Indirect Reputation Research Articles

Do sanctions imposed on peers have a deterrence effect on related-party transactions of observing firms? Evidence from Chinese listed firms

How to effectively discipline related-party transactions has garnered widespread attention from both academia and practice. While extensive literature has examined the factors influencing related-party transactions, it has overlooked the impact of sanctions imposed on peers on the related-party transactions of other firms. Using panel data covering Chinese listed firms from 2002 to 2021, we find that sanctions imposed on peers have a deterrence effect on observing firms. Increasing the certainty or severity of sanctions significantly reduces related-party transactions in observing firms. Mechanism tests demonstrate that media coverage, industry competition, and social trust strengthen this deterrence effect. Further analysis suggests the deterrence effect of sanctions is primarily driven by indirect reputation effects. Additionally, we find that deterrence effect exists in both state-owned enterprises (SOEs) and non-SOEs. Our study sheds light on the efficacy of enforcement in the Chinese capital market and offers important implications for enhancing regulatory efficiency in emerging economies.

Optimizing Collaborative Crowdsensing: A Graph Theoretical Approach to Team Recruitment and Fair Incentive Distribution.

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users' tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users' significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users' importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism.

WSN Network Node Malicious Intrusion Detection Method Based on Reputation Score

Wireless sensor network (WSN) is the Internet of Things by a large number of sensors in the external physical environment to obtain data information, and use wireless communication technology to provide users with information transmission services. At this stage, communication and security mechanisms are the main problems faced by WSN. This is because most of the existing sensors are powered by batteries with very limited energy, and most of them are deployed in an outdoor open environment, which is easy to be captured as a malicious node. Network attacks. However, the existing malicious node detection methods have shortcomings such as low efficiency, high energy consumption, and insufficient performance. Therefore, this paper proposes a WSN malicious node intrusion detection method based on genetic algorithm optimization of LEACH hierarchical routing protocol. Based on the optimization of the LEACH protocol by genetic algorithm, the method integrates the reputation evaluation mechanism, and screens and eliminates malicious nodes by calculating direct reputation, indirect reputation and comprehensive reputation, thereby ensuring the safe operation of WSN. The simulation results show that this method can effectively resist the attack of malicious nodes on WSN, and has obvious advantages over other methods.

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

Multiagent systems (MASs) have a wide range of industrial applications due to agents’ advantages. However, because of the agents’ dynamic behaviors, it is a challenge to ensure the quality of service they present. In this paper, to address this problem, we propose an adaptive agent trust estimation model where agents may decide to go from genuine to malicious or the other way around. In the proposed trust model, both direct trust and indirect reputation are used. However, the indirect reputation derived from the direct experience of third-party agents must have reasonable confidence to be useful. The proposed model introduces a near-perfect measure that utilizes consistency, credibility, and certainty to capture confidence. Moreover, agents are incentivized to contribute correct information (to be honest) through a credit mechanism in the proposed model. Simulation experiments are conducted to evaluate the proposed model’s performance against some of the previous trust models reported in the literature.

Intelligent Decision Support System of Emergency Language Based on Fog Computing

In recent years, various emergencies have frequently occurred worldwide, which has forced relevant service departments to pay more attention to decision-making and emergency management. Since emergency events are characterized by complex environments, unstable events, and time constraints, events usually involve multiple factors and promptly correct errors in the decision-making process. In fact, in many cases, emergency decision-making needs to select an optimal one from multiple alternatives for execution. The fog algorithm decision-making method can solve the problem of optimal solution selection, and it has been widely used in many fields. This article evaluates the emergencies that have occurred in the past 10 years. The evaluation indicators include direct economic loss, indirect reputation loss, ecological environment indicators, and healthy living indicators. The first two are cost-based indicators. The index value of direct economic loss and indirect reputation loss is as small as possible, while the index value of ecological environment index and healthy living index is the larger the better. Among the many selected emergencies, only the index evaluation scores of fires are reliable ( P < 0.01 ), and the evaluation scores of other emergencies belonging to natural disasters are a bit wrong ( P > 0.05 ). The reason for this may be that the direct economic losses caused by natural disasters are not well counted, and the families involved and the environment are too wide. Therefore, the emergency language intelligent decision support system based on fog computing has a good development prospect.

Investigating Indirect and Direct Reputation Formation in Asian Elephants (Elephas maximus).

Reputation is a key component in social interactions of group-living animals and appears to play a role in the establishment of cooperation. Animals can form a reputation of an individual by directly interacting with them or by observing them interact with a third party, i.e., eavesdropping. Elephants are an interesting taxon in which to investigate eavesdropping as they are highly cooperative, large-brained, long-lived terrestrial mammals with a complex social organisation. The aim of this study was to investigate whether captive Asian elephants (Elephas maximus) could form reputations of humans through indirect and/or direct experience in two different paradigms: (1) a cooperative string-pulling task and (2) a scenario requiring begging. Fourteen captive Asian elephants in Thailand participated in an experimental procedure that consisted of three parts: baseline, observation, and testing. In the observation phase, the subject saw a conspecific interact with two people—one cooperative/generous and one non-cooperative/selfish. The observer could then choose which person to approach in the test phase. The elephants were tested in a second session 2–5 days later. We found no support for the hypothesis that elephants can form reputations of humans through indirect or direct experience, but these results may be due to challenges with experimental design rather than a lack of capacity. We discuss how the results may be due to a potential lack of ecological validity in this study and the difficulty of assessing motivation and attentiveness in elephants. Furthermore, we highlight the importance of designing future experiments that account for the elephants' use of multimodal sensory information in their decision-making.

Wireless sensor networks secure routing algorithm based on trust value computation

In this paper, the security attack status in wireless sensor network (WSN) is analysed, and integrated with the features of WSN, a safety trust evaluation mechanism is proposed for WSN. It evaluates the nodes by delivery behaviour of sensor nodes and considers the subjective evaluation with trust value recommendation of other nodes, to establish trust evaluation model which is easy to be implemented. It refers to indirect reputation parameters of neighbour nodes and integrates them to the energy reputation parameters, to achieve comprehensive evaluation value of nodes. The credibility is determined by the degree that is beyond or lower that the threshold. Simultaneously, we also take into account the link quality of links and put forward improved routing setting strategy. The simulation results indicate the model can reflect the credibility of nodes accurately and it can effectively protect malicious libel, which also improves the system security and reliability, to shoe the fairness of network.

Wireless sensor networks secure routing algorithm based on trust value computation

In this paper, the security attack status in wireless sensor network (WSN) is analysed, and integrated with the features of WSN, a safety trust evaluation mechanism is proposed for WSN. It evaluates the nodes by delivery behaviour of sensor nodes and considers the subjective evaluation with trust value recommendation of other nodes, to establish trust evaluation model which is easy to be implemented. It refers to indirect reputation parameters of neighbour nodes and integrates them to the energy reputation parameters, to achieve comprehensive evaluation value of nodes. The credibility is determined by the degree that is beyond or lower that the threshold. Simultaneously, we also take into account the link quality of links and put forward improved routing setting strategy. The simulation results indicate the model can reflect the credibility of nodes accurately and it can effectively protect malicious libel, which also improves the system security and reliability, to shoe the fairness of network.

A Malicious Node Identification Strategy with Environmental Parameters Optimization in Wireless Sensor Network

In the wireless sensor network (WSN), nodes show a low forwarding rate under a poor-quality links environment and a resource-constrained state. The malicious nodes imitate this forwarding behavior, which can selectively forward date, eavesdropping, or discarding important dates. The traditional reputation model is challenging to identify with this kind of sub-attack nodes. To address these problems, a malicious node identification strategy based on time reputation model and environmental parameters optimization (TRM-EPO) is proposed in the WSN. First of all, the comprehensive reputation is calculated according to the direct reputation and the recommended indirect reputation. The environmental parameters matrix is based on nodes’ running state, taking into account nodes’ energy, data volume, number of adjacent nodes, and node sparsity. Besides, according to the environmental parameters matrix, and the recorded comprehensive reputation matrix, the next cycle’s trust can be predicted. Finally, a similarity of the actual reputation and predicted trust matrix is proposed to compare with an adaptive threshold to identify malicious nodes. The experimental results demonstrate that the proposed strategy improves sensor nodes’ security and reliability in a complex environment. Moreover, compared to comparison algorithms, the TRM-EPO improves the recognition rate above 1% and reduces the false-positive rate by more than 1%.

Effects of indirect reputation and type of rearing on food choices in chimpanzees (Pan troglodytes)

Chimpanzees and humans establish preferences over individuals they may benefit more from through scoring indirect reputation. However, humans prefer prosocial individuals even at their own cost. Giving preference to prosocial reputation over material rewards might have permitted the establishment of cooperative human societies. We tested the evolutionary roots of this propensity: importantly, in our study, the reputation scored had no food involved. Eighteen chimpanzees watched a performance where an antisocial experimenter hit a human victim and a prosocial experimenter interrupted the fight and consoled the victim. Next, the chimpanzees begged food from one of them. In Phase 2, the experimenters offered different food amounts (antisocial + 4 vs. prosocial + 1). Chimpanzees significantly prioritized rewards over reputation (i.e., chose antisocial). In Phase 3, both experimenters offered two pieces of food. Most of the subjects showed indifference to reputation (i.e., chose randomly). Watching fights produced significantly more arousal than consolations. Emotional engagement could not account for chimpanzees’ choices since their choices varied between phases but their arousal did not. Ontogeny and rearing history might play a role in chimpanzees’ choices: the adolescent males (n = 3) consistently chose the antisocial individual whereas hand-reared subjects chose significantly different from mother-reared. We discuss whether the valence of the reputation is species-specific. From an evolutionary perspective, being able to learn indirect reputation is relevant for the individual’s fitness. Both chimpanzees and humans have previously proved to choose those who will presumably behave in the future in a way they could benefit from, suggesting similar underlying cognitive processes that would have emerged at an earlier common ancestor. However, both species approach differently to prosocial individuals. Humans live in societies where there is common agreement about certain universal rights which should always prevail, and thus they are more willing to approach prosocial individuals, even at their own cost. By contrast, in our study, chimpanzees, whose societies are based on unequitable distribution of power and resources, were not that willing to consistently and costly approach prosocial individuals. Moreover, other interpersonal factors, such as the type of upbringing or age-related changes in behavior (aggressiveness during adolescence), might have accounted for these differences.

PSTRM: Privacy-aware sociopsychological trust and reputation model for wireless sensor networks

The high possibility of attack is greatly attributed to the broadcast nature of the communication medium in which the sensor nodes operate; this makes eavesdropping of messages possible on the network. This paper proposes a privacy-aware sociopsychological trust and reputation management (PSTRM) model. The paper presents a model that models the ability of a node as a continuum based on the current battery level and outage probability of the network. PSTRM also utilise an Elliptic-Curve Cryptography Diffie-Hellman (ECCDH) privacy-aware dissemination framework which encourages the sharing of accurate and credible indirect reputation information within network neighbourhoods. The following social constructs, viz., ability, benevolence and consistency were considered in the design of the proposed model. The detection rate of the proposed model was evaluated against that by Rathore et al., using MATLAB. PSTRM was found to have high detection rates than the proposal by Rathore et al.

On trust models for communication security in vehicular ad-hoc networks

Vehicular ad-hoc networks (VANETs) are prone to various network attacks due to its open, dynamic, and distributed nature. It is of increasing importance to assess the trustworthiness of vehicle nodes and messages for communication security in such networks. In this paper, we propose an integrated security scheme to help nodes in VANETs identify the legitimacy of messages for proper decision making. Within the proposed scheme, we first construct trust models to quantify the direct and indirect reputation of a participating node. Particularly, we use the certain-factor model to measure the node’s direct reputation by observing its communication behavior, and synthesize the information from the node’s neighbors and road side unit (RSU) reports to determine its indirect reputation. Both types of reputation are then combined using the uncertain deductive method. Based on these trust models, we further design an attribute-weighted K-means method to identify legitimate messages and a routing method for message delivery in VANETs. We also design and incorporate a dynamic game model-based incentive mechanism to encourage nodes to take cooperative actions and restrain them from selfish behaviors. Extensive simulations show that the proposed security scheme exhibits superior performances over existing methods in alleviating negative effects caused by selfish or malicious nodes.

A reputation based electronic government procurement model

Each year, governments spend billions in procurement. In this context, they generally select their suppliers based on the minimum price. However, other criteria than price may be considered to help governments minimize the procurement costs. This paper proposes a formal reputation model that is intended to determine the winners of a public procurement process. The proposed model combines three elements: (1) the direct reputation of the supplier, (2) the indirect reputation of the supplier, and (3) the difference in beliefs between the government and the supplier. The proposed model compares situations where reputation is integrated along price for selecting suppliers and where it is not. Results show that the proposed reputation model may lead to lower costs for governments.

Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method

Abstract It is a challenge to design a well balanced reputation system for an environment with millions of users. A reputation system must also represent user reputation as a value which is simple and easy to compare and will give users straightforward suggestions who to trust. Since reputation systems rely on feedbacks given by users, it is necessary to collect unbiased feedbacks In this paper we present a controversial, yet innovative reputation system. Hidden and Indirect (Probabilistically Estimated) Reputations - HIPER Method splits user reputation into two related values: Hidden Reputation (HR) is directly calculated from a set of feedbacks, Indirect Reputation (IR) is a probabilistically estimated projection of the hidden reputation and its value is public. Such indirect connection between received feedbacks and a visible reputation value allows users to provide unbiased feedbacks without fear of retaliation.

Direct and indirect reputation formation in nonhuman great apes (Pan paniscus, Pan troglodytes, Gorilla gorilla, Pongo pygmaeus) and human children (Homo sapiens).

Humans make decisions about when and with whom to cooperate based on their reputations. People either learn about others by direct interaction or by observing third-party interactions or gossip. An important question is whether other animal species, especially our closest living relatives, the nonhuman great apes, also form reputations of others. In Study 1, chimpanzees, bonobos, orangutans, and 2.5-year-old human children experienced a nice experimenter who tried to give food/toys to the subject and a mean experimenter who interrupted the food/toy giving. In studies 2 and 3, nonhuman great apes and human children could only passively observe a similar interaction, in which a nice experimenter and a mean experimenter interacted with a third party. Orangutans and 2.5-year-old human children preferred to approach the nice experimenter rather than the mean one after having directly experienced their respective behaviors. Orangutans, chimpanzees, and 2.5-year-old human children also took into account experimenter actions toward third parties in forming reputations. These studies show that the human ability to form direct and indirect reputation judgment is already present in young children and shared with at least some of the other great apes.

A Trust Value Computing Approach for Web Service

Building a trust mechanism for Web service is very important. First, Bayes method is used to evaluate the interaction success probability of Web service; then direct trust value, indirect trust value, reputation value and total trust value are defined respectively; third, the sample size determination is discussed; finally, the simulation experiment results demonstrate its feasibility and effectiveness.

A mechanism based on reputation in P2P networks to counter malicious packet dropping

In P2P (Peer-to-Peer) networks, some malicious peers can impact on overall networks performance. One of the malicious behaviors of these peers is malicious packet dropping. In this paper, our focus is to detect and to exclude peers that misbehave by dropping some or all packets. Here, we propose a reputation-based mechanism for solving the problem efficiently. The proposed mechanism uses both direct reputation information and indirect reputation information to compute comprehensive reputation of a peer. At the same time, history reputation information is also taken into account to provide faults tolerance capability and we regulate the imprecision based on the fact that the cause of packet dropping can be complex. Finally, the peers with bad comprehensive reputation can be detected easily and then will be excluded from the network. In this way, our proposed mechanism improves the performance of P2P networks without increasing computational overhead.

Repetition and Reputation: Implications for Trust and Trustworthiness in the Short and in the Long Run

Repeat transactions are not necessarily the rule in today's global economy. Indirect reputation systems, where buyers base their decisions on a seller's previous interactions with other buyers, are a potential substitute for personal interactions - provided such information is available. This paper examines experimentally to what degree indirect reputation building substitutes for direct reputation building in repeat interactions in the short run and analyzes the effects these environments have on behavior in the long run. We find that repeat interactions are the most effective institutional arrangement to foster trust and trustworthiness in the short and in the long run.