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With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This paper introduces an innovative adaptation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, integrating a Belief Structure (BTOPSIS) to improve the evaluation and interpretation of survey data. Our approach effectively addresses the distribution of responses across categories and the uncertainty often present in such data, including missing or ambiguous answers. Additionally, we perform a sensitivity analysis to assess the stability of the BTOPSIS rankings under varying utility function parameters, further validating the robustness of our method. We apply this framework to the 2023 'Quality of Life in European Cities' survey, analyzing diverse urban factors such as public transport, healthcare, cultural facilities, green spaces, education, air quality, noise levels, and cleanliness. Additionally, our study offers a comparative analysis of BTOPSIS against other multi-criteria methods used for evaluation data from this report, showcasing its strengths and limitations in addressing the dataset's complexity. Our findings reveal significant variations in residents' perceived QoL across European cities, both between cities and within countries. Zurich and Groningen rank highest in satisfaction, while Tirana, Skopje, and Palermo are ranked lowest. Notably, residents of cities with populations under 500,000 report higher satisfaction levels than those in larger cities, and satisfaction levels are generally higher in EU and EFTA cities compared to those in the Western Balkans, with the highest satisfaction observed in northern and western Member States. To aid urban planners and policy-makers, we propose a ranking tool using the BTOPSIS method, capturing nuanced resident perceptions of living conditions across cities. These insights provide valuable guidance for strategic urban development and advancing the smart city agenda across Europe. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of defining the weights in multiple criteria decision‐making analysis. In particular, we analyze if weights determined by decision makers (DMs) using the classic analytic hierarchy process (AHP) approach can be reproduced using more straightforward direct linguistic weights declarations (DLWD). Using the dataset from an online decision‐making experiment, we analyze the concordance of systems of weights obtained by DMs from AHP and DLWD. Applying optimization models, we check if the discrepancies between the AHP and DLWD weights result from (a) inefficiently declared mixes of linguistic labels in the symmetrically distributed scale or (b) using the unbalanced linguistic term set by DMs. The main findings are that the systems of weights obtained by DMs in the experiment from AHP and DLWD differ significantly. While using DLWD, most DMs defined criteria ranks contradictory to those resulting from AHP. They avoided extensive use of extreme labels from the 7‐point scale. The optimization results show that DLWD based on the symmetric scale might produce the most similar weights to AHP if a full range of linguistic terms was used, not only the intermediate ones. The differences in DMs' cognitive profiles seem not to affect the results. [ABSTRACT FROM AUTHOR]

The study aims to identify relationships among selected behavioral characteristics of decision-makers (DMs), i.e., experience in making complex decisions, decision-making style, and ability to use various multiple criteria decision-aiding (MCDA) methods coherently, and their impact on the evaluation of the latter functionality and recommendations for future use. The relationships were verified using experimental data through a structural equation model (SEM) and cluster analysis for three MCDA methods, i.e., AHP, SMART, and TOPSIS. One of the strongest effects identified by SEM was observed between coherence in methods' use and the DM's opinion on their functionality. DM's satisfaction and future willingness to use MCDA tools are related to the positive experience gained from using these tools in advance. Decision-making styles shape method selection, with TOPSIS favored by highly experienced DMs, SMART by highly rational, and AHP by those with low experience and a rational approach. [ABSTRACT FROM AUTHOR]

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig's method. To compare the influence of various normalization methods in both the EWM and Hellwig's method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max–min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig's method. The results are compared with the outcome obtained by Hellwig's method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig's method results. [ABSTRACT FROM AUTHOR]

In this paper we propose a multi-criteria model based on the fuzzy preferences approach which can be implemented in the prenegotiation phase to evaluate the negotiations packages. The applicability of some multi-criteria ranking methods were discussed for building a scoring function for negotiation packages. The first one is Simple Additive Weighting (SAW) technique which determines the sum of the partial satisfactions from each negotiation issue and aggregate them using the issue weights. The other one is Distance Based Methods (DBM), with its extension based on the distances to ideal or anti-ideal package, i.e. the TOPSIS procedure. In our approach the negotiator's preferences over the issues are represented by fuzzy membership functions and next a selected multi-criteria decision making method is adopted to determine the global rating of each package. The membership functions are used here as the equivalents of utility functions spread over the negotiation issues, which let us compare different type of data. One of the key advantages of the approach proposed is its usefulness for building a general scoring function in the ill-structured negotiation problem, namely the situation in which the problem itself as well as the negotiators preferences cannot be precisely defined, the available information is uncertain, subjective and vague. Secondly, all proposed variants of scoring functions produce consistent rankings, even though the new packages are added (or removed) and do not result in rank reversal.

Thorough preparation for a negotiation is considered critical for the achievement of successful relational and substantive results. Careful specification of preferences and determining the negotiation offer scoring systems is one of the most important preparation activities. To facilitate this process, preference elicitation aids have been designed and implemented in decision and negotiation support systems (NSSs). This paper shows that negotiators' motivation affects the use of simple elicitation aid and elicited preferences. We identify three types of motivations: epistemic, social, identity, and assign the factors that describe them. Then, using the dataset from electronic negotiation experiments, we apply logistic regression to identify those motivations that allow distinguishing negotiators who make errors in the determination of the scoring systems from those who do not make them. The key result allows us to identify relational‐ and learning‐oriented goals of the identity motivation as having a significant and direct impact on the negotiators' classification. Accommodating and competing approaches of social motivation impact agents' accuracy with the differences observed for gender. Surprisingly, epistemic motivation represented by rationality and experientiality factors does not affect users' accuracy in the prenegotiation phase. The results obtained can be used to design decision support tools adjusted to the motivational profiles of the NSS users. [ABSTRACT FROM AUTHOR]