CN112364468A - Corruption propagation model modeling simulation method based on agent social circle network - Google Patents
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Abstract
The invention relates to a corruption propagation model modeling simulation method based on an agent social circle network, and belongs to the field of computer simulation. Firstly, agent nodes are set in a two-dimensional space network; then establishing a social circle network of the agent; secondly, establishing a corruption propagation probability model; then reporting success rate and successful inspection rate are added; and finally, modeling and simulating the corruption propagation by utilizing the established social circle network. The invention can be used for counting various actual social network characteristics, and can flexibly describe the mechanism of large-scale social network coupling evolution; the individual network scale is different due to different social radiuses, the clustering coefficient is high, and the scale dynamically changes along with time; the corruption agent can change the connection relation inside the circle; can express the heterogeneity of complex social circles and social networks of people.
Description
Technical Field
The invention relates to a corruption propagation model modeling simulation method based on an agent social circle network, and belongs to the field of computer simulation.
Background
In a short period of time, the transition from high to low of the putrefaction state in the society is rare, most of the existing methods are used for researching the putrefaction routes at the national or organizational level, and few researches on individual decision processes for generating putrefaction behaviors are carried out. However, it cannot be underappreciated that whether an organization participates in corruption is primarily a decision from the administrator, i.e., the individual. In general, social and organizational causes do not fluctuate much, and it is therefore difficult to investigate the effective counterdecay measures in the country or organization from a macroscopic and mesoscopic level. Therefore, it is necessary to investigate the putrefaction on a microscopic level. An agent-based social circle network is constructed on a microscopic level, two parameters of reporting success rate and successful inspection rate are introduced for calculation simulation, and the transition process from a high-corruption state to a low-corruption state and the corruption reduction way thereof are analyzed and explored through a microscopic level emerging process. The corruption propagation network established by using the agent social circle theory is closer to the actual society, and can express the heterogeneity of social interaction circles of people, complex communities in the social network and hierarchical structures. The real social contact network is necessarily a dynamic evolution process driven by the constant change of daily behaviors of people, and the evolution process changes the network topological structure characteristics, so that the corruption propagation process is influenced.
Disclosure of Invention
The invention provides a corruption propagation model modeling and simulation method based on an agent social circle network, which is used for analyzing and exploring a transition process from a high corruption state to a low corruption state and a corruption reduction way through a microscopic level emergence process.
The technical scheme of the invention is as follows: a corruption propagation model modeling simulation method based on an agent social circle network comprises the steps of firstly, setting agent nodes in a two-dimensional space network; then establishing a social circle network of the agent; secondly, establishing a corruption propagation probability model; then reporting success rate and successful inspection rate are added; and finally, modeling and simulating the corruption propagation by utilizing the established social circle network.
In the corruption propagation model modeling and simulation method based on the agent social circle network, the setting of the agent nodes in the two-dimensional space network comprises the following steps: agent categories are divided into citizens and bureaucratic teams, and a certain number of people are distributed to each agent category; allocating social radiuses for agents, and setting population distribution proportions Spop%, Mpop% and Bpop% of small, medium and large social radiuses, wherein Spop% + Mpop% + Bpop% is 1; and setting the inherent attribute of the agent to the honesty attitude, namely the honesty.
In the corruption propagation model modeling and simulation method based on the agent social circle network, the establishment of the agent social circle network comprises the step that each agent node establishes a social network according to the social radius of the agent node.
In the corruption propagation model modeling and simulation method based on agent social circle network, the establishment of the corruption propagation probability model comprises behaviors and interactions among agents, and the method comprises the following specific steps:
(1) determining: each agent balances an individual's spoilage revenue x for spoilagepAnd non-corruption yield y to determine a policy (corruption or non-corruption). At xp>In the case of y, the agent decides to corrupt, and the corruption yield is calculated as:
xp＝(1-B)[Axi+(1-A)y]+B[y-ky] ①
here, xpRepresenting the spoilage benefit of pursuing a spoilage strategy. When x ispAbove the standard non-corruption cost y, agent will adopt the corruption strategy. B is the potential to be found septic; a is the probability of matching with a spoilage agent; x is the number ofiIs a considerable benefit in doing spoilage; k is the length of the inhibition criminal period. Thus, xpThe higher match rate a with which a corrupt agent interacts is directly related. But inversely proportional to the higher risk of being found caught B and the higher personal integrity. The cost of entering a prison, ky, is determined by the length of the prisoner penalty, k, i.e., the criminal phase, multiplied by the loss (lower limit) expenditure, y, in the prison, per time step, i.e., the non-corrupting benefit before entering the prison.
(2) Interaction: in each time step, each citizen chooses a random bureaucratic exchange at the nearest government office. It is in the process that if one agent in a citizen or bureaucratic is decaying while the other is not, the affected agent reports to an outside law enforcement agency the agent with which the proposed decay matches. (this results in an increase of 1 in the inverse of agent that proposed spoilage);
(3) calculating the corruption yield of agent: although revenue x is determined by the user at the beginning of each run, agents measure revenue according to their personal propensity to honesty, i, since agents have an inherent propensity to honesty, the ethical cost is high for i being completely honest, 1, and the ethical cost is not present for i being completely corrupt. Thus, even if x has a fixed value in the model, each agent experiences a different perception of x. A fully honest agent would get zeros from incorrect interactions, while only fully corrupted agents would get all x. Based on the above considerations, the spoilage benefit of agent is given by the following formula (II):
xi＝(1-i)x ②
thus, higher honesty and personal benefits x obtained by agents due to spoilageiIn inverse proportion.
(4) Probability of matching to septic agent: the agent estimates the probability A of matching the agent to the corruption agent according to the corruption interaction number N of another agent, wherein the total interaction number is N, namely A is N/N, and N represents the short-term behavior memory length of the agent determined by a user.
(5) Possibility of entering prison: agent determines the risk of entering prison by looking at the perceived probability of putrefaction guilty B, which is M/M. Where M represents the total number of friends in the prison and M represents the total number of corrupt interactions performed at the last time step.
(6) Enforcement is forced: after one agent reaches a certain number of corruption reports (the number of reports reported by another agent a is more than or equal to the maximum number of reports reported by law enforcement agencies b), law enforcement agencies outside the model send the agent into prisons. The agent being complained will obtain the final non-putrefactive profit y before entering the prison. In this regard, the maximum number of votes b determined by law enforcement can be used to indicate the strength of the government's attack on spoilage.
(7) And (3) updating memory: after the interaction is complete, the agent will update its memory to include the decisions made by the agent with which it interacted.
(8) And (3) changing the honesty: at the end of each step, is there a neighbor influence? After activating this switch, agents and bureaucratic persons change their honesty by a change in honesty, to better reflect the average honesty of their neighbours. If the agent's honesty has reflected his neighbors, this step does not make any changes.
(9) Entering the prison: the prison time of an inactive agent (a person currently in a prison) is one time unit of prison time. If they have completed entering the prison, they are released, their current policy is reset to be non-corrupt (so that neighbors "reform" them at least for that period of time), and can freely interact in a later period of time.
The reporting success rate and the successful inspection rate in the corruption propagation model modeling and simulation method based on the agent social circle network are used for more truly restoring the real society.
The invention has the beneficial effects that: according to the method, the social circle network model is improved, and the influence of the reporting success rate and the successful inspection rate is considered in the corruption propagation, so that the model is more practical. Through the established model, the emergence of microscopic individuals and the interaction between the individuals and the environment on a macroscopic level explains the process of the transition of the putrefaction state from high to low from the interaction of citizens and bureaucratic bureau, establishes a decay resistance mechanism from the aspects of moral scouting and penalizing strength, and respectively compares the decay resistance effects generated by the microcosmic individuals and the interaction between the individuals and the environment. The method is a method capable of counting various actual social network characteristics, and the agent constructs and manages social relationships by itself, so that the method is closer to the operation mechanism of an actual society and can flexibly describe the mechanism of large-scale social network evolution. The social radiuses of individuals in the network are different, the formed social circles are different in size, weak connection among the circles can be formed, the individuals with the large social radiuses tend to be connected with one another, and heterogeneity of complex communities and hierarchical structures of social communication circles and social networks of people can be expressed.
Drawings
FIG. 1 is a first diagram illustrating the relationship between individuals in a social circle according to the present invention;
FIG. 2 is a second schematic diagram of the relationship between individuals in the social circle of the present invention;
FIG. 3 is a third schematic diagram of the relationship between individuals in the social circle of the present invention;
FIG. 4 is a schematic diagram of an entity type overview in the present invention;
FIG. 5 is a flowchart of agent's behavior and interaction relationships in the present invention;
FIG. 6 is a simulation scenario one of the present invention;
FIG. 7 shows a second simulation scenario of the present invention.
Detailed description of the preferred embodiment
Example 1: as shown in FIGS. 1-7, a method for modeling and simulating a corruption propagation model based on an agent social circle network comprises the steps of firstly setting agent nodes in a two-dimensional space network; then establishing a social circle network of the agent; secondly, establishing a corruption propagation probability model; then reporting success rate and successful inspection rate are added; and finally, modeling and simulating the corruption propagation by utilizing the established social circle network.
Further, in the corruption propagation model modeling and simulation method based on the agent social circle network, the setting of the agent nodes in the two-dimensional space network includes: agent categories are divided into citizens and bureaucratic teams, and a certain number of people are distributed to each agent category; allocating social radiuses for agents, and setting population distribution proportions Spop%, Mpop% and Bpop% of small, medium and large social radiuses, wherein Spop% + Mpop% + Bpop% is 1; and setting the inherent attribute of the agent to the honesty attitude, namely the honesty.
Furthermore, in the corruption propagation model modeling and simulation method based on the agent social circle network, the establishment of the agent social circle network comprises the establishment of a social network by each agent node according to the social radius of the agent node.
Furthermore, the method for modeling and simulating the corruption propagation model based on the agent social circle network comprises the following steps of:
(1) determining: each agent balances an individual's spoilage revenue x for spoilagepAnd non-corruption yield y to determine a policy (corruption or non-corruption). At xp>In the case of y, the agent decides to corrupt, and the corruption yield is calculated as:
xp＝(1-B)[Axi+(1-A)y]+B[y-ky] ①
here, xpRepresenting the spoilage benefit of pursuing a spoilage strategy. When x ispAbove the standard non-corruption cost y, agent will adopt the corruption strategy. B is the potential to be found septic; a is the probability of matching with a spoilage agent; x is the number ofiIs a considerable benefit in doing spoilage; k is the length of the inhibition criminal period. Thus, xpThe higher match rate a with which a corrupt agent interacts is directly related. But inversely proportional to the higher risk of being found caught B and the higher personal integrity. The cost of entering a prison, ky, is determined by the length of the prisoner penalty, k, i.e., the criminal phase, multiplied by the loss (lower limit) expenditure, y, in the prison, per time step, i.e., the non-corrupting benefit before entering the prison. The revenue matrix is shown in FIG. 1, where x represents the corruption revenue before making a decision and y represents the non-corruption revenue;
TABLE 1 revenue matrix
| Spoilage | Is not putrefactive | |
| Spoilage | x | y |
| Is not putrefactive | y | y |
(2) Interaction: in each time step, each citizen chooses a random bureaucratic exchange at the nearest government office. It is in the process that if one agent in a citizen or bureaucratic is decaying while the other is not, the affected agent reports to an outside law enforcement agency the agent with which the proposed decay matches. (this results in an increase of 1 in the inverse of agent that proposed spoilage);
(3) calculating the corruption yield of agent: although revenue x is determined by the user at the beginning of each run, agents measure revenue according to their personal propensity to honesty, i, since agents have an inherent propensity to honesty, the ethical cost is high for i being completely honest, 1, and the ethical cost is not present for i being completely corrupt. Thus, even if x has a fixed value in the model, each agent experiences a different perception of x. A fully honest agent would get zeros from incorrect interactions, while only fully corrupted agents would get all x. Based on the above considerations, the spoilage benefit of agent is given by the following formula (II):
xi＝(1-i)x ②
thus, higher honesty and personal benefits x obtained by agents due to spoilageiIn inverse proportion.
(4) Probability of matching to septic agent: the agent estimates the probability A of matching the agent to the corruption agent according to the corruption interaction number N of another agent, wherein the total interaction number is N, namely A is N/N, and N represents the short-term behavior memory length of the agent determined by a user.
(5) Possibility of entering prison: agent determines the risk of entering prison by looking at the perceived probability of putrefaction guilty B, which is M/M. Where M represents the total number of friends in the prison and M represents the total number of corrupt interactions performed at the last time step.
(6) Enforcement is forced: after one agent reaches a certain number of corruption reports (the number of reports reported by another agent a is more than or equal to the maximum number of reports reported by law enforcement agencies b), law enforcement agencies outside the model send the agent into prisons. The agent being complained will obtain the final non-putrefactive profit y before entering the prison. In this regard, the maximum number of votes b determined by law enforcement can be used to indicate the strength of the government's attack on spoilage.
(7) And (3) updating memory: after the interaction is complete, the agent will update its memory to include the decisions made by the agent with which it interacted.
(8) And (3) changing the honesty: at the end of each step, is there a neighbor influence? After activating this switch, agents and bureaucratic persons change their honesty by a change in honesty, to better reflect the average honesty of their neighbours. If the agent's honesty has reflected his neighbors, this step does not make any changes.
(9) Entering the prison: the prison time of an inactive agent (a person currently in a prison) is one time unit of prison time. If they have completed entering the prison, they are released, their current policy is reset to be non-corrupt (so that neighbors "reform" them at least for that period of time), and can freely interact in a later period of time.
Furthermore, the reporting success rate and the successful inspection rate are added in the corruption propagation model modeling and simulation method based on the agent social circle network to more truly restore the real society.
Further, the method for modeling and simulating the corruption propagation by using the established social circle network in the agent social circle network-based corruption propagation model comprises the following claims 2-5.
Still further, the present invention presents the following experimental procedure:
firstly, agent nodes are arranged in a two-dimensional space network within the range of 250 x 241, attributes (social radius, income based on corruption, income based on lossiness, honesty, memory length, criminal period and honesty change quantity) of the agent nodes are arranged, a dynamic corruption propagation network is established by utilizing the social circle principle, in the established network, through interaction between citizen agents and bureau agents, after the reporting success rate and the successful detection rate are added, the social network is updated again by utilizing the social circle principle, and finally, the dynamic characteristics and the anti-corruption measures of corruption propagation are analyzed through simulation results.
The method for modeling and simulating the language competition model considering the language attitude and the bilingual teaching comprises the following specific steps of: the initial parameter design is shown in table 2:
TABLE 2 model principal parameters and initial values
Firstly, the number, social radius, integrity and other attributes of the individuals in the network are set.
In the network, individuals have social communication radiuses with different lengths, and the social circle of the individuals is formed by taking the individuals as centers and taking the social radius length as the radius. When the length Q of the connecting line of the central points of the two bodies is less than or equal to the smaller social radius r of the bodies at the two ends of the connecting line, the bodies establish a connection relationship to generate a connection. As shown in FIG. 1, Q > r, the individuals are not connected and do not generate relation, as shown in FIG. 2, Q < r, and the individuals are connected and are mutually neighbors. As shown in fig. 3, as the radius of society increases, the circle of society expands and the number of individuals having a connection relationship gradually increases. After the social circle network is constructed, the interaction between the citizen agents and the bureaucratic agents in the network is set, which is specifically explained as follows:
the behavioral interactions of a citizen agent and an bureaucratic agent must first be satisfied within social reach of each other before the behavioral interactions can take place. The concrete formula is shown as the formula I-II.
xp＝(1-B)[Axi+(1-A)y]+B[y-ky] ①
xi＝(1-i)x ②
In the formula, the meaning of each symbol is explained above, and is not described herein again.
The model takes Netlogo as a platform, Behavior space as a tool, and data obtained by repeating Behavior space for 35 times by taking each 100 time steps as one turn.
According to the introduction, the integrity is set to be in accordance with (0.5 ) normal distribution, the comparison criminal period k is 2, and the maximum reported number b determined by law enforcement agencies is 2; the criminal period k is 2, and the maximum reported number b determined by law enforcement agencies is 5; the criminal period k is 5, and the maximum reported number b determined by law enforcement agencies is 2; criminal phase k is 5 and law enforcement agencies determine the impact on the system of a maximum number of 5 invoices b. The result of the decay after 35 rounds of model operation is shown in fig. 6, and the result shows that the higher the criminal period, the smaller the maximum number of reported reports determined by law enforcement agencies, the fewer the number of people who can achieve the decay, and the more the number of people who have prison. Therefore, the transition from the putrefactive state to the low endogenous state is successfully simulated from the microscopic behavior of the individual, and the fact that the severe criminal penalty of the government has a certain effect on the anti-corrosion is shown.
The criminal period k is set to be 5, the maximum reported number b determined by law enforcement agencies is 2, the influence of normal distribution of compliance (0.2,0.8), (0.5 ) and (0.8,0.2) on the system is compared, and the corruption result after 35 runs of the model is shown in fig. 7. The results show that the higher the mean value of the honesty, the lower the standard deviation, the fewer the number of people who get rancid, and the more people are prison pris. Therefore, endogenous transition from high to low of the putrefaction state is successfully simulated from the individual microscopic behavior, and the moral introspection has a certain effect on the corrosion resistance.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (5)
1. A corruption propagation model modeling simulation method based on an agent social circle network is characterized by comprising the following steps: firstly, agent nodes are arranged in a two-dimensional space network; then establishing a social circle network of the agent; secondly, establishing a corruption propagation probability model; then reporting success rate and successful inspection rate are added; and finally, modeling and simulating the corruption propagation by utilizing the established social circle network.
2. The agent social circle network-based corruption propagation model modeling and simulation method of claim 1, wherein: the setting of agent nodes in the two-dimensional space network comprises the following steps: agent categories are divided into citizens and bureaucratic teams, and a certain number of people are distributed to each agent category; allocating social radiuses for agents, and setting population distribution proportions Spop%, Mpop% and Bpop% of small, medium and large social radiuses, wherein Spop% + Mpop% + Bpop% is 1; and setting the inherent attribute of the agent to the honesty attitude, namely the honesty.
3. The agent social circle network-based corruption propagation model modeling and simulation method of claim 1, wherein: the establishment of the social circle sub-network of the agent comprises the step that each agent node establishes a social network according to the social radius of the agent node.
4. The agent social circle network-based corruption propagation model modeling and simulation method of claim 1, wherein: the establishing of the corruption propagation probability model comprises behaviors and interactions among agents, and the concrete steps are as follows:
(1) determining: each agent balances an individual's spoilage revenue x for spoilagepAnd non-corruption yield y to determine a policy (corruption or non-corruption); at xp>In the case of y, the agent decides to corrupt, and the corruption yield is calculated as:
xp＝(1-B)[Axi+(1-A)y]+B[y-ky] ①
here, xpRepresents a spoilage benefit for pursuit of a spoilage strategy; when x ispAbove the standard non-corruption cost y, agent will adopt the corruption strategy, B is the probability of finding corruption, A is the probability of matching with a corrupt agent, xiIs a considerable gain in pursuit of spoilage, k is the length of the arrest period, and therefore,xpthe higher matching rate a with an corrupted agent interaction is directly related, but inversely proportional to the higher risk of being found caught B and the higher personal integrity, the cost of entering a prison, ky, is determined by the length of the prison penalty, i.e. the crime, k, multiplied by the loss per time step in the prison (lower limit) expenditure, y, i.e. the non-corrupting benefit before entering the prison;
(2) interaction: in each time step, each citizen chooses a random bureaucratic exchange at the nearest government office, and it is in this process that if one agent of the citizen or bureaucratic decays while the other does not, the affected agent reports the proposed decaying agent that matches it to an external law enforcement agency; (this results in an increase of 1 in the inverse of agent that proposed spoilage);
(3) calculating the corruption yield of agent: although revenue x is determined by the user at the beginning of each run, agents measure revenue according to their personal propensity to honesty, i, since agents have an inherent honesty tendency, the ethical cost is high for i to represent complete honesty, and the ethical cost is not present for i to represent complete spoilage, so even if x has a fixed value in the model, each agent experiences a different experience with x; an agent that is completely honest will get zeros from incorrect interactions, while only agents that are completely corrupt will get all x, and based on the above considerations, the corruption yield of an agent is given by the following equation:
xi＝(1-i)x ②
thus, higher honesty and personal benefits x obtained by agents due to spoilageiIn inverse proportion;
(4) probability of matching to septic agent: the agent estimates the probability A of matching the agent with the corruption agent according to the corruption interaction times N of another agent, wherein the total interaction number is N, namely A is N/N, and N represents the short-term behavior memory length of the agent determined by a user;
(5) possibility of entering prison: agent determines risk of entering prison by looking at perceived corruption crime probability B, which is M/M, where M is the total number of friends in the prison and M is the total number of corruption interactions performed at the last time step;
(6) enforcement is forced: after one agent reports a certain number of decayed reports (the number a reported by the other agent is more than or equal to the maximum reported number b determined by a law enforcement agency), the law enforcement agency outside the model sends the agent into the prison; the appealing agent will obtain final non-corruption income y before entering prison, and in the process, the maximum number b of lifted reports determined by law enforcement agencies can be used for representing the attack strength of the government on corruption;
(7) and (3) updating memory: after the interaction is finished, the agent updates the memory of the agent so as to include the decision made by the agent interacting with the agent;
(8) and (3) changing the honesty: at the end of each step, is there a neighbor influence? After the switch is activated, agents and bureaucratic persons change the honesty of the agents and the bureaucratic persons through the change of the honesty so as to better reflect the average honesty of the neighbors of the agents and if the honesty of the agents already reflects the neighbors of the agents, the step can not be changed;
(9) entering the prison: the prison time of an inactive agent (a person currently in a prison) is one time unit of prison time; if they have completed entering the prison, they are released, their current policy is reset to be non-corrupt (so that neighbors "reform" them at least for that period of time), and can freely interact in a later period of time.
5. The agent social circle network-based corruption propagation model modeling and simulation method of claim 1, wherein: the adding reporting success rate and the successful inspection rate are used for more truly restoring the real society.
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| CN113343149A (en) * | 2021-06-22 | 2021-09-03 | 深圳市网联安瑞网络科技有限公司 | Agent-based mobile terminal social media propagation effect evaluation method, system and application |
| CN114640643A (en) * | 2022-02-21 | 2022-06-17 | 华南理工大学 | Information cross-community propagation maximization method and system based on group intelligence |
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|---|---|---|---|---|
| CN113343149A (en) * | 2021-06-22 | 2021-09-03 | 深圳市网联安瑞网络科技有限公司 | Agent-based mobile terminal social media propagation effect evaluation method, system and application |
| CN114640643A (en) * | 2022-02-21 | 2022-06-17 | 华南理工大学 | Information cross-community propagation maximization method and system based on group intelligence |
| CN114640643B (en) * | 2022-02-21 | 2023-11-21 | 华南理工大学 | Information cross-community propagation maximization method and system based on group intelligence |
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