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The increased volume of rumors and attention on related topics during the COVID-19 pandemic have had a significant negative social impact. To combat rumors, it is crucial to study the actors involved in their spread. In this study, we first introduce prospect theory and construct an evolutionary game model between network operators and government regulators. We investigate collusion between network operators and Internet rumormongers as well as the regulatory behavior of government agencies. Second, we use prospect value to replace traditional expected utility to construct a profit prospect matrix and apply the dynamic replicator equation to analyze the equilibrium stability of the model. The stability conditions of the game between the two parties are closely related to the government's regulatory costs, and the strength of government punishment after collusion is detected. Finally, we propose relevant countermeasures against collusion problems during the network rumor control period. [ABSTRACT FROM AUTHOR]

We conduct an experimental study of cartel behaviour in the presence of an active competition authority. Treatments differ with respect to how responsive the authority is to collusion and suspicious pricing behaviour by firms. Subjects are successful in coordinating strategies to avoid antitrust detection as we observe gradual price increases and staggered pricing conforming with theory. We find that the presence of a more active antitrust regime substantially improves the internal stability of cartels. We attribute this result to the common-enemy (or esprit de corps) effect induced by the threat of cartel prosecution. A more responsive antitrust policy in this experiment provides higher cartel deterrence which lowers both prices for consumers and the cost of antitrust enforcement. [ABSTRACT FROM AUTHOR]

User-generated content (UGC) is developing rapidly as an emerging platform form, however, the problem of indirect copyright infringement by algorithms is becoming more and more prominent, and infringement governance has become a key act in the development of UGC platforms. When infringement occurs, recommendation algorithms expand the scope and results of infringement, while platforms choose to conspire with direct infringers for their own interests, making it difficult for infringed persons to defend their rights. In order to analyse the influence of different factors in the platform ecosystem on the subject's behavioural strategies, a "platform-infringer" evolutionary game model is constructed, and numerical simulation is used to verify the correctness of the stable results. Based on the simulation results, it is concluded that the factors of uncertain revenue, punishment and reputation loss have important influence on the decision-making behaviour of the subject of infringement governance, and accordingly, the proposed measures on the publishers, platforms and the legal level of the government are conducive to the evolution of the system to the point of positive regulation and stability of rights protection, with a view to promoting the healthier and more stable development of the UGC platforms. [ABSTRACT FROM AUTHOR]

Hierarchical quantum secret sharing has important theoretical value and realistic significance in quantum secret sharing schemes. In order to expand the access structure of participants and improve the performance of schemes, this paper presents a standard (k, n)-threshold hierarchical quantum secret sharing scheme. Firstly, we construct a specific pair of orthogonal entangled states and use their local distinguishable property to achieve the standard (k, n)-threshold structure at each level in the hierarchy, which not only enhances the practical value of hierarchical quantum secret sharing schemes, but also reduces the computational expenses. Secondly, our scheme does not require any quantum operations to recover the secret during the reconstruction phase, which greatly reduces the quantum computational complexity. Finally, performance analysis shows that our scheme owns higher information efficiency compared with the existing schemes. Security analysis shows that our scheme is resistant to not only a series of typical external attacks but also both personal attacks from dishonest internal participants and collusion attacks. [ABSTRACT FROM AUTHOR]

Collusion designates a specific type of transference-countertransference interaction between two or more persons, linked by an unconscious and shared unresolved issue, which they avoid on an intrapsychic level by externalizing it in the interpersonal space. To find a way to identify collusion and to delineate it from other transference-countertransference interactions. We conducted this study based on a case series approach. The study material consisted of audiotaped clinicians-centered supervisions with oncologists. The case series methodology involved the systematic examination of a purposive sample of supervisions, with the aim to understand how and why they differ from one another with respect to the relational dynamic between the physician and his/her patient. Four cases/supervisions were selected as they allowed to situate collusion on the spectrum of transference-countertransference interactions. We report on it by describing a countertransference reaction, two transference-countertransference interactions, and a collusion. The study reveals the challenges and pitfalls of research on collusion. The results allow to confirm the criteria of our working definition of collusion and to delineate collusion from other transference-countertransference interactions, which represents a first step for the empirical investigation of collusion. [ABSTRACT FROM AUTHOR]

Aim: To investigate the impact of changing from in‐person written assessments to open‐book online assessments and assess grade inflation, student preferences and potential cheating. Materials and Methods: Data from the Master of Clinical Dentistry in Fixed and Removable Prosthodontics (MClinDent FRP) postgraduate programme in dentistry was analysed. A comparison was made between the results from 3 years of traditional format written examinations (2017–2019) and the same examinations in an open‐book online format over 2 years (2020 and 2021). Results: A comparison of assignment grades (all in open‐book online format) over the 5 years 2017–2021 showed no significant difference in mean grades, suggesting the academic ability of each cohort was similar. The examination results increased by 2.5% (2020) and 1.9% (2021) compared with the equivalent data from 2017 to 2019. This shows modest grade inflation, which was highly significant (t 4.73, df 937, p <.00001) due to the change in format but suggests that no widespread collusion was taking place to raise the grades. Student feedback strongly endorsed the open‐book online format and was of the opinion that it was less stressful. Conclusions: The move to open‐book examinations was supported by student feedback, and the comparison of results, while showing an expected grade inflation, did not provide evidence that cheating was taking place through enhanced examination grades. [ABSTRACT FROM AUTHOR]

This study explores the social nature of learning and discusses its implications for student assessment. To this end, we analyzed a sample of unique first-hand accounts of students seeking help with academic work, relying on the grounded theory approach for the identification of incentives for academic support (n = 807), and used time-series analysis (n = 5,637) to identify temporal trends. Our findings demonstrate an overlap in collaboration, collusion, and contact cheating practices and highlight a trade element in peer-relationships. In contrast to outsourcing of academic work to commercial providers, whereby academic support is exchanged for money, students' tend to trade what they have available. The incentives offered in exchange for academic support included food, personal attention, money, alcohol, personal items, and sexual opportunities. The top subjects students sought help with were mathematics, history, and English. When examined on a timeline (2018–2023), the help-seeking behaviors persisted throughout the pandemic-related lockdowns; however, there was a shift toward monetary transactions. We argue that peer community can be considered an economy. Transacting with peers is more accessible, more affordable, and less risky than transacting with commercial providers. Furthermore, when students are partially involved in the production of academic work, it becomes harder to detect. [ABSTRACT FROM AUTHOR]

This paper studies the problem of minimizing the total cost, including computation cost and communication cost, in the system of two-sided secure distributed matrix multiplication (SDMM) under an arbitrary collusion pattern. In order to perform SDMM, the two input matrices are split into some blocks, blocks of random matrices are appended to protect the security of the two input matrices, and encoded copies of the blocks are distributed to all computing nodes for matrix multiplication calculation. Our aim is to minimize the total cost, overall matrix splitting factors, number of appended random matrices, and distribution vector, while satisfying the security constraint of the two input matrices, the decodability constraint of the desired result of the multiplication, the storage capacity of the computing nodes, and the delay constraint. First, a strategy of appending zeros to the input matrices is proposed to overcome the divisibility problem of matrix splitting. Next, the optimization problem is divided into two subproblems with the aid of alternating optimization (AO), where a feasible solution can be obtained. In addition, some necessary conditions for the problem to be feasible are provided. Simulation results demonstrate the superiority of our proposed scheme compared to the scheme without appending zeros and the scheme with no alternating optimization. [ABSTRACT FROM AUTHOR]

This article reviews and critically assesses the "cost coordination" theory of harm developed in a recent eponymous article, Harrington (2022), and its application to the EU trucks case. We conclude that, while the cost coordination theory is a valuable and interesting contribution to the academic literature, it assumes a number of market features that may not be present in the EU trucks case—in particular that list price coordination is persistent rather than occasional, that transaction prices are sufficiently transparent to implement an effective monitoring and retaliation mechanism, and that list price changes are interpreted by local price setters as reflecting changes in cost rather than changes in other variables. We also note that, compared with standard price coordination, cost coordination generally tends to be both harder to sustain and less effective at raising prices if it is sustained. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly aiding human decision makers in construction bidding processes by analyzing competitors' bidding patterns. However, concerns are emerging about the potential for AI-driven algorithmic collusion, which might inflate prices and disrupt fair competition in various sectors. Given the unique dynamics of the construction sector and its growing reliance on AI, understanding the impact of these algorithms on the bidding landscape is essential, both academically and practically. Thus, this study investigates the impact of AI in the construction bidding market on bid pricing patterns to predict how the landscape of the market might change as AI starts to play a more prominent role. We subjected AI bidding agents to repeated competitions with each other in computer-simulated construction bidding marketplaces. We focused on the markup decisions made by the AI bidders. Our findings indicate that AI bidders tend to develop cooperative strategies over time, leading to higher bids overall compared to lower, competitive bids. This collusive behavior was facilitated by frequent interactions (previous bidding competitions over time) between AI bidders. This collusive behavior was enabled by algorithms that aimed to maximize the profit and was hindered by algorithms that aimed to maximize the number of project wins. These findings highlight potential fairness and competitiveness issues in construction bidding with dominant AI bidders. [ABSTRACT FROM AUTHOR]

The story of Moses's birth and salvation entails a collusion between three female figures: his biological mother (a Hebrew slave), his sister, and his Egyptian adoptive mother, Pharaoh's daughter. Through a close reading of the biblical text, the author shows how mother and daughter deliberately place the reed basket (or "ark") where it might be found by Pharaoh's daughter; the princess then defies her father's decree not only by saving the infant but in adopting him and raising him within the palace precincts. While rabbinic midrash sees the Egyptian princess as exceptional, portraying her anachronistically as a model convert and renaming her Bityah (that is, Batyah, daughter of Yah, God), the author conjectures that this collusion between Hebrew slavewomen and Egyptian aristocracy to save the infant Hebrew boy may very well have been a prevalent phenomenon. The essay closes with an original midrashic retelling of the narrative that highlights the alliance between Moses's Egyptian/Hebrew mothers. [ABSTRACT FROM AUTHOR]

In this work, by constructing a principal‐agent model, we analyze the intrinsic causes of collusion between the government and enterprises, particularly through the central and local governments and resource‐based enterprises. The analysis has been conducted by introducing the public as a third‐party monitoring body to explore the positive role of public participation in preventing collusion between the government and enterprises, and henceforth entailing model analysis and validation with certain examples. The green transformation of resource‐based enterprises is an effective way for their sustainable development, besides being an inevitable requirement for China's high‐quality economic development and ecological civilization construction in the new era. In this perspective, our study reveals that: (1) Government‐enterprise collusion is motivated by the central government's improper assessment and incentive mechanism, besides the information deficit between the central government and the colluding parties. (2) The conditions for government‐enterprise collusion in development remain on the resource‐based enterprises and local governments that face fewer expected penalties than expected benefits, thus resulting in lower collusion risks. (3) Public participation in monitoring can effectively combat the willingness of the local governments and resource‐based enterprises to collude and significantly increase the level of effort of both parties in the green transition. (4) Public monitoring increases the probability of collusion detection, and prompt detection improves the timeliness and effectiveness of punishment. The findings from this study can provide a scientific basis for improving the regulatory system, thus improving public participation and strengthening the penal system. [ABSTRACT FROM AUTHOR]

I characterize the outcomes of a class of model-free reinforcement learning algorithms, such as stateless Q-learning, in a prisoner's dilemma. The behavior is studied in the limit as players stop experimenting after sufficiently exploring their options. A closed form relationship between the learning rate and game payoffs reveals whether the players will learn to cooperate or defect. The findings have implications for algorithmic collusion and also apply to asymmetric learners with different experimentation rules. [ABSTRACT FROM AUTHOR]

We conduct a series of Cournot duopoly market experiments with a high number of repetitions and fixed matching. Our treatments include markets with (a) complete cost symmetry and complete information, (b) slight cost asymmetry and complete information, and (c) varying cost asymmetries and incomplete information. For the case of complete cost symmetry and complete information, our data confirm the well-known result that duopoly players achieve, on average, partial collusion. However, as soon as any level of cost asymmetry or incomplete information is introduced, observed average individual quantities are remarkably close to the static Bayes-Nash equilibrium predictions. [ABSTRACT FROM AUTHOR]

We explore the incentives of firms to partition and shroud prices in a model where consumers may ignore a price component. In the static game shrouding can only arise under restrictive conditions if the market is sufficiently concentrated and the shrouded price component is sufficiently high. In the dynamic game we find strong pro-collusive effects of shrouding in that firms can more easily collude on shrouded prices than in the static game and collusion is also facilitated compared to standard Bertrand markets. Such collusive strategies are moderated if consumer learning is important. [ABSTRACT FROM AUTHOR]

On July 23, 2015, the Spanish National Markets and Competition Commission (CNMC) passed judgement (S/0482/13) against 20 automotive sector companies for alleged restrictive competition practices between March 2004 and August 2013. The exchange of information among the different companies would have had repercussions on the fixing of vehicle sale prices, homogenization of conditions and future commercial plans for the automobile sale and post-sale services in Spain. This paper estimates the loss suffered by consumers as a result of these collusive practices using the Before and After method. We have reconstructed the prices that would have prevailed in the absence of the collusive practice. The analysis reveals an increase of 10.16 percent. The observation of the prices and the production of automobiles during the period exhibits an anomalous behaviour with respect to GDP evolution and prices in the economy as a whole. [ABSTRACT FROM AUTHOR]

The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks. [ABSTRACT FROM AUTHOR]

In recent years, the development of IOTA, a new type of Distributed Ledger (DL) for internet of things (IoT), has gained significant attention. IOTA DL offers key features like scalability, fast and free transactions, making it an optimal choice for IoT devices. However, a major concern with IOTA DL is its reliance on a single coordinator for transaction confirmation. This default coordinator introduces issues of single point of failure and incomplete distribution. To address these limitations, this paper proposes the Multiple Coordinator Selection (MCS) algorithm. MCS aims to overcome the problem by involving multiple coordinators in the consensus process. Four metrics, namely "trust level," "distance from input transactions," "node activity," and "transaction distribution," are defined as properties for coordinator selection. Additionally, a checklist is employed to minimize the probability of collusion within the system. Furthermore, the paper introduces a three-layered architecture based on cloud and fog computing, where the MCS algorithm is implemented. Experimental results demonstrate improved security and distribution of the system, while reducing the chances of collusion and single point of failure. [ABSTRACT FROM AUTHOR]

Gay men in Kunming, China use collusive infrapolitics to foster and sustain LGBT communities under the premise of HIV/AIDS prevention. Collusive infrapolitics is the building of collaborative relationships between non-state actors and governing agencies to tacitly agree on publicly acceptable political projects, while leaving vague other deviating activities. Men who have Sex with Men HIV Community Based Organizations cooperate with the state around HIV/AIDS prevention and can then build collusive relationships with low level government cadres, who then ignore gay social activities and help to insulate LGBT organizations from political pressure. As the Chinese political environment becomes more restrictive of LGBT organizing, collusive infrapolitics provides an important theoretical explanation for how non-state actors can engage politically around sensitive topics. [ABSTRACT FROM AUTHOR]

The paper examines how pre‐play communication between players with partial credibility affects the ensuing strategic interaction. We consider an environment where players are uncertain about the economic and psychological costs of reneging on promises but learn these at the time of their implementation. We demonstrate that in the equilibrium both players make promises. The latter are partially effective in terms of achieving collusive outcomes and improving the players' payoffs under strategic complementarity, where promises are used to signal future collusive behavior. In contrast, under strategic substitutability the ability to make a promise can be used to signal future aggressive behavior and one of the players may even get a lower expected (before the type is revealed) payoff than in the game without communication. [ABSTRACT FROM AUTHOR]

Attribute‐based encryption (ABE) is a prominent cryptographic tool for secure data sharing in the cloud because it can be used to enforce very expressive and fine‐grained access control on outsourced data. The revocation in ABE remains a challenging problem as most of the revocation techniques available today, suffer from the collusion attack. The revocable ABE schemes which are collusion resistant require a semi‐trusted manager to update the secret keys of nonrevoked users in order to achieve revocation. This introduces computation and communication overhead, and also increases the overall security vulnerability. In this paper, we propose two collusion resistant revocable ABE schemes that do not require any semi‐trusted entity. Our first scheme supports revocation at the user‐level that is equivalent to revoking all the attributes from a user. Our second scheme supports revocation at the attribute‐level that enables more fine‐grained revocation by allowing selective attribute(s) revocation from a user. We call them user‐level revocable ABE (ULR‐ABE) and attribute‐level revocable ABE (ALR‐ABE), respectively. For both the schemes, the secret keys of the nonrevoked users are never affected and the decryption algorithm has the same performance as the baseline ABE scheme. We are able to achieve these at the cost of some increase (compared to the baseline scheme) in the size of the secret key and the ciphertext. [ABSTRACT FROM AUTHOR]

Purpose: This paper gives a model of collusion formation and a method of measuring the degree of it among the traders/bidders in the agricultural commodity markets in India. The important assumption is that the bidding is repetitive with a set of common bidders. The theory has been derived based on the behavior of the wholesale market of agricultural commodities in India. The paper is based on full information in the collusion formation. The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data. Design/methodology/approach: The paper used the standard theory of optimization and the theory of auction and probability statistics. Findings: This is a complete information model of cartel formation. The bidding is repetitive and continues forever in discrete time. Hence bidders behavior is observable. Using the proposed method, if the APMC measures for each market and publishes on a periodic basis, say weekly basis, then it will be easier to break the collusion in the market where relative collision is present. For example, if a farmer has three options to sell in three different markets, then the published data would help them to select the market where the degree of collusion is relatively lower. Moreover, the undesirable loss can be avoided based on the right choice of market. As a result, transaction costs will be optima. Originality/value: The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data. [ABSTRACT FROM AUTHOR]

In an agency model with adverse selection, we study how hidden interactions between agents affect the optimal contract. The principal employs two agents who learn their task environments through their involvement. The principal cannot observe the task environments. It is important to note that hidden interactions, such as acts of sabotage or help between the agents, have the potential to alter each other's task environments. Our analysis encompasses two distinct organizational structures: competition and cooperation. Without hidden interactions, the competitive structure is optimal because the cooperative structure only provides the agents with more flexibility to collusively misrepresent their task environments. With hidden interactions, however, the cooperative structure induces the agents to help each other to improve the task environments while removing sabotaging incentives at no cost once collusion is deterred. As a result, the cooperative structure can be optimal in such a case. We discuss the link between production technology and organizational structure, finding that complementarity in production favors cooperative structures. [ABSTRACT FROM AUTHOR]

In the bidding process of government investment engineering projects, collusion between the government and bidders occurs repeatedly, which seriously affects the quality of engineering projects and the effectiveness of the government investment. Therefore, it is necessary to analyze and discuss the collusion between the government and bidders in government investment engineering projects, so as to provide a healthy and sustainable environment for the government investment engineering bidding market. There are two main types of collusion in engineering bidding: horizontal collusion and vertical collusion, and this paper focuses on the vertical collusion process in the engineering bidding process. A conceptual framework of the grey interest chain based on three stages of benefit creation, benefit distribution, and benefit realization was established, 15 major nodes in the grey interest chain were identified, and a grey interest chain control model was constructed, which further identified and classified the nodes into four levels: key nodes, important nodes, general nodes, and unimportant nodes. Finally, through the application of the model in the case, measures such as establishing a cracking mechanism for grey resource integration, increasing the supervision of grey interest chain, and strengthening post-bid audit are proposed. Measures such as including the preparation of bidding documents into the work assessment system and entrusting consulting units or third parties to prepare bidding documents are proposed to establish a crack mechanism for grey resource integration. In the benefit distribution stage, the penalties for the government and the bidders can be appropriately increased, the responsibilities of enterprises and project leaders can be implemented in the system on a reciprocal basis, and a perfect reputation mechanism information can be established. At the stage of benefit realization, the bidding system should be improved and post-bid audit should be strengthened to increase the difficulty of grey benefit realization. This paper will provide a reference for the prevention and governance of vertical collusion in bidding and tendering. [ABSTRACT FROM AUTHOR]

High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumer's life style and poses serious security and privacy threats. Differential privacy (DP) based privacy models for smart grids ensure privacy when analysing energy consumption data for billing and load monitoring. However, DP models for smart grids are vulnerable to collusion attack where an adversary colludes with malicious smart meters and un-trusted aggregator in order to get private information from other smart meters. We first show the vulnerability of DP based privacy model for smart grids against collusion attacks to establish the need of a collusion resistant privacy model. Then, we propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) which not only provides resistance against collusion attacks but also protects the privacy of the smart grid data while providing accurate billing and load monitoring. We use differential privacy with a split noise cancellation protocol with multiple master smart meters (MSMs) to achieve collusion resistance. We propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) to protect the privacy of the smart grid data using a split noise cancellation protocol with multiple master smart meters (MSMs) to provide accurate billing and load monitoring and resistance against collusion attacks. We did extensive comparison of our E-DPNCT model with state of the art attack resistant privacy preserving models such as EPIC for collusion attack. We simulate our E-DPNCT model with real time data which shows significant improvement in privacy attack scenarios. Further, we analyze the impact of selecting different sensitivity parameters for calibrating DP noise over the privacy of customer electricity profile and accuracy of electricity data aggregation such as load monitoring and billing. [ABSTRACT FROM AUTHOR]

The multiparty‐mediated quantum secret sharing (MQSS) protocol proposed by Tsai et al. [Quantum Inf. Process., 2022, 21, 63] allows n restricted users with limited quantum capabilities to share secret information using a dishonest third party with full quantum capabilities. Although the MQSS protocol allows restricted users to achieve secret sharing with lightweight quantum capabilities, the qubit efficiency of this protocol can be further improved. Therefore, this study proposes a measurement property of the graph state to design an efficient mediated quantum secret‐sharing protocol in the same quantum environment as that of Tsai et al.'s protocol. The proposed MQSS protocol not only inherits the lightweight property of Tsai et al.'s protocol but also improves the qubit efficiency of Tsai et al.'s protocol by 2n−1$2{\;^{n - 1}}$ times. Security analysis is performed to show that the proposed MQSS protocol can avoid collective, collusion, and Trojan horse attacks. Furthermore, this study uses quantum network simulation software to implement Tsai et al.'s protocol and the proposed protocol to prove the feasibility of the proposed MQSS protocol and show that it is more efficient than Tsai et al.'s protocol. [ABSTRACT FROM AUTHOR]

This paper extracts the causes of collusion behavior based on literature analysis and expert interviews and obtains collusion causation data. The Apriori algorithm is used to mine the relationship between the causes of collusion behavior, and the network model of the causes of collusion behavior is constructed. The successive failures theory mines the most easily evolved causation chain of collusion behavior. The study results showed that: (1) The critical causes of the formation of collusion are self-discipline consciousness and difficulty of investigation. The strong control ability of causation network of collusion behavior is self-discipline consciousness, difficulty of investigation, and transparency of rights operation. (2) Based on the analysis of the group case data, eight causation chains are most likely to form collusion in actual cases, among which the causation chain of collusion behavior that occurs frequently is "difficulty of investigation⟶self-discipline consciousness⟶interest chain". (3) In view of the causation nodes in the causation chain of collusion behavior, we propose more effective preventive and preventive control measures for collusion between bidders and tenderers in construction projects from three aspects, namely, behavior awareness binding, collusion implementation dilemma and collusion supervision deterrence. [ABSTRACT FROM AUTHOR]

Cloud-based data sharing addresses the limited storage availability problem for resource-constrained users albeit at the cost of privacy and the need for access control mechanism. However, most of the techniques for secure data sharing with high access control are computationally intensive. Proxy re-encryption scheme is computationally light and provides secure cloud-based data sharing. Proxy re-encryption has a single semi-trust proxy for all intermediate re-encryption processes, which makes it a single point of failure and vulnerable to several attacks. In this paper, we propose, PB-TPRE, a threshold proxy re-encryption with the proactive property. The shares of re-encryption keys are sent to all proxies using shamir secret sharing. The shares may be leaked with passage of time or whenever any proxy leaves or joins the network, then, the secret needs to be change. The proactive property in threshold proxy re-encryption helps renew the shares without changing the secret. PB-TPRE scheme is collusion resistant against the proxies, users and cloud. We present a concrete construction for PB-TPRE that satisfies indistinguishability under chosen-plaintext attacks with a random oracle model and formally proves its security. We compared and discussed PB-TPRE scheme with other threshold proxy re-encryption schemes and found it to be efficient and secure for cloud-based data sharing applications. [ABSTRACT FROM AUTHOR]

There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering (HAC) for detecting aberrant test takers on Computer‐Based Testing (CBT) is explored. Random forest ensembles are used to evaluate the accuracy of the clustering and find the important features to classify the aberrant test takers. Testing data from a certification exam is used. The level of overlap between the exact response matches on incorrectly keyed items in the exam preparation material and HAC are compared. Integrating HAC as an investigation mean is promising in this field to improve the accuracy of classification of aberrant test takers. [ABSTRACT FROM AUTHOR]

Tailored data-driven "Digital Agriculture" solutions bring about many benefits. However, there are also challenges related to complicated data access needs in the farm-to-fork chain. Farm-specific data are mostly locked in by the first-mover companies. This prevents farmers from switching to a better or cheaper alternative. This also hinders agricultural data-driven innovation due to unanswered access seekers. Moreover, already powerful players build exclusive data exchange clusters that further exclude small rivals and other access seekers. Also, upstream input producers have exclusive control over their products' performance data, which generates critical advantages for their downstream data-driven services. These conditions endanger the digital agriculture sector and bring about the risk of reflection of the oligopolistic upstream players in this new sector. This paper explores the adequacy of EU competition law enforcement to address the ag-data access-related concerns – hoping to contribute to the sectoral literature that is currently dominated by regulation-centred discussions. [ABSTRACT FROM AUTHOR]

How do post-conflict societies contain large-scale criminal violence when state security forces that committed atrocities during a civil war remain unpunished and become key players in the criminal underworld? This article explores the impact on violence reduction of internationalized prosecution (IP): cooperation agreements between an international organization and a country's public prosecutors to dismantle state-criminal networks through judicial action. We assess the IP process by which the United Nations–sponsored International Commission Against Impunity in Guatemala (CICIG) and Guatemala's law enforcement dismantled over 70 criminal structures led by death squads and the civil war military establishment. Using synthetic control models, we estimate that Guatemala's IP process plausibly prevented the occurrence of between 20,000 and 30,000 homicides, from 2008 until 2019. Case studies show how IP contributed to violence reduction by removing criminal structures and deterring both state-criminal collusion and the state's use of iron-fist militarized policies against crime. [ABSTRACT FROM AUTHOR]

Some politicians engage in relationships with criminal actors in pursuit of mutual interests. How does their rise to power affect state capacity? I focus on one of Colombia's worst episodes of criminal politics to understand how the victory of paramilitary-friendly mayors impacted subnational taxation. Focusing on close races, I find that the victory of a paramilitary-friendly mayor in 2007 led to a substantive drop in property taxation in subsequent years. I argue that criminal collusion allows politicians and criminals to accumulate wealth and political power, but doing so requires them to deliberately undermine local state institutions. Indeed, the evidence suggests that municipalities governed by paramilitary-friendly politicians experienced a weakening of property rights, the worsening of local judicial institutions, and an alteration of the electoral playing field. Rather than a mere consequence of criminal collusion, state weakening is a politically strategic decision that serves the interests of both criminals and politicians. [ABSTRACT FROM AUTHOR]

There are rising concerns that reinforcement algorithms might learn tacit collusion in oligopolistic pricing, and moreover that the resulting 'black box' strategies would be difficult to regulate. Here, I exploit a strong connection between evolutionary game theory and reinforcement learning to show when the latter's rest points are Bayes–Nash equilibria, but also to derive a system of Pigouvian taxes guaranteed to implement an (unknown) socially optimal outcome of an oligopoly pricing game. Finally, I illustrate reinforcement learning of equilibrium play via simulation, which provides evidence of the capacity of reinforcement algorithms to collude in a very simple setting, but the introduction of the optimal tax scheme induces a competitive outcome. [ABSTRACT FROM AUTHOR]

To select a cloud service provider (CSP) out of immense number of relevant CSPs available, cloud customer (CC) trusts on the reviews provided by the CCs who have used the services provided by CSPs. However, relying on these feedbacks may be hazardous if the review(s) have been deliberately posted with wicked motive. To mitigate the impact of malicious feedbacks, a framework, namely highly available credible cloud trust management framework (HA2CTMF) has been proposed with the intent of revamping the reputation and availability parameters of QoS by evaluating the trust of cloud service providers (CSPs) based on the credibility of feedbacks furnished by CC. HA2CTMF detects collusion and Sybil attacks on the labeled CloudArmor dataset and increases availability of the trust service. Collusion attack has been detected by comparing review count; service count; & mean of reviews with their respective threshold values. Further, Sybil attack has been uncovered with the help of these aspects: deviation from mean of reviews; pattern detection; & comparison of polarity value from both lower & upper threshold values. Additionally, enhanced availability has been ensured by working on disaster management and diminishing repercussions of single point of failure. The results have been accessed on the following parameters: precision; recall; f1-score & availability percentage on the dataset comprising 8774 data points. This framework has exhibited precision of 85.69%, recall of 89.45%, f1-score of 87.53% and availability of 96.71% compared to CloudArmor framework values on the same parameters as: 44.47%; 62.21%; 51.85% & 70.43% respectively. As per the results attained, we conclude that proposed HA2CTMF has demonstrated better outcome. • HA2CTMF has been proposed with the intent of revamping reputation and availability. • Reputation has been enhanced by detecting Collusion and Sybil attacks. • Increased Availability has been ensured by arranging backup(s) of critical components. • Values of precision: 85.69%, recall: 89.45%, f1-score: 87.53% & availability: 96.71%. • Accuracy, specificity and MCC are 83.11%, 79.94% and 0.76 respectively. [ABSTRACT FROM AUTHOR]

So far, there have been several studies conducted on the subject of automotive cartels, and the majority of the studies you see focus exclusively on cartels. Collusion in the automotive industry was relatively uncommon in the past. However, due to the increasing level of competition in this industry, auto manufacturers are increasingly colluding in a number of areas to survive. The Scania cartel case provided us with a clearer understanding of the phenomenon of automotive cartels, as well as how EU competition law is applied to cartel member's violations and how the European Commission approaches complex infringements. Due to this case, it becomes apparent that there are many legal loopholes within the private enforcement sector, and that victims also face a number of challenges when seeking damages from auto manufacturers. [ABSTRACT FROM AUTHOR]

Annotation on the Judgment of the Court of Justice of the European Union (Fourth Chamber) of 15 September 2022 in Case C‑416/21 Landkreis Aichach-Friedberg v J Sch Omnibusunternehmen and K Reisen GmbH On 15 September 2022, the Court of Justice of the European Union delivered its judgment concerning the interpretation of Article 57 of Directive 2014/24 following the questions referred to by a German court.1 The case concerns the exclusion of two tenders, submitted by the same person on behalf of two different companies, from the award of a public contract for public transport bus services. The questions referred were aimed at determining the scope of the exclusion ground based on breach of competition law and clarifying the articulation between Article 57 of Directive 2014/24 and the principle of equal treatment. The CJEU clarifies that Article 57(4)(1)(d) of Directive 2014/24 has a broader scope than Article 101 TFEU since it also covers economic operators which have entered into agreements that do not fall within Article 101 TFEU. The CJEU also reiterates that the exhaustive list of optional grounds for exclusion provided for in Directive 2014/24 does not prevent the principle of equal treatment from precluding the award of the contract to economic operators which constitute an economic unit and whose tenders are neither autonomous nor independent. [ABSTRACT FROM AUTHOR]

Bidding consortia may be very beneficial to the public sector, but at the same time they can serve as bid rigging devices to eliminate competition. This article discusses illustrative European cases involving consortia or cooperative joint ventures between undertakings taking part in a public procurement procedure. It looks at the European Commission's Revised Guidelines for the assessment of horizontal co-operation agreements 2022, considering whether they clarified how economic operators can submit joint tenders without infringing EU competition law. [ABSTRACT FROM AUTHOR]

In Twelfth Night (1601–02), the pressure to enforce a comedic ending competes with problematic and overtly abusive behaviors towards certain characters, particularly the steward Malvolio, who is identified as a target for scapegoating by a persecuting group. As Malvolio is subject to mounting pressures to take in the projections of the group, the mistreatment levied against him gains in cruelty, culminating with his imprisonment in a dark room. While the "pranksters" seek to drive him mad, the figures in power ensure their own narcissistically satisfying ending, at the expense of those stripped of control and dignity. The role of the audience becomes a complex matter by the end, as applauding a problematic play meant to be funny invites a degree of collusion with those who would enforce scapegoating dynamics. [ABSTRACT FROM AUTHOR]

Reputation evaluation is an effective measure for maintaining secure Internet of Things (IoT) ecosystems, but there are still several challenges when applied in IoT-enabled pumped storage power stations (PSPSs), such as the limited resources of intelligent inspection devices and the threat of single-point and collusion attacks. To address these challenges, in this paper we present ReIPS, a secure cloud-based reputation evaluation system designed to manage intelligent inspection devices' reputations in IoT-enabled PSPSs. Our ReIPS incorporates a resource-rich cloud platform to collect various reputation evaluation indexes and perform complex evaluation operations. To resist single-point attacks, we present a novel reputation evaluation model that combines backpropagation neural networks (BPNNs) with a point reputation-weighted directed network model (PR-WDNM). The BPNNs objectively evaluate device point reputations, which are further integrated into PR-WDNM to detect malicious devices and obtain corrective global reputations. To resist collusion attacks, we introduce a knowledge graph-based collusion device identification method that calculates behavioral and semantic similarities to accurately identify collusion devices. Simulation results show that our ReIPS outperforms existing systems regarding reputation evaluation performance, particularly in single-point and collusion attack scenarios. [ABSTRACT FROM AUTHOR]

In the repeated Bertrand duopoly with capacity constraints, we introduce a sales maximization rationing rule, instead of a standard rationing rule, Efficient rule (E rule). Under our new rule, when the demand of a firm with a lower price exceeds its capacity, the consumers who are willing to buy at that price are rationed to that firm according to their unwillingness to buy. We find that the maximum one-shot total payoff under our rule is not less than that under E rule at any capacities; it is achieved by an asymmetric price pair rather than a symmetric monopoly price pair and it is strictly greater than that under E rule unless each firm's capacity is too large. Under the one-shot total payoff maximization, total surplus under our rule is also equal to or greater than that under E rule at any capacities. Hence, our rule is also appealing to the social planner, and it is implementable due to information technology which collects information about the consumers' willingness to pay. In the repeated game, patient firms can achieve the total payoff maximization by an equilibrium under our rule. Moreover, a range of discount factors within which the total payoff maximization can be sustained under our rule is wider than that under E rule at some capacities. [ABSTRACT FROM AUTHOR]

Machine learning has evolved and expanded as an interdisciplinary research method for educational sciences. However, cheating detection of test collusion among multiple examinees or sets of examinees with unusual answer patterns using machine learning techniques has remained relatively unexplored. This study investigates collusion on multiple‐choice tests by introducing feature representation methodologies and machine learning algorithms that can be jointly used as a promising method; they can be used not only to detect individual examinees involved in the collusion but also to evaluate test collusion with or without the groups of potentially dishonest examinees identified a priori. Furthermore, using small‐sample examples, the visual detection procedures of the current study were articulated to help identify questionable item response groups and simultaneously focus on the specific individuals providing anomalous answers. [ABSTRACT FROM AUTHOR]

Collusion permeates United States government procurement despite the strict statutory regime in place to confront it, specifically the Department of Justice (DoJ)Antitrust Division’s Leniency Program. The United States should look to other existing statutory regimes that have been successful in detecting and prosecuting other actions to supplement the Leniency Program, such as the False Claims Act (FCA). By incorporating a whistleblower program like that of the FCA, complete with a monetary incentive, the DoJ can increase instances of detecting and prosecuting collusion in government contracts by simply modifying existing centers within the Antitrust Division to process whistleblower information and qui tam suits. [ABSTRACT FROM AUTHOR]

This paper explores the impact of present bias on trigger pricing strategies that are used to support a cartel in a market where firms can only observe a noisy price signal. The present bias of firm management is modelled by using quasi-hyperbolic discounting that the discount factor between the present and next period is β δ , and between any two adjacent periods later it is δ. We demonstrate that there is a threshold value β ̲ (δ) , which is decreasing in δ. For β ≥ β ̲ (δ) , we show that there exists a Nash reversion trigger strategy for the cartel, featuring lower prices, higher outputs and longer periodic price wars. As a consequence, the trigger price, which is shown to be proportional to the cartel price, is always lower than that in the Green-Porter model. • We consider the impact of present bias on cartel strategy under a noisy price signal. • The present bias of firm management is modelled by using quasi-hyperbolic discounting. • The negative relationship between the optimal output and the discount factor δ is restored. • The cartel strategy shows lower prices, higher outputs and longer price wars under present bias. [ABSTRACT FROM AUTHOR]

Test collusion (TC) is a form of cheating in which, examinees operate in groups to alter normal item responses. TC is becoming increasingly common, especially within high-stakes, large-scale examinations. However, research on TC detection methods remains scarce. The present article proposes a new algorithm for TC detection, inspired by variable selection within high-dimensional statistical analysis. The algorithm relies only on item responses and supports different response similarity indices. Simulation and practical studies were conducted to (1) compare the performance of the new algorithm against the recently developed clique detector approach, and (2) verify the performance of the new algorithm in a large-scale test setting. [ABSTRACT FROM AUTHOR]

Quantum secret sharing (QSS) is an important branch of quantum cryptography. Identity authentication is a significant means to achieve information protection, which can effectively confirm the identity information of both communication parties. Due to the importance of information security, more and more communications require identity authentication. We propose a d-level (t , n) threshold QSS scheme in which both sides of the communication use mutually unbiased bases for mutual identity authentication. In the secret recovery phase, the sharing of secrets that only the participant holds will not be disclosed or transmitted. Therefore, external eavesdroppers will not get any information about secrets at this phase. This protocol is more secure, effective, and practical. Security analysis shows that this scheme can effectively resist intercept–resend attacks, entangle–measure attacks, collusion attacks, and forgery attacks. [ABSTRACT FROM AUTHOR]

Collusion between governments and enterprises has occurred in many economies around the world in the context of government investment projects and tenders. Not only is collusion an illegal act, but it may also lead to learning and imitation by non-colluding parties. Therefore, to control collusion and ensure the quality of government investment projects, investigating the spread of collusion in the bidding process of such projects is important. This study presents a simulation of the diffusion process of collusion among multiple entities through NetLogo, drawing on a contagious disease model. The effectiveness of the hypothesised control tools is validated through the changing trend of collusion in bidding in China. The findings provide a new approach to controlling collusion based on the perspective of the proliferation of bidding behaviour and have some reference value for the government to formulate policies. [ABSTRACT FROM AUTHOR]

This paper considers the collusive stability of downstream competition in a vertical market with network externalities and cost asymmetry. A dynamic collusion game is constructed, and backward induction is employed to solve the subgame perfect Nash equilibrium. We show that larger network externalities lead to less collusive incentive for an inefficient firm, while for an efficient firm, this depends on the efficiency gap. An increase in network externalities will destabilize the downstream collusion when the cost asymmetry is large and network externalities are relatively weak. [ABSTRACT FROM AUTHOR]

Tarikh Beyhaqi contains accurate information about the Ghaznavid period. ‘Khishkhaneh Herat’ is one of the anecdotes in this book, which deals with how Sultan Mahmud relates to his son Masood. The application of the theory of symbolic interaction to the verbal and non-verbal connections between the characters provides a more accurate understanding of the situation of the Ghaznavid court and their daily lives. The premise is that variables such as false representation, idealization, mystery, team collusion, hypocrisy, multi-kinship, maintaining dramatic control, and action and reaction in this anecdote make the application of the theory of symbolic interaction convincing. The analysis of these partial variables in Khishkhaneh Herat indicates an all-encompassing and macro-sociological reality and shows the unstable cultural atmosphere as well as secrecy, inspection, pretense, incoherence, and extreme distrust in the period. [ABSTRACT FROM AUTHOR]

This article presents a qualitative empirical study of elite collusion and its influence on village elections and rural land development in China. Drawing on ethnographic data collected from two Chinese villages, it investigates how village cadres collude with other rural elites, using bribery, gift-giving and lavish banquets, to establish reciprocal ties with township officials and other public officials. Meanwhile, the officials make use of formal organizations to corruptly obtain profits and form alliances with village elites. The article examines how rural elites, especially village cadres, use this collusion to profit from the misuse of villagers' collectively owned assets, the manipulation of village elections and the suppression of anti-corruption protests. It also offers new descriptive evidence of how recent reforms designed to strengthen the Party's overall leadership in rural governance may have actually facilitated elite capture and grassroots corruption. [ABSTRACT FROM AUTHOR]