CITES convention contract performance negotiation strategy simulation and optimization method based on game theory

Abstract

The invention discloses a CITES convention conclusion performance negotiation strategy simulation and optimization method based on a game theory, and relates to the technical field of performance negotiation, and the method comprises the following specific steps: building a data support system: building a database for classified storage of connective party attributes, subject correlation, historical negotiation and game parameter data, establishing a data interaction standard, a multi-check correction mechanism and an update iteration mechanism; according to the method, strategy simulation deduction is carried out by constructing the game model, decision responses, interactive behaviors and negotiation state changes of all connective parties can be accurately recorded, a standardized simulation result data set covering strategy execution paths, interest appeal satisfaction conditions and the like can be generated, comprehensive and accurate data support is provided for strategy optimization, and in the strategy optimization link, the strategy optimization efficiency is improved. A strategy evaluation system covering multiple core dimensions is constructed, index weights are configured by adopting a scientific method, an evaluation standard and a grading threshold are defined, a strategy evaluation score is calculated through a multi-objective optimization algorithm model, and an optimization direction and an adjustment amplitude are accurately determined.

Description

Technical Field

[0001] This invention relates to the field of contract compliance negotiation technology, specifically to a simulation and optimization method for CITES contract compliance negotiation strategies based on game theory. Background Technology

[0002] The CITES Convention aims to strictly control trade in endangered wild flora and fauna through cooperation in order to protect biodiversity. CITES implementation negotiations, as a crucial link in promoting the effective implementation of the Convention, involve numerous contracting parties. These parties differ significantly in resource endowment due to their geographical location, economic development level, and ecological resource status. Furthermore, based on economic, social, and other considerations, they have formed different policy objectives and interests. Against this backdrop, the contracting parties have developed complex and diverse game relationships in the implementation negotiations. The negotiation process is full of strategic interactions and balancing of interests. How to formulate scientific and effective negotiation strategies to satisfy their own interests while promoting negotiations in a direction conducive to the implementation of the Convention has become an important issue that urgently needs to be addressed.

[0003] Currently, the formulation of CITES compliance negotiation strategies mainly relies on experience-based judgment or static analysis. While experience-based judgment can provide strategic direction to some extent based on past negotiation experience, it lacks in-depth consideration of the dynamic interactions among contracting parties in the current complex negotiation scenario, making it difficult to accurately grasp the strategic adjustment trends of all parties. Static analysis, on the other hand, focuses on the analysis of specific moments or single factors, failing to comprehensively capture the dynamic changes and mutual influences among various factors during the negotiation process, and cannot well adapt to the dynamic characteristics of the negotiation scenario. This approach to negotiation strategy formulation based on experience-based judgment and static analysis, due to its failure to fully consider the interactive game logic among contracting parties and its lack of systematic support for strategic adjustments during the negotiation process, results in significant deficiencies in the targetedness and adaptability of the formulated strategies. It is difficult for these strategies to play an effective role in complex and ever-changing negotiation scenarios and cannot provide accurate and reliable technical support for negotiation decision-making. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies by providing a game theory-based method for simulating and optimizing CITES Convention compliance negotiation strategies. This method establishes a data support system, categorizes, stores, and verifies various negotiation-related data to ensure the reliability and timeliness of input data, providing a solid foundation for subsequent steps. Based on this data, it constructs a game theory model, accurately characterizing the attributes, relationships, and decision-making logic of contracting parties, providing clear and realistic support for the construction of game scenarios. Next, it decomposes and maps negotiation topics, clarifying topic boundaries, relationships, and interests, providing core content support for the game model. Based on the game theory framework, it constructs a negotiation game model, simulates and deduces the game process, and generates a standardized simulation result dataset. Finally, it iteratively optimizes the strategy using a multi-objective optimization algorithm, generating optimized strategy solutions adapted to different negotiation scenarios. This method can flexibly adapt to negotiation scenarios with different topics and combinations of contracting parties, providing precise and scientific technical support for CITES Convention compliance negotiations.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a simulation and optimization method for CITES Convention compliance negotiation strategies based on game theory, comprising the following specific steps:

[0006] Data support system construction: Build a database for classifying and storing contracting party attributes, issue-related data, historical negotiation and game parameters, and establish data interaction standards, multi-verification and correction mechanisms, and update and iteration mechanisms;

[0007] Game subject modeling: Based on database data, define multi-dimensional subject attribute dimensions, construct subject attribute models containing decision variables and constraints, establish a system of relationships between contracting parties and extract decision logic, and form a decision rule library covering different scenarios;

[0008] Negotiation topic decomposition and mapping: Based on the CITES Convention compliance requirements, core topic categories are divided, a correlation analysis algorithm is used to construct a topic correlation system and determine priorities, and an interest-topic mapping model is established to achieve precise correlation between the interests of contracting parties and topics;

[0009] Game strategy simulation: Integrate the subject model and issue mapping results to construct a negotiation game model, input the initial strategy set and advance the simulation according to preset rules, record the decision response and negotiation status changes of the contracting parties in real time, and generate a standardized simulation result dataset;

[0010] Multi-objective optimization and adjustment of strategy: Construct a strategy evaluation system, input simulation results to determine the optimization direction through multi-objective optimization algorithm, establish iterative optimization mechanism and convergence judgment rules, and output the optimized strategy scheme and adaptation description after cyclic verification.

[0011] Furthermore, in the data support system construction steps, a CITES Convention implementation negotiation database is built to classify and store contracting party attribute data, issue-related data, historical negotiation data, and game parameter data. At the same time, a unified data interaction standard and interface specification are established. On this basis, a multi-layered data verification and correction mechanism is constructed, including integrity verification, consistency verification, and rationality verification. An automatic warning and manual correction process is triggered for abnormal data. At the same time, a data update and iteration mechanism is designed, with clear data update cycles and triggering conditions set, supporting both manual update and automatic synchronization modes, so as to adapt to changes in negotiation scenarios and the needs of data type expansion.

[0012] Furthermore, in the game-playing entity modeling step, the specific steps for defining multi-dimensional entity attribute dimensions based on database data and constructing an entity attribute model containing decision variables and constraints are as follows:

[0013] Based on the economic strength, policy orientation, resource reserves, compliance records, and regulatory compatibility data of the contracting parties stored in the database, the controllable variables directly related to negotiation behavior in the subject attribute model are defined as core decision variables, including the choice of negotiation position, the setting of the concession range of the issue, the proposal of cooperation conditions, and the planning of the time limit for compliance commitments. At the same time, the value range and expression norms of each decision variable are clarified.

[0014] By combining the requirements of the CITES Convention, the resource carrying capacity limits of the contracting parties, relevant policy restrictions, and the boundary factors of the responsibility for fulfilling the Convention, a system of constraints for the model is constructed, and the insurmountable boundaries and logical constraints of the decision variables are defined.

[0015] Subsequently, the original data was normalized using data standardization methods. Based on the correlation logic between variables and constraints, the characteristic parameters corresponding to each decision variable were extracted. The initial values ​​of the parameters were then assigned by referring to historical negotiation behavior data and the quantitative statements in the publicly available policy documents of the contracting parties.

[0016] Establish a parameter rationality verification mechanism, eliminate abnormal parameters through cross-validation and logical consistency checks, and iteratively correct the parameters by combining the priority weights of the contracting party attribute dimensions to achieve accurate configuration of the subject attribute model parameters.

[0017] Furthermore, in the modeling steps of the game actors, the specific steps for establishing the inter-party relationship system and extracting decision-making logic are as follows: Based on the records of cooperative projects, policy coordination documents, minutes of interest dispute events, and historical negotiation position interaction data stored in the database, firstly, the core types of inter-party relationships are defined, including cooperative, competitive, neutral, and mixed relationships. Then, a multi-dimensional relationship evaluation index system is constructed, covering dimensions such as interaction frequency, cooperation depth, interest convergence, policy coordination, and conflict resolution efficiency. A quantitative evaluation method is used to convert the indicators of each dimension into quantitative values ​​of relationship strength, forming an inter-party relationship matrix. Simultaneously, the transmission paths of relationships are analyzed, clarifying the mechanisms of direct and indirect relationships. This study identifies key nodes and constraints in the transmission of influence. Based on this, using text mining and logical analysis methods, it extracts decision-making triggering conditions, basis for position adjustments, and standards for balancing interests under different relational scenarios from publicly declared statements of contracting parties, policy documents, historical negotiation and decision-making records, and adapted expressions of convention clauses. It categorizes and analyzes the collaborative decision-making logic in cooperation scenarios, the interest game logic in competition scenarios, the concession and negotiation logic in conflict scenarios, and the dynamic adjustment logic in mixed scenarios. Combining the strength of the relational relationship and the characteristics of the influence path, it constructs a decision-making logic rule system, clarifies the priority ranking, parameter constraints, and response modes of contracting parties' decisions under different relational states, and ensures that the decision-making logic is consistent with the actual interaction characteristics and interests of contracting parties.

[0018] Furthermore, in the steps of decomposing and mapping negotiation topics, based on the CITES Convention compliance requirements and combined with the Convention's appendix revision requirements and compliance supervision standards data stored in the database, the core negotiation topics are categorized, and the negotiation boundaries and specific compliance indicators of each topic are clarified. Then, an association analysis algorithm is used to construct a topic association system, presenting the mutual influence relationship between different topics in matrix form, and determining the topic priority ranking rules based on the hierarchical analysis method. Finally, a structured interest demand survey dimension is designed, and the core interest demands of each contracting party are extracted by combining publicly available documents of contracting parties and historical negotiation data in the database, establishing an interest-topic mapping model to achieve a precise correspondence between interest demands and negotiation topics.

[0019] Furthermore, in the step of decomposing and mapping negotiation topics, the algorithm formula for constructing the topic association system using the association analysis algorithm is as follows: ,in, Indicates the first The negotiation topic and the first The correlation strength value between the negotiation topics This indicates the total number of core impact dimensions representing the correlation between issues. Indicates the first The weight coefficients of each influencing dimension, Indicates the first The issue and the first The issue in Quantitative correlation values ​​under each influence dimension This represents the maximum value of the weighted sum of all issues' impact factors across multiple dimensions. This represents the correction factor.

[0020] Furthermore, in the negotiation topic decomposition and mapping step, the specific steps for determining the topic priority ranking rules based on the analytic hierarchy process (AHP) are as follows: A hierarchical structure is constructed, comprising a target layer, a criteria layer, and a solution layer. The target layer clarifies the priority ranking of the negotiation topics; the criteria layer defines the core evaluation dimensions based on the CITES Convention compliance requirements; and the solution layer comprises the decomposed negotiation topics. For each dimension of the criteria layer and each topic of the solution layer, based on the topic-related data stored in the database, a pairwise comparison judgment matrix is ​​constructed according to a preset scaling rule. The maximum eigenvalue and corresponding eigenvector are solved through matrix operations, and after normalization, the weights of each dimension of the criteria layer and the weights of each topic of the solution layer relative to each dimension of the criteria layer are obtained. Next, a consistency check is performed. By calculating the consistency index, random consistency index, and consistency ratio, the logical consistency of the matrix is ​​judged. If the consistency requirements are not met, the matrix elements are adjusted until the check passes. Finally, the weights of the criteria layer and the relative weights of the solution layer are weighted and aggregated to obtain the combined weights of each topic. The topic priority ranking rules are determined according to the magnitude of the combined weights, forming a standardized ranking result.

[0021] Furthermore, in the game strategy simulation and deduction step, based on the game theory framework, the constructed subject attribute model, relational system, decision rule base, and issue mapping results are integrated to build a negotiation game model. The game participants, action space, information structure, and payoff function are clearly defined, and the core parameters of the game model are configured. Initial strategy-related data is retrieved from the database, integrated to form an initial strategy set, and input into the constructed game model. Simultaneously, the simulation step size and deduction termination conditions are set, and the game process is progressively advanced according to preset deduction rules, recording the decision responses, interactive behaviors, and changes in negotiation status of each party in real time. After the deduction, a standardized simulation result dataset is generated, including strategy execution paths, satisfaction of interest demands, and dynamic data on the negotiation process.

[0022] Furthermore, in the multi-objective optimization and adjustment steps of the strategy, based on the core needs of negotiation decision-making, a strategy evaluation system is constructed, including feasibility, interest alignment, and negotiation progress efficiency. The analytic hierarchy process (AHP) is used to configure the weights of each indicator, clarifying the evaluation criteria and grading thresholds for each indicator. Subsequently, the generated simulation result dataset is input into the multi-objective optimization algorithm model to calculate the evaluation score of each strategy, accurately determining the strategy optimization direction and specific adjustment range. An iterative optimization mechanism is established, modifying strategy parameters according to the determined optimization direction to form an adjusted strategy scheme, which is then re-input into the game model for a second simulation to verify the optimization effect. Simultaneously, convergence judgment conditions, including evaluation score thresholds and strategy adjustment range thresholds, are set, and the strategy adjustment and simulation process is executed iteratively until multiple consecutive simulation results meet the convergence conditions. Finally, a standardized strategy output mechanism is designed to generate the optimized strategy scheme and explanatory documents adapted to different negotiation scenarios, thus completing the optimization.

[0023] Furthermore, in the multi-objective optimization adjustment step of the strategy, the specific steps for inputting the generated simulation result dataset into the multi-objective optimization algorithm model and calculating the evaluation score of each strategy are as follows: The simulation result dataset is standardized and preprocessed according to a preset format to unify the data dimensions and numerical ranges. Based on the constructed strategy evaluation system, the weight parameters of each indicator at the criterion layer are entered into the multi-objective optimization algorithm model, clarifying the objective function and constraints. The objective function corresponds to the score maximization requirement of each evaluation dimension, and the constraints match the hierarchical thresholds and logical association rules of the evaluation indicators. The preprocessed simulation result data is input into the model one by one according to the indicator category, and the individual scores of each strategy under the evaluation dimensions of feasibility, interest alignment, and negotiation progress efficiency are calculated, including logical solutions. According to the preset weighted aggregation rules, the individual scores of each dimension are multiplied and summed with the corresponding indicator weights to obtain the comprehensive evaluation score of each strategy.

[0024] Compared with existing technologies, this game theory-based simulation and optimization method for CITES Convention compliance negotiation strategies has the following advantages:

[0025] I. This invention, through the construction of a game theory model for strategy simulation and deduction, can accurately record the decision-making responses, interactive behaviors, and changes in negotiation status of each contracting party, generating a standardized simulation result dataset covering strategy execution paths, satisfaction of interest demands, etc., providing comprehensive and accurate data support for strategy optimization. In the strategy optimization stage, a strategy evaluation system covering multiple core dimensions is constructed, scientific methods are used to configure indicator weights, evaluation standards and grading thresholds are clarified, and a multi-objective optimization algorithm model is used to calculate the strategy evaluation score, accurately determine the optimization direction and adjustment range, and establish an iterative optimization mechanism to repeatedly execute the strategy adjustment and simulation process until the convergence condition is met. This effectively improves the adaptability of the strategy to the negotiation scenario, enhances the execution effect of the strategy in actual negotiations, and provides strong technical support for CITES Convention implementation negotiations.

[0026] Second, this invention constructs an issue association system through association analysis algorithms, presents the mutual influence relationships between issues in a matrix, and determines priority ranking rules, which helps to grasp the key points of negotiations and rationally allocate resources and energy. At the same time, it designs a structured dimension for interest demand research, extracts the core interest demands of each contracting party by combining database data, and establishes an interest-issue mapping model to achieve a precise correspondence between interest demands and negotiation issues. This not only makes the negotiation content more focused, but also allows all parties to clearly understand the relationship between their own interests and the issues, providing clear core content support for the construction of subsequent game scenarios and enhancing the pertinence and feasibility of negotiation strategies.

[0027] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0029] Figure 1 This is a flowchart of a simulation and optimization method for CITES Convention compliance negotiation strategies based on game theory;

[0030] Figure 2 This is a flowchart of the game agent modeling steps in a simulation and optimization method for CITES Convention compliance negotiation strategies based on game theory;

[0031] Figure 3This is a flowchart of the simulation and deduction steps of the game strategy for CITES Convention compliance negotiation strategy simulation and optimization method based on game theory. Detailed Implementation

[0032] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0033] Example 1

[0034] This invention provides a game theory-based method for simulating and optimizing CITES Convention compliance negotiation strategies. By establishing a data support system, various negotiation-related data are categorized, stored, and verified to ensure the reliability and timeliness of input data, providing a solid foundation for subsequent steps. Based on the data, a game subject model is constructed to accurately characterize the attributes, relationships, and decision-making logic of contracting parties, providing clear and realistic support for the construction of game scenarios. Next, negotiation topics are decomposed and mapped to clarify topic boundaries, relationships, and interests, providing core content support for the game model. A negotiation game model is constructed based on the game theory framework to simulate and deduce the game process, generating a standardized simulation result dataset. Finally, a multi-objective optimization algorithm is used to iteratively optimize the strategy, generating optimized strategy solutions adapted to different negotiation scenarios. This method can flexibly adapt to negotiation scenarios with different topics and combinations of contracting parties, providing accurate and scientific technical support for CITES Convention compliance negotiations.

[0035] This invention provides a simulation and optimization method for CITES Convention compliance negotiation strategies based on game theory. The method includes the following specific steps:

[0036] Data support system construction: Build a database for classifying and storing contracting party attributes, issue-related data, historical negotiation and game parameters, and establish data interaction standards, multi-verification and correction mechanisms, and update and iteration mechanisms;

[0037] In one embodiment, data collection is initiated first, comprehensively gathering attribute data on each participating party in the negotiations, including the resource reserves of terrestrial endangered species, the economic contribution of related industries, trade control regulatory systems, and past compliance records. Simultaneously, issue-related data is collected, covering CITES appendix amendments, conservation status reports, and technical standards for implementing trade control measures. Historical negotiation data is also compiled, including position papers of participating parties, negotiation process minutes, final agreement texts, and points of contention where consensus was not reached in negotiations on similar issues over the past decade. Game theory parameter data is collected, including interest balancing coefficients, decision-making response delay parameters, and quantitative reference indicators of willingness to cooperate in the negotiation scenario. All collected data is categorized and stored according to four main categories: party attributes, issue relevance, historical negotiations, and game theory parameters, constructing a dedicated CITES terrestrial endangered species trade control compliance negotiation framework. The database establishes unified data interaction standards and interface specifications, clearly defining the transmission formats, synchronization update frequencies, and access control rules for various types of data. This ensures smooth data interaction between the database and subsequent steps. A multi-layered data verification mechanism is constructed, including integrity, consistency, and rationality checks. This mechanism verifies whether core information is missing from the entered contracting party attribute data, the consistency between the timeline and position statements in historical negotiation data, and the logical rationality of the game parameter data. Any abnormal data detected triggers an automatic warning, which is then manually corrected by professionals. A data update and iteration mechanism is designed with a fixed update cycle. Updates are triggered by conditions such as revisions to convention provisions, adjustments to contracting party policies, and changes in the status of species protection. Both manual and automatic synchronization modes are supported, ensuring that the database data can adapt to the dynamic changes in the negotiation scenario in real time, providing comprehensive and reliable data support for subsequent steps.

[0038] Game subject modeling: Based on database data, define multi-dimensional subject attribute dimensions, construct subject attribute models containing decision variables and constraints, establish a system of relationships between contracting parties and extract decision logic, and form a decision rule library covering different scenarios;

[0039] In one embodiment, based on data from contracting parties stored in a database, five subject attribute dimensions are first defined: economy, policy, resources, compliance capacity, and willingness to cooperate. A subject attribute model containing decision variables and constraints is then constructed. The core decision variables of the model are defined, including adjustable variables such as the degree of support for various trade control measures, the extent of concessions made in discussions, the acceptable scope of cooperation terms, and the timeline for implementation. The reasonable value range and standardized expression of each decision variable are also clarified. The model is constructed by combining factors such as the mandatory requirements of the CITES Convention, the resource carrying capacity limits of each contracting party, the sustainable development constraints of relevant industries, and the legal boundaries of compliance responsibilities. The constraint system clearly defines the insurmountable boundaries and logical relationships of decision variables. Raw data is normalized using data standardization methods. Based on the logical relationship between decision variables and constraints, characteristic parameters corresponding to each decision variable are extracted. Initial parameter values ​​are assigned by referencing behavioral data of contracting parties in historical negotiations and relevant statements in publicly available policy documents. Abnormal parameters are then eliminated through cross-validation and logical consistency checks. The parameters are iteratively corrected based on the priority weights of each attribute dimension to achieve precise configuration of the subject attribute model parameters. Subsequently, a relationship system among contracting parties is established based on cooperation project records, policy coordination documents, and past data stored in the database. Data on positional interactions during negotiations and minutes of disputes over interests are used to define the types of relationships among the contracting parties. These include cooperative relationships based on resource complementarity, competitive relationships centered on market competition, neutral relationships with no direct overlap in interests, and hybrid relationships that involve both cooperation and competition. A relationship assessment index system is constructed, encompassing five dimensions: interaction frequency, cooperation depth, interest convergence, policy synergy, and conflict resolution efficiency. A pre-defined quantitative assessment method is used to convert these indicators into quantitative values ​​of relationship strength, forming a relationship matrix among the contracting parties. Simultaneously, the transmission paths of these relationships are analyzed, clarifying the mechanisms of direct and indirect relationships and identifying key nodes and constraints affecting transmission. Based on this, and through text mining and logical analysis, this study extracts decision-making triggering conditions, basis for position adjustments, and standards for balancing interests under different relational scenarios from the public statements, policy documents, historical negotiation and decision-making records, and expressions that are compatible with the provisions of the Convention from the public statements of each party. It categorizes and sorts out the collaborative decision-making logic in cooperation scenarios, the interest game logic in competition scenarios, the concession and negotiation logic in conflict scenarios, and the dynamic adjustment logic in hybrid scenarios. Combining the strength of the relational relationship and the characteristics of the influence path, a complete decision-making logic rule system is constructed, clarifying the priority ranking, parameter constraints, and response modes of the parties' decisions under different relational states, forming a main decision-making rule library covering various negotiation scenarios.

[0040] Negotiation topic decomposition and mapping: Based on the CITES Convention compliance requirements, core topic categories are divided, a correlation analysis algorithm is used to construct a topic correlation system and determine priorities, and an interest-topic mapping model is established to achieve precise correlation between the interests of contracting parties and topics;

[0041] In one embodiment, based on the compliance requirements of the CITES Convention and the core objectives of this negotiation, the negotiation topics are broken down into five core topic categories: licensing management, import inspection and quarantine standards, trade quota allocation, penalties for trade violations, and cross-border law enforcement cooperation. The negotiation boundaries for each topic are clearly defined, including non-negotiable bottom-line clauses and negotiable flexible clauses. Simultaneously, core compliance indicators for each topic are determined, such as the timeliness of export license approval processes, key technical indicators for import inspection and quarantine, and the basis for trade quota allocation. A weighted correlation strength algorithm based on multi-dimensional influencing factors is used to construct a topic correlation system, identifying five core influence dimensions: policy relevance, compliance dependence, overlap of interests, regulatory compatibility, and implementation relevance. The weight coefficients of each influence dimension are determined using the analytic hierarchy process. The topic-related data stored in the database are standardized to obtain quantitative correlation values ​​for each topic under different influence dimensions. Subsequently, the weighted correlation strength algorithm formula is applied. Calculate the pairwise correlation strength values ​​between all topics, where in the formula... Indicates the first The negotiation topic and the first The correlation strength value between the negotiation topics This indicates the total number of core impact dimensions related to the issues. These dimensions are determined based on the CITES Convention compliance requirements and include dimensions directly related to the negotiation issues, such as policy relevance, compliance dependence, overlap of interests, regulatory compatibility, and implementation relevance. Indicates the first The weight coefficients of each influencing dimension, Indicates the first The issue and the first The issue in The quantitative correlation values ​​under each influence dimension are obtained by standardizing the issue-related data in the database (including correspondences between convention clauses, historical negotiation topic interaction records, and data related to the implementation process, etc.), and the value range is [value range missing]. ; This represents the maximum value of the weighted sum of all issues' impact factors across multiple dimensions. This represents the correction factor, with a value range of [value missing]. This system is used to fine-tune the correlation strength values ​​based on the characteristics of CITES implementation negotiations (such as issue priority and negotiation stage requirements), ensuring that the correlation analysis results are adapted to the actual needs of the negotiations. A 5×5 issue correlation matrix is ​​constructed, arranged by issue number, to intuitively present the mutual influence relationships between different issues. Based on the analytic hierarchy process (AHP), the issue priority ranking rules are determined, and a hierarchical structure is constructed. The target layer clarifies the priority ranking of each issue in this negotiation. The criteria layer defines four core evaluation dimensions based on CITES implementation requirements: urgency of species protection, difficulty of implementation, scope of impact on interests, and degree of policy adaptability. The solution layer consists of five decomposed core issues. Based on issue-related data in the database, pairwise comparison judgment matrices are constructed between each dimension of the criteria layer according to preset scaling rules, as well as pairwise comparison judgment matrices of each issue relative to each dimension of the criteria layer. The maximum eigenvalue and corresponding eigenvector are solved through matrix operations, and after normalization, the values ​​of each eigenvalue in the criteria layer are obtained. The weights of the dimensions and the weights of each issue relative to the dimensions of the criteria layer are determined, followed by a consistency check. The corresponding consistency index, random consistency index, and consistency ratio are calculated sequentially. If the check results do not meet the consistency requirements, the elements of the judgment matrix are adjusted until the check passes. Finally, the weights of the criteria layer and the relative weights of the scheme layer are weighted and aggregated to obtain the combined weights of each issue. The priority ranking rules of the issues are determined according to the magnitude of the combined weights. A structured table of interest demand survey dimensions is designed. Data such as public policy documents, historical negotiation position statements, and interest demand disclosure materials of each party are extracted from the database. The core interest demands of each party in species protection, economic development, industrial transformation, and law enforcement capacity improvement are sorted out. An interest-issue mapping model is established to accurately associate the core interest demands of each party with the corresponding negotiation issues, clarify the degree to which each issue meets the interest demands of different parties, and provide clear core content support for the construction of subsequent game scenarios.

[0042] Game strategy simulation: Integrate the subject model and issue mapping results to construct a negotiation game model, input the initial strategy set and advance the simulation according to preset rules, record the decision response and negotiation status changes of the contracting parties in real time, and generate a standardized simulation result dataset;

[0043] In one embodiment, based on a game theory framework, a game model for the current negotiations on the implementation of trade controls on terrestrial endangered species is constructed by integrating the constructed subject attribute model, relational system, decision rule base, and issue mapping results. The game participants are clearly defined as all contracting parties involved in the negotiations. The action space of each participant is defined as the range of values ​​for the decision variables. The information structure of the game is defined as the publicly stated positions of each participant, historical behavioral information searchable in the database, and issue-related information. The payoff function of the game is set as the degree to which the core interests of each participant are satisfied. Relevant data, such as historical negotiation strategy cases, past negotiation strategy preferences of each contracting party, and pre-set initial strategy schemes within the industry, are retrieved from the database and integrated to form an initial strategy set. After standardization, this set is input into the constructed game model. The simulation step size is set in units of issue discussion stages. The simulation terminates upon completion of topic discussions, reaching a preliminary consensus, or the emergence of irreconcilable disputes. Following pre-set simulation rules, the game process progresses step-by-step. Within each simulation step, based on the game model's information structure and the decision-making rules of each party, the simulation demonstrates the parties' decision-making responses to the current topic discussion, including expressing positions, proposing solutions, and adjusting concessions. The simulation records each party's decision-making content, position change points, interactive feedback, and overall negotiation status changes in real time, ensuring the simulation strictly adheres to the logic and process of actual negotiations. After the simulation, a standardized simulation results dataset is generated. This dataset includes records of the execution paths of each initial strategy, statistics on the satisfaction of each party's core interests, dynamic data of key nodes in the negotiation process, and predictions of negotiation outcomes under different strategies, providing comprehensive and accurate simulation data support for subsequent strategy optimization steps.

[0044] Multi-objective optimization and adjustment of strategy: Construct a strategy evaluation system, input simulation results to determine the optimization direction through multi-objective optimization algorithm, establish iterative optimization mechanism and convergence judgment rules, and output the optimized strategy scheme and adaptation description after cyclic verification.

[0045] In one embodiment, based on the core decision-making needs of this negotiation, a strategy evaluation system is constructed that includes four core dimensions: feasibility, alignment of interests, negotiation progress efficiency, and controllability of performance costs. The Analytic Hierarchy Process (AHP) was used to configure the weights of each evaluation indicator, clarifying the evaluation criteria and grading thresholds for each dimension. The feasibility dimension focuses on whether the strategy complies with the requirements of the Convention and the actual implementation capabilities of the contracting parties. The interest alignment dimension focuses on the degree to which the strategy meets the core interests of each contracting party. The negotiation efficiency dimension emphasizes whether the strategy can shorten the negotiation cycle and reduce points of contention. The controllability of implementation costs considers whether the economic costs and technical investments required for strategy implementation are within a reasonable range. The generated simulation result dataset was standardized and preprocessed according to a preset format to unify data dimensions and numerical ranges, ensuring the comparability of data for each evaluation indicator. The preprocessed data was then input into a multi-objective optimization algorithm model, along with the weight parameters of each indicator in the criterion layer, clarifying the objective function and constraints. The objective function corresponds to maximizing the score of each evaluation dimension, while the constraints match the grading thresholds and logical association rules of the evaluation indicators. Through the built-in calculation logic of the multi-objective optimization algorithm model, the individual scores of each initial strategy under the four evaluation dimensions were calculated. Then, according to the preset weighted aggregation rules, the individual scores of each dimension were multiplied and summed with the corresponding indicator weights to obtain the comprehensive evaluation score of each strategy. This score was then output through the model output interface. A standardized dataset containing individual scores, overall scores, and score rankings for each strategy was exported. Based on the scores, the core optimization direction of the strategies was determined. For strategies with low overall scores, the problems in their score weakness dimension were analyzed in detail, and the specific adjustment range and optimization path were clarified. An iterative optimization mechanism was established, and the strategy parameters were modified according to the determined optimization direction to form the adjusted strategy scheme. The adjusted strategy scheme was then re-input into the constructed game model for a second simulation. The differences between the simulation results of the optimized strategy and the original results were recorded in detail, and convergence judgment conditions were set, including whether the overall score of each strategy reached the preset qualified threshold, whether the score fluctuation after two consecutive strategy adjustments was lower than the set standard, and whether the stability of multiple simulation results met the requirements. The strategy adjustment and simulation process was executed iteratively until the convergence conditions were met for three consecutive simulations. Finally, the optimized negotiation strategy scheme was generated through a standardized strategy output mechanism. An application instruction document adapted to different negotiation scenarios was also included, clarifying the applicable scope, key points of implementation, precautions, and adjustment plans for different contracting parties' reactions for each optimized strategy. This provided accurate and feasible strategic support for the CITES Convention on the Implementation of Trade Controls on Terrestrial Endangered Species.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A simulation and optimization method for CITES Convention compliance negotiation strategies based on game theory, characterized in that, The method includes the following specific steps: Data support system construction: Build a database for classifying and storing contracting party attributes, issue-related data, historical negotiation and game parameters, and establish data interaction standards, multi-verification and correction mechanisms, and update and iteration mechanisms; Game subject modeling: Based on database data, define multi-dimensional subject attribute dimensions, construct subject attribute models containing decision variables and constraints, establish a system of relationships between contracting parties and extract decision logic, and form a decision rule library covering different scenarios; Negotiation topic decomposition and mapping: Based on the CITES Convention compliance requirements, core topic categories are divided, a correlation analysis algorithm is used to construct a topic correlation system and determine priorities, and an interest-topic mapping model is established to achieve precise correlation between the interests of contracting parties and topics; Game strategy simulation: Integrate the subject model and issue mapping results to construct a negotiation game model, input the initial strategy set and advance the simulation according to preset rules, record the decision response and negotiation status changes of the contracting parties in real time, and generate a standardized simulation result dataset; Multi-objective optimization and adjustment of strategy: Construct a strategy evaluation system, input simulation results to determine the optimization direction through multi-objective optimization algorithm, establish iterative optimization mechanism and convergence judgment rules, and output the optimized strategy scheme and adaptation description after cyclic verification.

2. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 1, characterized in that, In the data support system construction steps, a CITES Convention implementation negotiation database is built to classify and store contracting attribute data, issue-related data, historical negotiation data, and game parameter data. At the same time, a unified data interaction standard and interface specification are established. On this basis, a multi-layered data verification and correction mechanism is constructed, including integrity verification, consistency verification, and rationality verification. An automatic warning and manual correction process is triggered for abnormal data. A data update and iteration mechanism is designed, with clear data update cycles and triggering conditions set, supporting both manual update and automatic synchronization modes to adapt to changes in negotiation scenarios and the expansion of data types.

3. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 1, characterized in that, In the game-playing agent modeling process, the specific steps for defining multi-dimensional agent attribute dimensions based on database data and constructing an agent attribute model containing decision variables and constraints are as follows: Based on the economic strength, policy orientation, resource reserves, compliance records, and regulatory compatibility data of the contracting parties stored in the database, the controllable variables directly related to negotiation behavior in the subject attribute model are defined as core decision variables, including the choice of negotiation position, the setting of the concession range of the issue, the proposal of cooperation conditions, and the planning of the time limit for compliance commitments. At the same time, the value range and expression norms of each decision variable are clarified. By combining the requirements of the CITES Convention, the resource carrying capacity limits of the contracting parties, relevant policy restrictions, and the boundary factors of the responsibility for fulfilling the Convention, a system of constraints for the model is constructed, and the insurmountable boundaries and logical constraints of the decision variables are defined. Subsequently, the original data was normalized using data standardization methods. Based on the correlation logic between variables and constraints, the characteristic parameters corresponding to each decision variable were extracted. The initial values ​​of the parameters were then assigned by referring to historical negotiation behavior data and the quantitative statements in the publicly available policy documents of the contracting parties. Establish a parameter rationality verification mechanism, eliminate abnormal parameters through cross-validation and logical consistency checks, and iteratively correct the parameters by combining the priority weights of the contracting party attribute dimensions to achieve accurate configuration of the subject attribute model parameters.

4. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 1, characterized in that, In the modeling steps of the game theory participants, the specific steps for establishing the inter-party relationship system and extracting decision-making logic are as follows: Based on the records of cooperation projects, policy coordination documents, minutes of interest dispute events, and historical negotiation position interaction data stored in the database, firstly, the core types of inter-party relationships are defined, including cooperative, competitive, neutral, and mixed relationships. Then, a multi-dimensional relationship evaluation index system is constructed, covering interaction frequency, cooperation depth, interest convergence, policy coordination, and conflict resolution efficiency. A quantitative evaluation method is used to convert each dimension of indicators into quantitative values ​​of relationship strength, forming an inter-party relationship matrix. Simultaneously, the transmission paths of relationships are analyzed, the mechanisms of direct and indirect relationships are clarified, and the influence of relationships is defined. The key nodes and constraints of impact transmission are identified. Based on this, text mining and logical analysis methods are used to extract decision-making triggering conditions, basis for position adjustment, and standards for balancing interests under different relational scenarios from public statements of contracting parties, policy documents, historical negotiation and decision-making records, and adaptation statements of convention clauses. The collaborative decision-making logic in cooperation scenarios, the interest game logic in competition scenarios, the concession and negotiation logic in conflict scenarios, and the dynamic adjustment logic in hybrid scenarios are categorized and analyzed. Combining the strength of the relational relationship and the characteristics of the impact path, a decision-making logic rule system is constructed to clarify the priority ranking, parameter constraints, and response modes of contracting parties' decisions under different relational states, ensuring that the decision-making logic is consistent with the actual interaction characteristics and interests of contracting parties.

5. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 1, characterized in that, In the steps of decomposing and mapping negotiation topics, based on the CITES Convention compliance requirements and combined with the Convention's appendix revision requirements and compliance supervision standards data stored in the database, the core negotiation topics are categorized, and the negotiation boundaries and specific compliance indicators of each topic are clarified. Then, an association analysis algorithm is used to construct a topic association system, presenting the mutual influence relationship between different topics in matrix form, and determining the topic priority ranking rules based on the hierarchical analysis method. Finally, a structured interest demand survey dimension is designed, and the core interest demands of each party are extracted by combining publicly available documents of the parties and historical negotiation data in the database, establishing an interest-topic mapping model to achieve a precise correspondence between interest demands and negotiation topics.

6. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 5, characterized in that, In the steps of decomposing and mapping negotiation topics, the algorithm formula for constructing the topic association system using the association analysis algorithm is as follows: ,in, Indicates the first The negotiation topic and the first The correlation strength value between the negotiation topics This indicates the total number of core impact dimensions representing the correlation between issues. Indicates the first The weight coefficients of each influencing dimension, Indicates the first The issue and the first The issue at the Quantitative correlation values ​​under each influence dimension This represents the maximum value of the weighted sum of the multi-dimensional influence factors for all issues. This represents the correction factor.

7. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 5, characterized in that, In the negotiation topic decomposition and mapping step, the specific steps for determining the topic priority ranking rules based on the analytic hierarchy process are as follows: A hierarchical structure is constructed, comprising a target layer, a criteria layer, and a solution layer. The target layer clarifies the priority ranking of the negotiation topics; the criteria layer defines the core evaluation dimensions based on the CITES Convention compliance requirements; and the solution layer comprises the decomposed negotiation topics. For each dimension of the criteria layer and each topic of the solution layer, based on the topic-related data stored in the database, a pairwise comparison judgment matrix is ​​constructed according to a preset scaling rule. The maximum eigenvalue and corresponding eigenvector are solved through matrix operations, and after normalization, the weights of each dimension of the criteria layer and the weights of each topic of the solution layer relative to each dimension of the criteria layer are obtained. Next, a consistency check is performed. By calculating the consistency index, random consistency index, and consistency ratio, the logical consistency of the matrix is ​​judged. If the consistency requirements are not met, the matrix elements are adjusted until the check passes. Finally, the weights of the criteria layer and the relative weights of the solution layer are weighted and aggregated to obtain the combined weights of each topic. The topic priority ranking rules are determined according to the magnitude of the combined weights, forming a standardized ranking result.

8. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 1, characterized in that, In the game strategy simulation and deduction steps, based on the game theory framework, the constructed subject attribute model, relational system, decision rule base, and issue mapping results are integrated to build a negotiation game model. The game participants, action space, information structure, and payoff function are clearly defined, and the core parameters of the game model are configured. Initial strategy-related data is retrieved from the database, integrated to form an initial strategy set, and input into the constructed game model. Simultaneously, the simulation step size and deduction termination conditions are set, and the game process is progressively advanced according to preset deduction rules, recording the decision responses, interactive behaviors, and changes in negotiation status of each party in real time. After the deduction, a standardized simulation result dataset is generated, including strategy execution paths, satisfaction of interest demands, and dynamic data on the negotiation process.

9. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 1, characterized in that, In the multi-objective optimization and adjustment steps of the strategy, based on the core needs of negotiation decision-making, a strategy evaluation system is constructed, including feasibility, interest alignment, and negotiation progress efficiency. The analytic hierarchy process (AHP) is used to configure the weights of each indicator, clarifying the evaluation criteria and grading thresholds for each indicator. Subsequently, the generated simulation result dataset is input into the multi-objective optimization algorithm model to calculate the evaluation score of each strategy, accurately determining the strategy optimization direction and specific adjustment range. An iterative optimization mechanism is established, modifying strategy parameters according to the determined optimization direction to form an adjusted strategy scheme, which is then re-input into the game model for a second simulation to verify the optimization effect. Simultaneously, convergence judgment conditions, including evaluation score thresholds and strategy adjustment range thresholds, are set, and the strategy adjustment and simulation process is executed iteratively until multiple consecutive simulation results meet the convergence conditions. Finally, a standardized strategy output mechanism is designed to generate the optimized strategy scheme and explanatory documents adapted to different negotiation scenarios, thus completing the optimization.

10. The method for simulating and optimizing CITES Convention compliance negotiation strategies based on game theory according to claim 9, characterized in that, In the multi-objective optimization adjustment step of the strategy, the specific steps for inputting the generated simulation result dataset into the multi-objective optimization algorithm model and calculating the evaluation score of each strategy are as follows: The simulation result dataset is standardized and preprocessed according to a preset format to unify the data dimensions and numerical ranges. Based on the constructed strategy evaluation system, the weight parameters of each indicator at the criterion layer are entered into the multi-objective optimization algorithm model, clarifying the objective function and constraints. The objective function corresponds to the score maximization requirement of each evaluation dimension, and the constraints match the hierarchical thresholds and logical association rules of the evaluation indicators. The preprocessed simulation result data is input into the model one by one according to the indicator category, and the individual scores of each strategy under the evaluation dimensions of feasibility, interest alignment, and negotiation progress efficiency are calculated, including logical solutions. According to the preset weighted aggregation rules, the individual scores of each dimension are multiplied and summed with the corresponding indicator weights to obtain the comprehensive evaluation score of each strategy.

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