Fair Mechanism Research Articles

The Outlook of Financial Consumer Protection in China: Learning from the American Approach

In China, the range of financial derivative products has expanded considerably in recent years, with a corresponding increase in the complexity of these instruments. Financial institutions now offer a diverse array of financial products to consumers. There is a considerable disparity between financial consumers and financial institutions in a number of areas, including economic strength, social status, professional composition, negotiation ability and even judicial resources. Consequently, the topic of financial consumer protection is receiving increasing attention, and this issue is also a matter of considerable concern to countries around the world. A unified and effective system for the protection of financial consumers has yet to be established in China. In the event of a dispute regarding the consumption of financial products, it is often challenging for consumers to pursue appropriate redress, resulting in a situation where they have no recourse. The question of whether China should enact a unified Financial Consumer Protection Law, establish an independent financial consumer protection agency, and integrate the measures and regulations for protecting financial consumers in the financial services market in order to provide a convenient and fair mechanism for handling financial consumption disputes is a key area of study.

Fair swarm learning: Improving incentives for collaboration by a fair reward mechanism

Swarm learning is an emerging technique for collaborative machine learning in which several participants train machine learning models without sharing private data. In a standard swarm network, all the nodes in the network receive identical final models regardless of their individual contributions. This mechanism may be deemed unfair from an economic perspective, discouraging organizations with more resources from participating in any collaboration. Here, we present a framework for swarm learning in which nodes receive personalized models based on their contributions. The results of this study demonstrate the efficacy of this approach by showing that all participants experience performance enhancements compared to their local models. However, participants with higher contributions receive better models than those with lower contributions. This fair mechanism results in the highest possible accuracy for the most contributive participant, comparable to the standard swarm learning model. Such incentive structure can motivate resource-rich organizations to engage in collaboration, leading to the development of machine learning models that incorporate data from more resources, which is ultimately beneficial for every party.

Fairness mechanism and stackelberg strategy for multi-agent systems: A case study of legends of the three kingdoms

Alongside the rapid advancement of artificial intelligence technologies, the complexity of society continues to escalate, and the design of multi-agent system rules becomes increasingly crucial, optimize participant engagement and addressing the issue of fairness and equilibrium win rates. The card game Legends of the Three Kingdoms (LTK), as a complex multi-agent system, represents an important abstraction of real-world scenarios, requiring the implementation of proper incentive mechanisms. Based on the mechanism and Stackelberg, this paper optimizes player strategies and system fairness. Firstly, we constructed a multi-agent Stackelberg game model for team battles. Then, we analyzed the impact of decision-making factors on players. Subsequently, we defined three kinds of fairness and improved the incentive mechanism. The results indicate that our mechanism optimizes participants' game behaviors, enhancing fairness among team players. Win rates were improved from 56.2% VS 43.8% to 51.4% VS 48.6%. With three different fairness measures, the win percentage fairness increased by 72 %, and the first elimination fairness increased by about 79 %. Our research provides a reference for understanding and analyzing complex computational models and facilitates the resolution of various resource allocation and system design issues.

Judicial lawmaking and judicial reform: Theoretical and practical aspects of the relationship

The intensification of legal development, increased interpenetration of legal systems, and transformation of legal and state phenomena in the context of globalisation and integration processes necessitate the investigation of the nature of judicial lawmaking and judicial reform and their correlation. The purpose of this study was to theorise the relationship between judicial lawmaking and judicial reform. This study was based on the historical, formal legal, and comparative methods employed in the context of natural law, positivist, sociological approaches, integrative type of legal understanding, theories of lawmaking and justice. The principal findings of this study lie in substantiation of a series of theoretical provisions on judicial lawmaking and judicial reform. It was found that the legal nature of judicial lawmaking is profound and multifaceted. Judicial lawmaking and judicial reform are closely interrelated. One of the manifestations of this interaction is that the introduction of judicial lawmaking is a task, vector, or result of judicial reform. The study proved that judicial lawmaking is a significant achievement of judicial reform, and not a side effect of the transformation of the judicial system. Judicial lawmaking is an essential factor that substantially affects judicial reform in material, procedural, and organisational aspects. Judicial reform, as an independent type of state transformation, is a crucial area of state- building and is aimed at transforming justice into a fair mechanism for resolving legal conflicts and disputes based on the rule of law. With each stage of judicial reform in Ukraine, the need for official recognition of judicial lawmaking becomes more urgent. The 2016 judicial reform did not positively resolve this issue. As of 2024, the need to introduce judicial lawmaking is mostly recognised at the doctrinal level, but the legislating body denies it, although there are various manifestations of the applied use of the lawmaking potential of courts in the national legal system of Ukraine. The practical value of the findings is that the highest authorities of Ukraine can use them to improve the efficiency of lawmaking, justice, and the transformation of the judicial system

Digital Transformation in Jordan and Poland - Implications on the Legal Protection of Cultural Heritage and Sustainable Development

This paper aims to clarify the principles of Jordanian and Polish copyright laws applicable to the preservation of cultural heritage, and to recommend the adoption of certain policies to facilitate this task. It is meant to serve as a guide for stakeholders who deal with digital cultural heritage, directed particularly at museums, but also institutions such as libraries, archives, and art galleries. The research aims to discuss the role of copyright law in protecting cultural heritage in the face of the rapid development of ICT technologies. This includes identifying the risks and challenges associated with digital heritage, framing the relationship between authors of digital cultural content, rights holders, and institutions responsible for documenting and preserving it, and the need to establish a fair mechanism that guarantees their rights. The study aims to provide a presentation of the importance of compliance with copyright principles related to the digitization of cultural heritage and other relevant regulations in the Jordan region and the Polish legislation. The article was created as a result of international scientific cooperation in combination with an innovative view at the law-making and application processes in the era of dynamic development of science and technology. The analysis will include identification of legal acts, which aim to ensure the effective, future-oriented, and long-term protection of cultural heritage in Jordan and Poland.

Determining target levels of power distribution system reliability indices using machine learning

Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions.

Decentralized Dreams - Crafting a Secure and Fair Blockchain Gaming Environment

This ambitions to discover the potential of blockchain generation in revolutionizing the gaming enterprise through developing a secure and honest gaming environment. This studies assignment delves into the concepts of decentralization, security, and fairness inside blockchain-based gaming systems, focusing on the improvement and essential evaluation of a prototype platform. Keywords: blockchain era, gaming industry, decentralization, safety, equity, prototype platform. Through an intensive literature overview, methodological technique, implementation, and vital analysis, this task investigates the challenges and opportunities in crafting a blockchain-based gaming environment that prioritizes consumer safety, asset possession, and honest gameplay. The results and findings from the implementation phase are analyzed to assess the platform's performance, scalability, and person revel in, supplying valuable insights for future improvements and advancements in blockchain gaming structures. Keywords—Blockchain Technology, Decentralized Gaming, Ownership, In-Game Assets, Smart Contracts, Security Measures, Fairness Mechanisms, User Experience, Scalability, Interoperability, Market Research, Regulatory Compliance, Innovation, Player Engagement, Future Improvements.

A Sequentially Fair Mechanism for Multiple Sensitive Attributes

In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the scientific community has developed a host of definitions and tools to solve this task, which work well in many practical applications. However, the applicability and effectivity of these tools and definitions becomes less straightfoward in the case of multiple sensitive attributes. To tackle this issue, we propose a sequential framework, which allows to progressively achieve fairness across a set of sensitive features. We accomplish this by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This method also provides a closed-form solution for the optimal, sequentially fair predictor, permitting a clear interpretation of inter-sensitive feature correlations. Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness. This extension permits a targeted prioritization of fairness improvements for a specific attribute within a set of sensitive attributes, allowing for a case specific adaptation. A data-driven estimation procedure for the derived solution is developed, and comprehensive numerical experiments are conducted on both synthetic and real datasets. Our empirical findings decisively underscore the practical efficacy of our post-processing approach in fostering fair decision-making.

Правовой анализ миграционных отношений России и Франции

This article examines the migration legal relations between Russia and France. It provides an overview of the current state and dynamics of migration processes between the two countries, as well as an analysis of the interaction between the migration legislation of Russia and France. The article discusses the main reasons and factors for migration between Russia and France, including economic, social, and political aspects. Various categories of migrants are considered, such as highly skilled professionals, students, refugees, and the main legal instruments regulating their status and rights are analyzed. The article emphasizes the importance of developing effective and fair mechanisms for regulating migration legal relations between Russia and France. It puts forward recommendations for improving legislation, ensuring the protection of migrants' rights, and facilitating their integration into society to ensure a mutually beneficial and harmonious migration process between the two countries.

Repressive nature of the application of administrative sanction provided by part 1 of article 20.25 of the CAO RF (failure to pay an administrative fine within the term provided by the CAO RF) and the need to take into account circumstances mitigating administrative punishment

In this article the authors have investigated the issue of the peculiarities of application of the sanction of article 20.25 of the CAO RF, presented statistical data indicating the dynamics of bringing to administrative responsibility under article 20.25 of the CAO RF for the last few years. In the course of writing this article the authors used universal dialectical, logical, statistical, formal-legal, hermeneutical methods of research. On the example of judicial practice the unfair and punitive nature of the sanction under consideration is shown. The authors present the positions of scientists regarding the goals of the legislator and law enforcer on the establishment and implementation of this administrative punishment, including: S.M. Zyryanov, P.P. Serkov, Z.I. Magomedova, A.V. Zhiltsov, F.K. Batenov and others. As a result of the study, the authors concluded that it is advisable to apply other (similar) legal means of influence on the offender who has not paid the fine on time, except for imposing a double fine. The present article is also a warning to legislators and law enforcers against potential injustice in the part of imposing a double fine. The authors' proposals contribute to the development of more balanced and fair mechanisms to influence the offender. The authors hope that their study will serve as a basis for reform in this area of law enforcement, contributing to the improvement of administrative legislation and judicial practice in favour of preventive over punitive policies.

Equity-oriented power sharing and demand response for enhancing green energy access in mixed communities

The distribution of renewable energy resources (RERs) is currently uneven and is largely influenced by their affordability across various communities. This has led to disparities in access to green energy for different community members. To address these inequalities, this study proposes an equity-oriented power-sharing mechanism that incorporates demand response (DR). The implementation of this approach will increase the availability of green energy for all members of the community in mixed neighborhoods. First, a DR program is introduced for conventional homes (i.e., homes without RERs) to maximize the consumption of RERs by shifting their load to RER excess intervals. Then, a proportionally fair mechanism is introduced to distribute the available resources among community members during times of high excess. The objective is to minimize the grid dependence of community members by reducing their reliance on purchasing power from the grid. To ensure renewable energy homes (i.e., homes with installed RERs) maintain their green energy access, additional constraints are put in place to prioritize their energy needs when compared to the non-sharing case. Three indices (green energy access, grid dependence, and cost reduction) are introduced to evaluate the performance of the proposed method in different cases: the base case, the power-sharing-only case, and the case of power-sharing with the DR. Finally, the impact of different seasons, day types, and user-defined parameters is analyzed. Simulations have shown that the proposed method can significantly increase the green energy access of conventional homes without affecting the access to green energy of renewable energy homes. It also decreases the grid dependence and the electricity cost of the entire community.

Truthful cake sharing

AbstractThe classic cake cutting problem concerns the fair allocation of a heterogeneous resource among interested agents. In this paper, we study a public goods variant of the problem, where instead of competing with one another for the cake, the agents all share the same subset of the cake which must be chosen subject to a length constraint. We focus on the design of truthful and fair mechanisms in the presence of strategic agents who have piecewise uniform (i.e., approval) utilities over the cake. On the one hand, we show that the leximin solution is excludably truthful (meaning it is truthful when it can block each agent from accessing parts of the cake that the agent does not claim to desire) and moreover maximizes the guaranteed normalized egalitarian welfare among all excludably truthful and position oblivious mechanisms. On the other hand, we demonstrate that the maximum Nash welfare solution is excludably truthful for two agents (as it coincides with leximin in that case) but not in general. We also provide an impossibility result on truthfulness when blocking is not allowed, and adapt notions of representation to our setting.

Whose Neighborhood Needs? Assessing the Spatial Distribution of Federal Community Development Funds

Local governments must balance their growth ambitions against needs arising from social inequities. The Community Development Block Grant (CDBG) program aims to redress these disparities by directing funds toward disinvested tracts. We ask whether a city's institutional design, public and private actor composition, and resource availability influence the decision to invest in communities with greater levels of social need. Utilizing a social equity framework, we connect place-level procedural fairness mechanisms with neighborhood-level access equity consequences. Combining U.S. local government survey data over two decades with census tract-level CDBG expenditures, we find that in neighborhood where 51 percent or more of the families are low-to-moderate income (LMI), its likelihood of receiving funds increases with its share of LMI population relative to the city's, but at a diminished rate compared to non-LMI tracts. Further, city-level factors moderate this relationship (e.g., including community development corporations in planning processes).

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study.

Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

A unified model for the fairness mechanism‐based coordinated vehicle route guidance

AbstractCoordinated vehicle route guidance is recognized as an effective way to alleviate the Braess' paradox that new congestion is generated since numerous vehicles obey the same guidance from Google Maps. In conventional route games, decision‐makers are assumed to be completely rational (maximized utility is taken as an optimization objective). However, behavioral research finds that not only the expected profit is pursued, but also the fairness psychology is concerned by decision‐makers in game theory. In existing studies, the fairness concern of players is widely accepted in supply chains. Inspired by the fairness psychology of supply chain members, the vehicle fairness coefficient is originally proposed in this paper. Moreover, a unified fairness mechanism of the routing game is built to perfect the existing theoretical framework of route games. Specifically, the formulated vehicle fairness coefficient is incorporated into the payoff functions of a perfectly rational‐based routing game, which makes the single‐objective problem to be multiple. Compared with the current completely rational‐based route games, this improvement allows vehicles to focus not only on travel time but also on route schemes' fairness. The consideration of vehicle fairness concern leads the original symmetric payoff matrix of the route game to be asymmetric. Since the existing solving theorems are not necessarily valid for the asymmetrical multi‐player game, a matched reinforcement learning algorithm is adopted to solve this new game. An experimental study shows that the averaged fairness coefficient is increased from 0.798 to 1.00 on the basis of the equal travel time due to the working of the designed fairness mechanism.

Peculiarities of pension provision of scientific workers: realities of today

The problems of pension provision in Ukraine today are one of the most acute issues, primarily due to its low level, the imperfection of the current legal regulation, the existence of various social and demographic problems in the state. It is noted that today the domestic pension system is financially unstable, does not provide the size of pensions in accordance with the labor contribution of the majority of citizens and at the same time creates conditions for preferential (mostly unfair) pension provision for a significant number of pensioners. The issue of the prosperity and quality of life of pensioners is becoming more and more urgent, because their financial condition is significantly lower than in European countries. Therefore, it is proposed to include the restoration of justice in the determination of pension payments – both today and in the future – among the important and priority tasks of the state. Because if a person has worked honestly all his life, he should be guaranteed a decent pension. The purpose of the article is to consider the features of pension provision for scientific workers in today’s conditions, on the basis of which to draw appropriate conclusions. It is noted that the legal regulation of the pension provision of scientific workers is imperfect, does not sufficiently ensure the prestige of scientific work and does not stimulate the systematic renewal of scientific personnel. Attention was drawn to the expediency of mandatory annual indexation of pensions, including the pensions of scientists, and the introduction of a fair mechanism for calculating their payments.Since the provision of economic, social and legal guarantees for employees, in particular the employees of the scientific sphere - scientists, as well as the creation of conditions for conducting effective scientific research, will increase the prestige of engaging in scientific activity. One of the ways to achieve the result and implement the functions assigned to the state should be to improve the legislative framework for the regulation of social security relations, as well as social protection and pension provision of scientific workers.It is noted that the implementation of state policy in this area is possible based on the improvement of legal regulation and adaptation of pension provision to international standards.

‘‘Two Tiers or Three Tier’ a Never-ending Saga of Dispute Settlement– A Delaying Mechanism Arbitration

Two Tier Arbitration or Appellate Arbitration is recognised by Court through its various judicial pronouncements. Even though Party Autonomy and fair mechanism are the stand points by which court gives importance or validity to Two Tier mechanism, it has unnecessarily opened the gates for delayed mechanism. The famous Centro trade Minerals and Metal Inc. v. Hindustan Copper Ltd. decision has further strengthened the principle that there is less chance of court intervention. All these shows the positive impact the particular mechanism will bring but the main question here is: Whether Arbitration mechanism is going far beyond speedy redressal of disputes? Or Whether the processes right now the Court’s rely on party autonomy is relatable to unnecessary court proceedings which we are tired off. In this Article the author’s try to analyse the repercussions that will create if Court starts supporting various appellate, revisions or review mechanisms in Arbitration proceedings. The Author’s surmises the Article by providing suggestions on appropriate implementation of Arbitration regime in India by comparing with other countries.

Psychiatry Match Numbers Increase for 12th Consecutive Year

Back to table of contents Previous article Next article Education & TrainingFull AccessPsychiatry Match Numbers Increase for 12th Consecutive YearMark MoranMark MoranSearch for more papers by this authorPublished Online:30 Mar 2023https://doi.org/10.1176/appi.pn.2023.05.5.043AbstractPsychiatry’s numbers have been increasing yearly since 2011. This year psychiatry was one of just 10 specialties with increases of more than 10% over the last five years.A total of 1,746 U.S. medical school graduates—1,343 from allopathic schools and 403 from osteopathic schools—matched into psychiatry residency programs this year as part of the 2023 National Resident Matching Program (NRMP). It marks the 12th year in a row that psychiatry’s match numbers have increased.Additionally, 351 U.S. and non-U.S. international medical graduates (IMGs) and 46 other applicants (allopathic and osteopathic graduates from a previous year) also matched into psychiatry, bringing the total to 2,143 medical school graduates. Just 21 psychiatry positions went unfilled.The annual match, in which the program choices of graduating medical students are paired with those of residency programs seeking trainees, is typically watched as an indicator of workforce size and makeup of the various medical specialties for the coming years. Psychiatry’s numbers have been increasing yearly since 2011. This year psychiatry was one of just 10 specialties with increases of more than 10% over the last five years.“There continues to be a sustained interest in psychiatric careers by both M.D. and D.O. medical graduates,” said Sidney Weissman, M.D., a former APA trustee and past president of the American Association of Directors of Psychiatric Residency Program Directors (AADPRT) who has been a longtime observer of workforce trends. “Their interest mirrors a heightened national focus on mental health and wellness. Since 2013 we have added over 130 new programs and 700 new positions in the match, and the number of U.S. senior medical students entering psychiatry has doubled. For the near term, these trends are likely to continue.”Vishal Madaan, M.D., APA’s chief of education, said, “It is heartening to see such a successful match for psychiatry. I am delighted that our medical students continue to rise to the occasion and meet the challenge of growing mental health needs in our communities, especially in the post-pandemic world. Not only does psychiatry offer an opportunity for our medical students to answer the societal call and gain a better understanding of social determinants of mental health, but also it allows them to sustain a career that offers job security and quality of life.”He pointed out, however, that the number of IMGs matching into psychiatry had declined from about 30% in 2013 to 16% in 2023, of whom just 6.9% are non-U.S. IMGs. “The number of IMG applications and matched positions, especially those for non-U.S. IMGs, continues to indicate a worrisome decline, creating potential workforce challenges for subspecialty recruitment and future public sector and rural psychiatry positions,” he said.APA CEO and Medical Director Saul Levin, M.D., M.P.A., urged new residents to visit the APA webpage for resources for residents and medical students. “Mental health is now on the national agenda in a way it has never been before, and our profession is critical to meeting our country’s mental health crisis,” he said. “Psychiatry is an exciting field with an expanding array of effective treatments for mental illness. I welcome our new physicians in training and urge them to consider joining APA. Our trainees are our future.”The NRMP is a private, nonprofit organization established in 1953 to oversee the match at the request of medical students to provide an orderly and fair mechanism for matching the preferences of applicants for U.S. residency positions with the preferences of residency program directors. The NRMP is sponsored by the American Board of Medical Specialties, the AMA, the Association of American Medical Colleges, the American Hospital Association, and the Council of Medical Specialty Societies. ■ResourceThe National Resident Matching Program website ISSUES NewArchived

Optimized allocation of renewable energy quota in Chinese provinces

A fair and efficient renewable energy quota allocation scheme is essential for China to implement the Renewable Portfolio Standards policy. Therefore, based on the principles of fairness and efficiency, this paper comprehensively considers the differences among provinces and then proposes and adopts an improved zero-sum gains data envelopment analysis method to reallocate quotas. Furthermore, for verifying the superiority of the proposed method, this paper establishes a composite index based on the Gini coefficient and the Theil index to compare the rationality of the distribution results. Finally, this paper discusses the relevant advice for the development of renewable energy. The results validate that the proposed method is superior to the traditional method. Additionally, according to the final quota distribution scheme, there are fewer renewable energy quotas in the northern provinces and more in the southern provinces. The quota of most provinces is lower than 50 × 109 kWh, while Guangdong, Sichuan, Yunnan, Jiangsu, and Hunan are the five provinces with the most renewable energy quota of over 100 × 109 kWh. In general, this study provides a more rational renewable energy quota redistribution approach, which will help the government to establish an efficient and fair mechanism of renewable energy quota allocation.

Real-time Pricing-based Resource Allocation in Open Market Environments

Open market environments consist of a set of participants (vendors and consumers) that dynamically leave or join the market. As a result, the arising dynamism leads to uncertainties in supply and demand of the resources in these open markets. In specific, in such uncertain markets, vendors attempt to maximise their revenue by dynamically changing their selling prices according to the market demand. In this regard, an optimal resource allocation approach becomes immensely needed to optimise the selling prices based on the supply and demand of the resources in the open market. Therefore, optimal selling prices should maximise the revenue of vendors while protecting the utility of buyers. In this context, we propose a real-time pricing approach for resource allocation in open market environments. The proposed approach introduces a priority-based fairness mechanism to allocate the available resources in a reverse-auction paradigm. Finally, we compare the proposed approach with two state-of-the-art resource allocation approaches. The experimental results show that the proposed approach outperforms the other two resource allocation approaches in its ability to maximise the vendors’ revenue.