Reputation Mechanism Research Articles

Denying the Obvious: Why Do Nominally Covert Actions Avoid Escalation?

Abstract In 2014, Russia denied that its military was assisting separatists in eastern Ukraine, despite overwhelming evidence. Why do countries bother to deny hostile actions like this even when they are obvious? Scholars have argued that making hostile actions covert can reduce pressure on the target state to escalate. Yet it is not clear whether this claim applies when evidence of responsibility for the action is publicly available. We use three survey experiments to test whether denying responsibility for an action in the presence of contradictory evidence truly dampens demand for escalation among the public in the target state. We also test three causal mechanisms that might explain this: a rationalist reputation mechanism, a psychological mechanism, and an uncertainty mechanism. We do find a de-escalatory effect of noncredible denials. The effect is mediated through all three proposed causal mechanisms, but uncertainty and reputational concern have the most consistent effect.

Blockchain-based UAV Continuous Authentication

With the growing utilization of unmanned aerial vehicles (UAVs), low-altitude airspace is becoming increasingly congested, posing significant challenges to Air Traffic Control (ATC). The Automatic Dependent Surveillance-Broadcast (ADS-B) system is used to report the location of UAVs. However, due to channel noise and adversarial interference, ADS-B data may be inaccurate. To address this issue, a position estimation method based on Bidirectional Long Short-Term Memory Multi-Layer Perceptron (BiLSTM-MLP) was proposed. In this method, ADS-B signal power is used to estimate the position of UAVs as a baseline approach. Experimental results demonstrate that the BiLSTM-MLP based method significantly improves estimation accuracy. Moreover, a blockchain-based dynamic reputation mechanism, aligned with the position estimation method, was proposed. The mechanism integrates information from the third-party UAV and the location data provided by the UAV itself to dynamically update the credibility of the UAV, and thus achieves decentralized access control of UAVs in low-altitude airspace.

Reputation, commitment, and financial market regulation

This paper explores the interplay between reputation mechanisms, pre-commitment strategies, and financial market regulation. Through a theoretical model, we analyze how repeated interactions between market participants, combined with regulatory oversight, can lead to improved compliance and market efficiency. The study demonstrates that reputation mechanisms serve as powerful self-regulatory tools, as negative reputational feedback exerts a stronger influence on market behavior than positive feedback, particularly when the cost of non-compliance is high. Additionally, we investigate the role of pre-commitment in promoting market cooperation and separating equilibria, where high-quality suppliers distinguish themselves from lower-quality competitors. However, in the absence of stringent regulatory enforcement, pre-commitments may be exploited by low-quality suppliers, leading to market inefficiencies. Our findings suggest that a reputation-based, tiered regulatory framework can optimize resource allocation and enhance the effectiveness of financial market regulation. The paper concludes with policy recommendations for integrating these mechanisms into current regulatory practices and outlines areas for future research.

Four-party evolutionary game analysis of enterprise environmental behavior.

With the implementation of the "Rural Revitalization Strategy" in China, it is common for enterprises to go to the countryside to develop business. However, enterprises often neglect the local environmental protection in rural areas while developing the economy to pursue profits. As the end of the national administrative system and the villagers' autonomous organization, the village committee needs to participate in monitoring enterprises' environmental behavior. With this in mind, this paper builds a game model of enterprises, grass-roots governments, farmers, and village committees and analyzes the impact of village committees, grass-roots governments, and farmers on enterprise environmental behavior. The conclusions are as follows: (i) it is difficult for the village committee to promote the positive environmental behavior of enterprises, which needs the supervision of the grass-roots government; (ii) Improving the coordination ability of village committees is conducive to reducing the burden of government supervision; (iii) Farmers' awareness of environmental protection can affect the environmental behavior of enterprises through the rights protection mechanism and reputation mechanism.

Peer conformity and competition shape how business managers evaluate withdrawals from Russia amid the Ukraine War

States have long used economic sanctions in response to violations of international law as a strategy to restore order. Increasingly, firms also reject doing business with violators. In response to the war in Ukraine, hundreds of multinational corporations voluntarily withdrew from Russia, even when policymakers were still debating the extent of sanctions. How did firm managers evaluate whether to withdraw from the Russian market? Using a survey experiment with Japanese firm managers conducted three months after the Russian invasion of Ukraine in 2022, we explore how peer effects-information on what other firms are doing in response to the crisis-influence support for withdrawal of business activity with Russia. Our findings show that information about withdrawal by other firms from a diverse set of countries promotes peer conformity that increases support. In contrast, information about ongoing business with Russia by Chinese firms fosters competition that reduces support. Market exposure moderates these reactions, although the concern about peer behavior does not appear to be driven by a reputation mechanism. Our research provides insight into how business actors perceive the strategic interplay of peer influence and market dynamics in the context of geopolitical conflicts.

ESG and Chinese corporate OFDI

In this era of sustainable development, ESG information disclosure and its capacity to fulfill goals have a significant influence on the outward foreign direct investment (OFDI) of Chinese firms. Using data from A-share listed businesses in Shanghai and Shenzhen from 2010 to 2020, this study empirically studies both the impact and mechanism of ESG information disclosure on corporate OFDI. According to the study, the greater a corporate's ESG rating, the more likely it is to engage in OFDI activities. Mechanism analysis shows ESG primarily supports corporate OFDI by easing financial limitations, lowering business risks, optimizing location choices, and strengthening reputation mechanisms. The heterogeneity study indicates this impact is stronger in large-scale firms, state-owned enterprises, and enterprises not subject to key supervision. Lastly, a further study indicates not only that ESG ambiguity reduces the promotion effect of ESG on corporate OFDI, but that high-quality disclosure also promotes OFDI more than purely symbolic low-quality ESG disclosure.

Graph Neural Network Based Asynchronous Federated Learning for Digital Twin-Driven Distributed Multi-Agent Dynamical Systems

The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growing concerns about issues such as data privacy and cybersecurity are major obstacles to improving the quality of distributed data annotation. In this paper, we propose a reputation-based asynchronous federated learning approach for digital twins. First, this paper integrates digital twins into an asynchronous federated learning framework, and utilizes a smart contract-based reputation mechanism to enhance the interconnection and internal interaction of asynchronous mobile terminals. In addition, in order to enhance security and privacy protection in the distributed smart annotation system, this paper introduces blockchain technology to optimize the data exchange, storage, and sharing process to improve system security and reliability. The data results show that the consistency of our proposed FedDTrep distributed intelligent labeling system reaches 99%.

ESG and customer stability: a perspective based on external and internal supervision and reputation mechanisms

According to external pressure theory and reputation theory, reputation-oriented enterprises will experience external pressure as societal attention increases, and enhanced ESG performance can mitigate information asymmetry and boost reputation, thereby signaling to customers the company’s commitment to sustainable development. Based on the data of A-share listed firms in China, this paper manually compiles the detailed information of corporate customers, and explores the relationship between firm’s ESG performance and customer stability using a two-way fixed-effects model. The results show that: (1) Firms with ESG advantages can enhance customer stability, and the higher the ESG score, the more significant the improvement in customer stability; (2) The mechanism test indicates that firms’ ESG performance improves customer stability by strengthening internal and external regulation as well as enhancing corporate reputation; (3) The heterogeneity analysis indicates that in high governance level and small-sized firms, the effect of firms’ ESG performance on customer stability is more significant, and in the ESG sub-test, environmental and social responsibility have a more significant impact on customer stability. This paper provides implications for improving firms’ social responsibility and stabilizing the supply chain.

Incorporating reputation into reinforcement learning can promote cooperation on hypergraphs

Most previous studies that investigate the evolutionary dynamics of cooperation in the multi-agent social dilemma only consider the pairwise interactions. However, pairwise interactions alone cannot adequately characterize the higher-order interaction relationships that are commonly present in real-world social systems. In this study, we propose a spatial public goods game on uniform random hypergraphs with reputation mechanism and self-regarding Q-learning algorithm, where agents take an action based on the Q-value and update their reputation by the first-order evaluation norm. We define the reward as a function of the agent’s payoff and their reputation influenced by the social attention degree. Simulation results indicate that an increase in the social attention degree can effectively facilitate cooperation, as social reputation incentivizes agents to adopt cooperative actions to obtain optimal rewards. Additionally, we find that the discount factor and the learning rate both significantly impact the evolution of cooperation, with the influence of the learning rate on the evolution of cooperation being dependent on the discount factor. Finally, the robustness of this model is demonstrated based on the results of theoretical analysis.

The role of reputation to reduce punishment costs in spatial public goods game

In the traditional spatial public goods game (SPGG), punishment mechanisms are considered an effective strategy to promote cooperation. However, in traditional punishment models, the high cost of punishment leads to ineffective cooperation promotion when the synergy factor is low. Therefore, this paper decides to establish a punishment model based on reputation to reduce the cost of punishment, to solve the above problems. Cooperation strategy can accumulate reputation as evolution progresses, where the high-reputation punishing group, acting as the 'main force' of law enforcement, can obtain stronger expulsion capabilities by reducing costs, and the decrease in the revenue difference with ordinary cooperators brings stronger survivability. The research compares the new punishment mechanism with traditional punishment mechanisms, finding that under low synergy factors, the reputation-based punishment model is more likely to achieve cooperation, and after introducing the reputation mechanism, it significantly enhances the promotive effect of punishers on system cooperation.

Secure and Efficient Data Sharing for IoT Based on Blockchain and Reputation Mechanism

Secure and Efficient Data Sharing for IoT Based on Blockchain and Reputation Mechanism

Reliable incentive mechanism in hierarchical federated learning based on two-way reputation and contract theory

Hierarchical federated learning (HFL) can effectively alleviate the communication bottleneck of traditional federated learning. However, the long-term healthy development of federated learning needs to continue to attract reliable participants with high-quality data. Therefore, we propose a novel reliable incentive mechanism based on two-way reputation and contract theory. Firstly, a two-way reputation mechanism named TWRM is introduced to evaluate the reliability of cluster heads and mobile devices simultaneously. Secondly, a reputation blockchain is designed to prevent malicious nodes from tampering with reputation scores, and effectively deal with malicious edge servers colluding with mobile devices. Finally, contract theory is used to reward customers based on data quality and encourage them to provide high-quality training data. Experimental results can verify that the scheme proposed in this paper effectively ensures the reliability and data quality of the participants, which can help to attract more reliable participants with high-quality data to join in federated learning.

Like Stars: How Firms Learn at Scientific Conferences

Scientific conferences are an underexplored channel by which firms can learn from science. We provide empirical evidence that firms learn from scientific conferences in which they participate but also that this is conditional on intense participation. Using data from conference papers in computer science since the 1990s, we show that corporate investments in participation are both frequent and highly skewed, with some firms contributing to a given conference scientifically, some as sponsors, and some doing both. We use direct flights as an instrumental variable for the probability that other scientists participate in the same conference as a firm, altering the knowledge set to which the firm is exposed. We find that a firm’s use of scientists’ knowledge increases when they participate in the same conferences. Greater participation efforts, where the firm seeks the spotlight by both sponsoring the conference and contributing to its scientific discourse, foretell research collaborations and a stronger learning effect. Such learning is disproportionately concentrated among the most prominent firms and scientists rather than benefitting those without alternative interaction channels. Therefore, on average, firms learn from scientists that they encounter at conferences, but the substantial heterogeneity of the effect reflects the influence of reputation mechanisms in social interactions. This paper was accepted by Ashish Arora, entrepreneurship and innovation. Funding: S. Baruffaldi acknowledges financial support from the Swiss Science National Foundation (Reference No.: P2ELP1-161847). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01373 .

Research on Stakeholder Behavior Strategy in Green PPP Project Financing

In the context of green development, while promoting green industry and sustainable development, green PPP faces the challenge of financing difficulties. Based on the evolutionary game theory of bounded rationality and taking government, social capital and financial institutions as the game players, this paper constructs an evolutionary game model for the behavioral decision-making of stakeholders in green PPP project financing. Using MATLAB software, we simulate and analyze the key factors affecting the behavioral strategies of each game player. The results indicate that: (1) The additional income obtained from participation in green PPP projects is a key factor affecting the decision-making of social capital and financial institutions, so it is necessary to improve the reasonable return mechanism of green PPP projects and attach importance to the construction of reputation mechanism; (2) Government subsidies can effectively encourage social capital and financial institutions to participate in green PPP projects, but the amount of subsidies should be within a reasonable range; (3) The government's reward and punishment intensity is a significant factor affecting the decision-making of social capital behavior. To this end, it is imperative for the government to enhance the performance evaluation system for green PPP projects and establish a balanced reward and punishment mechanism. This paper puts forward some suggestions on the decision-making of stakeholders in the financing process of green PPP projects, and provides references for the policy formulation of encouraging social capital and financial institutions to participate in green PPP projects.

Improvement of PBFT Consensus Algorithm Based on Affinity Propagation Clustering in Intellectual Property Transaction Scenarios

In response to the problems of random selection of primary nodes, high communication complexity, and low consensus efficiency in the current consensus mechanism for intellectual property transactions, a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on the Affinity-Propagation (AP) clustering algorithm, termed AP-PBFT, is proposed. Firstly, the election strategy of the leader node is constructed based on the reputation mechanism; the reward and punishment mechanism is designed to achieve the dynamic adjustment of the reputation value of the nodes in the PBFT consensus process, and the number of votes among the nodes is introduced to determine the node’s reputation value in collaboration with the reward and punishment mechanism to guarantee the precise ordering of the nodes. Secondly, nodes with high reputation values are selected as cluster centers to run the AP clustering algorithm, and clustering groups of knowledge property transaction nodes are constructed based on responsibility and availability. Finally, the three-stage consensus process of the PBFT consensus algorithm is optimized, and the consensus task is decomposed into two layers: the intra-consensus group and the inter-leader node group, reducing the communication complexity of transaction data in the blockchain. Experimental findings indicate a significant performance improvement of the algorithm over the PBFT consensus algorithm in communication complexity, throughput, and consensus efficiency in the simulation environment of multiple types of transactions in intellectual property transactions, including different types of large-scale transaction scenarios, such as purchases, sales, licenses, and transfers.

Double‐layer Byzantine fault‐tolerant grouping consensus algorithm based on raft

AbstractAddressing the scalability issues, excessive communication overhead, and challenges in adapting to large‐scale network node environments faced by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm currently employed in consortium blockchains, this paper proposes a Double Layer Consensus Algorithm Based on RAFT and PBFT Consensus Algorithms (DLCA_R_P). The nodes in the blockchain are initially divided into several groups to form the lower‐layer consensus network. Subsequently, the leaders of these groups constitute the upper‐layer consensus network, creating a dual‐layer consensus network structure. Within the lower‐layer consensus network, the PBFT consensus algorithm is employed for consensus among the groups, while the primary accountants form the upper‐layer RAFT consensus network. The algorithm incorporates a supervision mechanism and a reputation mechanism to enhance the security of the consensus network. Additionally, a grouping mechanism is introduced to transform the consensus network into a dynamic structure. Experimental results analysis demonstrates that compared to traditional PBFT consensus algorithms, DLCA_R_P reduces consensus latency by two orders of magnitude and improves throughput by one order of magnitude in a scenario with 100 nodes. Furthermore, it exhibits significant advantages over other improved algorithms. Thus, the DLCA_R_P consensus algorithm exhibits excellent scalability and can be widely applied in various scenarios within consortium blockchains.

Informational Efficiency of Cryptocurrency Markets

Abstract This study employs variance ratios (VRs) to assess the roles of regulation and liquidity on cryptocurrency market efficiency, focusing on crypto-assets subject to varying degrees of regulation. Our findings reveal that cryptocurrencies supervised by FinCEN-licensed exchanges (IEO-L) exhibit market efficiency similar to SEC-regulated traditional stock offerings (IPOs). Conversely, noncompliant crypto-assets display higher market inefficiency. We also establish a connection between regulatory compliance and issuing entity reputation mechanisms. Our results indicate that compliance with existing regulatory norms enhances efficiency and reduces investor risks in crypto-assets. Furthermore, assets voluntarily adhering to regulatory norms can attain efficiency akin to government-regulated assets.

An intrinsic integrity-driven rating model for a sustainable reputation system

In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets – where misleading practices like fake ratings are rampant – remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions.Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system’s foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain.

A Blockchain-based carbon emission security accounting scheme

To solve the problem of climate warming, countries around the world have paid special attention to the construction of carbon governance. Carbon emission accounting is an important policy tool to control the vented CO2. But at present, there are third-party agencies in carbon emission accounting that cannot ensure the fairness and impartiality of accounting, and there may be risks such as illegal use and leakage of sensitive information in the process of carbon emission data transmission. Therefore, We design the blockchain-based carbon emission security accounting scheme (BCESAS) and propose cross-chain verification contract to ensure the efficiency of cross-chain information accounting. In addition, bilinear pairing is used to ensure data integrity, and we encrypt private data using an improved and more secure homomorphic encryption algorithm to ensure that privacy is not leaked during the transfer of carbon emission data, which is more efficient than other homomorphic encryption algorithms. We also use reputation mechanism to regulate the behavior of carbon emission auditors. The theoretical and experimental analysis demonstrates that BCESAS can verify the integrity, correctness and privacy of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.

A framework for efficient cross-chain token transfers in blockchain networks

The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.