Analysis of the Prosperity Performances of G7 Countries: An Application of the LOPCOW-based CRADIS Method

The prosperity policies and strategies of major economies have the potential to significantly influence both the global economy and the prosperity of other nations. Therefore, the assessment of the prosperity performance of major economies holds paramount importance. In this context, the primary aim of this research is to evaluate the prosperity performance of G7 countries using the LOPCOW-based CRADIS method, leveraging sub-component values from the Legatum Prosperity Index. The secondary objective is to examine the relationship between a country's prosperity performance assessed through the LOPCOW-based CRADIS method and its quantifiability within the Legatum Prosperity Index (LPI) framework, as well as its associations with other Multi-Criteria Decision-Making (MCDM) methodologies. In the study, the first three most important LPI components according to countries were evaluated as Investment, Governance, and Safety & Security, while the first three least important components were Education, Living Conditions


Introduction
The increase in a country's level of prosperity extends beyond mere economic growth, encompassing advancements in social, cultural, and human rights domains.This upturn contributes to an enhancement in people's quality of life, encompassing factors such as access to healthcare services, educational opportunities, and essential public services.Furthermore, rising prosperity levels offer opportunities for the development of cultural and societal values, including gender Equation, environmental sustainability, and the strengthening of democratic institutions.Such progress can elevate a country's international standing, rendering it more respected on the global stage and enabling it to exert greater influence in international cooperation and diplomacy.Consequently, an elevation in the level of prosperity has the potential to augment a nation's overall development and enhance its capacity to make a more substantial contribution within the global context.
Measuring the prosperity performance of countries plays a critical role in both economic and social development.These measurements provide guidance to governments when formulating policies and support the effective distribution of resources.Additionally, international comparisons facilitated by these measurements enable countries to learn from each other's prosperity policies, aiming to improve their own prosperity.Prosperity measurements are instrumental in assessing the effectiveness of policies and programs aimed at enhancing people's quality of life and prosperity.Importantly, the dimension of prosperity encompasses not only economic growth but also a wide range of factors such as education, healthcare, environmental sustainability, and social equation.Consequently, measuring countries' prosperity performance is not just an economic endeavour; it is also a crucial tool for improving people's quality of life and building a sustainable future.
Specifically, G7 countries, unlike other countries, have not focused on economic orthodoxies based on total figures such as GDP, economic growth, and job creation as measures of prosperity.Instead, G7 countries can influence the global economy and prosperity formation with openness, inclusiveness, and democratic structures by using human-centered prosperity indices to develop strategies for prosperity and create awareness of prosperity performance.Therefore, it is important to examine the prosperity performance of G7 countries Moreover, considering that G7 countries, which control over half of global capital, can influence global economic policies and the prosperity development of other nations, the analysis of the prosperity performance of G7 countries holds particular significance (Moore et al., 2022).
This study aims to analyze the prosperity performance of G7 countries.The findings of this study will help to identify which countries need to improve their prosperity performance in order to make a greater contribution to the global economy and other dimensions related to the economy.This will increase the opportunity to make inclusive policies on the prosperity dimension globally and will affect countries around the world.In addition, when the prosperity literature is examined, no specific study has been found that focuses on the analysis of prosperity performance of G7 countries.In terms of method, it has been observed that there are limited studies that use the LOPCOW and CRADIS methods together in measuring the performance of Alphanumeric Journal Volume 11, Issue 2, 2023 decision alternatives or solving selection problems according to MCDM literature.Therefore, it is considered that this research contributes to both the prosperity and MCDM literature and enriches the relevant literature.The study is considered to have an original and unique quality.
In this context, the primary objective of this research is to measure the prosperity performance of G7 countries for the latest available year, which is 2022, using the values of the Legatum Prosperity Index (LPI) components through the LOPCOWbased CRADIS Multi-Criteria Decision-Making (MCDM) method.The secondary objective is to evaluate the feasibility of measuring countries' prosperity performance within the framework of LPI data using the LOPCOW-based CRADIS MCDM method and to assess the relationships between the LOPCOW-based CRADIS method and other MCDM techniques.In line with these objectives, the literature review section of the research elucidates the concept of prosperity in the context of the research topic, and in terms of methodology, it outlines previous research related to the LOPCOW and CRADIS MCDM methods.Subsequently, the research's conclusions and discussions are drawn based on the findings.

Literature Review
The most commonly used measure to gauge a country's overall income is Gross Domestic Product (GDP), which represents the value of all goods and services produced within a country's borders over the course of a year.International standards have been established to determine how GDP is calculated, and as a result, quantities derived from dividing GDP by a country's population have become an essential starting point for measuring prosperity (Mumford, 2016: 226).
The growing disparities between those benefiting from values determining prosperity (quality of life and opportunities) and those left behind in economies and societies have led to the recognition that the measures of progress need to extend beyond economic growth and GDP.This shift reflects the understanding that prosperity's development is crucial.It is now widely acknowledged that within the context of limited planetary resources, the economic sustainability of prosperity is constrained, and the global environmental degradation and climate change issues have adverse effects on prosperity (Moore and Mintchev, 2021: 3).In the pursuit of prosperity, it is not only essential for countries to increase their per capita GDP, but also to combat inEquation, promote social cohesion, protect the environment, and ensure the quality of education, healthcare, and employment opportunities (Woodcraft and Anderson, 2019: 5).Consequently, prosperity can be viewed as an economic, social, and psychological phenomenon that fosters the development of dimensions beyond the economic aspect, alongside the enhancement of social and quality-of-life aspects (Bate, 2009).Pociovălişteanu et al. (2010) have emphasized that while economic growth serves as a foundational element in prosperity, social, vital, and psychological dimensions are crucial factors in enhancing prosperity.According to the Legatum Institute (2023), prosperity is defined as a state in which all individuals have the opportunity to realize their unique potential, contributing to the empowerment of communities and nations.Consequently, the Legatum Institute (2023) underscores that prosperity is not solely a construct created by governments; rather, it can be reinforced by communities through the realization of potential, with Alphanumeric Journal Volume 11, Issue 2, 2023 government support.Thus, in accordance with the definition provided by the Legatum Institute (2023), governments play the role of facilitating prosperity potential for communities, while communities assume the role of moderating variables in shaping prosperity.
The measurement of national prosperity performance offers a number of benefits at the economic, social, and political levels.First, these measurements give governments the ability to allocate resources effectively.This allows countries to develop strategic policies for economic growth and social development.Second, prosperity measurements are used in international comparisons, increasing the comparability of countries and helping to share best practices.In addition, these measurements improve the capacity to assess the effectiveness of policies and programs, identify problems, and respond to emerging needs.Prosperity measurements provide a comprehensive view by taking into account a variety of factors, such as economic growth, education, health, environmental protection, and income inEquation, in addition to economic growth.As a result, the measurement of national prosperity performance is a key tool in achieving more equitable, sustainable, and human-centered development goals (Legatum Institute, 2023).
Prosperity is essential for societal improvement and, consequently, development.As countries are aware of their contribution to economic and social prosperity, they can take action to address their prosperity performance gaps, maintain their advantages, and improve their capabilities.Therefore, countries analyse their own and each other's performance to improve their prosperity levels.Within this scope, countries need international, impartial, and objective metrics to measure their prosperity performance (Legatum Institute, 2023).
The Legatum Prosperity Index (LPI) is the only metric that measures the prosperity performance of countries at an international level.The index is composed of three components, 12 subcomponents, and 67 variables.The arithmetic means of the variables, subcomponents, and components can be used to measure the prosperity index values of countries (Legatum Institute, 2023).The descriptions of the LPI's components, subcomponents, and variables are shown in Table 1.As countries improve their prosperity performance, they can also promote the development of economic and social dimensions, as well as other dimensions related to economy and society.Therefore, the prosperity dimension can be considered a broader concept than economic and social dimensions.In this regard, when considering the significance of the well-being dimension for countries, it is possible Güney (2014) examined the impact of the corruption dimension on the prosperity and sustainability performance dimensions using panel data analysis with data from the LPI, the Global Corruption Perception Index, the Control of Corruption Index, and the Environmental Performance Index between 2009 and 2013.The findings showed that corruption has a significant, negative, and very high impact on the prosperity and sustainability dimensions.Lee et al. (2017) examined the impact of the transparency dimension on the prosperity dimension of 96 countries between 2008 and 2015 using structural equation modeling.The study found that the transparency dimension has a positive, significant, and high-level impact on the prosperity dimension.Alotaibi and Alajlan (2021) analyzed the relationship between carbon dioxide (CO2) emissions, the Human Development Index (HDI), and the Legatum Prosperity Index (LPI) using quantile regression within the framework of the Environmental Kuznets Curve (EKC).The findings showed that both LPI and HDI have a negative relationship with CO2 emissions in the quantiles from 0.2 to 1.In addition, urbanization and trade openness were found to have a negative relationship.Butsaradragoon and Jitmaneeroj (2021) investigated the relationships between the dimensions of prosperity using data mining with data values related to the prosperity dimension of 142 countries.The findings showed that there are positive, significant, and high relationships between the different components that define prosperity.Based on these quantitative results, the study emphasized that different prosperity dimensions should not be given equal weight when designing policies to support national prosperity in the development of prosperity components.The study also concluded that the development of strategies for human capital and education, which are components of the prosperity dimension and contribute the most to the relational structure, will lead to the development of other prosperity components that determine prosperity.Bubnovskaia et al. (2021) examined the relationship between the security and health dimensions of the 2019 LPI and COVID-19 mortality indicators in 67 countries using Pearson correlation coefficients.The findings showed that the health dimension of a country is more positively associated with COVID-19 mortality than the security dimension.In addition, countries with higher security and health indices had higher mortality rates than countries with lower health indices.
Alphanumeric Journal Volume 11, Issue 2, 2023 Timmerman et al. (2021) examined the relationship between Hofstede's national culture dimensions and the LPI in 62 countries.In the study, Hofstede's national culture dimensions were tested as predictors of general prosperity, as measured by the LPI.The regression results showed that general prosperity has a negative relationship with power distance, but a positive relationship with individualism, longterm orientation, and indulgence.Kabakçı Günay and Sülün (2021) investigated the general impact of the social capital component on the prosperity levels of OECD countries by comparing the LPI values of countries with and without the social capital component, which is one of the components that determines the LPI value of countries.The findings showed that social capital has a positive impact on the prosperity rankings of Norway, Denmark, Iceland, New Zealand, Canada, Australia, the United States, Slovenia, Portugal, Israel, and Slovakia.On the other hand, it has a negative impact on the prosperity rankings of Switzerland, the United Kingdom, Luxembourg, France, Belgium, Hungary, the Czech Republic, Greece, Mexico, Latvia, Japan, Lithuania, South Korea, and Turkey.In addition, it was found that the social capital variable did not make a significant difference in the prosperity rankings of the Netherlands, Sweden, Austria, Ireland, Germany, Spain, Estonia, Italy, Chile, Colombia, and Poland.Alshamrani and Hezam (2023) measured the performance of the 19 countries with the worst performance in the world according to the 2021 LPI using the ENTROPYbased TOPSIS method.The study found that South Sudan had the worst ranking, while Cameroon had the best ranking.Azar et al. (2023) examined the relationships between the variables in Iran's LPI dimensions using canonical correlation with data from 2021.According to the quantitative results, the correlation coefficient between social capital and health was determined to be 0.89.According to the structural coefficients, life expectancy, physical health, mental health, care systems, preventive interventions, and high-risk behavioral factors were found to have the greatest impact on the canonical variable of health, respectively.According to the standard coefficients, interpersonal trust had the greatest impact on health, and institutional trust, social networks, civil and social participation, and personal and family relationships were found to be among the priority effects of social capital on health.Accordingly, the study concluded that social capital plays an important role in understanding the determinants of health and that it is essential for policy makers to pay special attention to it in order to eliminate health inequalities.In particular, it emphasized the need to give special attention to social capital, one of the most important determinants of health, in addition to equipment-and treatment-oriented strategies.
The Legatum Institute (2023) has ranked the G7 countries in terms of their prosperity performance according to the 2022 Legatum Prosperity Index (LPI) sub-component data.The findings are shown in Table 3 Based on Table 3, the LPI values of the countries are ranked as Germany, United Kingdom, Canada, Japan, United States, France, and Italy.The average LPI value of the countries was also measured, and it was found that the countries above the average value were Germany, United Kingdom, Canada, and Japan.
When reviewing the MCDM literature, it has been observed that many researchers utilize LOPCOW for calculating the weights of criteria and CRADIS for measuring the performance of decision alternatives or addressing selection problems.Therefore, in terms of the research methodology, the LOPCOW and CRADIS literature is indicated in Table 4 determine the weight coefficients of criteria, while CRADIS is often used to calculate the performance of decision alternatives or in selection problems.However, it has been observed in the literature that the LOPCOW and CRADIS methods are less utilized by researchers compared to some other methods (Weight Coefficient Calculation: ENTROPY, CRITIC, MEREC, SD, SVP, CILOS, IDOCRIW, SECA; Ranking of Decision Alternatives: ARAS, WASPAS, COPRAS, EDAS, TOPSIS) due to their relatively newer and more contemporary nature.

Research Objective, Analysis, Data Set, and Limitations of the Study
The main objective of this study is to measure the prosperity performance of G7 countries.To this end, the LOPCOW-based CRADIS method is used.The second objective of the study is to evaluate the performance of the LOPCOW-based CRADIS method in measuring the prosperity performance of countries.The data set of the study consists of the values of the LPI sub-components of the countries.For convenience, the abbreviations of the LPI sub-components are shown in Table 5.

Sub-Compenents
Abbrevations The research was conducted to measure the prosperity performance of G7 countries using the LOPCOW-based CRADIS method.The LPI index was chosen as the basis for measuring prosperity performance because it is more comprehensive, detailed, and up-to-date than other indices.The LPI sub-components were preferred over the LPI components because the number of components is small and the number of variables is large.The LOPCOW method does not have any restrictions on the number of criteria when calculating the importance of criteria for decision alternatives.The most important difference between the LOPCOW method and other objective weighting methods is that it removes the size difference of the data by calculating the standard deviation of the mean square quantity of the series in terms of percentages (Bektaş, 2022: 254-255).
The CRADIS method is a relatively new MCDM method.The most important feature of the method is that it has no restrictions on decision alternatives and criteria when measuring the performance of decision alternatives or in selection problems.The method also relies on simple mathematical operations.In addition, the method has acquired a hybrid and wide-ranging quality by being derived from the combination of ARAS, MARCOS, and TOPSIS methods (Puška et al., 2021).Therefore, the LOPCOWbased CRADIS method was used to measure the prosperity performance of countries due to the advantages of the aforementioned methods.
Due to the limitations of the study, data on prosperity components for only 2022 was used.It is thought that the data on prosperity components for other years of the Alphanumeric Journal Volume 11, Issue 2, 2023 countries should be taken into account for the study to have a more comprehensive, informative, and holistic nature.

The LOPCOW Method
The LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) method is an objective weighting method introduced to the MCDM literature by Ecer and Pamucar (2022).The logic of the method is based on obtaining the appropriate or ideal weights by bringing together data of different sizes.In addition, this method minimizes the gaps between the most important and least important criteria.In addition, LOPCOW takes into account the mutual relationships between criteria (Keleş, 2023: 125).The method is also not affected by negative raw data (Bektaş, 2022: 255).The application steps of the method are as follows (Ecer and Pamucar, 2022: 8).
Step 1: Provision of the Decision Matrix i:1,2,3… .: Number of decision alternatives j:1,2,3,… .: Number of criteria X: Decision matrix d ij : The decision matrix is constructed with the i-th decision alternative on the j-th criterion, using Equation 1.
Step 2: Normalization of the Decision Matrix(   ) The normalization process is achieved using Equation 2 for benefit-oriented (maximization) criteria and Equation 3 for cost-oriented (minimization) criteria, as specified in Equation 1.
For Benefit-Oriented Criteria: For Cost-Oriented Criteria: Step 3: Calculation of Weight Percentages for Each Criterion (PV) In this step, Equation 4 is used to calculate the mean square value as a percentage of the standard deviations of each criterion, such that it eliminates the variance attributable to the size of the data.In Equation 4,  represents the standard deviation, and ln stands for the natural logarithm.(5)

CRADIS Method
The CRADIS method is designed to determine the deviation of alternatives from ideal and anti-ideal solutions.This method is a combined integration of steps from ARAS, MARCOS, and TOPSIS methods.The CRADIS method is a contemporary modeling approach, representing a new way of utilizing steps from existing methods in a unique combination.In this method, alternatives are observed across all criteria, considering both ideal solutions representing the maximum value for an ideal solution and the minimum value for an ideal solution.The steps to apply the CRADIS method are explained below (Puška et al., 2021).
Step The decision matrix is constructed with the i-th decision alternative on the j-th criterion, using Equation 6.
Step 2: Normalization of the Decision Matrix For Benefit-Oriented Criteria: For Cost-Oriented Criteria: Step 3: Weighting the Decision Matrix The weighted decision matrix is obtained by multiplying the normalized decision matrix by the corresponding weights.The equation for this weighted decision matrix is described by Equation 9.
=   .  (9) Alphanumeric Journal Volume 11, Issue 2, 2023 Step 4: Determining the Ideal and Anti-Ideal Solutions The calculation of the ideal solution is determined by the largest value of   in the weighted decision matrix.The calculation of the anti-ideal solution, on the other hand, is identified by finding the smallest value of   in the weighted decision matrix.
Calculation of the Ideal Solution: Calculation of the Antı-Ideal Solution: Step (15) Step 7: Calculation of the Utility Function for Each Alternative Based on Deviations from Optimal Alternatives For the Ideal Solution For the Anti-ideal Solution: 0 + represents the optimal alternative with the least distance to the ideal solution.On the other hand,  0 − can be described as the optimal alternative with the greatest distance to the anti-ideal solution.
Step 8: Ranking Decision Alternatives The performance of decision alternatives is determined by the average deviation of the alternatives' utility degrees.The alternative with the highest   value is considered the best or the one with the highest performance.

Findings
In order to see the stages more clearly and systematically, the stages are presented in Figure 1, as the subject to be resolved in the decision problem consists of several different aspects and stages.In the context of the findings, first, the weight coefficients of LPI criteria were measured according to the LOPCOW method.In this regard, the decision matrix with relevant values is shown in Table 6 using Equation 1.In the second step of the LOPCOW method, the normalized matrix values were calculated using Equation 2, and the measured normalized values are presented in Table 7.

Sub-Components
In terms of findings, secondly, the prosperity performances of decision alternatives (countries) were measured within the framework of the CRADIS method, taking into account the weights of the LPI criteria determined within the LOPCOW method.Accordingly, within the CRADIS method, the decision matrix was first provided with Equation 6.This decision matrix had previously been created using Equation 1 with the assistance of the LOPCOW method, as shown in Table 6.Furthermore, within the CRADIS method, since all LPI components are benefit-oriented, the values of the normalized decision matrix were measured using Equation 7, and the measured values are presented in Table 9.
Alphanumeric Journal Volume 11, Issue 2, 2023 From a methodological perspective, a sensitivity analysis of the prosperity performance of countries has been provided using the LOPCOW-based CRADIS method.Sensitivity analysis in the MCDM literature can be conducted by comparing values and rankings obtained by applying different criteria weighting methods using the same data (Gigovič, 2016: 24).In this context, first, the weighting coefficient values of LPI components for countries were measured according to different weighting methods (ENTROPY, CRITIC, SVP: Statistical Variance Procedure, SD: Standard Deviation, MEREC), and the measured values and rankings are presented in Table 13.13 is examined, it can be observed that the rankings of the weight coefficients of LPI criteria determined by the LOPCOW method are mostly different from the rankings determined by other methods.In the sensitivity analysis, secondly; countries' LPI performances were measured using the CRADIS method based on CRITIC, SD, SVP, ENTROPY, and MEREC, and the measured values were ranked.The relevant quantities are presented in Table 14 14.Countries' LPI performances using the CRADIS method based on CRITIC, SD, SVP, ENTROPY, and MEREC When Table 14 is examined, it is observed that the rankings of countries' prosperity performance measured by the LOPCOW-based CRADIS method are largely different from the rankings of countries' prosperity performances measured by the other weight-based CRADIS method.Additionally, with respect to prosperity performances, the differentiation analysis graph of methods for countries is presented in Figure 2.  When examining Figure 3, it is observed that the methods are positioned differently in space.Particularly, according to Figure 2, the LOPCOW-CRADIS method exhibits a stronger positive proximity to the MEREC-CRADIS and CRITIC-CRADIS methods compared to other methods.Therefore, it is considered that the LOPCOW-CRADIS method has stronger positive relationships with the MEREC-CRADIS and CRITIC-CRADIS methods compared to other methods.Based on this, the relationship matrix between the methods is presented in Table 15.12 and 16 are examined together, it is observed that the ranking of countries' prosperity performance values calculated by the LOPCOW-based CRADIS method is fully consistent with the ranking of countries' prosperity performance values calculated by the LOPCOW-based MARCOS and MAIRCA methods.In addition, according to both tables, the ranking of countries' prosperity performance values measured by the LOPCOW-based CRADIS method is similar to the rankings of countries' prosperity performance values calculated by the LOPCOW-based ARAS and COPRAS methods.This is shown in Figure 4, along with the discriminant diagrams of the methods.16, the proximity in the discrimination distance visualized in Figure 3, and the correlation analysis in Table 17, it can be concluded that the LOPCOW-based CRADIS method is most similar to the LOPCOW-based MARCOS and MAIRCA methods.

Conclusion and Discussion
In today's global economy, the strategies that countries develop to enhance their own prosperity performance play a significant role in the development of the global economy and other dimensions related to economics.This is because the prosperity performance of countries plays a critical role in international economic and trade relations and in shaping economic policies, thus providing a fundamental resource to promote sustainable and inclusive economic growth worldwide.Particularly, the prosperity policies of major economies can impact the prosperity enhancement strategies of other countries, influencing global prosperity and the economy.In this context, this research measured the prosperity performance of G7 countries for the latest and most current year, 2022, using the LOPCOW-based CRADIS method based on the values of LPI components.
In the research, firstly, the weight coefficient values of LPI components for each country were measured, and the measured values were ranked.According to the findings, the top three most important LPI criteria for countries were determined to be Investment (LPI5), Governance (LPI3), and Safety & Security (LPI1), while the least important LPI criteria were found to be Education (LPI11), Living Conditions (LPI9), and Personal Freedom (LPI2), respectively.According to Table 4, the top three LPI criteria with the highest weight coefficients were Investment (LPI5), Governance (LPI3), and Safety & Security (LPI1), whereas the three LPI components with the lowest weight coefficients were identified as Education (LPI11), Living Conditions (LPI9), and Personal Freedom (LPI2).Therefore, based on this result, it can be observed that for G7 countries, strengthening the economy within the investment environment, especially in terms of economic stability in the investment climate, and in terms of governance, accountability, and oversight of governments to create economic policies, as well as overall ensuring security for economic investments and initiatives, are more critical.On the other hand, in general, G7 countries do not face significant issues in terms of living conditions, freedom, and education compared to In the literature, it has been observed that there is limited research examining countries' prosperity performances using MCDA methods.Furthermore, in the context of MCDA literature, the LOPCOW and CRADIS methods, being relatively new and up-to-date, have been less utilized compared to other methods.Therefore, this study is considered to contribute to the literature on countries' prosperity in terms of its research topic and enrich the MCDA literature by employing the LOPCOW and CRADIS methods.Limited data on prosperity components was only available for 2022, which constrained the scope of the study.To make the study more comprehensive, informative, and holistic, it is recommended that data on prosperity components for other years be collected and analyzed.

Recommendations in terms of policy and administrative implications
Under the recommendations, firstly, G7 countries can enhance their contributions to the global economy and global prosperity by implementing policies and initiatives aimed at the development of all LPI criteria.Specifically, they can focus on strategies to improve the criteria Safety & Security (LPI1), Governance (LPI3), Social Capital (LPI4), Investment (LPI5), Infrastructure & Market Access (LPI7), and Natural Environment (LPI12), which have weights greater than the average weight.Additionally, countries with prosperity performances below the average, such as the United States, France, and Italy, can provide solutions that are more prosperityoriented to contribute added value to the formation of global prosperity on a global scale.

Recommendations in the context of methodology
In terms of methods, countries' prosperity performances can be measured using LOPCOW and other objective weight-based methods (ENTROPY, CRITIC, SVP, SD, MEREC, SECA, CILOS) along with various multi-criteria decision-making techniques (TODIM, EDAS, VIKOR, ELECTRE, MABAC, MOOSRA, MULTIMOORA, PIV, OCRA, LBWA, TOPSIS, CODAS, etc.).This would allow for comprehensive comparisons of the measured values and rankings within the framework of different methods.Finally, to calculate countries' LPI performances more accurately, the number of components, subcomponents, and variables related to countries' prosperity performances can be increased, or country-specific LPI components, subcomponents, and variables can be developed.

5 :
Measurement of Deviations from Ideal and Anti-Ideal Solutions Measurement of Deviations from Ideal Solutions:  + =   −   (12) Measurement of Deviations from Anti-Ideal Solutions:  − =   −   (13) Step 6. Measurement of Deviation Values of Individual Alternatives from Ideal and Anti-Ideal Solutions Measurement of Deviation Value from Ideal Solutions:

Figure 2 .
Figure 2. Radar Visualization of Methods by CountriesWhen examining Figure2, it can be observed that the methods generally do not intersect on the same axis for countries and are generally located at different points accordingly.Especially in Italy, France, the United States, and Japan, the distinction between the methods has become more pronounced, while in Canada, Germany, and the United Kingdom, the proximity of the methods to each other has occurred.Additionally, the visual representation of the separation distance of methods by countries is shown in Figure3

Figure 3 .
Figure 3. Discriminant Visualization of Methods by Countries

Figure 4 .
Figure 4. Discriminant Visualization among LOPCOW-Based MCDM Method When examining Figure 4, it can be observed that the proximity of the LOPCOW-based CRADIS method to the LOPCOW-based ARAS, COPRAS, WASPAS, GRA, MARCOS, MAIRCA, and ROV methods is very high, and the relationships between these methods are positive and very strong.The correlation values among LOPCOW-based MCDM methods are shown in Table17.

Table 2 .
Indexes Associated with LPI

Table 4 .
. LOPCOW and CRADIS Literature As shown inTable 4, LOPCOW and CRADIS techniques are relatively new and up-todate methods in the literature.However, it is observed that LOPCOW is often used to Alphanumeric Journal Volume 11, Issue 2, 2023

Table 5 .
LPI Sub-Components Abbreviations Determination of Criterion Weights (Degrees of Importance (  )

Table 6 .
Decision Matrix Values

Table 8 .
Ranking of Criteria Based on   and Weight Coefficient Values

Table 13 .
Weights of LPI criteria and Rankings of Values According to MethodsWhen Table .

Table 15 .
Correlation Values between LOPCOW, CRITIC, SD, SVP, ENTROPY, and MEREC-Based CRADIS Methods According to Table 15, the LOPCOW-based CRADIS method has significant positive relationships with other weight-based CRADIS methods.In particular, the LOPCOWbased CRADIS method exhibits very high positive correlations with the MEREC-based CRADIS and CRITIC-based CRADIS methods.Furthermore, in terms of methodology, the prosperity performance of countries was measured based on LOPCOW, and the measured values were ranked alongside commonly used methods in the MCDA literature, such as TOPSIS, ARAS, WASPAS, EDAS, ROV, COCOSO, MAIRCA, and MARCOS.These rankings are presented in Table16.

Table 16 .
Prosperity Performance Values and Rankings of Countries According to Different MCDM Methods Based on LOPCOW When Tables

Table 17 .
Correlation Values among LOPCOW-Based MCDM Methods According to Table 17, it has been observed that the LOPCOW-based CRADIS method has the highest level of relationships with LOPCOW-based ARAS, COPRAS, WASPAS, GRA, MARCOS, MAIRCA, and ROV methods.Based on the consistency ranking in Table