Verifiable policy simulation system and method thereof with integrating multi-criteria decision making and system dynamics

Abstract

A policy simulation method includes: providing a validation model; collecting relevant data; using the relevant data to initially simulate and establish an evaluation index; using the evaluation index to provide several preliminary expert questionnaires to obtain preliminary expert questionnaire results; using the preliminary expert questionnaire results to establish a hierarchical architecture of a network procedure analysis method; establishing several ANP expert questionnaires to obtain ANP expert questionnaire results; using the ANP expert questionnaire results to calculate weights to obtain a model weight value; using the ANP expert questionnaire results to establish an index of a system dynamics model; using the ANP expert questionnaire results and the index of the system dynamics model to draw a causal feedback graph; inputting the index of the system dynamics model and the causal feedback graph into the validation model to obtain model validation analysis data; and comparing the expert questionnaire results of the network procedure analysis method with the model validation analysis data to obtain simulation comparison analysis results data.

Description

Technical Field

[0001] This invention relates to a verifiable policy simulation system and method that integrates multi-criteria decision-making and system dynamics, employing an analytical network process (ANP) based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) framework. Specifically, this system and method achieve overall simplification of the processing flow and reduce the amount of data processed. Prior Technology

[0002] Conventional machining strategy analysis systems employing DANP technology include, for example, the Republic of China Patent Publication No. TW-I769798, "Machining Strategy Analysis System for Vertical Cutting Centers," which discloses a machining strategy analysis system for vertical cutting centers. This system includes an acquisition module, a memory module, an information module, and a calculation module, with the acquisition module, memory module, and calculation module coupled to the information module.

[0003] As mentioned above, the computing module of TW-I769798 has a first calculation program, a second calculation program and a third calculation program. The first calculation program adopts the Fuzzy Delphi Method, the second calculation program adopts the Decision-Making Laboratory Method, and the third calculation program is the Network Hierarchical Analysis Method (DANP) based on the Decision-Making Laboratory Analysis.

[0004] Another commonly used development decision system for medical wearable devices employing DANP technology is the Republic of China Patent Publication No. TW-M605334, entitled "Development Decision System for Medical Wearable Devices," which discloses a development decision system for medical wearable devices. This development decision system includes a data processing device, which has an input module, a database, and a processing module.

[0005] Continuing from the above, the processing module of TW-M605334 mentioned above has a key factor calculation program. This program processes an influence relationship matrix into a super matrix, and sums the row vectors of the super matrix to obtain a key factor ranking of the weights of each indicator. Furthermore, the program utilizes the Network Hierarchical Analysis (DANP) method based on decision laboratory analysis to remove the total influence value of each criterion from the influence relationship matrix, transposing it into a normalized influence relationship matrix, and then calculating the super matrix from this normalized influence relationship matrix.

[0006] Another commonly used method for evaluating the level of agricultural modernization development using DANP technology is the invention patent application No. CN-113610444 entitled "An Evaluation Method for the Level of Agricultural Modernization Development Based on the Correlation of Indicators". It discloses an evaluation method for the level of agricultural modernization development based on the correlation of indicators, which includes: Step 1, selecting an evaluation indicator for the level of agricultural modernization development based on an agricultural modernization development goal and the current situation, and establishing an evaluation indicator system for the level of agricultural modernization development.

[0007] Continuing from the above, the evaluation method for the level of agricultural modernization development based on the correlation degree of indicators in CN-113610444 includes: Step 2, based on the correlation degree between evaluation indicators, a grey relational analysis is used to calculate the grey relational degree between each indicator, and a direct influence matrix is ​​constructed. Furthermore, a fuzzy GRA-DANP method is introduced to construct a fuzzy GRA-DANP method in order to determine the weight of each evaluation indicator.

[0008] Continuing from the above, the evaluation method for the level of agricultural modernization development based on the correlation of indicators in CN-113610444 includes: Step 3, using the TOPSIS method to comprehensively evaluate the level of agricultural modernization development. Due to the mutual influence relationships between the evaluation indicators, the fuzzy GRA-DANP method is constructed to calculate the weights to ensure the reliability of each indicator weight. Furthermore, the TOPSIS model is used to evaluate the level of agricultural modernization development, so that the evaluation results are more comprehensive and accurate.

[0009] Another commonly used method and system for comprehensively evaluating the performance of demand response execution in load management using DANP technology is Chinese Patent Publication No. CN-115759711, entitled "Comprehensive Evaluation Method and System for Demand Response Execution Performance in Load Management," which discloses a comprehensive evaluation method for demand response execution performance in load management. This comprehensive evaluation method for demand response execution performance in load management includes: Step 1, constructing an evaluation index system suitable for demand response execution performance in load management.

[0010] Continuing from the above, the comprehensive evaluation method for the performance of demand response to load management as described in CN-115759711 includes: Step 2, obtaining a subjective evaluation weight vector based on the DANP method for the evaluation index system constructed in Step 1; Step 3, obtaining an objective evaluation weight vector based on the anti-entropy weight method for the evaluation index system constructed in Step 1.

[0011] Continuing from the above, the comprehensive evaluation method for the performance of demand response to load management as described in CN-115759711 includes: Step 4, using the subjective evaluation weight vector and the objective evaluation weight vector, a comprehensive evaluation weight is obtained by using a combined weighting method; Step 5, using an evaluation index and a comprehensive evaluation weight, a final evaluation level and a score are obtained by using a gray cloud model.

[0012] Another common application of data transmission methods, apparatus, devices, and dual-connection PRP nodes employing DANP technology is Chinese Patent Publication No. CN-115914138, entitled "Data Transmission Method, Apparatus, Device, and Dual-Connection PRP Node," which discloses a dual-connection Parallel Redundancy Protocol (PRP) node. This dual-connection PRP node includes a decision logic submodule, a two-in-one submodule, and a DANP submodule.

[0013] As mentioned above, the judgment logic sub-module of CN-115914138 is provided with a sending unit and a receiving unit. The sending unit is connected to the receiving unit and the two-in-one sub-module respectively, and determines whether to send a data to be sent via a communication link formed by the receiving unit or via a communication link formed by the two-in-one sub-module according to a destination IP address.

[0014] As mentioned above, the receiving unit of CN-115914138 is connected to the transmitting unit and the DANP sub-module respectively, and determines, based on a source IP address, whether to receive data through a communication link formed by the transmitting unit or through a communication link formed by the DANP sub-module.

[0015] As mentioned above, the dual-mode sub-module of CN-115914138 is connected to the DANP sub-module and merges the data to be transmitted into one data stream, or copies the data received by the DANP sub-module into two data streams. The DANP sub-module is used to copy the data stream and upload it to an optical network and a power grid respectively, or to merge the data from the optical network and the power grid to obtain a received data stream.

[0016] However, the aforementioned Republic of China Patent Publications No. TW-I769798, No. TW-M605334, and Chinese Patent Publications No. CN-113610444, No. CN-115759711, and No. CN-115914138 only disclose various simple applications of DANP technology. Therefore, they do not provide any improvements to DANP technology (e.g., verifiable analysis functions). Thus, there is a need for further improvements to meet the requirements for enhancing the technology.

[0017] Another commonly used method, apparatus, equipment and system for verifying and evaluating toolsets that support intelligent decision-making is Chinese Patent Publication No. CN-115456347, entitled "A Method for Verifying and Evaluating Toolsets that Support Intelligent Decision-Making". It discloses a method for verifying and evaluating toolsets that support intelligent decision-making, which includes the following steps: S1, dividing a toolset that supports intelligent decision-making into several different toolsets, each containing an algorithm and several functional modules.

[0018] Continuing from the above, the verification and evaluation method for the toolset supporting intelligent decision-making in CN-115456347 includes the following steps: S2, verifying the reliability of several different internal component algorithms and several functional modules of the toolset in order to obtain several verification results; S3, using a fuzzy evaluation algorithm to evaluate the several verification results of the toolset.

[0019] However, the aforementioned Chinese patent publication CN-115456347 only discloses the verification technology of a toolset supporting intelligent decision-making, and it is only applicable to the entire process of manufacturing or enterprises in their design, manufacturing, operation and maintenance services. Therefore, it is not applicable to the relevant DANP technology improvement (e.g., integrating multi-criteria decision-making and system dynamics for policy simulation technology). Therefore, it must be further improved to meet the needs of improving its technology.

[0020] Another commonly used method and system for credit verification and evaluation management related to decision support is the invention patent application No. CN-115511596 entitled "A method and system for credit verification and evaluation management to support decision support". It discloses a method for credit verification and evaluation management to support decision support, which includes: performing asset statistics on a user to obtain the user's asset information; and constructing a custom decision model based on an open-source interactive computing engine.

[0021] Continuing from the above, the credit verification assessment management method for the aforementioned decision-making assistance in CN-115511596 includes: inputting the user's asset information into the custom decision model to obtain a user credit assessment result; synthesizing a credit management key using multiple verification information of the user's credit; and encrypting the user's credit assessment result using the credit management key to obtain encrypted information of the user's credit.

[0022] Continuing from the above, the credit verification and assessment management method for assisting decision-making in the aforementioned CN-115511596 includes: establishing a credit model management platform and generating a rule file using the user's encrypted credit information; verifying a business person, and if the business person's verification is successful, obtaining a credit verification request; based on the credit verification request, loading rules into the credit model management platform, and calling, parsing, and deploying the rule file to assist a business person in credit management of a user.

[0023] However, the aforementioned Chinese patent publication CN-115511596 only discloses credit verification and assessment management technology for decision support, and it is only applicable to credit verification and assessment management technology for decision support in the banking or financial industry. Therefore, it does not provide any improvements to the DANP technology (e.g., integrating multi-criteria decision-making with system dynamics for policy simulation technology). Therefore, it must need further improvement to meet the needs of enhancing its improved technology.

[0024] Clearly, the aforementioned Republic of China Patent Publications No. TW-I769798, No. TW-M605334, and Chinese Patent Publications No. CN-113610444, No. CN-115759711, No. CN-115914138, No. CN-115456347, and No. CN-115511596 are merely references to the technical background of this invention and to illustrate the current state of technological development, and are not intended to limit the scope of this invention.

[0025] In view of this, to meet the aforementioned technical problems and needs, the present invention provides a verifiable policy simulation system and method integrating multi-criteria decision-making and system dynamics. It provides a verification model and collects relevant literature or data, and uses this literature or data to initially simulate and establish at least one evaluation index. It also uses this evaluation index to provide several preliminary expert questionnaires to obtain at least one preliminary expert questionnaire result, and uses the preliminary expert questionnaire result to establish a hierarchical architecture for network procedure analysis. Furthermore, it establishes several expert questionnaires for network procedure analysis to obtain at least one expert questionnaire result for network procedure analysis, and then uses the network procedure analysis... The expert questionnaire results of the method are weighted to obtain at least one model weight value. The expert questionnaire results are used to establish at least one system dynamics model index. The expert questionnaire results of the network program analysis method and the index of the system dynamics model are used to draw at least one causal feedback graph. The index of the system dynamics model and the causal feedback graph are input into the validation model to obtain at least one model validation analysis data. The expert questionnaire results of the network program analysis method and the model validation analysis data are compared to obtain at least one simulation comparison analysis result data, so as to improve the technical problems of conventional DANP technology without any improvement to DANP technology. Summary of the Invention

[0026] The main objective of this invention is to provide a verifiable policy simulation system and method that integrates multi-criteria decision-making and system dynamics. It provides a verification model and collects relevant literature or data. Using this literature or data, it initially simulates and establishes at least one evaluation index. Using this evaluation index, it provides several preliminary expert questionnaires to obtain at least one preliminary expert questionnaire result. Using these preliminary expert questionnaire results, it establishes a hierarchical architecture for network procedure analysis (LPA) and establishes several LPA expert questionnaires to obtain at least one LPA expert questionnaire result. Finally, it utilizes the LPA expert questionnaire results... The questionnaire results are weighted to obtain at least one model weight value. At least one system dynamics model index is established using the expert questionnaire results. At least one causal feedback graph is plotted using the expert questionnaire results of the network program analysis method and the index of the system dynamics model. The index of the system dynamics model and the causal feedback graph are input into the validation model to obtain at least one model validation analysis data. The expert questionnaire results of the network program analysis method and the model validation analysis data are compared to obtain at least one simulation comparison analysis result data, so as to achieve the purpose and effectiveness of simplifying the overall processing flow, reducing the amount of data processing, and verifying the analysis.

[0027] To achieve the above objectives, the preferred embodiment of the present invention, a policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis, includes: At least one validation model is used to validate policy simulations; At least one data collection unit is used to collect a relevant document or relevant data; At least one simulated evaluation indicator unit, which uses the relevant data to initially simulate and establish at least one evaluation indicator; At least one preliminary expert questionnaire unit, which uses the evaluation index to provide several preliminary expert questionnaires in order to obtain at least one preliminary expert questionnaire result; At least one hierarchical architecture for network program analysis was established using the preliminary expert questionnaire results; Several ANP expert questionnaire units are used to establish several ANP expert questionnaires in order to obtain expert questionnaire results for at least one network procedure analysis method, and to use the expert questionnaire results of the network procedure analysis method to calculate weights to obtain at least one model weight value; and A computing unit has a causal feedback module, which uses the expert questionnaire results to establish an index of at least one system dynamics model, and uses the expert questionnaire results of the network program analysis method and the index of the system dynamics model to draw at least one causal feedback diagram; The system dynamics model indicators and causal feedback diagrams are input into the validation model to obtain at least one model validation analysis data. The network program analysis method expert questionnaire results and the model validation analysis data are compared to obtain at least one simulation comparison analysis result data.

[0028] In a preferred embodiment of the present invention, the expert questionnaire results and model weight values ​​are sorted in order of decision-making using a sequential optimization method, and the sequential optimization method is selected from the TOPSIS method.

[0029] The verification model in a preferred embodiment of the present invention includes at least one model test, and the model test is used to verify a system dynamics mode.

[0030] In a preferred embodiment of the present invention, the model test is selected from a unit consistency test, a behavior refactoring test, or a combination thereof.

[0031] In a preferred embodiment of the present invention, the computing unit further includes a comparison unit, and the expert questionnaire results of the network program analysis method and the model verification analysis data are compared in the comparison unit.

[0032] In a preferred embodiment of the present invention, the influence relationship of several criteria is employed, and the influence relationship of the several criteria is defined using several ANP expert questionnaires.

[0033] In a preferred embodiment of the present invention, several mutual influence relationship values ​​are used, and these several mutual influence relationship values ​​are obtained from the influence relationship of several criteria.

[0034] In a preferred embodiment of the present invention, the calculation unit further includes at least one direct influence matrix, and the direct influence matrix is ​​established using several of the mutual influence relationship values.

[0035] In a preferred embodiment of the present invention, the calculation unit further includes a normalization module, and uses the direct influence matrix to establish at least one normalized influence matrix, and uses the normalized influence matrix to perform calculations to obtain at least one total influence matrix, and performs normalization processing on the total influence matrix to obtain a normalized total influence matrix, and uses the normalized total influence matrix to perform limit calculations to obtain at least one limited super matrix, so as to obtain the weight values ​​of several criteria.

[0036] To achieve the above objectives, the preferred embodiment of the present invention provides a policy simulation method integrating multi-criteria decision-making and system dynamics for verifiable analysis, comprising: Provide at least one validation model, which is used to validate policy simulations; Collect relevant literature or relevant materials; At least one evaluation indicator should be preliminarily established using relevant literature or materials; Several preliminary expert questionnaires were provided using this evaluation indicator in order to obtain at least one preliminary expert questionnaire result; A hierarchical architecture for network program analysis was established using the preliminary expert questionnaire results. Several expert questionnaires for network program analysis were developed in order to obtain expert questionnaire results for at least one network program analysis method. The results of the expert questionnaire using this network program analysis method are used to calculate weights to obtain at least one model weight value; The results of an expert questionnaire using this network program analysis method were used to establish indices for at least one system dynamics model; At least one causal feedback graph was plotted using the expert questionnaire results and indicators of the system's dynamics model based on this network program analysis method. The system's dynamic mode indicators and causal feedback diagram are input into the validation model to obtain at least one model validation analysis data; and The results of the expert questionnaire and the model validation analysis data of this network program analysis method are compared to obtain at least one simulated comparison analysis result.

[0037] In a preferred embodiment of the present invention, the expert questionnaire results and model weight values ​​are sorted into decision order using a sequential optimization method, wherein the sequential optimization method is selected from the TOPSIS method.

[0038] The verification model in a preferred embodiment of the present invention includes at least one model test, and the model test is used to verify a system dynamics mode.

[0039] In a preferred embodiment of the present invention, the model test is selected from a unit consistency test, a behavior refactoring test, or a combination thereof.

[0040] A comparison unit is used to compare the expert questionnaire results and the model verification analysis data of the preferred embodiment of the present invention.

[0041] In a preferred embodiment of the present invention, several expert questionnaires of the network program analysis method are used to define the influence relationship of several criteria in order to obtain several mutual influence relationship values.

[0042] In a preferred embodiment of the present invention, at least one direct influence matrix is ​​established using several of the mutual influence relationship values, and at least one normalized influence matrix is ​​established using the direct influence matrix.

[0043] In a preferred embodiment of the present invention, the normalized influence matrix is ​​used to perform calculations to obtain at least one total influence matrix, and the total influence matrix is ​​normalized to obtain a normalized total influence matrix.

[0044] In a preferred embodiment of the present invention, the normalized total influence matrix is ​​used to perform limit calculations to obtain at least one limited super matrix in order to obtain the weight values ​​of several criteria. Simple Explanation of the Diagram

[0045] Figure 1: Schematic diagram of a policy simulation system integrating multi-criteria decision-making and system dynamics, based on a preferred embodiment of the present invention, for verifiable analysis. Figure 1A: Schematic diagram of the architecture of the preferred embodiment of the present invention, which integrates multi-criteria decision-making and system dynamics for policy simulation and applies the verifiable analysis of the system and method to marine debris or waste reduction. Figure 2: Flowchart of the method for policy simulation integrating multi-criteria decision-making and system dynamics, based on verifiable analysis of the first preferred embodiment of the present invention. Figure 2A: A schematic diagram of the architecture of a policy simulation system and method for integrating multi-criteria decision-making and system dynamics with verifiable analysis according to a preferred embodiment of the present invention to obtain a corrected reduction in marine debris or waste. Figure 3: A schematic diagram of the causal feedback graph obtained by the policy simulation system and method integrating multi-criteria decision-making and system dynamics for verifiable analysis according to a preferred embodiment of the present invention. Figure 4: A schematic diagram of the policy simulation system and method for verifiable analysis of the present invention, which integrates multi-criteria decision-making and system dynamics, and employs a network program analysis method (DANP) based on decision laboratory analysis for expert integration computation. Figure 4A: Schematic diagram of the architecture of the policy simulation system and method integrating multi-criteria decision-making and system dynamics for verifiable analysis according to a preferred embodiment of the present invention, using expert questionnaire processing. Figure 5: A flowchart illustrating the second preferred embodiment of the present invention, which uses a network procedure analysis method (DANP) based on decision laboratory analysis for expert integration calculations, to perform policy simulation based on verifiable analysis of integrated multi-criteria decision-making and system dynamics. Figure 6: A flowchart illustrating the policy simulation system and method for integrated multi-criteria decision-making and system dynamics based on the third preferred embodiment of the present invention, which employs the network procedure analysis method (DANP) based on decision laboratory analysis for expert integration calculation. Figure 7: Schematic diagram of a policy simulation system integrating multi-criteria decision-making and system dynamics, based on verifiable analysis of the fourth preferred embodiment of the present invention. Implementation

[0046] To fully understand the present invention, preferred embodiments will be described in detail below with reference to the accompanying drawings, but these are not intended to limit the present invention.

[0047] The preferred embodiment of the present invention provides a verifiable analysis of an integrated multi-criteria decision-making and system dynamics policy simulation system. Its methods and operation are applicable to various human-completed questionnaires and related application fields, such as various public opinion surveys and their analysis, various market surveys and their analysis, various industry surveys and their analysis, various environmental surveys and their analysis, various ecological surveys and their analysis, various policy surveys and their analysis, various cultural studies or surveys and their analysis, or other surveys and their analysis. However, it is not intended to limit the scope of the present invention.

[0048] Generally speaking, traditional policy simulation systems and methods that integrate multi-criteria decision-making and system dynamics may fail to fully express or reflect the true mental patterns of respondents due to biases caused by human questionnaire completion. Therefore, the preferred embodiment of the present invention, which uses the Network Process Analysis (DANP) method based on decision laboratory analysis for expert integration calculation, its method and operation can improve the calculation process and solve the problem of biases caused by human questionnaire completion.

[0049] Continuing on, the preferred embodiment of the present invention, which integrates multi-criteria decision-making and system dynamics for policy simulation, along with its methods and operation, is applicable to various verification analysis model tests, such as: boundary adequacy test, structure assessment test, dimensional consistency test, sensitivity analysis test, parameter assessment test, integration error test, behavior reproduction test, behavior anomaly test, surprise behavior test, extreme condition test, family member test, system improvement test, or other verification analysis model tests. However, this is not intended to limit the scope of the present invention.

[0050] Continuing on, the verifiable analysis of the integrated multi-criteria decision-making and system dynamics policy simulation system of the preferred embodiment of the present invention, its method and operation method can be applied to marine waste reduction policy simulation or other policy simulations, and it can be applied to various scenario strategies or policy simulations, such as: zero-plan, policy norms, corporate extended producer responsibility, education and promotion of public participation, government effective or hotspot removal of waste, prevention of garbage entering the ocean or other policy simulations, but it is not intended to limit the scope of the present invention.

[0051] Figure 1 illustrates a schematic diagram of a policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis according to a preferred embodiment of the present invention. Referring to Figure 1, for example, the policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis according to a preferred embodiment of the present invention includes an input unit 1, a data collection unit 1a, a preliminary expert questionnaire unit 10a, an ANP expert questionnaire unit 10b, a calculation unit 2, a simulation evaluation index unit 2a, an ANP hierarchical structure 2b, a system dynamic model index 2c, an output unit 3, and a verification model 4.

[0052] Please refer to Figure 1 again. For example, another preferred embodiment of the present invention, a policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis, includes at least one comparison unit 5 or other units with similar comparison calculation functions, as shown in the lower left corner of Figure 1.

[0053] Please refer to Figure 1 again. For example, the input unit 1 can be selected from a computer data input unit or a unit with similar data input functions. The input unit 1 can be connected to the data collection unit 1a using appropriate technology (e.g., wired or wireless communication). The data collection unit 1a can be selected from a computer data storage unit or a unit with similar data storage functions.

[0054] Please refer again to Figure 1. For example, in another preferred embodiment of the present invention, the computing unit 2 can be connected to various database units by appropriate technical means. The computing unit 2 can be selected from a workstation computer, a desktop computer, a notebook computer, a tablet personal computer, a mobile communication device, a smartphone, or other devices with computer functions, but this is not intended to limit the scope of the present invention.

[0055] Please refer to Figure 1 again. For example, the output unit 3 can be selected from a computer data output unit or a unit with similar data output function, and the input unit 1, data collection unit 1a and output unit 3 can be connected to the computing unit 2 by appropriate technology (e.g., wired or wireless communication).

[0056] Please refer to Figure 1 again. For example, the output unit 3 is connected to the computing unit 2 by appropriate technical means, and the output unit is further connected to a terminal device, a display device, a projection device, a storage device, a server device, an information management center, or any combination thereof by appropriate technical means.

[0057] Please refer to Figure 1 again. For example, the computing unit 2 has the simulation evaluation index unit 2a, the hierarchical architecture of ANP 2b, and the index 2c of the system dynamics model. The simulation evaluation index unit 2a, the hierarchical architecture of ANP 2b, and the index 2c of the system dynamics model can be appropriately configured in the computing unit 2.

[0058] Please refer to Figure 1 again. For example, the verification model 4 can be used to provide verification policy simulation or similar functions, and the verification model 4 can be used to provide a predetermined confidence level, and subsequent related policy simulations can be performed based on the predetermined confidence level generated by the verification model 4.

[0059] Figure 1A illustrates the architecture of a preferred embodiment of the present invention, which integrates multi-criteria decision-making and system dynamics for policy simulation, and applies the method thereof to marine debris or waste reduction. Referring to Figure 1A, for example, the architecture for marine debris or waste reduction includes a first layer (top layer), a second layer (middle layer), and a third layer (bottom layer).

[0060] Please refer to Figure 1A again. For example, the first, second and third layers are arranged from top to bottom. The first layer can be selected as a target layer, the second layer can be selected as a surface layer, and the third layer can be selected as a criterion layer (e.g., an index of system dynamics). Three experts from academia, government, and NGOs can be selected.

[0061] Please refer again to Figure 1. For example, the execution method of the policy simulation system and method integrating multi-criteria decision-making and system dynamics of the preferred embodiment of the present invention is a computer-executable process step, which can be executed on various computer devices, such as workstations, desktop computers, laptops, tablets, mobile communication devices, smartphones or other devices with computer functions, but it is not intended to limit the scope of the present invention.

[0062] Figure 2 illustrates a schematic diagram of the policy simulation method integrating multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention, which corresponds to the policy simulation system integrating multi-criteria decision-making and system dynamics of the preferred embodiment of the present invention in Figure 1.

[0063] Please refer to Figures 1 and 2. The first preferred embodiment of the present invention, the method for policy simulation by integrating multi-criteria decision-making and system dynamics for verifiable analysis, includes step S0: First, for example, at least one or more of the verification models 4 are provided by appropriate technical means (e.g., automated, semi-automated or manual methods), and the verification models 4 are used to verify the policy simulation (e.g., integrated multi-criteria decision-making simulation).

[0064] Please refer to Figures 1 and 2. The verification model 4 can be selected to provide at least one policy simulation calculation verification function, at least one policy simulation calculation analysis function, or other similar calculation execution functions. The verification model 4 can also be selected to be provided to the calculation unit 2 or other units with similar calculation functions.

[0065] Please refer again to Figures 1 and 2. The method for policy simulation of integrated multi-criteria decision-making and system dynamics with verifiable analysis in the first preferred embodiment of the present invention includes step S1: Next, for example, a relevant document or relevant data is collected in the data collection unit 1a or other unit with similar data collection functions by appropriate technical means (e.g., automated, semi-automated or manual methods).

[0066] Please refer to Figures 1 and 2 again. The policy simulation method for integrated multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S2: Next, for example, by means of appropriate technical means (e.g., automated, semi-automated or manual), the relevant literature or relevant data may be used to conduct a preliminary simulation to establish at least one evaluation index, or it may be applied to the computing unit 2 or other units with similar computing functions.

[0067] Please refer again to Figures 1 and 2. The policy simulation method for integrated multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S3: Next, for example, using appropriate technical means (e.g., automated, semi-automated or manual), several preliminary expert questionnaires (e.g., Delphi method expert questionnaires) are provided through the preliminary expert questionnaire unit 10a using the evaluation index, so as to obtain at least one preliminary expert questionnaire result, or it can be selected to be applied to the calculation unit 2 or other units with similar calculation functions.

[0068] Please refer again to Figures 1 and 2. The policy simulation method for integrated multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S4: Next, for example, using appropriate technical means (e.g., automated, semi-automated or manual), a hierarchical architecture of network program analysis (ANP) is established using the preliminary expert questionnaire results, or it can be applied to the computing unit 2 or other units with similar computing functions.

[0069] Figure 2A illustrates the architecture of a policy simulation system and method for verifyable analysis integrating multi-criteria decision-making and system dynamics according to a preferred embodiment of the present invention, which obtains a revised reduction in marine debris or waste. Referring again to Figures 1, 2, and 2A, the policy simulation method for verifyable analysis integrating multi-criteria decision-making and system dynamics according to a first preferred embodiment of the present invention includes step S5: Next, for example, using appropriate technical means (e.g., automated, semi-automated, or manual methods), several expert questionnaires for network procedure analysis are established via the ANP expert questionnaire unit 10b to obtain at least one expert questionnaire result for network procedure analysis, or it can be selectively applied to the computing unit 2 or other units with similar computing functions.

[0070] Please refer again to Figures 1 and 2. The method for policy simulation by integrating multi-criteria decision-making and system dynamics in the first preferred embodiment of the present invention includes step S6: Next, for example, by means of appropriate technology (e.g., automated, semi-automated or manual), the weights can be calculated using the expert questionnaire results of the network program analysis method in the computing unit 2 or other units with similar computing functions to obtain at least one model weight value (as shown in Table 2), or it can be applied to the computing unit 2 or other units with similar computing functions.

[0071] Please refer again to Figures 1 and 2. The policy simulation method for integrated multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S7: Next, for example, by means of appropriate technical means (e.g., automated, semi-automated or manual), at least one indicator of the system dynamics model (such as the third layer shown in Figure 2A, i.e. the criterion layer as the indicator of system dynamics) 2c can be established in the computing unit 2 or other units with similar computing functions using the expert questionnaire results of the network program analysis method, or it can be applied to the computing unit 2 or other units with similar computing functions.

[0072] Figure 3 illustrates a schematic diagram of the causal feedback graph obtained by the policy simulation system and method integrating multi-criteria decision-making and system dynamics of the present invention, according to a preferred embodiment of the present invention. Referring again to Figures 1, 2, and 3, the policy simulation method integrating multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S8: Next, for example, using appropriate technical means (e.g., automated, semi-automated, or manual methods), at least one causal feedback graph 30 can be drawn in the computing unit 2 or other units with similar computing functions using the expert questionnaire results of the network program analysis method and the index 2c of the system dynamics model, as shown in Figures 1 and 3.

[0073] Please refer again to Figures 1, 2 and 3. The policy simulation method for integrated multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S9: Next, for example, by means of appropriate technical means (e.g., automated, semi-automated or manual), the index 2c of the system dynamics model and the causal feedback graph 30 can be input into the verification model 4 in the computing unit 2 or other units with similar computing functions to obtain at least one model verification analysis data 40.

[0074] Please refer to Figures 1, 2, and 3 again. For example, the verification model includes at least one model test to verify a system dynamics pattern. The model test can be selected from a unity consistency test, a behavior reengineering test, or a combination thereof. The model test can also be selected from a model boundary suitability test, a model structure evaluation test, a sensitivity test, a parameter verification test, an integration error test, a behavior anomaly test, an unexpected behavior test, an extreme condition test, a family member test, a system improvement test, or other verification analysis model tests.

[0075] Please refer again to Figures 1, 2, and 3. The policy simulation method for integrated multi-criteria decision-making and system dynamics of the first preferred embodiment of the present invention includes step S10: Next, for example, by means of appropriate technical means (e.g., automated, semi-automated, or manual), the expert questionnaire results of the network program analysis method and the model verification analysis data 40 can be compared by the comparison unit 5 in the computing unit 2 or other units with similar computing functions to obtain at least one simulation comparison analysis result data 50.

[0076] Figure 4 illustrates a preferred embodiment of the present invention, a policy simulation system and method integrating verifiable analysis of multi-criteria decision making and system dynamics, employing a network procedure analysis based on decision laboratory analysis (DANP) for expert integration computation. Referring to Figure 4, for example, the preferred embodiment of the expert integration computation system using DANP includes an input unit 1, an expert questionnaire unit 10, a calculation unit 2, and an output unit 3.

[0077] Please refer to Figure 4. For example, the computation unit 2 has at least one normalization module 20. Generally speaking, there are two main normalization methods: the first uses the maximum value among the sums of all column vectors as the normalization benchmark, and the second uses the maximum value among the sums of all row vectors and column vectors as the normalization benchmark.

[0078] Figure 4A illustrates the architecture of a policy simulation system and method for integrating multi-criteria decision-making and system dynamics, based on verifiable analysis of a preferred embodiment of the present invention, using expert questionnaire processing. This corresponds to the expert integration computation system in Figure 4, which uses the Network Program Analysis Method (DANP) based on decision laboratory analysis.

[0079] Please refer to Figures 4 and 4A. For example, the expert questionnaire unit 10 contains several expert questionnaires 11, and these expert questionnaires 11 are prepared and processed by several experts (e.g., by completion or other tasks). Generally, the system has various criteria, and these criteria are appropriately compared by executing the expert questionnaires 11.

[0080] Figure 5 illustrates a schematic diagram of the policy simulation system and method for verifiable analysis integrating multi-criteria decision-making and system dynamics according to the second preferred embodiment of the present invention, which employs the Network Process Analysis Based on Decision Laboratory Analysis (DANP) method for expert integration calculation. Referring to Figures 4, 4A, and 5, the expert integration calculation method for the Network Process Analysis Based on Decision Laboratory Analysis (DANP) method according to the second preferred embodiment of the present invention includes step S10: First, for example, using appropriate technical means (e.g., automated, semi-automated, or manual methods), several expert questionnaires 11 are used to define the influence relationships of several criteria 21 in order to obtain several mutually influent relation values ​​22.

[0081] Please refer again to Figures 4, 4A, and 5. For example, in a preferred embodiment of the present invention, the influence relationship 21 of several criteria is compared pairwise using several expert questionnaires 11. The mutual influence relationship value 22 includes no influence, low influence, moderate influence, high influence, and extremely high influence. The mutual influence relationship values ​​of no influence, low influence, moderate influence, high influence, and extremely high influence have a predetermined scale, and the predetermined scale includes influence degree value 0, influence degree value 1, influence degree value 2, influence degree value 3, and influence degree value 4.

[0082] Please refer again to Figures 4, 4A and 5. The second preferred embodiment of the method for expert integration calculation based on Decision Laboratory Analysis (DANP) includes step S20: Next, for example, at least one initial-influence matrix X is established using several of the mutual influence relationship values ​​22 by appropriate technical means (e.g., automated, semi-automated or manual methods).

[0083] In a preferred embodiment of the present invention, after defining the magnitude of the influence, a direct influence matrix X can be established. If there are n evaluation criteria, the criteria are compared pairwise according to their degree of influence, presenting an n*n direct influence matrix. [X=[X] ij []],in [X]ij represents the degree to which criterion i influences criterion j, while the diagonal of the matrix represents the degree to which each criterion influences itself. Since it is considered to have no influence, it is set to 0.

[0084] The direct influence matrix of the preferred embodiment of the present invention [X] is as follows: in [X]ij represents the degree to which criterion i influences criterion j.

[0085] Please refer again to Figures 4, 4A and 5. The second preferred embodiment of the method for expert integration calculation based on the Decision Laboratory Analysis-based Network Program Analysis (DANP) of the present invention includes step S30: Next, for example, at least one normalized direct-influence matrix (i.e., normalized influence relation matrix) is established using the direct influence matrix X with appropriate technical means (e.g., automated, semi-automated or manual methods).

[0086] The normalized influence matrix of the preferred embodiment of the present invention [D] is as follows: The normalized influence matrix is: [D], where the normalization datum is k.

[0087] Please refer again to Figures 4, 4A and 5. The second preferred embodiment of the method for expert integration calculation based on Decision Laboratory Analysis (DANP) includes step S40: Next, for example, the normalized influence matrix D is calculated using appropriate technical means (e.g., automated, semi-automated or manual) to obtain at least one total-influence matrix T, i.e., the total influence relationship matrix.

[0088] Please refer again to Figures 4, 4A and 5. For example, in the second preferred embodiment of the present invention, the total influence matrix T (i.e., the total influence relationship matrix) can be formed by appropriately combining the direct influence matrix X (i.e., the direct influence relationship matrix) with an indirect influence matrix ID (i.e., the indirect influence relationship matrix).

[0089] In a preferred embodiment of the present invention, after obtaining the normalized influence matrix D (i.e., the normalized influence relation matrix), the total influence matrix T (i.e., the total influence relation matrix) is equivalent to the direct influence matrix D (i.e., the direct influence relation matrix) plus the indirect influence matrix ID (i.e., the indirect influence relation matrix).

[0090] Continuing on the above, in a preferred embodiment of the present invention, computationally, a sub-stochastic matrix can be obtained by similarly deleting rows or columns that lead to absorbing states from an absorbing Markov chain, thereby obtaining the following: Where 0 is the zero matrix, and [I] is the identity matrix.

[0091] The total influence matrix T of the preferred embodiment of the present invention is as follows:

[0092] Please refer again to Figures 4, 4A and 5. The second preferred embodiment of the method for expert integration calculation based on Decision Laboratory Analysis (DANP) includes step S50: Next, for example, the total influence matrix T is normalized by appropriate technical means (e.g., automated, semi-automated or manual) to obtain a normalized total-influence matrix TC.

[0093] In the second preferred embodiment of the present invention, the total influence matrix of the criteria calculated in the previous step is listed in matrix TC, and its formula is as follows: Where tij is the influence value of criterion i on criterion j, and n is the total number of criteria.

[0094] In a second preferred embodiment of the present invention, the normalization criterion of the total influence matrix T is calculated, and fi is defined as the normalization criterion. The formula is as follows:

[0095] In the second preferred embodiment of the present invention, the "influence value" criterion is used to normalize the "influenced value" of the total influence matrix T. The formula for normalizing the total influence matrix TC* is as follows:

[0096] Please refer again to Figures 4, 4A and 5. In the second preferred embodiment of the present invention, each criterion and its definition are determined using the Delphi method. When defining the influence relationship of several criteria in several expert questionnaires, a surface influence is omitted. When normalizing the total influence matrix, a unified normalization operation is used.

[0097] Please refer again to Figures 4, 4A and 5. The second preferred embodiment of the method for expert integration calculation based on Decision Laboratory Analysis (DANP) includes step S60: Next, for example, using appropriate technical means (e.g., automated, semi-automated or manual), limit calculation is performed using the normalized total influence matrix TC to obtain at least one limited supermatrix L in order to obtain the weight of criteria 23.

[0098] The second preferred embodiment of the present invention adopts the influence of the criterion as a computable surface, so there is no need to perform weighted operation on the super matrix of the criterion. Since the sum of each row vector of the weighted super matrix of the criterion is 1, the value of each column of the matrix tends to be stable when performing limit operation, and this characteristic is used to calculate the weight value of each criterion.

[0099] In the second preferred embodiment of the present invention, the limiting supermatrix L is obtained by continuously multiplying a weighted supermatrix by itself until the weighted supermatrix reaches a stable state.

[0100] The formula for the extreme supermatrix L in the second preferred embodiment of the present invention is as follows:

[0101] In a second preferred embodiment of the present invention, the weighted supermatrix is ​​continuously self-multiplied until it reaches a stable state, where m is the number of self-multiplications performed when the stable state is reached, and the weight values ​​of each criterion under each surface are added together to obtain the weight value of each surface.

[0102] Figure 6 illustrates a schematic diagram of the policy simulation system and method for verifiable analysis integrating multi-criteria decision-making and system dynamics according to the third preferred embodiment of the present invention. This system employs a network procedure analysis based on decision laboratory analysis (DANP) method for expert integration calculation. This corresponds to the expert integration calculation method using DANP based on decision laboratory analysis in Figure 5. Referring to Figures 5 and 6, compared to the second preferred embodiment, the expert integration calculation method using DANP based on decision laboratory analysis in the third preferred embodiment of the present invention has a more complex overall processing flow and increases the amount of data processed.

[0103] Please refer to Figures 4, 4A and 6. The expert integration calculation method based on Decision Laboratory Analysis (DANP) in the third preferred embodiment of the present invention includes step S10a: First, for example, using appropriate technical means (e.g., automated, semi-automated or manual), several expert questionnaires 11 are used to define the influence relationship 21 of several criteria in order to obtain several mutual influence relationship values ​​22 (as shown in step S10 of Figure 5).

[0104] Please refer again to Figures 4, 4A and 6. The expert integration calculation method based on Decision Laboratory Analysis (DANP) in the third preferred embodiment of the present invention includes step S20a: Next, for example, at least one direct influence matrix X is established using several of the mutual influence relationship values ​​by appropriate technical means (e.g., automated, semi-automated or manual methods) (as shown in step S20 of Figure 5).

[0105] Please refer again to Figures 4, 4A and 6. The expert integration calculation method based on the Decision Laboratory Analysis Network Program Analysis (DANP) of the third preferred embodiment of the present invention includes step S30a: Next, for example, at least one normalized influence matrix D is established using the direct influence matrix X using appropriate technical means (e.g., automated, semi-automated or manual methods) (as shown in step S30 of Figure 5).

[0106] Please refer again to Figures 4, 4A and 6. The expert integration calculation method based on Decision Laboratory Analysis (DANP) in the third preferred embodiment of the present invention includes step S40a: Next, for example, the normalized influence matrix is ​​used to perform calculations using appropriate technical means (e.g., automated, semi-automated or manual) to obtain at least one total influence matrix T (as shown in step S40 of Figure 5).

[0107] Please refer again to Figures 4, 4A and 6. The expert integration calculation method based on Decision Laboratory Analysis (DANP) in the third preferred embodiment of the present invention includes step S50a: Next, for example, the total influence matrix T is normalized and transposed by appropriate technical means (e.g., automated, semi-automated or manual) to obtain an unweighted super-matrix W.

[0108] The total influence matrix TC of the calculation criteria in the third preferred embodiment of the present invention is as follows: Where tij is the influence value of criterion i on criterion j, and n is the total number of criteria.

[0109] In the third preferred embodiment of the present invention, when calculating the normalization criterion of the total influence matrix T, the normalization criterion is set as fi, and the calculation method is as follows: The fi values ​​must be calculated separately for different planes. For example, criteria 1 to 5 belong to the first plane, and criteria 6 to 13 belong to the second plane. The normalization criterion for the first 5 rows of values ​​contained in the first plane is calculated by summing the column vectors of the first 5 rows in matrix TC, which yields n fi values. The normalization criterion for the values ​​of the 6th to 13th rows of values ​​contained in the second plane is calculated by summing the column vectors of the 6th to 13th rows in matrix TC, which yields n fi values.

[0110] In the third preferred embodiment of the present invention, the total influence matrix T is normalized as follows: The normalization calculation must be performed according to the surface. Following the assumptions of the previous step, the normalization process for the first 5 rows of values ​​in the first surface of the normalized total influence matrix is ​​to divide the values ​​of the first 5 rows by the normalization reference value of each column, and perform n calculations for each row. The normalization process for the values ​​of the 6th to 13th rows of values ​​in the second surface is to divide the values ​​of the 6th to 13th rows by the normalization reference value of each column, and perform n calculations for each row.

[0111] In the third preferred embodiment of the present invention, the unweighted supermatrix W is obtained by normalizing and transposing the total influence matrix T as follows:

[0112] Please refer again to Figures 4, 4A and 6. The expert integration calculation method based on Decision Laboratory Analysis (DANP) in the third preferred embodiment of the present invention includes step S60a: Next, for example, weighted calculation is performed using the unweighted supermatrix W by appropriate technical means (e.g., automated, semi-automated or manual) to obtain at least one weighted supermatrix.

[0113] In a third preferred embodiment of the present invention, the total influence matrix of the surface obtained by DEMATEL can be selected as the basis for weighted calculation of the unweighted super matrix W, and the total influence matrix of the surface calculated in the previous step can be normalized to form TD, which has the following form: Where tDij is the total influence value of surface i on surface j, and n is the total number of surfaces.

[0114] In the third preferred embodiment of the present invention, when calculating the normalization datum of the surface, the normalization datum is set to vi, and the total influence matrix of the normalized surface is set to TD*, the formula of which is as follows:

[0115] In the third preferred embodiment of the present invention, after transposing the normalized total influence matrix of the surface, a weighted supermatrix S is obtained by multiplying it by a weighted value according to the relative relationship of each surface in the unweighted supermatrix. The formula is as follows:

[0116] In the third preferred embodiment of the present invention, after normalizing the total influence matrix of the surface, it must be transposed, and then weighted according to the relative positions after transposition. Otherwise, the property that the sum of the row vectors of the weighted super matrix is ​​1 cannot be satisfied, which would lead to the inability to perform the operation of the limit super matrix. In addition, when performing weighted calculation, attention must also be paid to the relative positions of the criteria contained in the surface, otherwise calculation errors are easy to occur.

[0117] Please refer again to Figures 4, 4A and 6. The expert integration calculation method based on Decision Laboratory Analysis (DANP) in the third preferred embodiment of the present invention includes step S70a: Next, for example, limit calculation is performed using the weighted supermatrix with appropriate technical means (e.g., automated, semi-automated or manual) to obtain at least one limited supermatrix L in order to obtain the weight values ​​23 of several criteria (as shown in step S60 of Figure 5).

[0118] In the third preferred embodiment of the present invention, since the sum of the row vectors of the weighted supermatrix S is 1, the values ​​of each column of the matrix tend to stabilize during limit operations. Therefore, this characteristic is used to calculate the weight values ​​of each criterion. The limitized supermatrix L of the third preferred embodiment of the present invention is as follows:

[0119] In a third preferred embodiment of the present invention, the weighted supermatrix is ​​continuously multiplied by itself until the weighted supermatrix reaches a stable state, where m is the number of self-multiplications performed when the stable state is reached.

[0120] Please refer to Figures 5 and 6 again. Compared with the second preferred embodiment (steps S10 to S60 as shown in Figure 5), the expert integration calculation method of the third preferred embodiment of the present invention based on the Decision Laboratory Analysis-based Network Program Analysis (DANP) has a more complex overall processing flow and increases the amount of data processed (steps S10a to S70a as shown in Figure 6).

[0121] Figure 7 illustrates a schematic diagram of a policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis according to a fourth preferred embodiment of the present invention. Referring again to Figure 7, compared to the first embodiment, the policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis according to a fourth preferred embodiment of the present invention further includes a decision ranking unit 100.

[0122] Please refer to Figure 7 again. For example, the decision ranking unit 100 has at least one sequential optimization method. The expert questionnaire results and model weight values ​​are ranked according to this sequential optimization method. This sequential optimization method is selected from the TOPSIS method, which is a multi-criteria decision-making method applied to multi-attribute decision-making models under certain circumstances. It first defines its ideal solution and negative ideal solution. The positive ideal solution has the highest evaluation value of the benefit attribute and the lowest evaluation value of the cost attribute among all alternative solutions. Conversely, the negative ideal solution has the lowest evaluation value of the benefit attribute and the highest evaluation value of the cost attribute among all alternative solutions. Therefore, when selecting a solution, the decision solution is compared with the best solution and the worst solution in terms of their relative distance, which serves as a method for ranking the merits of the solutions.

[0123] First, assume the decision matrix is ​​D: n*m and the weight matrix is ​​W: 1*m (Σw i = 1). The preferred embodiment of the TOPSIS method of this invention includes step one: calculating the normalized evaluation value; to avoid the influence of extreme qualities, the scoring matrix must be normalized to reduce the difference in scores among different options under the same attribute, and also to eliminate the different attribute value ranges resulting from different scales for different attributes. The calculation is as follows: xij is the original evaluation value of the i-th scheme for the j-th attribute. Next, the TOPSIS method of the preferred embodiment of the present invention includes step two: calculating the weighted normalized evaluation value; after determining the relative weights of each attribute, multiplying the normalized evaluation value by the criterion weight, and the calculation is as follows: v ij =W jr ij i represents the scheme, j represents the attribute, and wj represents the weight value of the j-th attribute. Next, the TOPSIS method of the preferred embodiment of the present invention includes step three: determining the ideal solution A* and the negative ideal solution A-, and using the positive ideal solution A* and the negative ideal solution A- respectively. A-={V1-,V2-,...,VN-}. Next, the preferred embodiment of the TOPSIS method of the present invention includes step four: calculating the Euclidean distance between the ideal solution and the negative ideal solution, wherein the Euclidean distances of the positive ideal solution and the negative ideal solution are respectively... Next, the TOPSIS method of the preferred embodiment of the present invention includes step five: calculating the relative approximation of each alternative solution to the ideal solution, and the calculation is as follows: Where 0 ≦ Ci* ≦ 1. Next, the TOPSIS method of the preferred embodiment of the present invention includes step six: ranking the alternative schemes according to their relative similarity and, based on the calculated... The order of priority of the sorting schemes is determined by the size of the values. The higher the value, the greater the preference for the option, and vice versa. Next, in a preferred embodiment of the present invention, after determining the indices of the system dynamics model, the indices established by the Delphi method are applied to the construction of the system dynamics model. These indices will serve as the inputs and outputs of the model, used to describe and evaluate the behavior and effects of the system.

[0124] The foregoing preferred embodiments are merely illustrative of the present invention and its technical features. The technology of these embodiments can still be implemented with various substantially equivalent modifications and / or substitutions; therefore, the scope of the present invention must be determined by the scope defined in the appended claims. This copyright application is limited to the uses specified in the Republic of China patent application.

[0125] 1: Input Unit 1a: Data Collection Unit 10: Expert Questionnaire Unit 10a: Preliminary Expert Questionnaire Unit 10b: ANP Expert Questionnaire Unit 11: Expert Questionnaire 100: Decision Ranking Unit 2: Calculation Unit 2a: Simulation evaluation index unit 2b: Hierarchical Architecture of ANP 2c: Indicators of System Dynamics Model 20: Regularization Module 21: The Influence of Criteria 22: Mutual Influence Relationship Value 23: Weight values ​​of the criteria 3: Output Unit 30: Cause-and-effect feedback diagram 4: Validate the model 40: Model Validation Analysis Data 5: Comparison Unit 50: Simulation comparison analysis results data

Claims

1. A policy simulation system integrating multi-criteria decision-making and system dynamics for verifiable analysis, comprising: at least one validation model for validating policy simulation; at least one data collection unit for collecting relevant literature or data; at least one simulation evaluation indicator unit for initially simulating and establishing at least one evaluation indicator using the relevant data; at least one preliminary expert questionnaire unit for providing several preliminary expert questionnaires using the evaluation indicator to obtain at least one preliminary expert questionnaire result; and at least one hierarchical architecture of network procedure analysis for establishing the hierarchical architecture of network procedure analysis using the preliminary expert questionnaire result. The system comprises several ANP expert questionnaire units for establishing several ANP expert questionnaires to obtain expert questionnaire results for at least one network procedure analysis method, and for calculating weights using the expert questionnaire results of the network procedure analysis method to obtain at least one model weight value; and a calculation unit having a causal feedback module, which establishes an index of at least one system dynamics model using the expert questionnaire results, and plots at least one causal feedback graph using the expert questionnaire results of the network procedure analysis method and the index of the system dynamics model; wherein the index of the system dynamics model and the causal feedback graph are input into the validation model to obtain at least one model validation analysis data, and the expert questionnaire results of the network procedure analysis method and the model validation analysis data are compared to obtain at least one simulation comparison analysis result data.

2. A policy simulation system integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 1, wherein the expert questionnaire results and model weight values ​​are sorted in order of decision-making using a sequential optimization method.

3. A policy simulation system integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 1, wherein the verification model includes at least one model test, and the model test is used to verify a system dynamics pattern.

4. A policy simulation system integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 3, wherein the model test is selected from a unit consistency test, a behavior reengineering test, or a combination thereof.

5. A policy simulation system integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 1, wherein the calculation unit further includes a comparison unit, and the expert questionnaire results of the network program analysis method and the model verification analysis data are compared in a comparison unit.

6. A verifiable analysis-integrated multi-criteria decision-making and system dynamics approach for policy simulation, comprising: providing at least one validation model for validating policy simulation; collecting relevant literature or data; using the relevant literature or data to initially simulate and establish at least one evaluation indicator; using the evaluation indicator to provide several preliminary expert questionnaires to obtain at least one preliminary expert questionnaire result; using the preliminary expert questionnaire result to establish a hierarchical framework for network procedure analysis; establishing several expert questionnaires for network procedure analysis to obtain at least one expert questionnaire result for network procedure analysis; using the expert questionnaire result for network procedure analysis to perform weight calculations to obtain at least one model weight value; using the expert questionnaire result for network procedure analysis to establish an indicator for at least one system dynamics model; and using the expert questionnaire result for network procedure analysis and the indicator for the system dynamics model to plot at least one causal feedback graph. The system dynamics model indicators and causal feedback diagram are input into the validation model to obtain at least one model validation analysis data; and the expert questionnaire results of the network program analysis method and the model validation analysis data are compared to obtain at least one simulation comparison analysis result data.

7. A policy simulation method integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 6, wherein the expert questionnaire results and model weight values ​​are sorted into decision order using a sequential optimization method.

8. A policy simulation method integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 7, wherein the verification model includes at least one model test, and the model test is used to verify a system dynamics pattern.

9. A policy simulation method integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 8, wherein the model test is selected from a unit consistency test, a behavior reengineering test, or a combination thereof.

10. A policy simulation method integrating multi-criteria decision-making and system dynamics based on verifiable analysis according to claim 6, wherein the expert questionnaire results of the network procedure analysis method and the model verification analysis data are compared in a comparison unit.

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