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In recent years, research on applications of three-way decision (e.g., TWD) has attracted the attention of many scholars. In this paper, we combine TWD with multi-attribute decision-making (MADM). First, we utilize the essential idea of TOPSIS in MADM theory to propose a pair of new ideal relation models based on TWD, namely, the three-way ideal su...
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... visualize the SRCC and enhance the readability of the data, this paper uses a heatmap, which is a matrix that shows the data in terms of color changes that represent the magnitude of the correlation coefficient, thus showing the correlation between different indicators and different samples. The heatmap of Table 24 described above is shown in Figure 1. From Table 24, for the IS method we proposed, the SRCC values of IF and TOPSIS method is low, only 0.5714 and 0.3810, respectively; however, the SRCC values between it and PROMETHEE, Ye's method, Zhang's method and Jia's method are all greater than 0.648, indicating that the IS method has certain rationality and feasibility. ...
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... In the second case, we set the preference parameter k 2 with a fixed value and adjust the value of risk-aversion coefficient τ to obtain the following classification and sorting results for the eight objects in the TWD ideal inferiority model. Table 34 and Figures 10 and 11, we analyze from the perspective of the optimal object, discovering that except for τ = 0, the rest of the optimal objects are O 7 . Furthermore, regardless of the value of τ, O 3 and O 8 are both classified into the negative domain, which indicate that O 3 and O 8 are not considered objects. ...
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... τ = 0.1 and τ = 0.2, the results of classification and ranking are identical. In Figure 10, it can be seen that the red line and the green line are coincident, and the sorting result is O 7 O 1 O 4 O 6 O 5 O 2 O 3 O 8 . In Figure 11, the division of the three areas of the histogram is the same: O 7 is in the positive domain, O 1 , O 2 , O 4 , O 5 , O 6 are in the boundary domain, and O 3 and O 8 are in the negative domain. ...
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... Figure 10, it can be seen that the red line and the green line are coincident, and the sorting result is O 7 O 1 O 4 O 6 O 5 O 2 O 3 O 8 . In Figure 11, the division of the three areas of the histogram is the same: O 7 is in the positive domain, O 1 , O 2 , O 4 , O 5 , O 6 are in the boundary domain, and O 3 and O 8 are in the negative domain. When τ = 0.4 and τ = 0.5, although they are sorted the same, the classification is different. ...
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Aiming at the problems of filling missing information and calculating conditional probability and loss function in incomplete information systems, this paper provides a novel three-way decision model based on incomplete information systems. Firstly, a new information table is obtained by filling in the missing information based on similarity, and t...
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... It succeeds in rationally assigning semantic interpretations to the positive, negative, and boundary domains of rough sets, which correspond to acceptance, rejection, and delay decisions, respectively, in practical decision making. Since the proposal of the three-way decisions, many scholars have refined and extended it, and it has been widely used in many fields [19][20][21][22][23][24][25]. ...
Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment.
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Individuals working within a team have unique attributes which lead to the emergence of different team dynamics that could promote or hinder team performance. Implicit motivation is one such aspect that brings diversity. Research in the Psychology literature has identified three motive profiles that characterize individuals as achievement, affiliation and as power motivated. In this work, we personify agents with these motive profiles and analyze their performance through a series of experiments using a prey-predator task domain. Throughout the experiments, we analyzed the productivity of teams with different motivation compositions (heterogeneous teams) and homogeneous teams with motive profiles created by perturbing the strengths of the three original motive profiles. Furthermore, we investigated how much an agent has to suppress its motivation to pick a more rational goal and quantified this inhibition by introducing a new metric named Perceived Tension. Our experiments concluded that when working collaboratively, having teams with a diverse motivation portfolio is beneficial. It was also evident that diversely motivated teams perform well across different test settings making such teams robust to different task difficulty levels. Furthermore, results showed that predominantly affiliation-motivated teams are less tensed when working on collaborative easy tasks, however their productivity is significantly poor. The framework introduced in this paper can be used to understand the implications for workplaces and leaders when putting together organic teams to create a productive, healthy and relaxed environment for their employees.
