Collusive Behavior Research Articles

Post and hold regulation and competitive conduct: Evidence from the U.S. beer industry

The literature argues that Post and Hold (pH) laws facilitate tacit collusive price-setting behavior among suppliers of alcoholic beverages. Yet there is no explicit empirical test of this claim. We specify and estimate a structural model designed to identify the extent to which pH laws induce tacit collusive price-setting behavior among beer suppliers. Our estimates reveal evidence of pH law-induced collusive behavior that causes higher prices and lower consumption. Furthermore, we find that an alcohol content tax as a replacement for pH regulation yields the highest surplus to consumers compared to a sales tax or the pH regulation.

The collusion behavior of venture capitalists and entrepreneurs based on “guanxi”: evidence from China

The role of venture capital in promoting entrepreneurship and funding innovation cannot be underestimated. However, it is important to acknowledge noteworthy concerns associated with collusive behavior in the venture capital process. “Guanxi” is a relatively intimate and closed social relationship, which can impact venture capital through trust and social obligations. Drawing on the theories of unethical pro-relational behavior and social networks, using data from GEM-listed companies supported by venture capital from 2009 to 2021, this research investigates the phenomenon of collusion behavior between venture capitalists and entrepreneurs, with a particular focus on the influence of “guanxi”. The research finds that the presence of “guanxi” between venture capitalists and entrepreneurs tends to facilitate collusive behavior. Specifically, “guanxi” tends to increase entrepreneurial self-interest behavior, and venture capitalists’ returns, infringe upon minority shareholders’ interests, and increase corporate operational risk. Moreover, the study further explores the moderator effect of equity incentives on such collusion behavior. This research not only enriches the theoretical framework of the relationship between “guanxi” and venture capital activities but also provides insight into the ethical implications associated with “guanxi”, contributing to the broader literature on social relationships and unethical behavior.

Carbon Reduction Effects in Transport Infrastructure: The Mediating Roles of Collusive Behavior and Digital Control Technologies

Carbon Reduction Effects in Transport Infrastructure: The Mediating Roles of Collusive Behavior and Digital Control Technologies

Large Scale Anonymous Collusion and its detection in crowdsourcing

Crowdsourcing aims to aggregate collective intelligence from independent work of individuals using online platforms. However, some participants disregard platform regulations for higher payment, collaborating overtly or covertly to reduce individual workload—a behavior known as collusion in crowdsourcing. Prior research on collusion has focused on small groups or offline formations, neglecting the potential for large-scale anonymous collusion. Our study unveils a feasible method for large-scale anonymous collusion, making current detection methods less effective. In this paper, we propose a novel framework for large-scale collusion, demonstrating its negative impacts. Workers using this framework can earn more commissions with reduced workload, while at the same time easily evading current detection systems. Meanwhile, to address such collusive behavior, we present a graph-oriented collusion detection algorithm that can successfully identifies colluding workers. Experimental results show that our method outperforms previous ones, significantly reducing adverse effects on crowdsourced data by excluding collusion cases.

담합 사례 연구 : 철스크랩 구매가격 담합 사건에서 개별 기업의 담합 실행 여부에 대한 실증적 검증

담합 사례 연구 : 철스크랩 구매가격 담합 사건에서 개별 기업의 담합 실행 여부에 대한 실증적 검증

Optimal production decision of hybrid power generation enterprises in multi-quota policy coupled markets

At present, China is adopting both Carbon Emission Trading (CET) and Tradable Green Certificate (TGC) markets to optimize the resource allocation of its power industry. To mitigate the impacts of the above dual markets, more and more power generation enterprises are adopting hybrid production mode, generating electricity from thermal and new energy units simultaneously. In these complex production and market conditions, enterprises are difficult to arrange their generation activities. This research describes the production decision process of hybrid power generation enterprises with different market positions, obtains their optimal yield, and then offers market equilibrium results. In addition, the consequences of enterprise collusion behaviors and government interventions are also measured. Numerical experiment results confirm the correctness of the production decision model and reveal the following facts: 1) CET and TGC markets effectively control the carbon emission level and promote the consumption of new energy, but magnify the failure of electricity market to a certain extent. 2) CET and TGC markets promote each other in function. The existence of one market can help the other to achieve the anticipated goal. 3) Enterprises first offset the impact of market condition changes by adjusting the share of new energy generation, and then digest the residual influence by adjusting the total power generation. 4) Enterprise collusion behaviors aggravate the welfare loss of electricity consumer and then lower the expected effects of CET and TGC markets.

Using technology to prevent fraud in high stakes national school examinations: Evidence from Indonesia

Cheating reduces the signaling value of examinations. It also shifts the focus of teachers and students away from learning. Combating widespread cheating is difficult as students, teachers, and bureaucrats all benefit from high reported grades. We evaluate the impact of computer-based testing (CBT), an at-scale policy implemented by the Indonesian government to reduce widespread cheating in the national examinations. Exploiting the phased roll-out of the program from 2015 to 2019, we find that test scores declined dramatically, by 0.5 standard deviations, after the introduction of CBT. Schools with response patterns that indicated cheating prior to CBT adoption experienced a steeper decline. The effect is similar between schools with and without access to a computer lab, indicating that the reduction in the opportunity to cheat is the main reason for the test score decline. In districts with high adoption of CBT, schools that still used paper-based exams cheated less and scored lower, indicating spillovers of CBT. The results highlight the potential role of technology in improving the effectiveness in efforts to overcome collusive behavior in the education sector.

AI Pricing: Adoption of Artificial Intelligences and Collusive Price

With the growing integration of artificial intelligence (AI) in determining pricing strategies, there is an increasing concern about its potential to foster collusive behavior. Harrington (2012, 2018) underscores the challenge: if AI proves more adept at tacit collusion than humans or if AI-driven collusion is inherently tacit, then it presents a significant hurdle for prosecution under the prevailing interpretation of US antitrust laws. Validating these concerns, Assad et al. (2020) observed collusive price surges linked to the adoption of pricing algorithms among German gas stations. Drawing from game theory—specifically the repeated game paradigm—this paper crafts a foundational mathematical model to analyze competition versus collusion dynamics. It also evaluates the resultant welfare implications of both scenarios. The paper further delves into the broader challenges posed by AI-powered pricing and advocates for potential policy countermeasures, including algorithmic regulation and collusion detection mechanisms.

Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation

Corporate procurement management assumes a pivotal role within the contemporary business landscape, yet confronts an array of challenges as markets continue to evolve and globalize. Conventional procurement management systems frequently grapple with issues of inefficiency, resource depletion, and noncompliance, necessitating the exploration of innovative avenues for optimization. This paper delves into the realm of risk mitigation associated with collusion behavior in the administration of intelligent procurement systems, presenting a novel procurement collusion identification model founded on a convolutional neural network (CNN) with reinforcement learning techniques. This framework commences with the application of a CNN and Long Short-Term Memory (LSTM) network for in-depth feature analysis and initial identification of historical procurement data, subsequently leveraging reinforcement learning methodologies to enhance the model's autonomy and intelligence for the purpose of optimization. Throughout the experimental phase, diverse domains of procurement data were meticulously selected for analysis. The empirical findings unequivocally demonstrate the model's proficiency, with an average recognition accuracy of 95.1% across five publicly available datasets. This performance surpasses existing machine learning methodologies employed in contemporary research and common recognition networks, thereby offering a pioneering reference point for the intelligent administration and optimization of future procurement systems.

Industrial location policy, exporting activity, and firms’ collusive behavior: scrutinizing the unintended effect of industrial location policy in Indonesia

Industrial location policy, exporting activity, and firms’ collusive behavior: scrutinizing the unintended effect of industrial location policy in Indonesia

Analysis of the Tripartite Evolutionary Game between MRV Institutions, Government, and Enterprises in the Context of the Carbon Trading Market

As the global carbon emission trading market develops, the role of Monitoring, Reporting, and Verification (MRV) institutions is becoming increasingly important. This paper analyzes the tripartite evolutionary game among MRV institutions, local government, and enterprises within the carbon trading market context and explores the impact of various factors on market behavior through sensitivity analysis. We find that changes in carbon prices, market maturity, the rent-seeking cost of enterprises, penalty policies of local government, and the supervisory intensity of MRV institutions significantly influence the strategic choices of the participating entities. Notably, high carbon prices may lead to collusive behaviors between enterprises and local government, while strengthened supervision and a reasonable penalty mechanism can promote fairness and justice in the market.

Identification and Evolutionary Analysis of User Collusion Behavior in Blockchain Online Social Media

Blockchain technology has given rise to a series of new blockchain online social medias (BOSMs), of which Steemit is representative. Such communities are based on a token reward system and attempt to engross users in the knowledge activities of the community through knowledge payment. Studies have found that the reward system of such communities has been abused (e.g., collusion for profit), but few studies have performed an in-depth analysis for this phenomenon. Consequently, real data for Steemit are used as a case study herein to examine the collusion of users in BOSMs. Two user collusion behaviors (group-voting and vote-buying) are defined and measured. On this basis, an identification and evolutionary survival analysis of the two collusion behaviors are conducted for colluding users and colluding groups, and the behavior patterns of user collusion under the token system are deconstructed. The results of this study improve stakeholders’ understanding of user participation behavior in new online communities, and serve as a reference for decision-making in community governance and token design.

Detecting Fraudulent Student Communication in a Multiple Choice Online Test Environment

Online evaluation systems, pervasive nowadays, are known to be susceptible to higher fraud risks. This work proposes a novel and robust method to detect potential fraud acts in online multiple-choice question (MCQ) exams. For the first time, the communication probability between the examinees is statistically assessed based on the concordance of responses and answer time against null expectations and is subsequently used to identify potential fraud behavior. The model is sensitive to the direction of communication acts, distinguishing content consumption from production, as well as multiwise communication channels. Online remote tests from engineering courses at Técnico Lisboa are used as a case study. We show that the cumulative contribution of concordant responses between students, when recurrent, offers a way of signaling fraud behavior. Separating content production from consumption reveals the underlying student role played in potential fraud acts. Collusion behavior is assessed against null models of fraud and conformity, and therefore being statistically framed and offering a solid criterion to guide tutors in ascertaining fraud and discouraging communication.

DECODING COLLUSION: AN ANALYSIS OF COOPERATIVE BEHAVIOR UNDER DIFFERENT PRICING SCHEMES

This research delves into the intricate dynamics of collusion behavior within markets, exploring cooperative interactions among entities under various pricing schemes. Titled "Decoding Collusion," the study employs game theory and econometric analyses to investigate how different pricing mechanisms influence collusion tendencies. By scrutinizing the behaviors of market participants across diverse pricing structures, the research aims to unravel the complexities of cooperative strategies and shed light on the factors influencing collusion in economic environments.

Can the Conditional Rebate Strategy Work? Signaling Quality via Induced Online Reviews

Online reviews are an important part of product information and have important effects on consumers’ purchasing decisions. Some sellers try to manipulate the market by inducing online reviews. In this study, a signal game model based on Bayesian conditional probability is constructed to analyze the preconditions, decision-making process, and effect on market demand and profit of this behavior. The results show that first, when consumer sensitivity to rebates reaches a certain threshold, low-quality sellers will adopt a conditional rebate strategy to induce consumers to give positive reviews. Second, the optimal rebate cost (β*) is obtained, where β* increases with the product price (p), but it is not necessarily monotonic in consumers’ sensitivity to rebates (ρ) or the proportion of high-quality products (α). Third, the conditional rebate strategy can only work in a market dominated by low-quality goods. Using the conditional rebate strategy in a market dominated by high-quality goods will not bring benefits to low-quality sellers but will harm their profits. This study proposes that some developing online markets have collusive behaviors owing to a lack of regulations and laws, as well as consumers’ concern for small interests. Ensuring the orderly development of online markets will require joint efforts by platform enterprises, government agencies, and consumers.

The reliability of the standard deviation bid method for awarding construction contracts

This study examines the complex test of observing to time, cost, and quality factors when executing construction projects in developing countries, which frequently becomes more difficult by awarding projects based on low bids. By compared to collusion implications and e-bidding benefits using the standard deviation bid and average bid methods, the study provides light on critical aspects. An examination of contracts in Nepal’s Gandaki province between 2018 and 2022 yields interesting results. When there is no collusion, both bidding methods produce similar results. The average bid method, on the other hand, is subject to collusion, whereas the standard deviation bid method is not. To address the constraints associated with low bids, a cautious recommendation for the average bid method is made, along with vigilance against collusion. The standard deviation bid method, on the other hand, emerges as an effective obstacle to collusive behavior. To enhance the bid procedures, the study encourages for additional qualification criteria such as financial weighting and enhanced performance bonds. These amendments to Nepal’s Public Procurement Act (PPA) and Public Procurement Regulation (PPR) of 2007 are intended to address issues raised by low bids. The importance of e-bidding practices is highlighted for digital transformation and corruption prevention. The study emphasizes the importance of putting in place mechanisms to detect collusion across bid selection methods. The research concludes with an invitation for experimental exploration of the Standard Deviation Bidding Method (SDBM), with an evaluation of its efficiency across time, cost, and quality domains. This investigation has the potential to reveal the best bid selection methodology for construction projects.

Evolutionary game analysis on decision-making behaviors of participants in mega projects

Mega projects have a huge impact on the economy and society. Preventing collusive behavior and introducing public participation in supervision are conducive to the governance of mega projects and are crucial to the success of mega projects. The owner, the construction party, and the supervisor of the mega project are regarded as a collusion-body that may collude. Based on the evolutionary game theory, an evolutionary game model with collusion-body, the government, and the public as the participants is constructed. The stability of each participant’s strategy selection is analyzed, the conditions for achieving the evolutionarily stable strategy are obtained, and the evolutionary path of the strategy is simulated. The results show that the costs and benefits have important effects on the behavior of the three participants. Each participant’s strategy choice is affected by other participants, and there is a certain critical probability for each participant. If the critical value is greater than or less than, the participant’s strategy will change. There are five evolutionary stable strategies in the game system, and the costs and benefits conditions to achieve each evolutionary stable strategy are different. In the case of active government intervention and public participation in supervision, the ideal stabilization strategy can be achieved by changing the costs and benefits parameters of the participants, and then the governance effectiveness of the collusive behavior of mega projects can be improved.

Clustered bids in first-price auctions: Collusion or competition?

Clustering of the two most competitive bids in first-price auctions can be indicative of collusive behavior. However, it can also reflect non-cooperative strategies under complete information. I propose a simple procedure to distinguish between the two observationally equivalent explanations. This method requires the knowledge of winning bids and the determinants of the bid of the second most competitive bidder.

Evolutionary Game Analysis of Copyright Protection for NFT Digital Works Considering Collusive Behavior

The non-fungible tokens trading of digital content works, as an emerging business model, has rapidly developed while also posing challenges to current copyright protection. The NFT infringement incidents in recent years have exposed many issues, such as lack of government regulation, imperfect copyright protection mechanisms, and illegal profits from service platforms. Considering the collusive behavior during the NFT minting process, this study uses evolutionary game theory to model a game composed of three populations: digital content creators; NFT service platforms; and government regulatory agencies. We derived and analyzed the replication dynamics of the game to determine the evolutionary stability strategy. In addition, combined with numerical simulations, we also analyzed the impact of individual factors on the stability of system evolution. This study identifies that the incentives and fines set by the government must be above a certain threshold in order for game results to develop toward an ideal equilibrium state, and the government can try to improve the efficiency of obtaining and updating market information and set dynamic punishment and reward mechanisms based on this. This study also found that excessive rewards are not conducive to the government fulfilling its own regulatory responsibilities. In this regard, the government can use information technology to reduce the cost of regulation, thereby partially offsetting the costs brought about by incentive mechanisms. In addition, the government can also enhance the governance participation of platforms and creators to improve the robustness of digital copyright protection by strengthening media construction and cultivating public copyright awareness. This study helps to understand the complex relationship between NFT service platforms, digital content creators, and government regulatory authorities and proves the practical meaning of countermeasures and suggestions for improving government digital copyright regulations.

Causation network analysis of collusion behavior between tenderers and bidders in construction project

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.