Reputation Information Research Articles

Clarifying social norms which have robustness against reputation costs and defector invasion in indirect reciprocity

The evolution of cooperation through indirect reciprocity is a pivotal mechanism for sustaining large-scale societies. Because third parties return cooperative behaviour in indirect reciprocity, reputations that assess and share these third parties’ behaviour play an essential role. Studies on indirect reciprocity have predominantly focused on the costs associated with cooperative behaviour, overlooking the costs tied to the mechanisms underpinning reputation sharing. Here, we explore the robustness of social norms necessary to secure the stability of indirect reciprocity, considering both the costs of reputation and the resilience against perfect defectors. Firstly, our results replicate that only eight social norms, known as the ‘leading eight,’ can establish a cooperative regime. Secondly, we reveal the robustness of these norms against reputation costs and perfect defectors. Our analysis identifies four norms that exhibit resilience in the presence of defectors due to their neutral stance on justified defection and another four that demonstrate robustness against reputation costs through their negative evaluation of unjustified cooperation. The study underscores the need to further research how reputational information is shared within societies to promote cooperation in diverse and complex environments.

Symbolic association networks: A case study of orchestral programming’s effect on the reputation of composers

A type of symbolic association network for the development of reputation is described and tested. Associations between people in these networks are not based on individual interaction, but rather are created by “reputational entrepreneurs” based on perceived symbolic association between people. We argue the intent of this type of connection is to add to the reputational information about those connected and we test whether a network of such associations influence cultural recognition. To do this, we use dyadic connections between classical music composers created by conductors for orchestra performance and determine whether a composer’s symbolic association network (SAN) aids recognition in publications. We find SANs to have a significant impact on the extent of reputational recognition, even when holding a composer’s individual status achievements constant. Composers with a large symbolic association network and those who bridge unconnected composers tend to receive more recognition. We discuss the influence of symbolic association networks on perception of reputational significance. We suggest SANs may advance research in reputation and culture particularly when considering actors whose reputation is active beyond their work or lifetime, such as artists, writers, musicians, and historical figures.

Cooperation and social organization depend on weighing private and public reputations

To avoid exploitation by defectors, people can use past experiences with others when deciding to cooperate or not (‘private information’). Alternatively, people can derive others’ reputation from ‘public’ information provided by individuals within the social network. However, public information may be aligned or misaligned with one’s own private experiences and different individuals, such as ‘friends’ and ‘enemies’, may have different opinions about the reputation of others. Using evolutionary agent-based simulations, we examine how cooperation and social organization is shaped when agents (1) prioritize private or public information about others’ reputation, and (2) integrate others’ opinions using a friend-focused or a friend-and-enemy focused heuristic (relying on reputation information from only friends or also enemies, respectively). When agents prioritize public information and rely on friend-and-enemy heuristics, we observe polarization cycles marked by high cooperation, invasion by defectors, and subsequent population fragmentation. Prioritizing private information diminishes polarization and defector invasions, but also results in limited cooperation. Only when using friend-focused heuristics and following past experiences or the recommendation of friends create prosperous and stable populations based on cooperation. These results show how combining one’s own experiences and the opinions of friends can lead to stable and large-scale cooperation and highlight the important role of following the advice of friends in the evolution of group cooperation.

The effect of surface on knee landing mechanics and muscle activity during a single-leg landing task in recreationally active females

ObjectiveInvestigate the effect of surface on frontal plane knee angle, knee moment and muscle activity. DesignRandomised cross over. SettingUniversity Laboratory. MethodsTwenty females performed single-leg hop-landings onto sand, grass and firm surfaces. Kinematic, kinetic and muscle activity data were obtained. Compatibility curves were used to visualise parameter estimates alongside P- values, and S-value transforms. ResultsKnee angle for firm-sand (mean difference (d)‾ = −2.2°; 95% compatibility interval (CI): −4.6 to 0.28, p = 0.083, s = 3.6) and firm-grass (d‾ = −1.9; 95% CI: −4.3 to 0.5, p = 0.125, S = 3) yielded <4 bits of reputational information against the null hypothesis (H). 5 bits (p = 0.025) of information against H were observed for knee moment between firm-sand (d‾ = 0.17 N m/kg-1. m-1; 95% CI: 0.02 to 0.31) with similar effects for firm-grass (d‾ = 0.14 N m/kg-1. m-1; 95% CI: −0.02 to 0.29, p = 0.055, S = 4). Muscle activity across surfaces ranged from almost no (S = 1) reputational evidence against H (Quadriceps and Hamstrings) to 10–13 ‘bits’ against H for lateral gastrocnemius (lower on sand). ConclusionsOur study provides valuable information for practitioners of the observed effect sizes for lower-limb landing mechanics across surfaces in asymptomatic females.

Platforms and trust as two columns of sharing economy

In the past two decades, economic models based on platform-based sharing, driven by the IT revolution, have caused a fundamental shift in traditional business practice, global capitalism, and societal values. The platform, along with all its features, has taken over the role of the organizational form since they have brought together strangers with under-utilized capacities and belongings, with those who need them without looking for ownership. The entire economic ecosystem - marketing techniques, business practices, supply chains, and consumption habits - has undergone a significant transformation. Consumer behavior is changing rapidly, and consumers are looking for appropriate and efficient access to goods and services through the Internet, without the financial, emotional, and logistical burdens of ownership. The radius of trust which was initially confined to family, friends, and local communities; now encompasses strangers who do not speak a common language and who are in different countries. Trust driven by Digital Identity (DI) and Trust and Reputation Information (TRI) have enabled what was considered unbelievable or even impossible some years ago. Further expansion and deepening of trust, based on new technologies combined with the international legal framework, has the potential to rewrite modern capitalism and societal values. This paper introduces the role of platforms in the evolution of the sharing economy, the role of trust in promoting such platforms, and the role of well-designed platforms in fostering and nurturing trust which is essential for promoting and sustaining sharing economy models.

Client selection and resource scheduling in reliable federated learning for UAV-assisted vehicular networks

Federated Learning (FL), a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks (VECN), allows a distributed approach to train datasets of nodes locally, e.g., for mobile vehicles, and exchanges model parameters to obtain an accurate model without raw data transmission. However, the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models. Moreover, the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources. In view of this, we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks (FLVN). We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles. Further, to fortify the FL reliability, we adopt the consortium blockchain to oversee the reputation information, which boasts tamper-proof and interference-resistant qualities. Finally, we formulate the resource scheduling problem by jointly optimizing the computation capability, the transmission power, and the number of local training rounds, aiming to minimize the cost of clients while guaranteeing accuracy. To this end, we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy. Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.

Endogenous monitoring through voluntary reporting in an infinitely repeated prisoner's dilemma game: experimental evidence

AbstractExogenous reputational information is known to improve cooperation. This study experimentally investigates how people create such information by reporting their partner's action choices, and whether endogenous monitoring helps to sustain cooperation, in an indefinitely repeated prisoner's dilemma game with random matching. The experimental results show that most subjects report their opponents' action choices, thereby successfully cooperating when reporting does not involve costs. However, when reporting is costly, participants are strongly discouraged from doing so. Consequently, they fail to achieve strong cooperative norms when the reported information is conveyed privately only to their next‐round interaction partners. Costly reporting occurs only occasionally even when there is a public record whereby all future partners can check the reported information, but significantly more frequently relative to the condition in which it is sent to the next partner only. With public records, groups can foster cooperative norms aided by reported information that gradually accumulates and becomes more informative over time.

Who is a Leader in the Leading Eight? Indirect Reciprocity under Private Assessment

Indirect reciprocity is a mechanism that explains large-scale cooperation in human societies. In indirect reciprocity, an individual chooses whether to cooperate with another based on reputation information, and others evaluate the action as good or bad. Under what evaluation rule (called “social norm”) cooperation evolves has long been of central interest in the literature. It has been reported that if individuals can share their evaluations (i.e., public reputation), social norms called “leading eight” can be evolutionarily stable. On the other hand, when they cannot share their evaluations (i.e., private assessment), the evolutionary stability of cooperation is still in question. To tackle this question, we create a novel method to analyze the reputation structure in the population under private assessment. Specifically, we characterize each individual by two variables, “goodness” (what proportion of the population considers the individual as good) and “self-reputation” (whether an individual thinks of him or herself as good or bad), and analyze the stochastic process of how these two variables change over time. We discuss the evolutionary stability of each of the leading-eight social norms by studying the robustness against invasions of unconditional cooperators and defectors. We identify key pivots in those social norms for establishing a high level of cooperation or stable cooperation against mutants. Our finding gives an insight into how human cooperation is established in a real-world society. Published by the American Physical Society 2024

Context matters: The influence of the social situation on source memory

AbstractPrevious research has demonstrated that people remember negative reputational information particularly well. However, most of these experiments manipulated the type of information associated with each face, rather than manipulating the circumstances under which people learn this information. The present experiment examines the effect of the social situation on memory for social-exchange relevant information. Faces were paired with descriptions of cheating, trustworthy, or neutral behavior. In addition, the importance of the social situation was manipulated: Participants had either to decide if they would want to work with the described person on a student project (socially relevant scenario) or if they would want to ask this person what time it is while waiting at an airport (socially irrelevant scenario). A multinomial processing tree model was used to measure old–new item discrimination and source memory. Only in the socially relevant scenario a source memory advantage for cheaters was found.

Children are sensitive to the number of sources when relying on gossip.

Gossip allows children to effectively identify cooperative or trustworthy partners. However, the risk of being deceived must be faced because gossip may be false. One clue for determining gossip's veracity is the number of its sources since multiple informants spreading identical reputational information about others might imply that another's moral traits are viewed unanimously among members of a social group. We investigated whether 7-year-olds (N = 108) would trust gossip from multiple independent sources. In our study, they received multiple pieces of positive/negative reputational information about one agent and neutral information about another agent by gossip from either single or multiple informants. Then they allocated rewards to and chose rewards from the gossip targets. The 7-year-olds acted upon positive gossip from multiple informants and did not rely on positive gossip from a single informant. By contrast, they relied on negative gossip regardless of the number of informants. In either valence, however, they were more likely to allocate rewards based on gossip from multiple informants than a single informant. This result indicates they are sensitive to an objective index, specifically the number of sources, for judging the veracity of gossip.

A Study on the Measuring Methods of Website Security Risk Rate

Traditionally, website security risks are measured using static analysis based on patterns and dynamic analysis by accessing websites with user devices. Recently, similarity hash-based website security risk analysis and machine learning-based website security risk analysis methods have been proposed. In this study, we propose a technique to measure website risk by collecting public information on the Internet. Publicly available DNS information, IP information, and website reputation information were used to measure security risk. Website reputation information includes global traffic rankings, malware distribution history, and HTTP access status. In this study, we collected public information on a total of 2000 websites, including 1000 legitimate domains and 1000 malicious domains, to assess their security risk. We evaluated 11 categories of public information collected by the Korea Internet &amp; Security Agency, an international domain registrar. Through this study, public information about websites can be collected and used to measure website security risk.

Information-driven cooperation on adaptive cyber-physical systems

Cooperation among competitive populations hinges on trustworthy information usually obtained through personal experiences (private information) and social influence (public information) which often is obtained through interactions in the cyberspace. To reveal the coupled dynamics between information propagation on the cyberspace and evolutionary games in the physical world, we propose a double-layer dynamical graph model for adaptive cyber-physical systems. The reputation information aggregated from the cyber layer can be used for the decision-making in the physical layer, meanwhile the population evolution and competition in the physical layer will reorganise the information network in the cyber layer. Our results show that public and private information on the trustworthiness of others can play significantly different roles in the decision-making to cooperate or defect, leading to very different performance in the global prosperity of the population. Limited consideration for the available public information reduces the benefits of the reorganization of the information network in the cyber layer leading to the increasing of mutual defections. On the other way, when public information is prioritized, the reorganization of the cyber layer can facilitate cooperation. However, the optimal decision-making to maximize population prosperity strongly depends on the way the cyber layer is reorganized and on the opportune balance of public and private information. Our results highlight the importance of the interplay between information reliability and restructuring of the cyber layer for the resilience of cooperation and prosperity in cyber-physical systems.

Tit for tattling: Cooperation, communication, and how each could stabilize the other

Indirect reciprocity is a mechanism by which individuals cooperate with those who have cooperated with others. This creates a regime in which repeated interactions are not necessary to incent cooperation (as would be required for direct reciprocity). However, indirect reciprocity creates a new problem: how do agents know who has cooperated with others? To know this, agents would need to access some form of reputation information. Perhaps there is a communication system to disseminate reputation information, but how does it remain truthful and informative? Most papers assume the existence of a truthful, forthcoming, and informative communication system; in this paper, we seek to explain how such a communication system could remain evolutionarily stable in the absence of exogenous pressures. Specifically, we present three conditions that together maintain both the truthfulness of the communication system and the prevalence of cooperation: individuals (1) use a norm that rewards the behaviors that it prescribes (an aligned norm), (2) can signal not only about the actions of other agents, but also about their truthfulness (by acting as third party observers to an interaction), and (3) make occasional mistakes, demonstrating how error can create stability by introducing diversity.

Evolution of norms for judging social behavior

Reputations provide a powerful mechanism to sustain cooperation, as individuals cooperate with those of good social standing. But how should someone's reputation be updated as we observe their social behavior, and when will a population converge on a shared norm for judging behavior? Here, we develop a mathematical model of cooperation conditioned on reputations, for a population that is stratified into groups. Each group may subscribe to a different social norm for assessing reputations and so norms compete as individuals choose to move from one group to another. We show that a group initially comprising a minority of the population may nonetheless overtake the entire population-especially if it adopts the Stern Judging norm, which assigns a bad reputation to individuals who cooperate with those of bad standing. When individuals do not change group membership, stratifying reputation information into groups tends to destabilize cooperation, unless individuals are strongly insular and favor in-group social interactions. We discuss the implications of our results for the structure of information flow in a population and for the evolution of social norms of judgment.

A Framework for Decentralized, Real-Time Reputation Aggregation in IoV

With the rapid increase in the number of connected and autonomous vehicles, there is a growing concern about the potential road accidents and collisions caused by malicious vehicles. A reputation system can help to mitigate these concerns and allow users to have safe journeys by providing a way to identify and estimate the behaviour of individual vehicles and to take appropriate actions in case of any malicious behaviour. Centralised reputation systems are widely used for reputation aggregation, but this setup requires Peer trust and could be a single point of attack. The alternative to a centralised system is the decentralised reputation system for IoV, in which the reputation information is collected and maintained by the vehicles rather than a central authority. There are several key considerations when designing a secure reputation aggregation system for the IoV. These include: i) It should ensure that vehicle feedback about other vehicles is kept private; ii) vehicles' interaction networks and positions should be protected; and iii) computations should be decentralised and not resource-intensive. Adopting a decentralised reputation system within IoV using blockchain can enhance security and privacy and mitigate many security concerns. In this article, we proposed a blockchain-based reputation system which ensures the privacy of participants and provides secure and resilient reputation computation. The reputation value reflects the aggregate trustworthiness of vehicles and this is computed via feedback provided by the vehicles in a decentralized way. We analysed the security and privacy of the proposed system and provided the computation and communication performance.

조직 평판으로서의 ESG 시그널:구성원 이직 의도, 인지와 도구성 효과에 관한 연구

As the social and environmental role of the corporation in society has been emphasized, many firms have sought to achieve good social and environmental reputations. Based on signaling theory and organizational reputation literature, this study examines how ESG (environmental, social, and government) reputations function as an important signal that reduces employees’ turnover intentions. Additionally, a mediating role in employees’ reputation awareness and a moderating role in their reputation instrumentality perception are further investigated. For the study, we collected organizational reputation information from a secondary source and conducted an online survey of 987 employees from 31 companies. By analyzing a multilevel model using MPlus software, we found strong support for our hypotheses. The ESG reputation of a company reduced its employees’ turnover intentions and employees’ awareness of their company’s ESG behaviors mediated the relationship. In addition, we found that employees’ beliefs in the importance of ESG behaviors on the long-term survival of their company, that is, ESG reputation instrumentality, positively moderated the mediation relationship. These findings suggest that signaling theory and organizational reputation literature at the organizational level can be consistently applied to employees at the individual level. We also discuss the theoretical contributions and practical implications of our study.

Dynamic Decentralized Reputation System from Blockchain and Secure Multiparty Computation

In decentralized environments, such as mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs), traditional reputation management systems are not viable due to their dependence on a central authority that is both accessible and trustworthy for all participants. This is particularly challenging in light of the dynamic nature of these networks. To overcome these limitations, our proposed solution utilizes blockchain technology to maintain global reputation information while remaining fully decentralized, and to secure multiparty computation to ensure privacy. Our system is not limited to specific settings, such as buyer/seller or provider/client scenarios, where only a subset of the network are raters while the others are ratees. Instead, it allows all nodes to participate in both rating and being rated. In terms of security, the system maintains feedback privacy in the semi-honest model, even in the presence of up to n−2 dishonest parties, while requiring only O(n) messages and having an O(n) computation overhead. Furthermore, the adopted techniques enable the system to achieve unique characteristics such as accessibility, consistency, and verifiability, as supported by the security analysis provided.

Generalized Secure and Dynamic Decentralized Reputation System With a Dishonest Majority

Reputation management systems are essential for establishing trust among network users. They are tools for reinforcing cooperation and sanctioning malicious behavior. This importance becomes a requirement in decentralized environments such as mobile ad-hoc networks (MANETs), peer-to-peer systems (P2P), wireless sensor networks (WSNs), or decentralized social networks (DSNs) where there is no trusted third party to monitor and enforce good behavior among users. In this paper, we propose a dynamic decentralized reputation system that fits such network characteristics, namely decentralization, dynamism, and openness, without conceding on security. The novel system is a general-purpose system that uses blockchain to gather and supply global reputation information while remaining fully decentralized. Unlike previous works on decentralized reputation systems where reputation information is inconsistent and limited to users’ direct experience and recommendations from peers (neighbors), our system gathers feedback from all over the network and stores reputation information on a distributed ledger fully accessible to all users. In terms of security, the proposed method achieves privacy utilizing secure multiparty computation, a cryptographic primitive that preserves feedback privacy even with a dishonest majority reaching <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n-2$ </tex-math></inline-formula> malicious parties while requiring only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> messages. The employed techniques enable the system to achieve unique characteristics like consistency, conservation, and verifiability in addition to privacy. The security analysis we provide confirms these properties, and the performed simulation shows the protocol’s effectiveness.

A blockchain-based reputation system for trusted VANET nodes

Vehicular network applications, such as Local Danger Warnings (LDW), have become necessary as they provide services to drivers with warnings and instructions to ensure safe driving. However, before any vehicle reacts to an alert, it is essential to verify its veracity because an incorrect warning misleads other vehicles and causes unnecessary delays and detours in traffic. On the other hand, ignoring an alert or taking too long to verify it can cause accidents. A common way to address this problem is via a reputation system, where the veracity and plausibility of an alert message are assessed based on the reputation of the vehicle that discloses it. This paper describes a decentralized reputation system based on a consortium blockchain and smart contracts called BRS4VANETs. This system analyzes the reliability of data generated by a vehicle, detects malicious behavior, and contributes to decision-making. The complete functional architecture of the system, detailing the algorithms, evaluation metrics, communication protocol, and message formats, is described and implemented. Experiments with network and traffic simulators evaluated the risk of a false message attack, the effectiveness of the system and its overheads. The assessment demonstrates the importance of reputation systems and also shows the feasibility of having a decentralized system to store and disseminate reputation information based on blockchain technologies. Finally, the results also demonstrate that BRS4VANETs can successfully detect suspicious and malicious vehicles.

Why do people gossip? Reputation promotes honest reputational information sharing.

Gossip, sharing information about an absent person, is an important way of spreading reputational information, crucial in fostering human cooperation. However, why should information possessors engage in gossip, and why should they be honest? We addressed this question based on a reputational account. In study 1, we found that when observed by potential investors, people were willing to pay more to share reputational information with those in need. Moreover, engaging in gossip did confer reputational benefits for gossipers by receiving more trust from investors, and gossipers' willingness to gossip predicted their subsequent trustworthiness. Study 2 further verified that observability promoted only honest information sharing and deterred dishonest gossiping. Consistently, only honest gossipers were trusted more, while dishonest gossipers were perceived to be even less trustworthy than non-gossipers, and only the motivation for honest gossiping could reflect gossipers' trustworthiness. These findings suggest that reputation can provide a solution to encourage gossiping and secure honesty.