Auction Theory Research Articles

An Improved MOEA/D with an Auction-Based Matching Mechanism

Multi-objective optimization problems (MOPs) constitute a vital component in the field of mathematical optimization and operations research. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a MOP into a set of single-objective subproblems and approximates the true Pareto front (PF) by optimizing these subproblems in a collaborative manner. However, most existing MOEA/Ds maintain population diversity by limiting the replacement region or scale, which come at the cost of decreasing convergence. To better balance convergence and diversity, we introduce auction theory into algorithm design and propose an auction-based matching (ABM) mechanism to coordinate the replacement procedure in MOEA/D. In the ABM mechanism, each subproblem can be associated with its preferred individual in a competitive manner by simulating the auction process in economic activities. The integration of ABM into MOEA/D forms the proposed MOEA/D-ABM. Furthermore, to make the appropriate distribution of weight vectors, a modified adjustment strategy is utilized to adaptively adjust the weight vectors during the evolution process, where the trigger timing is determined by the convergence activity of the population. Finally, MOEA/D-ABM is compared with six state-of-the-art multi-objective evolutionary algorithms (MOEAs) on some benchmark problems with two to ten objectives. The experimental results show the competitiveness of MOEA/D-ABM in the performance of diversity and convergence. They also demonstrate that the use of the ABM mechanism can greatly improve the convergence rate of the algorithm.

Potential and goal conflicts in reverse auction design for bioenergy with carbon capture and storage (BECCS)

Bioenergy with carbon capture and storage (BECCS) is considered as a future key technology to provide baseload electricity, heat, pulp, paper, and biofuels, while also enabling atmospheric carbon dioxide removal (CDR). Sweden seeks to lead the way in bringing this technology up to scale, introducing a EUR 3.6 billion reverse auction scheme to facilitate market entry of companies producing BECCS. We explore instrument design preferences among politicians, regulators, and prospective BECCS operators to identify trade-offs and explore feasible policy design. Based on 35 interviews with experts in the latent BECCS sector in Sweden, we identify under which circumstances prospective operators would be willing to place bids and discuss how actor preferences both align with and challenge auction theory. The analysis concludes that at least four dilemmas need attention. These concerns how to: (1) balance the state’s demand for BECCS to be implemented already in 2030 against the prospective BECCS operators’ fear of the winner’s curse, i.e., a fear of bidding for a contract that turns out to be too costly to implement; (2) allocate contracts at the margin of the auctioneer’s demand for BECCS without driving up costs; (3) design compliance mechanism to achieve effectiveness without undermining efficiency, and; 4) integrate the auction with the voluntary carbon market—if at all—in a manner that safeguards the environmental integrity of the auctions.

Herding behavior in NFT Auction: The role of visual complexity and familiarity

Despite the growing popularity of non-fungible tokens (NFTs) in the market, they still lack established valuation guidelines. Invoking herding and auction theories, this study investigates how NFT collectors’ bidding tendencies are influenced by other collectors’ bidding decisions (herding behavior). This study also examines how NFT visual characteristics, such as complexity and familiarity, moderate herding behavior. We use a computer vision technique to quantify the visual complexity of an NFT and apply transfer learning to capture its visual familiarity. Examining data from a widely used NFT platform, we find evidence of herding behavior in such auctions. Furthermore, our results show that there are nonlinear moderating effects of visual characteristics on herding behavior. Specifically, we find that consumers exhibit a stronger herding tendency at the two extremes of complexity distribution and at the lower extreme of familiarity distribution. Further analysis of the NFT resale market suggests that auction winners consider herding in the primary sale of an NFT as a signal of high demand; thus, they are more willing to resell it and set a higher price premium in the resale. These findings can be taken as input by the NFT market platforms in their NFT display decisions to boost primary market sales and promote resale market participation. The findings can also help content creators formulate creative strategies that lead to higher bidding probability or sales performance.

Access strategies in mmWave cell-free network: A matching and auction theory based approach

The mmWave cell-free network is viewed as an energy-efficient and emerging architecture for beyond 5G systems. However, many practical issues should be solved for this system before the benefits are enjoyed, whereof feasible implementation of access strategy is one of the most vital. To this end, the access strategy including user scheduling and bandwidth allocation is investigated in this paper. Firstly, to maximize the defined utility function, the joint optimization problem of user scheduling and bandwidth allocation is formulated as a mixed integer nonlinear programming problem, which is intractable to search for an optimal solution in polynomial time. Secondly, a two-stage matching and sealed-auction-based access strategy is proposed to obtain a sub-optimal solution, where UEs with high-quality channel conditions are scheduled in the first-stage matching as much as possible, and the rest unmatched UEs are scheduled in the second-stage matching with the sealed-auction based bandwidth allocation. Thirdly, a simplified full-matching based access strategy with lower complexity is proposed for the high-dynamic scenario caused by the high-speed movement of UEs, and the complexities and convergence of the two proposed strategies are quantitatively analyzed. Finally, the performance of proposed strategies is evaluated through abundant simulations in different scenarios, and the superiorities of the proposed strategies are demonstrated through the comparison with reference strategies.

On the complexity of inverse bivariate multi-unit assignment valuation problems

Inverse and bilevel optimization problems play a central role in both theory and applications. These two classes are known to be closely related due to the pioneering work of Dempe and Lohse (2006), and thus have often been discussed together ever since. In this paper, we consider inverse problems for multi-unit assignment valuations. Multi-unit assignment valuations form a subclass of strong-substitutes valuations that can be represented by edge-weighted complete bipartite graphs. These valuations play a key role in auction theory as the strong substitutes condition implies the existence of a Walrasian equilibrium. A recent line of research concentrated on the problem of deciding whether a bivariate valuation function is an assignment valuation or not. In this paper, we consider an inverse variant of the problem: we are given a bivariate function g, and our goal is to find a bivariate multi-unit assignment valuation function f that is as close to g as possible. The difference between f and g can be measured either in ℓ 1 - or ℓ ∞ -norm. Using tools from discrete convex analysis, we show that the problem is strongly NP -hard. On the other hand, we derive linear programming formulations that solve relaxed versions of the problem.

Blockchain-Based Joint Auction Model for Distributed Energy in Industrial Park Microgrids

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration.

A Reinforcement Learning-Based Reverse Auction Enforcing Smart Pricing Policies towards B5G Offloading Strategies

In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users’ needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user’s request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements.

Optimization of cross-border E-commerce (CBEC) supply chain management based on fuzzy logic and auction theory

People have benefited enormously from e-commerce’s explosive expansion in recent years. E-commerce, in contrast to the traditional business environment, is dynamic and complicated, which poses a number of challenges. The prediction market can create mixed intelligence for sales forecasting, which is essential for e-commerce enterprises, to handle this difficulty. Combining the usage of human analysts and machine learning algorithms can accomplish this. To accurately anticipate retailer volume and allot resources, a novel methodology for optimizing supply chain management at CBEC is proposed in this paper. The framework improves efficiency and profitability by using fuzzy logic and auction theory to make strategic decisions. Thanks to this creative strategy, managers can now make more informed decisions, ultimately enhancing the efficiency of CBEC’s supply chain. The results of this paper reveal that our proposed method is superior to previous comparable methods, with RMSE and MAE values of 22.31 and 18.76, respectively. This approach offers a promising solution to the challenges faced by e-commerce businesses, and can help them achieve greater success in the dynamic and complex world of online commerce.

Looking at the bid picture: A framework for identifying reverse auctions in ecological systems.

Biological market theory can be used to explain intraspecific cooperation, interspecific mutualism, and sexual selection through models of game theory. These models describe the interactions between organisms as two classes of traders (buyers/sellers) exchanging commodities in the form of goods (e.g. food, shelter, matings) and services (e.g. warning calls, protection). Here, we expand biological market theory to include auction theory where bidding serves to match buyers and sellers. In a reverse auction, the seller increases the value of the item or decreases the cost until a buyer steps forward. We provide several examples of ecological systems that may have reverse auctions as underlying mechanisms to form mutualistic relationships. We focus on the yellow baboon (Papio cynocephalus) mating system as a case study to propose how the mechanisms of a reverse auction, which have the unintended but emergent consequence of producing a mutually beneficial outcome that improves collective reproductive benefits of the troop in this multi-female multi-male polygynandrous social system. For the yellow baboon, we posit that the "seller" is the reproductively cycling female, and the "buyer" is a male looking to mate with a cycling female. To the male, the "item for the sale" is the opportunity to sire an offspring, the price is providing safety and foraging time (via consortship) to the female. The "increasing value of the item for sale" is the chance of conception, which increases with each cycle since a female has resumed cycling post-partum. The female's sexual swelling is an honest indicator of that cycle's probability of conception, and since resident males can track a female's cycle since resumption, there is transparency. The males presumably know the chance of conception when choosing to bid by offering consortship. Across nature, this reverse auction game likely exists in other inter- and intraspecific social relationships. Considering an ecological system as a reverse auction broadens our view of social evolution and adaptations through the lens of human economic structures.

The privacy preserving auction mechanisms in IoT-based trading market: A survey

Nowadays, the Internet of Things (IoT) technology has greatly promoted the transaction behavior. It has led to a shift from the traditional face-to-face, bartering business to today’s online virtual goods business, such as energy trading, spectrum, and so on. However, there exist two critical challenges in IoT-based trading market, which include “efficiency” and “safety”. “Efficiency” indicates a fair-trading decision-making rule, and “safety” indicates that the bidders’ privacy must be protected. To address the above challenges, numerous researchers are working on privacy-preserving auction mechanisms for IoT-based transactional markets, drawing insights from electronic auction theory and information security theory. However, a predominant focus on specific scenarios characterizes most existing works, lacking a comprehensive synthesis of the collective contributions to date. Therefore, this paper systematically elucidates privacy-preserving auction mechanisms for IoT-based transactional markets. Firstly, we expound the foundational knowledge of IoT-based auction markets, delving into auction theory and privacy theory through concise categorization. Secondly, we address both theoretical considerations (from the integration perspective of various privacy protection methods and auction mechanisms) and practical aspects (evaluating various real-world application scenarios). Each aspect undergoes meticulous scrutiny, providing practical assessments and prospects. Lastly, we propose future research directions. Key challenges include the absence of inference attack models for differential privacy, a dearth of algorithmic designs with privacy-preserving capabilities at the auction mechanism level, and the intricate balance between privacy and efficiency. Proposed solutions for future research directions include leveraging Bayesian inference and neural networks for effective attacks, designing autonomous privacy protection mechanisms, and addressing the privacy-efficiency trade-off through the application of Markov decision processes.

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. In order to avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider: i) the complexity of human behavior when interacting with power conservation systems; and ii) realistic home-level power dynamics. As a consequence, this leads to approaches that are i) ineffective due to poor long-term user engagement; and ii) too abstract to be used in real-world settings. In this paper, we propose an auction-theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator (SO) sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine-learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine-learning-based Power Saving Predictions (PSP) algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial-time algorithm called Greedy Ranking Allocation (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

An Algorithmic Solution to the Blotto Game Using Multimarginal Couplings

Computing the solution of Blotto games, a century-year-old open problem The Blotto game has been introduced by Borel more than a century ago in a seminal game theory paper. Surprisingly, characterizing and computing the optimal solutions (or the Nash equilibria) of this game in general was still open! This paper combines theoretical tools from statistics and auction theory to provide an almost explicit characterization of the strategies that are then computed using techniques from optimal transport (and other algorithmic ideas). The complexity of the final algorithm is even polynomial in the number of battlefields (a key parameter of a Blotto game) and the approximation level.

Kinetic theory of active particles meets auction theory

In this paper we study Nash equilibria in auctions from the kinetic theory of active particles point of view. We propose a simple learning rule for agents to update their bidding strategies based on their previous successes and failures, in first-price auctions with two bidders. Then, we formally derive the corresponding kinetic equations which describe the evolution over time of the distribution of agents on the bidding strategies. We show that the stationary solution of the equation corresponds to the symmetric Nash equilibrium of the auction, and we prove the convergence to this stationary solution when time goes to infinity. We also introduce a more general learning rule that only depends on the income of agents, and we apply to both first- and second-price auctions. We show that agents learn the Nash equilibrium in first- and second-price auctions with these rules. We present agent-based simulations of the models, and we discuss several open problems.

Effective Knowledge Dissemination Modeling and Regulation in Blended Learning Networks

Blended learning networks (BLNs) based on the integration of online learning networks and offline learning environments provide new opportunities and platforms for people to acquire and update useful knowledge and carry out all kinds of learning activities anytime and anywhere. Effective modeling and regulation of the knowledge dissemination process can accurately grasp its dissemination process, promote knowledge innovation and collaborative sharing among learners, and accelerate the maximization of knowledge dissemination. However, it is a challenge to establish a comprehensive dynamics model and adopt the optimal regulation for the knowledge dissemination process under the constraints of a limited budget in large-scale BLNs with diverse learners. To this end, we first explore the evolution process of knowledge dissemination in BLNs and the blended learning interaction process of learners. Based on the system dynamics modeling theory, a dynamics model of knowledge dissemination is established. Second, two kinds of effective regulation strategies are proposed. We establish an optimal regulation system intending to maximize the dissemination of knowledge and use the optimal control theory to tackle the optimal solution distribution of regulation strategies. Then, we propose a knowledge dissemination regulation task allocation method based on the collaborative participation of users, and the reverse auction theory is used to quickly solve the task allocation scheme while ensuring performance. Finally, we demonstrate the effectiveness of proposed models and methods through extensive simulation experiments based on real datasets.

Cost Control Management of Construction Projects Based on Fuzzy Logic and Auction Theory

Cost Control Management of Construction Projects Based on Fuzzy Logic and Auction Theory

Analysing the efficiency of public procurement procedures using game-theory models

Subject. Public procurement plays an important role in providing public services and ensuring that the target programmes of government structures are implemented. Therefore, it is crucial to improve the efficiency of procurement procedures. The efficiency of public procurement is largely determined by the conditions and format of the procurement procedure. So, it is of particular interest to identify the factors influencing the customer’s choice of the optimal procedure for the procurement of different types of goods, as well as to assess the impact of these factors on the effectiveness of existing procedures.Objectives. The aim of the study was to develop a model for selecting procurement procedures for different types of goods and to assess the effectiveness of current public procurement procedures.Methodology. In the study, we used the findings of auction theory, qualitative and quantitative analysis techniques to identify public procurement, determine its value to participants, and assess its effectiveness. The following goods were selected for empirical assessment: paper, medicines, automotive components, and construction services. The empirical basis of the study was the data obtained from the Unified Information System (UIS) in Procurement website.Results. We developed and estimated a model for choosing a supplier selection procedure (for empirical assessment, the probability of choosing a certain procedure was determined by the number of uses of the procedures among completed procurement procedures). We also designed and estimated a model of price effectiveness for public procurement procedures that are actually in use (price effectiveness in this study means the final price of the contract, as well as the reduction of the final price against the initial maximum price).Conclusions. Based on the analysis, we concluded that it is necessary to introduce negotiations as an intermediate procedure between the auction and procurement from a single supplier. Such a procedure is used in other countries. The auction procedure fails to provide the best quality goods, especially in the case of complex goods or services with a high level of uncertainty. For simple goods, an auction is the most favoured procedure under the conditions of high competition and a preference for quality. It allows selecting the most experienced and financially reliable supplier.

Auction design and order of sale with budget-constrained bidders

The presence of financial constraints changes traditional auction theory predictions. In the case of multiple items, such constraints may affect revenue equivalence and efficiency of different auction formats. We consider a simple complete information setting with three financially constrained bidders and two items that have different values common to all the bidders. Using a laboratory experiment, we find that, as predicted by theory, it is more beneficial for the seller to sell the higher value item first. We then show that the first-price sealed-bid auction yields higher revenue than the English auction, with significant differences in learning of equilibrium strategies.

Teaching Auction Theory to Finance Students

Auctions are generally taught in economics courses in a discussion of game theory or mechanism design. While the topic is important from a theoretical perspective, it is also crucial to students' understanding of how capital markets work, yet few instructional techniques exist for incorporating auctions into classroom discussions in finance courses. This articlesummarizes a great exercise/game that allows students to learn about auctions through participation in a series of actual auctions. The exercise is designed for a single class period of eighty to ninety minutes. We have used the game as an introduction to courses in both corporate finance and financial markets and institutions both at the undergraduate and graduate levels. It has been well received by students at numerous universities.

Auction Theory and Device Bidding Functions for Transactive Energy Systems: A Review

Auction Theory and Device Bidding Functions for Transactive Energy Systems: A Review

A Comparative Study of Auction Theory Applications: Insights from India and Global Perspectives

This research paper presents a comprehensive comparative study of auction theory applications, exploring insights from both the Indian context and global practices. Auction theory plays a vital role in resource allocation, market efficiency, and economic decision-making across various industries. The study analyses different auction mechanisms used in India and other countries, evaluating their effectiveness and suitability in diverse economic environments. The paper delves into specific sectors where auction theory is applied, such as spectrum auctions for telecom operators, infrastructure project auctions, and public procurement auctions for goods and services. By examining auction outcomes, bidder strategies, and their impact on industries, the study provides valuable insights into the adaptation and effectiveness of auction theory in addressing unique challenges and opportunities. The research investigates challenges faced in implementing auction-based systems in India and other economies, offering potential solutions and policy measures to address issues like information asymmetry and regulatory complexities. It compares the economic implications of auction outcomes in India and other countries, including revenue generation and market efficiency. Moreover, the paper explores government initiatives that promote auction-based resource allocation and assesses policy considerations and regulatory frameworks governing auctions in different sectors. Emphasis is placed on the importance of inclusive auction practices in promoting equitable outcomes and fostering financial inclusion. By showcasing international case studies of successful and challenging auctions, the study draws lessons for India's future auction initiatives. The research contributes to the understanding of effective auction-based resource allocation strategies and their potential impact on diverse economic landscapes. In conclusion, this comparative study provides valuable insights into the adaptation, effectiveness, and economic implications of auction theory applications, benefiting policymakers, businesses, and economists seeking to optimize resource allocation and market efficiency. The findings offer valuable guidance for shaping auction mechanisms and promoting fair market practices in both Indian and global contexts. As the study enhances the understanding of auction theory's applications, it contributes to evidence-based decision-making, fostering efficient resource allocation and economic growth.