Multi-subject water environment governance decision-making method fusing reporting mechanism

Abstract

The invention relates to the technical field of environmental governance, and discloses a multi-subject water environmental governance decision-making method fusing a reporting mechanism, and the method comprises the steps: building a multi-subject evolutionary game model; establishing a replication dynamic equation selected by each subject strategy, and describing a dynamic evolution process adjusted by each subject strategy; fusing report mechanism parameters into the multi-subject evolutionary game model, and quantifying the influence of the report mechanism on the selection of each subject strategy; combining the multi-subject evolutionary game model with a system dynamics method, and establishing a water environment governance dynamics model comprising a state variable, a flow variable and an auxiliary variable; the strategy evolution trajectory and the water environment treatment effect of each subject are simulated through various scenes, and an optimal decision result is determined based on a simulation result. According to the invention, accurate prediction of the dynamic evolution process of the water environment treatment system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental governance, and in particular to a multi-agent water environment governance decision-making method integrating a reporting mechanism. Background Art

[0002] The existing water environment management methods have the following main deficiencies: First, existing governance approaches fail to effectively address the complex dynamics between multiple stakeholders. Water environmental governance involves multiple actors, including the central government, local governments, polluting enterprises, and the public, each with distinct interests and behavioral strategies. The central government pursues global social welfare maximization, while local governments may prioritize local economic interests. Polluting enterprises may engage in illegal pollution discharges to maximize profits. The public, as the bearer of environmental impacts, requires effective participation and oversight mechanisms. Existing research, which mostly employs two- or three-party game models, struggles to fully capture the complex interactions among these multiple actors.

[0003] Second, oversight relies on government power, while social oversight mechanisms are underutilized. Current water environment governance relies primarily on government oversight and corporate self-regulation, with insufficient public participation. Reporting mechanisms, as a crucial means of social oversight, have received insufficient attention, and there is a lack of systematic research into the behavioral characteristics and influence of whistleblowers as independent actors. Existing reporting systems are generally plagued by insufficient rewards, inadequate protection, and inefficient processing, resulting in low reporting willingness and limited effectiveness.

[0004] Third, static analysis methods struggle to reveal the evolution of strategies. Existing water environment governance research largely employs static game analysis, which struggles to reveal the dynamic evolution of strategies chosen by various actors and their long-term stability. Environmental governance is a long-term, dynamic process that requires consideration of the dynamic adjustments and mutual influences among various actors' strategies.

[0005] Fourth, there is a lack of systematic policy optimization methods. Existing research lacks a comprehensive analytical framework that effectively combines the results of multi-party game theory with system dynamics methods, making it difficult to achieve scientific predictions and optimize decision-making on the long-term evolution path of the system under different scenario combinations. Summary of the Invention

[0006] In response to the above-mentioned deficiencies in the existing technology, the present invention provides a multi-agent water environment governance decision-making method that integrates a reporting mechanism to solve technical problems such as the difficulty in accurately clarifying the complex game relationship among multiple subjects in existing water environment governance, the lack of a public supervision mechanism, and the difficulty in dynamically predicting the strategy evolution process.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A multi-agent water environment governance decision-making method integrating a reporting mechanism includes the following steps: According to the multiple subjects involved in water environment governance, the optional strategy set and benefit function of each subject are determined, and a multi-agent evolutionary game model is established; Based on the multi-agent evolutionary game model, a replication dynamic equation for each agent's strategy selection is established to describe the dynamic evolution process of each agent's strategy adjustment. Integrate the reporting mechanism parameters into the multi-agent evolutionary game model to quantify the impact of the reporting mechanism on the strategic choices of each agent; Combining the multi-agent evolutionary game model with the system dynamics method, a water environment governance dynamics model including state variables, flow variables and auxiliary variables is established; Through multiple scenarios, the strategy evolution trajectory and water environment governance effects of each subject are simulated, and the optimal decision-making result is determined based on the simulation results.

[0008] Furthermore, the optional strategy set and benefit function of each subject are determined based on the multiple subjects involved in water environment governance, including: According to the optional strategy sets of active governance and passive governance of each subject, the average expected benefits of each subject's governance behavior are determined as:

[0009]

[0010]

[0011]

[0012] in, is the average expected benefit of the superior management's governance behavior, is the average expected benefit of the subordinate management’s governance behavior, is the average expected benefit of corporate governance behavior, is the average expected benefit of reporting behavior, The probability of active governance by the superior management, The governance cost of the passive governance of the superior management, Environmental losses caused by the superior management when the enterprise is passive in governance. Quantify the loss of credibility of the passive governance of the superior management, is the probability of reporting, The environmental benefits to higher management when enterprises actively govern, The probability of active corporate governance, The probability that the lower-level management party will actively govern, The cost of governance for the active governance of the superior management, The penalty cost for passive governance by lower-level management, The governance cost of passive governance by lower-level management. Environmental losses caused by lower-level management when enterprises are passive in governance. Quantify the loss of credibility of passive governance by lower-level management, The environmental benefits to lower-level management when enterprises actively govern, The governance costs of active governance by lower-level management, The penalty cost for enterprises’ passive governance, The governance cost of passive corporate governance, For the economic benefits of passive corporate governance, Quantify the loss of credibility of passive corporate governance, The cost of rewarding companies for reporting negative corporate governance. The governance costs of active governance for enterprises, is the reward cost for lower-level management to actively manage the enterprise, To report expected returns, To avoid reporting expected returns, For reporting costs, Rewards for reporting to lower-level management.

[0013] Furthermore, based on the multi-agent evolutionary game model, the replication dynamic equations for the strategy selection of each agent are established, including: The replication dynamic equation of the superior management's positive governance behavior is:

[0014] The replication dynamic equation of the subordinate management's active governance behavior is:

[0015] The replication dynamic equation of the enterprise's active governance behavior is:

[0016] The replication dynamic equation of whistleblowing behavior is: .

[0017] Furthermore, the reporting mechanism parameters include: Reporting reward coefficient, reporting success rate and reporting cost.

[0018] Furthermore, the calculation method for the whistleblowing reward cost when the enterprise is passive in governance is:

[0019] in, is the reporting reward coefficient, The success rate of reporting.

[0020] Furthermore, the critical conditions for reporting are: .

[0021] Furthermore, the state variables include the probability of active governance by the superior management, the probability of active governance by the subordinate management, the probability of active governance by the enterprise, the probability of reporting, and the water environment quality index; Flow variables include the rate of adjustment of each agent's strategy and the rate of change of water environment quality; Auxiliary variables include the income of each entity, the average income of each entity, the detection rate of illegal pollution discharge, the environmental damage rate, the water quality improvement rate, the overall social welfare and the reporting effectiveness index; Parameter variables include regulatory cost coefficient, environmental damage coefficient, and reporting reward coefficient.

[0022] Furthermore, based on the simulation results, the optimal decision result is determined through comprehensive evaluation indicators, specifically:

[0023] in, is a comprehensive evaluation index. , is the weight coefficient, is the water environment quality index, is the total cost, is the time required for the system to reach a stable state, The reporting effectiveness index.

[0024] Furthermore, the water environment quality index is calculated as follows:

[0025] in, is the initial environmental quality index, is the environmental improvement factor, is the environmental damage coefficient.

[0026] Furthermore, the reporting effectiveness index is calculated as follows:

[0027] in, It is the reporting protection factor.

[0028] The present invention has the following beneficial effects: The present invention firstly constructs a four-party evolutionary game model to systematically consider the complex interactive relationship between multiple subjects involved in water environment governance, thereby improving the scientific nature and pertinence of water environment governance strategies; secondly, by incorporating reporting mechanism parameters into the game model, the impact of the reporting mechanism on the behavioral decision-making of each subject is quantified, thereby enhancing social supervision, reducing environmental supervision costs, and increasing the detection rate of illegal pollution discharge behaviors; furthermore, by combining system dynamics methods, dynamic simulation and prediction of the strategy evolution process of each subject in the water environment governance system are achieved; finally, through scenario simulation and parameter optimization, the optimal policy combination is determined to improve the efficiency and effectiveness of water environment governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flowchart of a multi-agent water environment governance decision-making method that integrates a reporting mechanism. DETAILED DESCRIPTION

[0030] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0031] The present invention systematically considers the complex game relationship among multiple subjects involved in water environment governance, quantitatively analyzes the impact of the reporting mechanism on the behavioral decision-making of each subject, and combines the system dynamics method to accurately predict the dynamic evolution process of the water environment governance system.

[0032] like Figure 1 As shown, an embodiment of the present invention provides a multi-agent water environment governance decision-making method integrating a reporting mechanism, including the following steps S1 to S5: S1. Determine the optional strategy set and benefit function of each subject based on the multiple subjects involved in water environment governance, and establish a multi-agent evolutionary game model; In an optional embodiment of the present invention, step S1 sets parameters based on evolutionary game theory to construct a multi-agent evolutionary game model of multiple agents participating in water environment governance.

[0033] In this example, the multiple entities involved in water environment governance can include higher-level management, lower-level management, polluting enterprises, and the public. Higher-level management is responsible for overseeing local government implementation; lower-level management is responsible for cooperating with higher-level management's governance measures, managing polluting enterprises within their jurisdiction, and accepting public reports; polluting enterprises choose whether to strictly discharge pollutants based on the cost of pollution and the reward and punishment mechanism; and the public decides whether to report violations based on the reward mechanism and the reporting cost. Model parameters, the strategy set, and payoff function of each entity are defined, and a payoff matrix for the four-party evolutionary game is established.

[0034] In this embodiment, the policy set of the superior management party includes {strict supervision, loose supervision}, and the probabilities are ; The expected benefit of the superior management choosing “strict supervision” is :

[0035] The expected benefit of the central government choosing “relaxed regulation” is :

[0036] The average expected benefit of the central government's actions is :

[0037] In this embodiment, the strategy set of the subordinate management party includes {active cooperation, passive cooperation}, and the probabilities are ; The expected benefit of the local government choosing “active cooperation” is :

[0038] The expected benefit of the local government choosing “passive cooperation” is :

[0039]

[0040] In this embodiment, the policy set for pollutant-discharging enterprises includes {strict pollution discharge, illegal pollution discharge}, and the probabilities are ; The expected benefit of a polluting enterprise choosing “strict pollution discharge” is :

[0041] The expected benefit of a polluting enterprise choosing “non-strict pollution discharge” is :

[0042] The average expected benefit of the polluting enterprise behavior is :

[0043] In this embodiment, the strategy set for the whistleblower includes {active reporting, passive reporting}; the probabilities are ; The expected benefit of the whistleblower choosing to report is :

[0044] The expected benefit of the whistleblower choosing not to report is :

[0045] The average expected benefit of the whistleblower's behavior is :

[0046] Where x represents the probability that the central government will choose strict regulation. y represents the probability that the local government will choose to actively cooperate. z represents the probability that the polluting enterprise will choose to strictly discharge pollutants. w represents the probability that the whistleblower will choose to participate in the report. It represents the regulatory cost when the central government strictly regulates. It represents the cooperation cost of local government’s active cooperation. It represents the pollution discharge cost of polluting enterprises in strictly discharging pollutants. It represents the regulatory cost when the central government has loose regulation. It represents the cooperation cost of local government’s passive cooperation. It refers to the pollution discharge costs of polluting enterprises that do not strictly discharge pollutants. It represents the reporting cost for the whistleblower if he chooses to report. It refers to the economic benefits obtained by polluting enterprises by not strictly discharging pollutants. It refers to the environmental benefits brought to the central government when polluting enterprises strictly discharge pollutants. It refers to the environmental benefits brought to local governments when polluting enterprises strictly discharge pollutants. It refers to the environmental losses caused to the central government when polluting enterprises do not strictly discharge pollutants. It refers to the environmental losses caused to local governments when polluting enterprises do not strictly discharge pollutants. It means the central government’s subsidy to local governments for their active cooperation. It means that local governments provide environmental protection subsidies to polluting enterprises that strictly control pollution discharge. It represents the subsidy that the local government provides to whistleblowers for choosing to report. It refers to the compensation paid to whistleblowers for participating in reporting when polluting enterprises do not strictly discharge pollutants. It indicates the loss of credibility of the central government when whistleblowers discover lax regulation by the central government. It refers to the loss of credibility of local governments when whistleblowers find that local governments are passively cooperating. It refers to the reputational loss suffered by a polluting enterprise when a whistleblower discovers that the enterprise is not strictly discharging pollutants. It refers to the central government’s punishment for local governments’ passive cooperation. It refers to the punishment imposed by local governments on polluting enterprises that do not strictly control their discharge of pollutants.

[0047] S2. Based on the multi-agent evolutionary game model, a replication dynamic equation for each agent's strategy selection is established to describe the dynamic evolution process of each agent's strategy adjustment; In an optional embodiment of the present invention, step S2 establishes a replication dynamic equation of each subject's behavior based on the four-party evolutionary game payoff matrix obtained in step S1, and explores the dynamic evolution process of each subject's strategy adjustment by taking partial derivatives of each replication dynamic equation.

[0048] The replication dynamic equation of the central government’s “strict supervision” behavior is: :

[0049] make =0, the solution obtained may be the equilibrium point of the evolution process: (1) When hour, , indicating that the points on the x-axis are in a stable state, that is, the central government’s strategic choices do not change over time.

[0050] (2) When When and for According to the stability theorem of the replica dynamic equation, when When , this point is the evolutionary game stable strategy point (ESS).

[0051] The replication dynamic equation of the local government's "active cooperation" behavior is: :

[0052] make , the solution obtained may be the equilibrium point of the evolution process: (1) When hour, , indicating that the points on the y-axis are in a stable state, that is, the strategic choices of local governments at this time do not change over time.

[0053] (2) When When and for According to the stability theorem of the replica dynamic equation, when When , this point is the evolutionary game stable strategy point (ESS).

[0054] The replication dynamic equation of the “strict pollution discharge” behavior of polluting enterprises is: :

[0055] make , the solution obtained may be the equilibrium point of the evolution process: (1) When hour, , indicating that the points on the z-axis are in a stable state, that is, the strategic choices of the pollutant-discharging enterprises do not change with time.

[0056] (2) When When and for According to the stability theorem of the replica dynamic equation, when When , this point is the evolutionary game stable strategy point (ESS).

[0057] The replication dynamic equation of the whistleblower's "reporting" behavior is: :

[0058] make , the solution obtained may be the equilibrium point of the evolution process: (1) When hour, , indicating that the points on the w-axis are in a stable state, that is, the whistleblower’s strategic choice does not change with time.

[0059] (2) When When and for According to the stability theorem of the replica dynamic equation, when When , this point is the evolutionary game stable strategy point (ESS).

[0060] S3. Integrate the reporting mechanism parameters into the multi-agent evolutionary game model to quantify the impact of the reporting mechanism on the strategic choices of each agent; In an optional embodiment of the present invention, step S3 integrates the reporting mechanism parameters into the multi-agent evolutionary game model. Specifically, parameters such as reporting probability, reporting reward, and reporting cost are incorporated into the game model to quantify the impact of the reporting mechanism on the strategy choices of each agent.

[0061] Reporting mechanism parameters include: Reporting compensation coefficient : represents the ratio of the reward received by the whistleblower to the fines imposed on the illegal pollutant-discharging enterprise, ranging from [0,1]; the success rate of the report : represents the probability that the reporting behavior is effectively handled and leads to the investigation and punishment of illegal pollutant-discharging enterprises, ranging from [0,1]; reporting cost : Represents the time cost, economic cost and possible risks incurred by the whistleblower during the reporting process; the calculation formula for whistleblowing compensation is: , the critical conditions for reporting are: , when reporting reward When it is greater than this critical value, the whistleblower will tend to choose the whistleblowing strategy.

[0062] S4. Combine the multi-agent evolutionary game model with the system dynamics method to establish a water environment governance dynamics model that includes state variables, flow variables, and auxiliary variables; In an optional embodiment of the present invention, step S4 constructs a system dynamics model including the following variables: state variables: including the probability of strict supervision by the central government x, the probability of active cooperation by local governments y, the probability of strict pollution discharge by enterprises z, the probability of active reporting by whistleblowers w, and the water environment quality index E; flow variables: including the strategy adjustment rate of each subject 、 、 、 , water environment quality change rate ; Auxiliary variables: including the income of each subject , the average income of each entity , and the detection rate of illegal pollution discharge , environmental damage rate , water quality improvement rate , overall social welfare , Reporting Effectiveness Index Parameter variables: including regulatory cost coefficient, environmental damage coefficient, reporting reward coefficient, etc. Quantity: including regulatory cost coefficient, environmental damage coefficient, reporting reward coefficient, etc.

[0063] S5. Simulate the strategy evolution trajectory and water environment governance effects of each subject through multiple scenarios, and determine the optimal decision-making result based on the simulation results.

[0064] In an optional embodiment of the present invention, step S5 verifies the validity of the model by assigning reasonable values to the parameters set in the evolutionary game model, and designs multiple policy scenarios to simulate the strategy evolution trajectory and water environment governance effects of each subject under different scenarios. Specifically, by adjusting the reporting compensation coefficient , reporting protection coefficient , Central regulatory inspection frequency , local government assessment weight Parameters such as , construct different policy scenarios and conduct simulation analysis.

[0065] Step S5 determines the optimal policy combination based on the simulation results under different policy scenarios in step S5 to provide decision support for water environment governance.

[0066] The optimal scenario can be further determined by setting the comprehensive evaluation index J: in, To simulate the environmental quality index at the end of the period, is the overall social cost, is the time required for the system to reach a stable state, is the reporting effectiveness index, is the weight coefficient, whose value range is [0,1] and satisfies ; Water Environment Quality Index The calculation formula is: in, is the initial environmental quality index, is the environmental improvement factor, is the environmental damage coefficient; the calculation formula of the reporting effectiveness index EI is: in, is the probability of reporting by the whistleblower, The success rate of reporting, Strict pollution discharge probability for enterprises, is the reporting protection coefficient; the calculation formula for the overall social cost C is:

[0067] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0070] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0071] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A multi-agent water environment governance decision-making method integrating a reporting mechanism, characterized by: The following steps are involved: According to the multiple subjects involved in water environment governance, the optional strategy set and benefit function of each subject are determined, and a multi-agent evolutionary game model is established; Based on the multi-agent evolutionary game model, a replication dynamic equation for each agent's strategy selection is established to describe the dynamic evolution process of each agent's strategy adjustment. Integrate the reporting mechanism parameters into the multi-agent evolutionary game model to quantify the impact of the reporting mechanism on the strategic choices of each agent; Combining the multi-agent evolutionary game model with the system dynamics method, a water environment governance dynamics model including state variables, flow variables and auxiliary variables is established; Through multiple scenarios, the strategy evolution trajectory and water environment governance effects of each subject are simulated, and the optimal decision-making result is determined based on the simulation results.

2. A multi-agent water environment management decision-making method integrating a reporting mechanism according to claim 1, characterized in that: The optional strategy set and benefit function of each subject determined based on the multiple subjects involved in water environment governance include: According to the optional strategy sets of active governance and passive governance of each subject, the average expected benefits of each subject's governance behavior are determined as: in, is the average expected benefit of the superior management's governance behavior, is the average expected benefit of the subordinate management’s governance behavior, is the average expected benefit of corporate governance behavior, is the average expected benefit of reporting behavior, The probability of active governance by the superior management, The governance cost of the passive governance of the superior management, Environmental losses caused by the superior management when the enterprise is passive in governance. Quantify the loss of credibility of the passive governance of the superior management, is the probability of reporting, The environmental benefits to higher management when enterprises actively govern, The probability of active corporate governance, The probability that the lower-level management party will actively govern, The cost of governance for the active governance of the superior management, The penalty cost for passive governance by lower-level management, The governance cost of passive governance by lower-level management. Environmental losses caused by lower-level management when enterprises are passive in governance. Quantify the loss of credibility of passive governance by lower-level management, The environmental benefits to lower-level management when enterprises actively govern, The governance costs of active governance by lower-level management, The penalty cost for enterprises’ passive governance, The governance cost of passive corporate governance, For the economic benefits of passive corporate governance, Quantify the loss of credibility of passive corporate governance, The cost of rewarding companies for reporting negative corporate governance. The governance costs of active governance for enterprises, is the reward cost for lower-level management to actively manage the enterprise, To report expected returns, To avoid reporting expected returns, For reporting costs, Rewards for reporting to lower-level management.

3. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 2 is characterized in that: According to the multi-agent evolutionary game model, the replication dynamic equations of each agent's strategy selection are established, including: The replication dynamic equation of the superior management's positive governance behavior is: The replication dynamic equation of the subordinate management's active governance behavior is: The replication dynamic equation of the enterprise's active governance behavior is: The replication dynamic equation of whistleblowing behavior is: 。 4. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 3 is characterized in that: Reporting mechanism parameters include: Reporting reward coefficient, reporting success rate and reporting cost.

5. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 4 is characterized in that: The calculation method for the whistleblowing reward cost when the enterprise is passive in governance is: in, is the reporting reward coefficient, The success rate of reporting.

6. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 5 is characterized in that: The critical conditions for reporting are: 。 7. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 6 is characterized in that: State variables include the probability of active governance by superior management, the probability of active governance by subordinate management, the probability of active governance by enterprises, the probability of reporting, and the water environment quality index; Flow variables include the rate of adjustment of each agent's strategy and the rate of change of water environment quality; Auxiliary variables include the income of each entity, the average income of each entity, the detection rate of illegal pollution discharge, the environmental damage rate, the water quality improvement rate, the overall social welfare and the reporting effectiveness index; Parameter variables include regulatory cost coefficient, environmental damage coefficient, and reporting reward coefficient.

8. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 7 is characterized in that: Based on the simulation results, the optimal decision-making results are determined through comprehensive evaluation indicators, specifically: in, is a comprehensive evaluation index. , is the weight coefficient, is the water environment quality index, is the total cost, is the time required for the system to reach a stable state, The reporting effectiveness index.

9. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 8 is characterized in that: The calculation method of water environment quality index is: in, is the initial environmental quality index, is the environmental improvement factor, is the environmental damage coefficient.

10. The multi-agent water environment management decision-making method integrating the reporting mechanism according to claim 9 is characterized in that: The reporting effectiveness index is calculated as follows: in, It is the reporting protection factor.

Images