A reputation-based network space collaborative governance incentive method and device

Abstract

The application discloses a reputation-based network space cooperative governance incentive method and device, wherein the method comprises the following steps: observing and evaluating the historical information of the interaction between the evaluator autonomous domain and the evaluated autonomous domain by using a server; wherein the historical information comprises that the evaluator autonomous domain initiates a data or service request to the evaluated autonomous domain; obtaining the reputation evaluation result of the evaluated autonomous domain by an evaluation algorithm according to the historical information; and updating the reputation of the evaluated autonomous domain based on the reputation evaluation result of the evaluated autonomous domain to encourage the autonomous domains to participate in cooperative governance; wherein the reputation update comprises two modes of event-triggered and batch processing. The application encourages the autonomous domains to actively participate in cooperative governance and contribute their data and services, so that the rational selfish autonomous domains actively contribute data and services in the mechanism, which conforms to the personal interests and becomes the dominant strategy.

Description

A reputation-based incentive method and apparatus for collaborative governance in cyberspace Technical Field

[0001] This invention relates to the field of Internet applications, and in particular to a reputation-based method and apparatus for incentivizing collaborative governance in cyberspace. Background Technology

[0002] Effective cyberspace governance relies on the active cooperation of various organizations. However, the current global internet management is relatively decentralized and independent—loosely coupled among independent Autonomous Systems (AS), each managing its own network. Effective inter-domain collaboration is essential for cyberspace governance.

[0003] Inter-domain collaborative governance is specifically reflected in the requirement for participating ASs to openly share data and services, such as network security alerts, measurement data, probe resources, and information query services. However, providing data and services consumes the AS's own resources, requiring initial investment of manpower and materials, and incurring storage, electricity, and maintenance costs later. Furthermore, providing data and services can easily lead to privacy leaks and security issues. Selfish ASs may refuse to provide data and services to other ASs, or provide low-quality data and services to reduce their own expenses. Such selfish behavior will significantly undermine the efficiency of collaborative governance.

[0004] Current cyberspace collaborative governance suffers from incentive problems: without incentives, collaboration cannot become a dominant strategy in the system. Existing inter-domain collaborative governance lacks profitability and central control over participating ASs, failing to constrain their selfish behavior.

[0005] A typical example of selfish behavior is that when sharing data and services among ASs, an AS may request data from other ASs, but refuse to provide the corresponding services when it receives requests from other ASs to use those services. This selfish behavior not only hinders the resolution of cyberspace security issues but also severely dampens the enthusiasm of other participants in collaborative governance. Summary of the Invention

[0006] The present invention aims to at least partially solve one of the technical problems in the related art.

[0007] Therefore, the purpose of this invention is to incentivize ASs to actively participate in collaborative governance and contribute their data and services. It designs a reputation-based incentive method for inter-domain collaborative governance in cyberspace, so that rational and selfish ASs will actively contribute data and services in line with their personal interests when interacting under the mechanism, which becomes the dominant strategy.

[0008] Another objective of this invention is to propose a reputation-based incentive device for collaborative governance in cyberspace.

[0009] To achieve the above objectives, this invention proposes a reputation-based incentive method for collaborative governance in cyberspace, comprising the following steps:

[0010] The system utilizes a server to observe historical information about interactions between the evaluator's autonomous region (AGN) and the AGN being evaluated. This historical information includes a single data or service request initiated by the evaluator's AGN to the AGN being evaluated. Based on this historical information, an evaluation algorithm is used to obtain a reputation evaluation result for the AGN being evaluated. Based on this reputation evaluation result, the reputation of the AGN being evaluated is updated to incentivize all AGNs to participate in collaborative governance. This reputation update includes both event-triggered and batch processing modes.

[0011] The reputation-based incentive method for collaborative governance in cyberspace, as described in this invention, can provide high-quality data and services, and greatly improve the efficiency of collaborative governance.

[0012] In addition, the reputation-based cyberspace collaborative governance incentive method according to the above embodiments of the present invention may also have the following additional technical features:

[0013] Furthermore, the observation includes direct observation and indirect observation; wherein, direct observation refers to assessing the reputation of the assessed autonomous region based on direct interaction information between the evaluator's autonomous region and the assessed autonomous region; wherein, the direct interaction information is primary information; the indirect observation refers to assessing the reputation of the assessed autonomous region based on the evaluator's autonomous region observing indirect interaction information between the assessed autonomous region and other assessed autonomous regions; wherein, the indirect interaction information is secondary information.

[0014] Furthermore, the primary information and the secondary information are assigned different weights, which are represented by a weight matrix as follows:

[0015] w = (w ij ) i，j∈N

[0016] Among them, w ij ∈[0,1] represents the degree to which the evaluator's autonomous region adopts the observations of the evaluated autonomous region regarding the target, where i is the reputation of the evaluator's autonomous region and j is the reputation of the evaluated autonomous region.

[0017] Furthermore, the multidimensional history vector of the autonomous region being evaluated, which has interacted with the evaluator's autonomous region, is represented as follows:

[0018]

[0019] Where f represents the frequency of interaction between i and j, and c represents the frequency with which j is willing to provide data or services. This represents i's evaluation of the quality of the data or services provided by j.

[0020] Furthermore, when the evaluator's autonomous region evaluates the service quality of the evaluated autonomous region, an exponential smoothing method is used to calculate a weighted average of the evaluations at different times:

[0021] φ(t)=τ·φ(t-1)+(1-τ)φ(t)

[0022] Among them, the right end This is a one-time evaluation of the service quality of the target AS at time t in this round. It is a comprehensive evaluation of the service quality of the target AS before time t, on the left side. It is the comprehensive evaluation of service quality updated at time t.

[0023] Furthermore, in the multidimensional history vector, f,c, The calculation formula is:

[0024]

[0025]

[0026] φ ij =τ·φ ij (t-1)+(1-τ)φ ij (t)

[0027] Where I∈{0,1} is the indicator function, τ is the time factor, and the real-time evaluation at time t is... It is a measured value.

[0028] Furthermore, the formula for calculating the comprehensive reputation assessment of the assessor's autonomous domain on the assessed autonomous domain is as follows:

[0029] δ ij (t-1)=w ii ·f·c·φ+∑ k∈N\i w ij δ kj

[0030] Among them, w ii The weights for the reputation assessments adopted by the evaluator's autonomous region based on its direct observations of the evaluated autonomous region are defined as follows: f is the frequency at which i sends requests to j, and c is the frequency at which j provides data or services to i. This is i's evaluation of j.

[0031] To achieve the above objectives, another aspect of the present invention proposes a reputation-based incentive device for collaborative governance in cyberspace, comprising:

[0032] An information collection module is used to observe historical information about interactions between the evaluator's autonomous region (AGN) and the AGN being evaluated using a server; wherein, the historical information includes a data or service request initiated by the evaluator's AGN to the AGN being evaluated; a reputation evaluation module is used to obtain the reputation evaluation result of the AGN being evaluated based on the historical information and through an evaluation algorithm; a reputation update module is used to update the reputation of the AGN being evaluated based on the reputation evaluation result of the AGN being evaluated to incentivize all AGNs to participate in collaborative governance; wherein, the reputation update includes two modes: event-triggered and batch processing.

[0033] The reputation-based cyberspace collaborative governance incentive device of this invention can provide high-quality data and services, greatly improving the efficiency of collaborative governance.

[0034] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0035] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0036] Figure 1 is a flowchart of a reputation-based incentive method for collaborative governance in cyberspace according to an embodiment of the present invention;

[0037] Figure 2 is a schematic diagram of a decentralized reputation mechanism framework according to an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of the structure of a reputation-based cyberspace collaborative governance incentive device according to an embodiment of the present invention; Detailed Implementation

[0039] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] The following describes the reputation-based cyberspace collaborative governance incentive method and apparatus according to embodiments of the present invention with reference to the accompanying drawings. First, the reputation-based cyberspace collaborative governance incentive method according to embodiments of the present invention will be described with reference to the accompanying drawings.

[0042] Figure 1 is a flowchart of a reputation-based incentive method for collaborative governance in cyberspace according to an embodiment of the present invention.

[0043] As shown in Figure 1, the reputation-based incentive method for collaborative governance in cyberspace includes the following steps:

[0044] Step S1: Use the server to observe the historical information of the interaction between the evaluator's autonomous domain and the evaluated autonomous domain; wherein, the historical information includes a data or service request initiated by the evaluator's autonomous domain to the evaluated autonomous domain.

[0045] Understandably, this invention models the system as time-discrete, with one round of interaction occurring at each time step. Assume there are N ASs in the system, denoted as N. In each round of interaction, each AS needs to initiate a data or service request to a specific AS in the system.

[0046] Specifically, the first step in reputation assessment is the collection of reputation information. Each AS's reputation should reflect its history of interactions with other ASs. Each AS collects reputation information from other ASs through two methods: direct observation and indirect observation. Direct observation refers to AS i using its direct interaction experience with AS j as the basis for assessing AS j's reputation; the information obtained is called primary information. Indirect observation refers to AS i using the experience of AS j interacting with other ASs as the basis for assessing AS j's reputation; the information obtained is called secondary information.

[0047] On the one hand, primary information can assess reputation more accurately. Because of discrimination, an AS may react differently to different ASs, rendering secondary information inapplicable. On the other hand, for a specific pair of ASs, the number of direct interaction samples is small, and primary information accumulates slowly, hindering the rapid identification of selfish ASs. Utilizing secondary information can accelerate this process.

[0048] To accelerate AS type recognition by utilizing secondary information while reducing its inaccuracy, this invention assigns different weights to primary and secondary information, using a weight matrix:

[0049] w = (w ij ) i，j∈N

[0050] It means that w ij ∈[0,1] represents the degree to which AS i adopts the observations of AS j regarding the target. w represents the weights on the diagonal. ii Setting a higher value indicates a greater degree of adoption of first-hand information.

[0051] Furthermore, the historical interaction information of the AS is updated after each round of interaction. After the interaction history is updated, the ASes need to maintain synchronization of secondary information. Secondary information needs to be continuously propagated in the network to maintain information synchronization among ASes. To alleviate the pressure on network communication bandwidth consumption, it is stipulated that the evaluated reputation information δ is propagated when ASes synchronize secondary information. ij Instead of the entire history vector, this is used. After each round of interaction, each AS i updates its interaction history information and calculates the reputation assessment δ for the AS j that it interacted with. ij It spread across the entire internet.

[0052] Step S2: Based on historical information, obtain the reputation evaluation results of the autonomous region of the evaluated entity through an evaluation algorithm.

[0053] Specifically, this invention relates to the format of historical information collected for reputation calculation and the reputation value algorithm based on historical information. When evaluating the reputation of a target AS, each AS stores historical vectors of interactions with other ASes, and reputations are synchronized between ASes. AS1 updates its reputation evaluation of target AS0 based on the synchronized historical reputation and the stored historical vectors.

[0054] To comprehensively assess reputation by considering the influence of multiple factors, this study uses multidimensional vectors to record the historical behavioral information of AS and evaluates AS's reputation based on these multidimensional historical vectors.

[0055] Specifically, each AS i maintains a multidimensional vector about the AS j it has interacted with:

[0056]

[0057] Where f represents the frequency of interaction between i and j, and c represents the frequency with which j is willing to provide data or services. This represents i's evaluation of the quality of the data or services provided by j.

[0058] Furthermore, because ASs continuously optimize their strategies and switch between different strategies, the type of AS may change over time. An AS that performed well initially may refuse to provide services later; an AS that acted selfishly in the early stages may later cooperate. Historical information at different times has varying reference value for determining the type of AS. Considering the impact of time on the reference value of information, a time factor τ was designed when assessing the importance of information. Information closer to the current time has a greater time weight, while information farther away from the current time has a smaller time weight.

[0059] When evaluating the service quality of a target AS for each AS, an exponential smoothing method is used to calculate a weighted average of evaluations at different times:

[0060] φ(t)=τ·φ(t-1)+(1-τ)φ(t)

[0061] Among them, the right end This is a one-time evaluation of the service quality of the target AS in this round (time t). It is a comprehensive evaluation of the service quality of the target AS before time t, on the left side. It is the comprehensive evaluation of service quality updated at time t.

[0062] Exponential smoothing assigns a weight to historical evaluations that decreases exponentially over time. Compared to treating all historical behavior of an AS equally, giving greater weight to an AS's recent performance in the evaluation allows ASs that were selfish in the early stages to recover their evaluations by actively contributing high-quality data and services again. On the other hand, it incentivizes ASs that have already accumulated high evaluations to continue contributing high-quality data and services.

[0063] Furthermore, reputation assessment relies on historical vectors. However, newly added ASs lack historical interaction information with other ASs. Assessing the reputation of a newly added AS is a cold-start problem.

[0064] A common approach to handling the cold start problem is to assign a default initial reputation value to new users. In the collaborative governance incentive problem, this invention assigns a minimum initial reputation value to newly joined ASs. This is to prevent newly joined ASs from gaining a reputation advantage, which could lead to selfish ASs frequently leaving and rejoining the system to "launder" their reputation. Newly joined ASs must first build their reputation by providing data and services to other ASs before they can obtain data and services from other ASs employing reputation-based strategies. Simultaneously, this initial value setting helps maintain system stability—ASs that have already established a high reputation will not easily leave the system. Because once they leave, when they need to re-acquire data or services from other ASs, they must rebuild their reputation from scratch.

[0065] Furthermore, considering the time effect, AS at time t applies the multidimensional history vector f,c, The specific calculation method is as follows:

[0066]

[0067]

[0068] φ ij =τ·φ ij (t-1)+(1-τ)φ ij (t)

[0069] Where I∈{0,1} is the indicator function, and τ is the time factor. Real-time evaluation at time t. It is a measurement value. The higher the service quality provided by j, the higher the real-time evaluation of j by i.

[0070] The multidimensional history vector is distributed and stored across different ASs. The history vector requested by AS i from AS j is stored in ASi. This is done to prevent AS j from tampering with the vector. Because the vector contains i's evaluation of j, j has an incentive to tamper with the vector.

[0071] When AS i evaluates the reputation of AS j, it needs to comprehensively consider both the reputation assessment of j by i based on primary information and the secondary information disseminated—the reputation assessments of j by other ASs that have interacted with j. The formula for calculating the comprehensive reputation assessment of j by AS i is as follows:

[0072] δ ij (t-1)=w ii ·f·c·φ+∑ k∈N\i w ij δ kj

[0073] Among them, w ii Let f be the weight of AS i's reputation assessment derived directly from its observations of AS j, and c be the frequency at which i sends requests to j, and c be the frequency at which j provides data or services to i. This is i's evaluation of j. The higher the frequency of interaction between i and j, the more reliable and valuable the resulting reputation assessment. The more frequently j has been willing to provide data or services in past interactions, the higher i's evaluation of j. The higher the quality of data and services provided by AS j in historical interactions, the higher i's reputation assessment of j. j'j Weights are assigned to AS i based on secondary information received from AS j'. The weight settings satisfy:

[0074] ∑ j∈N w ij =1

[0075] Ensure δ ij ∈[0, 1]. δ kj This refers to the reputation assessment of target AS j by other AS k. For ASs that interact very infrequently with other ASs, the reputation assessment of them by other ASs is also very low.

[0076] The formula consists of two parts: the evaluation of AS j based on direct historical interactions by AS i, and the indirect evaluation based on the evaluations of AS j by other AS k. The higher the direct evaluation of AS j by AS i, the higher the reputation of j; the higher the evaluations of AS j by other AS k, the higher the reputation of j.

[0077] Step S3: Based on the reputation assessment results of the assessed autonomous domain, update the reputation of the assessed autonomous domain to incentivize each autonomous domain to participate in collaborative governance; wherein, the reputation update includes two modes: event-triggered and batch processing.

[0078] Specifically, reputation updates generally follow two modes: event-triggered and batch processing. Event-triggered reputation updates cause reputation calculation to depend on the order of AS interactions within the same round; ASs that interact earlier propagate their reputation assessments first, affecting subsequent AS interactions. To avoid the interdependence of reputation assessment calculations between different ASs and to achieve fair synchronization, the mechanism specifies a batch processing approach for reputation updates. In each round, when calculating reputation, each AS i first utilizes the secondary information δ synchronized from the previous round. kj Calculate the reputation assessment δ for target AS j ij After all AS calculations are completed in this round, all ASs in the system will then uniformly propagate the updated reputation assessments to other ASs, synchronizing secondary information δ. ij .

[0079] In summary, the reputation mechanism can promote cooperative behavior in cyberspace collaborative governance. Experiments have shown that, under specific conditions, this reputation incentive for actively contributing services to high-performing participants (i.e., the reputation strategy) is evolutionarily stable, and within a certain range, the higher the service value relative to the cost, the more participants will ultimately choose the reputation strategy.

[0080] As shown in Figure 2, the reputation mechanism design mainly includes three aspects: reputation collection, evaluation, and updating. To evaluate an AS's reputation, it is necessary to collect historical information about the AS's interactions with other ASs. Reputation information can be collected through direct or indirect observation. Based on the collected historical information, the mechanism uses an evaluation algorithm to form a reputation score for each AS. ASs in the system spontaneously react to each AS's reputation, mainly by penalizing ASs with low reputations and refusing to provide them with data and services. Simultaneously, the mechanism incorporates a time factor, to some extent forgetting AS behaviors that occurred earlier, allowing selfish ASs to be reinstated by other ASs after a period of good performance. An AS's reputation score is formed based on its performance, directly affecting the reactions of other ASs to that AS, thus deterring AS behavior. Under the condition of satisfying system and mechanism parameters, the dominant strategy should be to actively provide high-quality data and services to ASs with high reputations, aligning with the individual interests of each AS.

[0081] The reputation-based incentive method for collaborative governance in cyberspace according to embodiments of the present invention can provide high-quality data and services, greatly improving the efficiency of collaborative governance. It incentivizes each AS to actively participate in collaborative governance and contribute its own data and services, designing a collaborative incentive mechanism for inter-domain collaborative governance in cyberspace. This mechanism ensures that rationally self-interested ASs, when interacting under its framework, actively contributing data and services aligns with their personal interests and becomes the dominant strategy.

[0082] To implement the above embodiments, as shown in FIG3, this embodiment also provides a reputation-based cyberspace collaborative governance incentive device 10, which includes: an information collection module 100, a reputation evaluation module 200, and a reputation update module 300.

[0083] The information collection module 100 is used to observe the historical information of the interaction between the evaluator's autonomous domain and the evaluated autonomous domain using the server; wherein, the historical information includes a data or service request initiated by the evaluator's autonomous domain to the evaluated autonomous domain.

[0084] The reputation assessment module 200 is used to estimate the reputation assessment results of the assessed entity's autonomous region based on historical information and through an assessment algorithm.

[0085] The reputation update module 300 is used to update the reputation of the autonomous region being evaluated based on the reputation evaluation results of the autonomous region being evaluated, so as to incentivize each autonomous region to participate in collaborative governance; the reputation update includes two modes: event-triggered and batch processing.

[0086] Furthermore, the aforementioned information collection module 100 includes:

[0087] The direct observation module is used to assess the reputation of the assessed autonomous region based on the direct interaction information between the assessor's autonomous region and the assessed autonomous region; where the direct interaction information is first-hand information.

[0088] The indirect observation module is used to assess the reputation of the assessed autonomous region based on the indirect interaction information between the assessed autonomous region and other assessed autonomous regions by the evaluator's autonomous region; wherein, the indirect interaction information is secondary information.

[0089] Furthermore, primary and secondary information are assigned different weights, represented by a weight matrix:

[0090] w = (w ij ) i，j∈N

[0091] Among them, w ij ∈[0,1] represents the degree to which the evaluator's autonomous region adopts the observations of the evaluated autonomous region regarding the target, where i is the reputation of the evaluator's autonomous region and j is the reputation of the evaluated autonomous region.

[0092] The reputation-based cyberspace collaborative governance incentive device according to embodiments of the present invention can provide high-quality data and services, greatly improving the efficiency of collaborative governance. It incentivizes each AS to actively participate in collaborative governance, contributing its own data and services. This design creates a collaborative incentive mechanism for inter-domain collaborative governance in cyberspace, ensuring that rationally self-interested ASs, when interacting under this mechanism, actively contributing data and services aligns with their personal interests and becomes a dominant strategy.

[0093] It should be noted that the foregoing explanation of the embodiment of the reputation-based cyberspace collaborative governance incentive method also applies to the reputation-based cyberspace collaborative governance incentive device of this embodiment, and will not be repeated here.

[0094] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0095] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0096] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A reputation-based incentive method for collaborative governance in cyberspace, characterized in that, Includes the following steps: The system utilizes a server to observe historical information about interactions between the evaluator's autonomous region (AGN) and the AGN being evaluated. This historical information includes a data or service request initiated by the evaluator's AGN to the AGN being evaluated. Based on this historical information, an evaluation algorithm is used to obtain the reputation evaluation result of the AGN being evaluated. Based on the reputation evaluation result of the AGN being evaluated, its reputation is updated to incentivize AGN participation in collaborative governance. This reputation update includes both event-triggered and batch processing modes. Event-triggered reputation updates cause reputation calculation to depend on the order of AS interactions within the same round; ASs that interact first propagate their reputation evaluations first, affecting subsequent AS interactions. To avoid interdependence in reputation evaluation calculations between different ASs and to achieve fair synchronization, the mechanism specifies a batch processing method for reputation updates. In each round, when calculating reputation, each AS i first utilizes secondary information synchronized from the previous round. Calculate the reputation assessment of target ASj After all AS calculations are completed in this round, all ASs in the system will then uniformly propagate the updated reputation assessments to other ASs, synchronizing secondary information. The observations include direct observation and indirect observation; wherein, direct observation refers to assessing the reputation of the assessed autonomous region based on direct interaction information between the evaluator's autonomous region and the assessed autonomous region; wherein, the direct interaction information is primary information; and indirect observation refers to assessing the reputation of the assessed autonomous region based on indirect interaction information between the evaluator's autonomous region and other assessed autonomous regions; wherein, the indirect interaction information is secondary information.

2. A reputation-based cyberspace collaborative governance incentive device using the method described in claim 1, characterized in that, include: The information collection module is used to observe historical information about interactions between the evaluator's autonomous region (AGN) and the AGN being evaluated using a server; wherein, the historical information includes a data or service request initiated by the evaluator's AGN to the AGN being evaluated; the reputation evaluation module is used to obtain the reputation evaluation result of the AGN being evaluated based on the historical information and through an evaluation algorithm; the reputation update module is used to update the reputation of the AGN being evaluated based on the reputation evaluation result of the AGN being evaluated to incentivize all autonomous regions to participate in collaborative governance; wherein, the reputation update includes two modes: event-triggered and batch processing.

3. The apparatus according to claim 2, characterized in that, The information collection module includes: a direct observation module, used to assess the reputation of the assessed autonomous region based on direct interaction information between the evaluator autonomous region and the assessed autonomous region; wherein the direct interaction information is primary information; and an indirect observation module, used to assess the reputation of the assessed autonomous region based on indirect interaction information observed by the evaluator autonomous region through observation of indirect interactions between the assessed autonomous region and other assessed autonomous regions; wherein the indirect interaction information is secondary information.

4. The apparatus according to claim 3, characterized in that, The primary information and the secondary information are assigned different weights, which are represented by a weight matrix: in, This indicates the degree to which the evaluator's autonomous region adopts the observations of the evaluated autonomous region regarding the objective, where i is the reputation of the evaluator's autonomous region and j is the reputation of the evaluated autonomous region.

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