Attitude-based entropy function and applications in decision-making
01 Sep 2021-Engineering Applications of Artificial Intelligence (Pergamon)-Vol. 104, pp 104290
TL;DR: In this article, a new entropy function is introduced specifically for the human decision making, which considers an agent's degree of sensitivity towards uncertainty, i.e., the tendency to exaggerate or downplay the inherent uncertainty.
Abstract: The popular entropy functions are rigorously analysed in the context of uncertainty in the real world decision making. Based on the findings, a new entropy function is introduced specifically for the human decision making. The proposed function considers an agent’s degree of sensitivity towards uncertainty, i.e., the tendency to exaggerate or downplay the inherent uncertainty. The proposed entropy function is equipped to deal with both the subjective and probabilistic uncertainties alike, which are often interlinked in a decision-making context. The properties of the proposed entropy function are rigorously studied. A real case-study in portfolio diversification highlights the usefulness of the entropy function. It was found that the attitude plays a profound role, when there are a large number of uncertain systems (portfolios) to compare and choose from, or when the portfolios are more diversified.
TL;DR: In this paper , the integrated entropy-CoCoSo approach for evaluating the sustainability of road transportation systems is introduced, and the framework process is proposed to define the weight of the decision criteria based on the real data.
Abstract: Road haulage solutions are incredibly adaptable, having the capacity to link domestically and internationally. Road transportation offers a greener, more efficient, and safer future through sophisticated technology. Symmetry and asymmetry exist widely in industrial applications, and logistics and supply chains are no exception. The multi-criteria decision-making (MCDM) model is considered as a complexity tool to balance the symmetry between goals and conflicting criteria. This study can assist stakeholders in understanding the current state of transportation networks and planning future sustainability measures through the MCDM approach. The main purpose of this paper is to evaluate and compare the sustainable development of existing road transportation systems to determine whether any of them can be effectively developed in the Organization for Economic Cooperation and Development (OECD) countries. The integrated entropy–CoCoSo approach for evaluating the sustainability of road transportation systems is introduced, and the framework process is proposed. The entropy method defines the weight of the decision criteria based on the real data. The advantage of the entropy method is that it reduces the subjective impact of decision-makers and increases objectivity. The CoCoSo method is applied for ranking the road transportation sustainability performance of OECD countries. Our findings revealed the top three countries’ sustainability performance: Japan, Germany, and France. These are countries with developed infrastructure and transportation services. Iceland, the United States, and Latvia were in the last rank among countries. This approach helps governments, decision-makers, or policyholders review current operation, benchmark the performance of other countries and devise new strategies for road transportation development to achieves better results.
TL;DR: In this article , a tensorial multi-criteria decision analysis (TMCDA) approach is proposed to predict the future values of the criteria values based on the past data.
Abstract: Multi-criteria Decision Analysis (MCDA) is a methodology that has been classically used to rank alternatives according to a set of decision criteria. The MCDA techniques have been shown to be an efficient tool in a number of real-life engineering problems. Nevertheless, most of the proposed approaches in the field do not consider the temporal characteristic of the criteria values, which can be an interesting information to be explored in order to predict future rankings. The present work proposes a novel MCDA methodology in which the past data of the criteria are considered to predict their future values. Our approach is based on a tensorial formulation, together with the use of the recursive least mean squares method in the prediction step. In addition, we consider a probabilistic prediction and use the Stochastic Multi-criteria Acceptability Analysis, which allows the decision maker to observe the degree of uncertainty in the ranking. Numerical experiments with synthetic and actual data attest to the proposal’s relevance in scenarios in which the criteria values change over time.
TL;DR: In this paper , the authors proposed the decomposable Deng entropy, which is an extension of the decompositionable entropy for the Dempster-Shafer evidence theory, and showed that the proposed model can effectively decompose the Deng entropy.
Abstract: Dempster–Shafer evidence theory is an extension of classical probability theory in the evidential environment. Evidential environment is an environment in which Dempster–Shafer evidence theory is used. The decomposable entropy for the Dempster–Shafer evidence theory can efficiently decompose the Shannon entropy for the Dempster–Shafer evidence theory, and has high theoretical and application value. This article proposes the decomposable Deng entropy, which is an extension of the decomposable entropy for the Dempster–Shafer evidence theory. The decomposable Deng entropy can effectively decompose the Deng entropy. When the cardinalities of all focal elements of a mass function are 1, then the decomposable Deng entropy will collapse to the decomposable entropy for the Dempster–Shafer evidence theory. Many calculation examples are used to verify the performance of the proposed model in decomposing Deng entropy. Experimental results show that the proposed model can efficiently decompose the Deng entropy.
TL;DR: New attitude-based variants of Shannon's, Pal & Pal, and Aggarwal’s probabilistic entropies are introduced and extended to consider the agent’'s specific attitude, providing a wide range of entropy values with the conventional entropy functions as their special cases.
Abstract: In this paper, new entropy functions are formulated based on an agent’s perceived uncertainty that inevitably affects the agent’s choice. The role of the decision-maker’s (DM’s) attitude is emphasized as one of the key determinants of such an entropy function. More specifically, new attitude-based variants of Shannon’s, Pal & Pal, and Aggarwal’s probabilistic entropies are introduced. The extant fuzzy entropies are also extended to consider the agent’s specific attitude. The proposed entropy functions provide a wide range of entropy values with the conventional entropy functions as their special cases. The special cases of the proposed entropies are examined. The wide applicability of the proposed entropy functions in multi criteria decision making is highlighted. A case-study is included to showcase the usefulness of the proposed entropy functions in the real world.
TL;DR: In this paper , a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy.
Abstract: Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Abstract: In this final installment of the paper we consider the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now. To a considerable extent the continuous case can be obtained through a limiting process from the discrete case by dividing the continuum of messages and signals into a large but finite number of small regions and calculating the various parameters involved on a discrete basis. As the size of the regions is decreased these parameters in general approach as limits the proper values for the continuous case. There are, however, a few new effects that appear and also a general change of emphasis in the direction of specialization of the general results to particular cases.
TL;DR: In this paper, a generalized form of entropy was proposed for the Boltzmann-Gibbs statistics with the q→1 limit, and the main properties associated with this entropy were established, particularly those corresponding to the microcanonical and canonical ensembles.
Abstract: With the use of a quantity normally scaled in multifractals, a generalized form is postulated for entropy, namelyS q ≡k [1 – ∑ i=1 W p i q ]/(q-1), whereq∈ℝ characterizes the generalization andp i are the probabilities associated withW (microscopic) configurations (W∈ℕ). The main properties associated with this entropy are established, particularly those corresponding to the microcanonical and canonical ensembles. The Boltzmann-Gibbs statistics is recovered as theq→1 limit.
TL;DR: A functional defined on the class of generalized characteristic functions (fuzzy sets), called “entropy≓, is introduced using no probabilistic concepts in order to obtain a global measure of the indefiniteness connected with the situations described by fuzzy sets.
Abstract: A functional defined on the class of generalized characteristic functions (fuzzy sets), called “entropy≓, is introduced using no probabilistic concepts in order to obtain a global measure of the indefiniteness connected with the situations described by fuzzy sets. This “entropy≓ may be regarded as a measure of a quantity of information which is not necessarily related to random experiments. Some mathematical properties of this functional are analyzed and some considerations on its applicability to pattern analysis are made.
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