards over sucrose pellets, and preference for one balanced diet pellet formulation over another, in a simultaneous choice test using a low effort fixed ratio operant test. All mice, of both sexes and both CD1 and C57 background strains, showed the same strong preferences among these three types of reinforcers. In contrast, flavorings added to the reward pellets had relatively small and more variable effects on preference. The preference for balanced diet pellets over sucrose pellets was seen also in the total numbers of rewards consumed in low effort tests with food pellets or only sucrose pellets available. However, progressive ratio testing showed that mice worked harder for sucrose pellets than for the preferred balanced diet pellets. These findings indicate that reinforcers with similar and very different preference profiles are readily available and that testing with different rewards can produce different, and sometimes unexpected, results.

A bias-controlled statistical procedure to highlight host-plant preferences and the resulting prevalences in phytophagous insects: illustrative case study with leaf-miners on Lamiaceae

Summary. Quantitative, rigorously derived and statistically validated information regarding the relative preferences/relative prevalences of phytophagous insects for/upon their different host plant species is too rarely made available in the literature, at least for wild host plant species, which are usually lacking economic interest. This seems likely to be due (i) in part to the usual lack of field samplings sufficiently wide (both numerically and spatially) to ensure the statistical significance of results; and (ii) to the current unavailability of a dedicated procedure for appropriately dealing with the unavoidable sources of biases affecting raw field data. Accordingly, a new methodological approach is proposed to overcome both kinds of difficulties, involving in particular: a procedure of evaluation of the relative preferences for hosts and relative prevalences on hosts, making them insensitive to the biases inevitably linked to the unavoidable large disparities among the respective abundances of the focused phytophagous insects on the one hand, and to the hardly controllable heterogeneities in sampling intensities of the different host plant species, on the other hand; an appropriately modified version of the standard Chi-2 testing procedure, applied to control the degree of statistical significance of the estimations of relative preferences and prevalences. The practical implementation of this newly derived approach is illustrated with a case study, dealing with leaf-mining insects upon host plant species belonging to Lamiaceae. From a purely descriptive point of view, more or less strong discrepancies turn out to single out the relative preference profiles, as well as the relative prevalence profiles of 10 leaf miners for four host-plant species (despite the relative phenotypic homogeneity of hosts within Lamiaceae). Highlighted, at a more speculative level, is the significant role played by phylogenetic affinities (between the exploiting species as well as between the exploited species) upon the arrangement of the food web that connects them.

Ruin Theory for User Association and Energy Optimization in Multi-Access Edge Computing

In this correspondence, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) user association phase</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2) task offloading phase</i> . In the first phase, a ruin theory-based approach is developed to obtain the users association considering the users' transmission reliability and resource utilization efficiency. Meanwhile, in the second phase, an optimization-based algorithm is used to optimize the data offloading process. In particular, ruin theory is used to manage the user association phase, and a ruin probability-based preference profile is considered to control the priority of proposing users. Here, ruin probability is derived by the surplus buffer space of each edge node at each time slot. Giving the association results, an optimization problem is formulated to optimize the amount of offloaded data aiming at minimizing the energy consumption of users. Simulation results show that the developed solutions guarantee system reliability, association efficiency under a tolerable value of surplus buffer size, and minimize the total energy consumption of all users.

Ascending Probabilistic Max-min Extended Choice Correspondence

In this paper, we provide an axiomatic characterization of the ascending probabilistic max-min extended choice correspondence for a decision-maker who has state-dependent preferences (represented by a linear order) over a set of alternatives and a (subjective) probability vector over states of nature, where both the preferences and probability vectors are variable. Further on the domain of all extended preference profiles for which the Ascending Probabilistic Max-Min Extended Choice Correspondence is resolute, the same choice correspondence is completely characterized by just two of the three axioms that are required for the axiomatic characterization on the more general domain. A significant feature of our solution concept and the related axiomatic analysis is that we use no more information than the probability with which each alternative realizes each rank.

Learning User Preferences for Complex Cobotic Tasks: Meta-Behaviors and Human Groups

In complex tasks (beyond a single targeted controller) requiring robots to collaborate with multiple human users, two challenges arise: complex tasks are often composed of multiple behaviors which can only be evaluated as a collective (a meta-behavior) and user preferences often differ between individuals, yet successful interactions are expected across groups. To address these challenges, we formulate a set-wise preference learning problem, and validate a cost function that captures human group preferences for complex collaborative robotic tasks (cobotics). We develop a sparse optimization formulation to introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinctiveness</i> metric that aggregates individuals with similar preference profiles. Analysis of anonymized unlabelled preferences provides further insight into group preferences. Identification of the mode average most-preferred meta-behavior and minimum covariance bound allows us to analyze <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">group cohesion</i> . A user study with 43 participants is used to validate group preference profiles.

Ballot Length in Instant Runoff Voting

Instant runoff voting (IRV) is an increasingly-popular alternative to traditional plurality voting in which voters submit rankings over the candidates rather than single votes. In practice, elections using IRV often restrict the ballot length, the number of candidates a voter is allowed to rank on their ballot. We theoretically and empirically analyze how ballot length can influence the outcome of an election, given fixed voter preferences. We show that there exist preference profiles over k candidates such that up to k-1 different candidates win at different ballot lengths. We derive exact lower bounds on the number of voters required for such profiles and provide a construction matching the lower bound for unrestricted voter preferences. Additionally, we characterize which sequences of winners are possible over ballot lengths and provide explicit profile constructions achieving any feasible winner sequence. We also examine how classic preference restrictions influence our results—for instance, single-peakedness makes k-1 different winners impossible but still allows at least Ω(√k). Finally, we analyze a collection of 168 real-world elections, where we truncate rankings to simulate shorter ballots. We find that shorter ballots could have changed the outcome in one quarter of these elections. Our results highlight ballot length as a consequential degree of freedom in the design of IRV elections.

The sophistry of the neutral tool. Weaponizing artificial intelligence and big data into threats toward social exclusion

In late modernity societies, the double meaning of ‘monitoring’ is not a coincidence insofar, it can be a subject or an object attribution, thus suggesting a phenomenological intersection between surveillance studies and technological deployments in mass media. Emerging applications of Artificial Intelligence (AI) tools corroborate an intensified and optimized collection of personal data, either objective ones granted by individuals themselves or subject ones silently taken by algorithmic learning. From an eventual possibility to an invisible probability, Big Data may be used from devising purchase preference profiles to political bias in election periods and to reinforce bigotry against social minorities, especially transphobia. This paper’s objective is to address the use of AI and Big Data as social surveillance systems tools for the establishment of more sophisticated strategies of social control. Before late modernity, disciplinary discursive power was an addressed tool to perform social control in Western societies by institutions such the Roman Catholic Church. Currently, AI technologies are tooled to perform a security-based society regulation, potentially deploying gathered data as threats against social categories that deviate from moral-based norms. Incapable of broadly embracing all cultural and social developments throughout history, such norms refer to social regulation and standardization that turn out to be exclusionary for the existence of distinctive individuals whose identities don’t conform to such moral standards. The issue of ethical AI regulation is therefore grounded in questioning to what extent Western culture values and practices are still consistent in the standardized and global deployment of social and ethical policies addressed to cultures that may hold distinctive cultural perceptions and values. Theoretical reflections on post-modern panoptic frameworks, such as synoptic and banoptic devices, were carried out to assess the impact of emerging surveillance technologies as social control strategies for the reinforced marginalization of categories of exclusion. Instances of recent technology-based violence discriminations, such as misogyny, religious intolerance, racism, xenophobia, and transphobia, are provided and seen through the lens of current AI development and transphobia. The efforts of global, universal, and unilateral influence of Western culture’s values on AI ethical regulation is counteracted with a reflection on decentralized bottom-up approaches to culture by means of applied ethnographic research to bring the potential of local culture into AI policy making. It is expected to corroborate future research on local-based ethical AI approaches designed within a specific culture’s values to mitigate and avoid social vulnerability and violence.

Drivers of Purchase Decisions Among Consumers of Dried Flower Cannabis Products - A Discrete Choice Experiment.

Cannabis was legalized for non-medical use in Canada in 2018. However, with a long-established illegal market, it is important to understand cannabis consumers preferences in order to create a market that encourages purchasing cannabis through legalized channels. A survey including a discrete choice experiment was conducted to estimate preference weights for seven attributes of dried flower cannabis purchases (price, packaging, moisture level, potency, product recommendations, package information and regulation by Health Canada). Participants were at least 19 years of age, lived in Canada, and purchased cannabis in the last 12 months. A multinomial logit (MNL) model was used for the base model, and latent class analyses to identify sub-groups preference profiles. 891 participants completed the survey. The MNL model showed that all attributes significantly influenced choice, except product recommendations. Potency and package information were most important. A three-group latent class model showed that ~30% of the sample were most concerned with potency, while two groups, jointly making up the remaining ~70% were most concerned with package type ( ~40% preferred bulk packaging, ~30% preferred pre-rolled joints). Consumer purchase preferences for dried flower cannabis was influenced by different attributes. Preference patterns can be grouped into three groups. About 30% of the population appeared to have their preferences met by the legalized market, while another 30% appeared to be more loyal to the unlicensed market. The remaining 40% represented a group that may be influenced through regulatory changes to simplify packaging and increasing availability of product information.

A Society Can Always Decide How to Decide: A Proof

Infinite regress lies within every democratic procedural choice. If society members try to select an appropriate rule [social choice correspondence (SCC)] entirely endogenously, they will need an appropriate rule to choose such a rule. However, this should also be selected by an appropriate rule to choose a rule to choose a rule, and so on. This paper explores how to solve this infinite regress. A preference profile over the set of alternatives is said to converge if, at a sufficiently high level, every feasible SCC in the menu ultimately results in the same alternative, and hence, further regress has no effective meaning. A menu is said to be convergent if all preference profiles converge under the menu (i.e., infinite regress can “always” be resolved). First, we characterize the convergent menus under a special case. Then, we prove two general possibility theorems: (1) there exists a menu of SCCs that is strongly convergent (i.e., the outcome is uniquely determined); (2) any set of scoring rules can be extended to a superset that is asymptotically convergent for a large society (i.e., the probability of a convergent profile occurring goes to one as the population goes to infinity). Therefore, such a large society can “almost always” resolve the infinite regress by adding multiple SCCs. These theorems are expected to build new ground for SCCs in a distinct way from the axiomatic characterizations of standard social choice theory.

Strategy-proofness implies minimal participation under single-peakedness

We study a model in which agents with single-peaked preferences can participate in a costly voting procedure to determine the value of a one-dimensional variable. We show that, for all positive participation costs and all profiles of individual preferences, there exists a unique equilibrium outcome with one single participant whenever the voting rule is strategy-proof, anonymous, and responsive in the sense that the outcome reacts to a unanimous move of the votes of all agents in the same direction; moreover, the single participant is always one of the ‘extremist’ voters, i.e. either one with the lowest or one with the highest peak. While this uncovers a strong tension between strategy-proofness and participation for all deterministic voting rules on the single-peaked domain (just as in the case of an unrestricted domain), there are simple probabilistic and strategy-proof voting rules that induce full participation in equilibrium.