Preference Function Research Articles

No Evidence of Responding Individuals Constraining the Evolution of the Pheromone Signal in the Pine Engraver Ips avulsus

Chemical signals are important mediators of interactions within forest ecosystems. In insects, pheromone signals mediate intraspecific interactions such as mate location and acceptance. The evolution of pheromones in insects has been mostly studied from a theoretical perspective in the Lepidoptera. With this study, we aimed to broaden our understanding of pheromone communication in bark beetles. We first demonstrated that the enantiomeric ratios of ipsdienol produced by male I. avulsus, showed little variation. Subsequently, with field trapping trials we characterized the influence of the enantiomeric ratio of ipsdienol (pheromone component of I. avulsus) on I. avulsus captures and observed a great amount of variation in the receiver preference function. Most importantly, we demonstrated that responding individuals responded indiscriminately to all the enantiomeric ratios produced by the emitting individuals. These observations are consistent with the asymmetric tracking model which postulates that if the limiting sex is the emitting sex, responding individuals should not discriminate between emitted ratios. Consequently, responding individuals do not constrain the evolution of the signal. Our data suggest that, in I. avulsus, the composition of the aggregation pheromone signal might be more responsive to external selection forces, such as predation and metabolic constraints, as suggested by the asymmetric tracking model.

OP94 Online Elicitation Of Personal Utility Functions (OPUF): A New Tool For Eliciting EQ-5D-5L Value Sets On The Societal-, Group-, Subgroup-, And Individual-Level

IntroductionThe ‘Online elicitation of Personal Utility Functions’ (OPUF) is a new method for valuing health states. It is based on compositional preference elicitation techniques. In contrast to established, decompositional techniques, such as time trade-off or discrete choice experiment (DCE), the OPUF approach does not require hundreds or thousands of respondents, but allows estimating utility functions for small (patient) groups and even on the individual level. The objective of this study was to generate and compare EQ-5D-5L value sets on the societal-, group-, subgroup-, and individual-level.MethodsA demonstration version of the EQ-5D-5L OPUF Tool is available at: https://eq5d5l.me. It broadly consists of three valuation steps: dimension weighting, level rating, and anchoring. Responses were combined on the individual level to construct personal utility functions. Every respondent also completed three conventional DCEs. Preferences were aggregated across individuals to estimate a societal and various group-level preference functions. We then assessed the heterogeneity of preferences between groups using descriptive statistics and k-means cluster analysis.ResultsA representative sample (n = 1,000) of the United Kingdom (UK) population was recruited through the prolific online platform. On average, it took participants about 7 minutes to complete the survey. Data of 874 respondents were included in the analysis. For each respondent, we constructed a personal EQ-5D-5L value set. The derived utility functions predicted respondents’ choices in DCE with an accuracy of 78 percent. On the societal level, the predicted values for the EQ-5D-5L health states ranged from -0.376 to 1. Health state preference varied greatly between groups. This was largely due to differences in the anchoring, while there was near consensus on the relative importance of the five EQ-5D dimensions between groups. Demographic characteristics explained only a small proportion of the variability.ConclusionsUsing the OPUF approach, we were not only able to estimate a new EQ-5D-5L value set for the UK, but also to examine the underlying individual preferences in an unprecedented level of detail.

Finding preferred solutions under weighted Tchebycheff preference functions for multi-objective integer programs

Many interactive approaches in multi-objective optimization assume the existence of an underlying preference function that represents the preferences of a decision maker (DM). In this paper, we develop the theory and an exact algorithm that guarantees finding the most preferred solution of a DM whose preferences are consistent with a Tchebycheff function for multi-objective integer programs. The algorithm occasionally presents pairs of solutions to the DM and asks which one is preferred. It utilizes the preference information together with the properties of the Tchebycheff function to generate solutions that are candidates to be the most preferred solution. We test the performance of the algorithm on a set of three and four-objective combinatorial optimization problems.

Investors’ Preference for Creating Impact: (A study to understand impact investors’ preference while making investment in India)

Purpose: The main aim of this article is to analyse the preference of impact investors when it comes to the selection of ventures for investment. The study has been conducted with the sole objective of trying to evaluate the choices of impact investors in the Indian market. In addition to this, the present study strives to find out how a certain venture turns out to be the most preferred area of investment and what instrument is preferred to invest in for impact investments, and the criteria that guide investors to arrive at their choice of investment portfolios in Indian impact investment market. Design/Methodology/Approach: A choice-based conjoint experiment has been done in which respondents have to rank the different options which were generated by orthogonal arrays based on their preference. The preference function was computed by using OLS regression. Findings: It has been revealed through this study that while deciding to invest for impact, first preference is accorded to stage of investment in terms of highest importance value which was ensued by their behavioural penchant to do impact investment. Impact investors are inclined to invest in social ventures having the most innovative ideas in response to addressing social problems. The most preferred area of investment is the healthcare sector which is ensued by agriculture and education. Research Limitations: The real behaviour of an individual cannot be predicted through an online survey, which restricts the generalization of the study. Practical Implications: This study presents a simple yet empirical tool that will undoubtedly aid social entrepreneurs, impact investors and other investors to understand the leanings of impact investors and allows them to understand in a better way, how investors’ behaviour differs while making investment decisions in India. The findings will also help policymakers to create a more conducive environment for impact investing in India. Originality/Value: The article is unique in the sense that it endeavours to analyse the impact investors’ preferences and choices with the application of conjoint analysis in the Indian perspective.

The Generalized Shapley Value of Cooperative Games as a Social Preference Function

The Generalized Shapley Value of Cooperative Games as a Social Preference Function

An asymmetric PROMETHEE II for cryptocurrency portfolio allocation based on return prediction

Portfolio allocation, portfolio selection, and portfolio optimization are recognized as three crucial problems in the financial field. Using different criteria in addition to return and risk in the portfolio allocation problem based on the multi-criteria decision-making (MCDM) methods makes it more practical in the real world. The emergence of new and volatile assets such as cryptocurrencies has recently increased the need to use portfolio allocation models. In order to reduce inequalities and alleviate poverty as one of the sustainable development goals, cryptocurrency portfolio construction leads to sustainable income and wealth. This paper proposes a cryptocurrency portfolio allocation model based on the asymmetric Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) method using eight criteria and nine cryptocurrencies. To reduce the uncertainty of the problem, the return prediction obtained from the Auto-Regressive Integrated Moving Average (ARIMA), the Long Short-Term Memory (LSTM), and the Random Forest Regression (RFR) models as return-related criteria and has been used from the SlideVaR, along with the Value at Risk (VaR) and the Conditional Value at Risk (C-VaR) as risk-related criteria to consider investor insight from the market situation. It is also proposed that an asymmetric preference function be proposed to consider gain and loss asymmetry as a behavioral phenomenon in the model. The out-of-sample performance of the proposed model in the last three months of 2021 confirms the superiority of the proposed model in terms of average return (= 0.017) and standard deviation (= 0.036) among other proposed models.

The Impact of Perceived Self-Skill Levels on Product Choice: An Exploratory Study of the Moderating Influence of Mood

In view of previous research on consumers’ single-peaked product preference and “matching” process, this paper examines the effect of price as a necessary condition for consumers’ ideal-point formation process in making choices in a product array that has monotonically increasing values on the benefit dimension. Building on the “mood-as-information” perspective and incorporating the emerging stream of mood study, this paper also studies the moderating impact of mood on the effects of perceived self-skill levels on consumers’ purchase intentions. Using experiments, this research generates and replicates consumers’ single-peaked product preferences in a consumer behavioral lab. Results show that subjects always go for the best product option in the absence of price information (i.e., preference is monotonically increasing, rather than single-peaked), and subjects exhibit different product preference patterns when they are in different moods. This paper extends previous research by explicitly including and testing the effect of price as a cost dimension, and incorporating the emerging trend of mood study, and therefore, this paper gains a deeper understanding of consumers’ single-peaked preference function.

A dynamic model of adaptive consumers with endogenous unimodal preferences

We consider a discrete-time dynamic model to describe the repeated choices of adaptive consumers that at each time period adjust their request for a given good according to the discrepancies between the observed price and the fair price perceived on the basis of their utility function. Moreover, consumers’ preferences are endogenously modified on the basis of past consumption experience. The model considered is derived from the one proposed in D’Orlando and Rodano (2006), with a different assumption about the way the utility function changes according to the past consumption. In fact, in D’Orlando and Rodano (2006) the consumption preferences increase whenever past consumption increases, whereas in the model proposed in this paper a saturation effect is introduced, so that the same assumption holds for low and moderate past consumption, whereas current consumption decreases if the quantity consumed in the previous time period was too high. This leads to a unimodal preference function instead of an increasing one, which implies that the two-dimensional map, whose iteration represents the time evolution of the consumers’ choices, is transformed from an invertible map to a noninvertible one. Hence different global dynamic properties are obtained that influence the structure of the attractors and basins of attraction. These global dynamic features, described by the method of critical curves, interact with the property that the map is also characterized by the presence of a denominator that can vanish, giving rise to different kinds of singularities denoted as focal points and prefocal curves in Bischi et al. (1999), Bischi et al. (2003), Bischi et al. (2005), that strongly influence the structure of the basins of attraction. We describe the structure of the basins of attraction, and the contact bifurcations that change the qualitative properties of their global structure, by using geometric and numerical methods guided by the combined study of critical and prefocal curves.

Extended PROMETHEE approach with 2‐tuple linguistic m‐polar fuzzy sets for selection of elliptical cardio machine

AbstractThe Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) is an outranking series of multi‐attribute decision making used to assess a finite collection of alternatives based on conflicting criteria. One of its significant benefits is its adjustability in response to a set of acceptable preference functions that may quantify the differences between alternatives conditional on the kind and structure of the criteria. This article offers the novel approach of the PROMETHEE method, named the 2TLmF PROMETHEE method, which combines the multi‐polarity with linguistic information of decision problems. The customary procedure of the offered technique begins with the normalized weights of the attributes assigned by decision‐makers. After that, Gaussian and the usual preference functions are utilized to determine the preferences of the alternatives. The ultimate choice is based on the alternative's positive and negative outranking flow. The positive outranking demonstrates how one alternative outranks all other options, while the negative preference flow implies an alternative is superior to all other options. There are several modifications to the PROMETHEE approach, two of which are discussed in this article. The first is PROMETHEE I, which uses positive and negative preference flows to generate a partial ranking. Secondly, PROMETHEE II gives a comprehensive rating list based on the net flow, representing the balance of positive and negative preference flows. Further, a comprehensive flowchart is used to show the entire picture of the proposed methodology. As an application, a real‐life situation related to selecting an elliptical machine for a home gym is chosen to illustrate the transparency and reliability of the presented work. Finally, a comparative study is carried out to illustrate the strength and applicability of the proposed method.

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

The problem of improving the methodological and algorithmic support of the complexing process by developing models of adaptive redundant complexing of weighted interval data has been solved. The object of this study is the process of complexing interval data obtained from several independent sources; the subject is the algebraic methods of excessive complexing of weighted interval data. The relevance of the task is due to the severity of the problem of consolidating homogeneous data in order to obtain more accurate and relevant information about the object or process under study. Models have been developed that, unlike the existing ones, make it possible a posteriori to take into account the accuracy of experts at the preliminary stage of expert evaluation. A single analytical form of the model for processing weighted interval and point estimates with the possibility of structural and parametric tuning is proposed. It allows one to increase the degree of automation of processing expert assessments under conditions of interval uncertainty. Recommendations for the practical application of the proposed models have been formulated. Options for parametric configuration of preference functions were indicated depending on the characteristics of weighted interval estimates. The commonality of the limiting cases of the proposed models with previously known ones is proved. The example shows the shift of the integrated assessment to the side of more accurate assessments at the previous stage of source assessment. The adaptability of the proposed models is illustrated. At the same time, a slight, on average, about 10 %, expansion of the complexed interval relative to the primary ones was registered. The built models and algorithms can be used in automated expert systems, as well as in cascade models of information processing and compression.

Accelerated charge transfer of g-C3N4/BiVO4 Z-scheme 2D heterojunctions by controllably introducing phosphate bridges and Ag nanocluster co-catalysts for selective CO2 photoreduction to CO

Artificial Z-scheme heterojunctions by mimicking photosynthesis have been widely investigated for photoreduction of CO2, yet the activity is hindered by the weak interfacial interactions and insufficient CO2 activation sites. Here, a cascade Z-scheme g-C3N4/BiVO4 (CN/BVO) heterojunction has been tailored by the dual modification of phosphates and Ag nanoclusters for CO2 reduction in pure water, in which phosphates are modified in the interface of CN/BVO by a facile impregnation method, while Ag nanoclusters are anchored on the surface of CN by a light induced in-situ deposition strategy under a low-temperature environment created by liquid N2. The optimal Ag-CN/PO-BVO heterojunction delivers a ca.48 μmol g−1h−1 CO generation rate with 97 % selectivity, which exhibits a 24-fold increment in CO production rate compared with that of pristine BVO. The improved photoactivity is mainly ascribed to the accelerated Z-scheme charge transfer from the built PO-bridged dimension-matched 2D interfaces and from the introduced Ag nanoclusters with preferable catalytic functions for CO2 reduction mainly by means of the time-resolved surface photovoltage responses and fluorescence spectra. Moreover, temperature-programmed desorption curves and in-situ DRIFTS results demonstrate that the introduced Ag is favorable for CO2 activation and CO desorption with COOH intermediates, responsible for the high CO selectivity.

Equilibrium Seeking and Optimal Selection Algorithms in Peer-to-Peer Energy Markets

We consider a clearing problem in peer-to-peer energy markets, where prosumers can trade energy among each other and with the main grid to meet their energy demands. By using a game-theoretic formulation and exploiting operator-theoretic methods for generalized Nash equilibrium seeking, we propose two variants of the state-of-the-art distributed market clearing mechanism with improved convergence speeds. Furthermore, we design a third variant that allows for equilibrium selection, i.e., computing a specific market solution based on a convex preference function of the network operator, e.g., a congestion cost. We provide convergence guarantees and numerically show the advantages of our proposed algorithms in terms of convergence speed up and obtaining reduced grid congestion.

Ambiguity of scientific probability predictions and willingness-to-pay for climate change mitigation policies

We estimate a smooth ambiguity preference function, wherein an individual faces multiple probability predictions of policy outcomes, and then empirically measure their willingness-to-pay for the policy, ambiguity attitude, and ambiguity premium. Climate change mitigation policy is used as the example. The estimation results reveal that most people have ambiguity-seeking attitudes, but that these attitudes are heterogeneous across individuals. People who are older, are university graduates, have higher income, or trust more in science show stronger ambiguity-seeking attitudes. Their willingness-to-pay can be underestimated if ambiguity is not considered. Moreover, individuals with stronger ambiguity-seeking attitudes support aggressive mitigation policies more strongly. Our estimation strategy is generally applicable to policy evaluations wherein policy outcomes are ambiguous.

Multicriteria Decision-Making Methods for Optimal Treatment Selection in Network Meta-Analysis.

Network meta-analysis exploits randomized data to compare multiple interventions and generate rankings. Selecting an optimal treatment may be complicated when multiple conflicting outcomes are evaluated in parallel. The present study suggested the incorporation of multicriteria decision-making methods in network meta-analyses to select the best intervention when multiple outcomes are of interest by creating partial and absolute rankings with the TOPSIS, VIKOR, and PROMETHEE algorithms. The TOPSIS and VIKOR techniques represent distance-based methods for compromise intervention selection, whereas the PROMETHEE analysis method allows the definition of preference and indifference thresholds. In addition, the PROMETHEE technique allows a variety of modeling options by selecting alternative preference functions. Different weights may be applied to outcomes objectively with the entropy method as well as subjectively with the analytic hierarchy process, enabling the individualization of treatment choice depending on the clinical scenario. Visualization of decision analysis may be performed with multicriteria score-adjusted scatterplots, while league tables may be constructed to depict the PROMETHEE I partial ordering of interventions. A simulated study was performed assuming equal weights of outcomes, and the TOPSIS, VIKOR, and PROMETHEE II methods were compared using a similarity coefficient, indicating a high degree of agreement among methods, especially with higher numbers of interventions. Multicriteria decision analysis provides a flexible and computationally direct way of selecting compromise interventions and visualizing treatment selection in network meta-analyses. Further research should provide empirical data about the implementation of multicriteria decision analysis in real-world network meta-analyses aiming to define the most suitable method depending on the clinical question. Multicriteria decision-making methods can be implemented in network meta-analysis to indicate compromise interventions.The TOPSIS, VIKOR, and PROMETHEE methods can be used for optimal treatment selection when conflicting outcomes are evaluated.The weights of outcomes can be defined objectively or subjectively, reflecting the priorities of the decision maker.

Use of aerodynamically favorable tapered form in contemporary supertall buildings

Today, supertall buildings can be constructed in unusual forms as a pragmatic reflection of advances in construction techniques and engineering technologies, together with advanced computational design tools for architectural design. As with many other buildings, architectural and practical principles play a crucial role in the form of a supertall building, where aerodynamic behavior shaped by wind-induced excitations also becomes a critical design input. Various methods are used to meet the functional needs of these towers and reduce excitations, including aerodynamic modification methods directly related to the building form. Tapered forms are one of the most frequently used and most effective methods in today's skyscrapers, which significantly affect architectural design. To date, no study has been conducted in the literature that provides an understanding of the interrelationships between tapered building forms and main planning criteria, considering the aerodynamic design concerns of the tapering effect in supertall buildings (≥300 m). This important issue is explored in this article with data gathered from 41 supertall case studies, considering location, function, structural system, and structural material as well as the aerodynamic taper effect. The main findings of the study highlighted the following: (1) Asia was where tapered towers were most favored, with a wider margin in all regions; (2) mixed-use was the most preferred function in selected supertall buildings with tapered form; (3) outriggered frame systems were mainly used; (4) tapered supertall cases were mostly built in composite; (5) the sample group included 17 cases that used the tapering effect with aerodynamic design concerns, some of which were accompanied by corner modifications. It is believed that this study will be a basic guide for design and construction professionals including architectural and structural designers, and contractors.

Exploring the role and mechanism of gut microbiota in methamphetamine addiction using antibiotic treatment followed by fecal microbiota transplantation.

Recently, the role of the gut microbiota in the context of drug addiction has attracted the attention of researchers; however, the specific effects and underlying mechanisms require further exploration. To accomplish this, C57BL/6 mice were firstly treated with methamphetamine (MA). Conditioned place preference (CPP) behavior changes, gut permeability and function, microglial activation, and inflammatory cytokine expression were systematically analyzed in antibiotics-treated mice with microbiota depletion and in fecal microbiota transplantation mice with microbiota reconstitution. MA treatment altered microbiota composition and caused gut dysbiosis. Depletion of gut microbiota with antibiotics inhibited MA-induced CPP formation, and fecal microbiota transplantation reversed this inhibition. Mechanistic analyses indicated that antibiotic treatment decreased gut permeability and neuroinflammation, while fecal microbiota transplantation offset the impact of antibiotic treatment. Additionally, MA-induced microglial activation was suppressed by antibiotics but restored by microbiota transplantation, and this correlated well with the CPP score. Compared to antibiotic treatment, microbiota transplantation significantly increased 5-HT4 receptor expression in both the nucleus accumbens and the hippocampus. Furthermore, when fecal microbiota from healthy mice was transplanted into MA-treated mice, the CPP scores decreased. Our results provide a novel avenue for understanding MA addiction and suggest a potential future intervention strategy.

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

SummaryInternet of Things (IoT) is the rapidly growing domain that facilitates seamless connectivity to physical objects that makes them suitable and ideal entity of smart environment. The enforcement of trust between the physical objects is essential for completely utilizing the significant potentiality of these connected IoT objects. The traditional security strategies are not potent enough in provisioning comprehensive protection to the smart world. In this paper, Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model (BFPSMTM) is proposed for determining the outranking class that aids in superior trust estimation of interconnected IoT objects to guarantee reliable data dissemination. This BFPSMTM adopted different preference functions depending on the nature and type of criteria or attributes considered for evaluating the trust of the IoT objects during data dissemination. It utilized bipolar fuzzy number for assessing trust that represents an asymmetrical associated between the different judgmental factors considered for decision making. It represented the bipolar linguistic terms into trapezoidal bipolar fuzzy numbers to handle the issue of imprecise and vague knowledge during trust computation. It utilized a trapezoidal bipolar fuzzy number‐based ranking function and identified the crisp real preferences of trusted IoT objects during data dissemination. It incorporated an entropy weighting information and computed the attribute weights based on the normality condition to prevent bias during trust estimation. The results of BFPSMTM confirmed improved throughput of 22.18%, reduced delay of 20.86%, minimized energy consumption of 19.32%, and better packet forwarding rate of 19.84%, with different number of IoT objects in the environment.

Mate Preferences and Choosiness Are Distinct Components of Mate Choice in Eastern Gray Treefrogs (Hyla versicolor).

AbstractMate choice is an important cause of natural and sexual selection, driving the evolution of ornaments and promoting diversification and speciation. Mate choice decisions arise from the interaction of several components, and knowledge of whether they interact, and how, is crucial for understanding their contributions to selection. Here we focus on the relationship between preference functions (attractiveness ranking of prospective mates) and choosiness (effort invested in obtaining the preferred mate) and test the hypothesis that they are independent components of mate choice decisions. We examine individual variation in preference functions and choosiness for call duration in female Hyla versicolor treefrogs and show that measures describing preference functions and choosiness are not correlated. We also found a suggestive but inconclusive pattern that both components are influenced by different factors (body measures and hormones). Independence of preference and choosiness suggests that the joint study of variation in both components is required to gain a complete understanding of how mate choice contributes to sexual selection and speciation.

Incorporating the status quo effect into the decision making process: The case of municipal companies merger

Decision-making for cooperation between municipal entities involves elected members serving public interest who argue in both quantitative (e.g. financial performance), and qualitative (e.g. social acceptance) grounds. Policy support should consider conflicting objectives in a context of uncertainty and imprecision as well as in presence of organizational lock-ins that tend to favor incumbent arrangements. In search of appropriate algorithm to support decision making in such cases we opted for the PROMETHEE outranking method integrating novel findings of behavioral theory. Thus its preference functions are specified so that they reflect bounded rationality that shapes risk attitudes of decision makers within the uncertain environment of public management. Moreover alternative weighting procedures have been applied for group decisions. The prospect theory multi-criteria methodology, termed PT-PROMETHEE is implemented relating proposed options to observed behavior providing useful insights especially regarding status quo options also allowing for sensitivity analysis. Thus acceptance of MCDM support by the stakeholders is enhanced leading to solid compromise institutional arrangements.

2DLIF-PROMETHEE methods for multiple attribute decision making under 2-dimension linguistic intuitionistic fuzzy environments

<h2>Abstract</h2> In this paper, how to run 2DLIF-PROMETHEE methods are introduced to deal with the multiple attribute decision making (MADM) problems evaluated by 2-dimension linguistic intuitionistic fuzzy variables (2DLIFVs). Firstly, the Euclidean distance and the cosine similarity measures for 2DLIFSs are introduced respectively. Then a hybrid distance for 2DLIFSs is introduced by integrating these two measures. Based on the hybrid distances for 2DLIFSs, six improved preference functions are designed to form the kernel part of 2DLIF-PROMETHEE methods. Finally, the partial ranking order of alternatives or the total ranking order of alternatives may be obtained by 2DLIF-PROMETHEE methods.