Preference Theory Research Articles

Word order evolves at similar rates in main and subordinate clauses

Abstract In syntactic change, it remains an open issue whether word orders are more conservative or innovative in subordinate clauses compared with main clauses. Using 47 dependency-annotated corpora and Bayesian phylogenetic inference, we explore the evolution of S/V, V/O, and S/O orders across main and subordinate clauses in Indo-European. Our results reveal similar rates of change across clause types, with no evidence for any inherent conservatism of subordinate or main clauses. Our models also support evolutionary biases towards SV, VO, and SO orders, consistent with theories of dependency length minimization that favor verb-medial orders and with theories of a subject preference that favor SO orders. Finally, our results show that while the word order in the proto-language cannot be estimated with any reasonable degree of certainty, the early history of the family was dominated by a moderate preference for SVO orders, with substantial uncertainty between VO and OV orders in both main and subordinate clauses.

Locke on Prerogative

John Locke’s role in the advent of modernity has been debated widely. His work has been (ab)used by those arguing from libertarian, democratic, communitarian, socialist, feminist, or postcolonial points of view, either portraying him as a forefather of their preferred political theory or as an antagonist to their avowed political and philosophical goals. In this paper, we are primarily concerned with highlighting the importance of the executive’s prerogative in Locke’s philosophy, as we argue that this concept, often banished to the side-lines, is indeed central to Locke’s political theory. This partial neglect entails that some of the most popular readings of Locke are potentially based on faulty premises. We focus on how Locke’s prerogative is antithetical to the project of seeing Locke as a forefather of libertarianism. Furthermore, this paper’s arguments could also support challenges to interpretations of Locke as an early and important proponent of democracy. Rather than seeking to find support for modern theories through anachronistic and dubious readings of Locke, we focus on how he conceived of an exceedingly strong executive whose actions are exclusively restricted by the public good. We conclude that there is good reason to consider his philosophy proto-utilitarian, something that further highlights the conflict with libertarianism. As such, we conclude that libertarian or democratic theories postulating Locke as their direct predecessor should be re-examined to account for these findings.

Group decision making with incomplete interval-valued linguistic intuitionistic fuzzy preference relations

With the increasing complexity and indeterminacy intensity of the decision making environment, there is a growing lack of widespread utility of traditional preference representations. As a practical yet valid preference expression, interval-valued linguistic intuitionistic fuzzy preference relations (IVLIFPRs) can efficiently represent the pairwise cognitions qualitatively and provide the inclusive support. This study aims at forming a novel group decision making (GDM) method based on the consistency and consensus preference analysis for IVLIFPRs. First, the new concepts of additive consistency and acceptably additive consistency for IVLIFPRs are defined. Based on these definitions, two optimization models are constructed to obtain the incomplete information and acceptably additive consistent IVLIFPRs, respectively. Then, inspired by the Markov model, the derivation approach of DMs' weights is designed. Afterward, the consensus improving process is constructed under the premise of acceptable consistency and smallest information distortion. Accordingly, the normalized priority weights of alternatives are further determined. Thus, a mechanism for the GDM method with incomplete and unacceptably consistent IVLIFPRs is developed. Finally, an application and comparison analysis show that the proposed method comprehensively considers the key steps in the GDM process, so that the logical rationality and technical superiority are well guaranteed.

Fine-Gained Recurrence Graph: Graphical Modeling of Vibration Signal for Fault Diagnosis of Wind Turbine

Benefiting from the recent successes of convolutional neural networks (CNNs), many studies have modeled the vibration signal of energy system into a two-dimensional (2D) input graph to amplify and highlight fault features. However, most works seldomly consider the system dynamic characteristics, which may affect the knowledge discovery and diagnosis quality. To address this issue, this paper proposes a novel graphical modeling approach, termed as fine-gained recurrence graph (FRG), to capture dynamic characteristics of vibration signals from the view of nonlinear dynamics. FRG focuses on modeling the temporal correlations and tendencies between state vectors in phase space and then visualizes the characteristics to represent intrinsic dynamic changes of the vibration signals into 2D graphs. The information representation capability of the proposed FRG is verified using different dynamic signals. Based on this finding, an ensemble fault diagnosis model is proposed fusing FRG with deep CNNs. Meanwhile, transfer learning is accompanied to deal with the training difficulties of deep CNNs. Finally, case studies on experimental data and real wind turbine data illustrate its effectiveness and feasibility. Comparisons with four state-of-the-art approaches have confirmed the preferable information representation and diagnosis performance of the proposed approach.

LINKING PROBABILITY AND STATISTICS IN YOUNG STUDENTS’ REASONING WITH CHANCE

This article reports on a study in which third-grade students (8–9 years) were given a degree of agency in conducting chance experiments and representing the outcomes. Students chose their own samples of 12 coloured counters, ensuring all colours were represented. They predicted the outcomes of item selection, tested their predictions, explained the outcomes, quantified their chances of colour selections, and created two representations displaying the probabilities. Children displayed awareness of randomness and variation, together with proportional reasoning, as evident in their identification of one or more colours as having a greater chance of being selected, or equal chances when proportions of colours were equal. Evidence of children’s metarepresentational competence appeared in their creation of two representations to display their probabilistic outcomes, with bar and circle graphs, as well as stacked bars, created. The inclusion of their own forms of inscription revealed a range of probability and statistics understandings. In selecting and justifying their preferred representations for conveying their outcomes, students favoured both bar and circle graphs, with a focus on how accurately, effectively, and efficiently their representation displayed the data, with the importance of the inscriptions highlighted.

Konflik Kepentingan Dalam Kebijakan Dividen

Dividends are part of the profits earned by the company and are announced to shareholders. Company shares can be owned by national institutions, foreign institutions, and the public. This research aims to obtain empirical evidence of the influence of national institutional ownership, foreign institutions, public ownership and debt policy on dividend policy. There are 37 sample companies with 130 sample observations from 77 manufacturing companies in the consumer goods industry sector listed on the Indonesia Stock Exchange in 2015-2019. The sampling technique used was purposive sampling. The data was analyzed using multiple regression, and it was found that national institutional ownership and foreign institutional ownership had a negative effect on dividend policy. The results support the tax preference theory and the client effect theory, but do not support the bird in the hand theory. Meanwhile, public ownership and debt policy have no effect on dividend policy.
 Keywords : Dividend Policy; National Institutional Ownership; Foreign Institutional Ownership; Public Ownership; Debt Policy.

Economic policy uncertainty and stock market in G7 Countries: A panel threshold effect perspective.

Based on the literature, it is commonly understood that stock prices (SP) are influenced by economic policy uncertainty (PU), with a rise in PU typically having a negative impact on SP. However, the relationship between PU and SP may not always be linear due to the varying risk preferences of individuals. Risk preference theory posits that individuals respond differently to different levels of risk. Therefore, this study aims to investigate whether PU determines SP asymmetrically (i.e., in a non-linear manner) by considering risk preferences and addressing a gap in the literature. To answer this question, the study employs a panel threshold approach to examine the effect of PU on SP in the Group of Seven (G7) countries, namely Canada, France, Germany, Italy, Japan, UK, and the US. In contrast to previous research, this study finds evidence of an asymmetric effect of PU on SP in the G7 countries. Specifically, the panel threshold results reveal that the impact of increased PU on SP is positive up to a certain level (Threshold1), beyond which it becomes negative (Threshold2). These findings are in line with information asymmetry hypothesis, prospect theory, behavioural finance hypothesis, and market liquidity hypothesis and shed light on the asymmetric behaviour of SP in response to varying levels of PU. The implications of these findings are significant for understanding how to manage risks effectively in the financial markets.

FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network

In recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this issue, this paper proposes a Generative Adversarial Network (GAN) model comprising a filter-enhanced Multi-Layer Perceptron (MLP) generator and a linear discriminator to mitigate bias and improve the accuracy of the recommendation. The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. This adversarial process enables each component to optimize and improve itself while strengthening the correction effect. To enhance the accuracy of G generation, we evaluate the influence of three different input lists on the filter effect. Finally, we validate our model on two real-world datasets by comparing the effect of filter-augmented MLP and pure MLP generators. Our results demonstrate the effectiveness of filters, and our model achieves better recommendation accuracy than other baseline models.

Round offer prices in M&A transactions: costly negotiation and psychological preference

PurposeThe authors of this study aim to investigate possible explanations of the prevalence of price clustering in the final offer prices of mergers and acquisitions (M&A).Design/methodology/approachThe authors use final offer price in M&A deals to investigate the price clustering phenomena. The authors used regressions and logistic regressions to examine potential factors that might affect pricing strategy by looking into one-time acquirers and experienced serial acquirers.FindingsPrice clustering increases with negotiation uncertainties characterized as competitive bidding, number of bidders, challenged deals and duration. Moreover, the authors find persistent price clustering in experienced serial acquirers that are more experienced and better equipped with handling uncertainties, suggesting a preference of using round numbers regardless of levels of uncertainties. The authors' evidence shows that price clustering results from a combination of Harris' (1991) costly negotiation hypothesis where round prices may be used to lower search costs and psychological bias and preference.Originality/valueThe authors appear to be the first to investigate alternative theories that support M&A offer price clustering behavior, finding that both the costly negotiation and psychological bias and preference theories apply to M&A final price formation. Thus, the authors' major contribution, specific to the M&A process, is a clarification of physical and psychological factors associated with bidding and negotiation behavior. The authors are confident that the authors' study impacts conventional knowledge regarding M&A deal negotiation strategies, including bidding behavior, contract negotiation, financial analysis, management practices and risk management.

A novel image recommendation model based on user preferences and social relationships

Recent work on personalized recommendation advocates that exploring user visual preferences from their interactions with images has become a future endeavor for image recommendation. However, there are inevitable discrepancies between the images favored and the visual preference representations reflected by users, therefore we strive to reveal user visual preferences from their behaviors and social relationships. In this work, we propose a collaborative graph model of user preferences for image recommendation, which establishes a higher-order collaborative relationship between user visual preferences and image semantic information. In user visual preferences, the underlying visual preferences hidden in social relationship is explored by combining the idea of contrast learning. In the higher-order collaboration module, a recursive interest propagation strategy is used to aggregate user preferences and image visual information. The experimental results clearly show that the model has an improvement of about 8.82% and 7.25% on Recall@20 and NDCG@20 compared to the latest methods.

Effect of Capital Adequacy on Liquidity of Microfinance Banks in Kenya

The study sought to assess the effect of capital adequacy on the liquidity of microfinance banks in Kenya. The capital buffer theory and liquidity preference theory were employed for the investigation. The study used an explanatory research design and a positivist worldview. The 13 Kenyan microfinance banks that were operational between 2012 and 2018 were the demographic that was targeted. The study used a census methodology and concentrated on all 13 MFBs in Kenya. The study used secondary data from published financial statements and Central Bank regulatory reports. Descriptive analysis and panel regression analysis was used to analyze the data. At a significance threshold of 0.05, the hypothesis was tested. The study discovered that adequate capital has a substantial impact on Kenyan microfinance banks' liquidity. Notably, higher capital held above the minimum requirement depletes the liquidity levels of banks; hence, managers need to strike a balance between capital levels and optimum liquidity levels. This, in turn, will allow the Microfinance Banks to carry out other liquidity functions without interruptions emanating from rising capital adequacy.

تأثير سياسة توزيع الأرباح على قيمة الوحدة الاقتصادية

This research aims to test the theories explaining the profit distribution policy, by applying it to 4 sectors listed in the Iraq Stock Exchange, which are (industry sector, hotel and tourism sector, agricultural sector, banking sector), during the period (2013-2016), dealing with This research tested the policy of dividend distribution on the market value of the economic units, and the results of the research are consistent with the return preference theory, which was that the market value of the economic unit is affected by the dividend distributions. of shareholders at the same time, because in the event that this is achieved, the tendency of investors to invest in the economic unit is great, and this will provide a good source of financing for the unit.

MKGCN: Multi-Modal Knowledge Graph Convolutional Network for Music Recommender Systems

With the emergence of online music platforms, music recommender systems are becoming increasingly crucial in music information retrieval. Knowledge graphs (KGs) are a rich source of semantic information for entities and relations, allowing for improved modeling and analysis of entity relations to enhance recommendations. Existing research has primarily focused on the modeling and analysis of structural triples, while largely ignoring the representation and information processing capabilities of multi-modal data such as music videos and lyrics, which has hindered the improvement and user experience of music recommender systems. To address these issues, we propose a Multi-modal Knowledge Graph Convolutional Network (MKGCN) to enhance music recommendation by leveraging the multi-modal knowledge of music items and their high-order structural and semantic information. Specifically, there are three aggregators in MKGCN: the multi-modal aggregator aggregates the text, image, audio, and sentiment features of each music item in a multi-modal knowledge graph (MMKG); the user aggregator and item aggregator use graph convolutional networks to aggregate multi-hop neighboring nodes on MMKGs to model high-order representations of user preferences and music items, respectively. Finally, we utilize the aggregated embedding representations for recommendation. In training MKGCN, we adopt the ratio negative sampling strategy to generate high-quality negative samples. We construct four different-sized music MMKGs using the public dataset Last-FM and conduct extensive experiments on them. The experimental results demonstrate that MKGCN achieves significant improvements and outperforms several state-of-the-art baselines.

Liquidity Capacity and Financial Performance of Commercial Banks in Kenya

Purpose: The objective of the study was to assess the impact of liquidity capacity on the financial performance of commercial banks in Kenya.
 Methodology: This study employed the explanatory research design. The 42 Kenyan commercial banks were the study's target population. The essential financial data for analysis was extracted and compiled using a data collection routine from the yearly reports. Panel data was mined from 42 commercial banks for six years between 2012 and 2018. The data were assessed by employing descriptive statistics as well as inferential statistics. Descriptive statistics employed involved the standard deviation, median, and average. Inferential statistics used involved panel regression. The data analysis was aided by STATA software.
 Findings: The findings from the regression analysis indicate that Net Stable Funding and Liquidity Coverage exert a significant positive impact on the financial performance of commercial banks in Kenya. Conversely, provisioning for Non-Performing Loans, Liquidity Gap, and Provisioning for Nonperforming Loans demonstrate a notable negative effect on the financial performance of commercial banks in the country. Moreover, the study reveals that bank competition plays a significant moderating role in the relationship between liquidity capacity and the financial performance of commercial banks in Kenya.
 Unique Contribution to Theory, Practice and Policy: The study was anchored on Anticipated Income Theory and Liquidity Preference Theory. The study recommended that the regulatory body for commercial banks, the CBK, facilitates open channels of communication between policy makers and senior management of commercial banks in Kenya. This discussion is critical to ensuring that the monetary policies developed are practical and beneficial to the expansion of the commercial banking industry. Lastly, the study suggests that all commercial banks in Kenya integrate considerations of liquidity costs, benefits, and risks into their performance measurement, pricing, and approval processes for significant business activities.

Empirical studies on the impact of booking status on customers’ choice behavior in online appointment systems

We consider customers' choice behavior in online appointment systems. In three online experiments, we investigate whether and to what extent customers are impacted by the number of available slots by asking subjects to choose between two providers of several service areas with different occupancy rates. In line with previous literature, we find some evidence that customers infer quality from a utilized system compared to an empty schedule; that is, any demand (very few booked appointments) is preferred to no demand (empty booking schedule). A too-small offer set, in contrast, shows congestion and leads to an opposed scarcity effect because customers expect the provider to be in a rush, the waiting room to be crowded, and further value flexibility in the choice of the appointment time. We contribute to the literature by analyzing the interaction of the two nonlinear effects and further find that the presence of the quality-inference effect depends on the type of service. For a standardized service, we find no evidence of the quality-inference effect. For nonstandardized services, however, we find an inverse U-shaped preference in the number of offered slots, showing that customers prefer a medium utilization of the service provider. We find that this is a robust representation of customers' preferences, even if other quality signals, such as star ratings and prices, are available.

Student Online Learning Behavior Characteristics Based on Multidimensional Cognitive Model

Analysis of student learning behavior characteristics is an important means for educators to better understand students and improve the quality and effectiveness of teaching in the field of education. It is necessary to refer to students' cognitive levels for analysis of student learning behavior characteristics. However, existing algorithms only focus on the overall performance and grades of students, ignoring the individual differences in learning cognitive levels among students, which affects the accuracy of the analysis results. Therefore, this paper conducts research on student online learning behavior characteristics based on a multidimensional cognitive model. Firstly, a multidimensional and multilevel model for evaluating students' cognitive levels is constructed, and the process of evaluating students' cognitive levels is sustainable and can be adjusted in real-time as students' cognitive levels change. By considering the differences in evaluation levels and students' cognitive levels, targeted observation and extraction of students' online learning behavior characteristics can be achieved. A new model based on variational autoencoder neural network is proposed to perform decoupled representation of students' implicit preferences. By using a regularization term based on maximum mean difference, the model can learn independent hidden vectors sensitive to dynamic and static factors from students' online learning behavior history data and multidimensional cognitive evaluation history data. The experimental results verify the effectiveness of the constructed model.

Co-Creation of Breast Cancer Risk Communication Tools and an Assessment of Risk Factor Awareness: A Qualitative Study of Patients and the Public in India.

Low awareness of BC and its associated risk factors causes delays in diagnosis and impacts survival. It is critical to communicate BC risk to patients in a format that they are easily able to understand. Our study aim was to develop easy-to-follow transmedia prototypes to communicate BC risk and evaluate user preferences, alongside exploring awareness of BC and its risk factors. Prototypes of transmedia tools for risk communication were developed with multidisciplinary input. A qualitative in-depth online interview study was undertaken using a pre-defined topic guide of BC patients (7), their relatives (6), the general public (6), and health professionals (6). Interviews were analyzed using a thematic approach. Most participants preferred pictographic representations (frequency format) of lifetime risk and risk factors and storytelling using short animations and comic strips (infographics) for communicating genetic risk and testing: "In a short time, they explained it very well, and I liked it". Suggestions included minimizing technical terminology, decreasing the delivery speed, "two-way dialogue", and using local "language for different locations". There was low awareness of BC, with some understanding of age and hereditary risk factors but limited knowledge of reproductive factors. Our findings support use of multiple context-specific multimedia tools in communicating cancer risk in an easy-to-understand way. The preference for storytelling using animations and infographics is a novel finding and should be more widely explored.

The association between settlement intention and migrant entrepreneurship in China: Explanation from human capital and risk aversion

In the new era, one of the keys to promoting sustainable economic growth is to give full play to the main role of migrants in innovation and entrepreneurship, and improve the overall employment quality of society. Based on the 2017 China Migrants Dynamic Survey (CMDS) data, this paper attempts to use the reference group theory, self-motivation theory, human capital theory, and risk preference theory to provide the theoretical framework. Then, the relationship between immigrants’intention and entrepreneurship is examined by using PSM, Heckman-two stage model and IV method. The results clearly show that settlement intention is negatively associated with migrant entrepreneurship for both necessity-based entrepreneurship and opportunity-based entrepreneurship. Further study shows that this negative effect is achieved by changing migrants’ human capital and their risk preference. The heterogeneity analysis shows that the negative effect of settlement intention on migrant entrepreneurship is among those migrants who are between 25 and 45 years old, who are female migrants, who have rural hukou, and who live in central and western China. In a sense, this study identifies a side effect of settlement intention that sublimates and complements the theory of migrant entrepreneurship.

Trusting and Working with Robots: A Relational Demography Theory of Preference for Robotic over Human Co-Workers

Trusting and Working with Robots: A Relational Demography Theory of Preference for Robotic over Human Co-Workers

Integrated preference argumentation and applications in consumer behaviour analyses

Consumer preference studies in economics rest heavily on the behavioural interpretation of preference especially in the form of Revealed Preference Theory (RPT). Viewing purchasing decisions as a kind of human reasoning, in this paper we are interested in generalising behaviourism to preference-based argumentation where existing frameworks are arguably governed by the opposing mentalistic interpretation of preference. Concretely our contributions are three-fold. First we re-construct and unify two main approaches to RPT in argumentation terms by developing a Revealed Preference Argumentation (RPA) framework. We show that key RPT-based consumer analyses such as different rationality checks of a consumer behaviour and various kinds of such behaviour's extrapolations can be translated to computational tasks in RPA. RPA is in turn subsumed by the second contribution - an Integrated Preference Argumentation (IPA) framework which fully integrates mentalism and behaviourism. In particular we show that RPA is just a special case of IPA with only revealed preference, while existing preference-based argumentation frameworks can be viewed as IPA frameworks with only stated preference. As the third contribution, we develop a complete set of IPA algorithms and establish their correctness and termination for a general class of IPA frameworks. For demonstration we implement the algorithms in Prolog to obtain an IPA reasoning engine and test the engine with various RPT-based consumer behaviour analyses. In sum, it is argued that the current paper provides not only a theory of Integrated Preference Argumentation, but also a related development tool for future applications of argumentation to behavioural economics - a largely unexplored area so far though Dung used the stable marriage problem in microeconomic game theory to partly motivate the argumentation theory.