Auction Model Research Articles

Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

Urban environmental evaluation using an affiliated private value auction model

Urban environmental evaluation using an affiliated private value auction model

Bid-Aggregation Based Clearing of Day-Ahead Electricity Markets

In this work we propose a heuristic clearing method for a simplified model of European day-ahead electricity auction markets, considering divisible standard bids and block orders. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints, reducing the feasibility region of the clearing problem. By removing bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block orders the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to run multiple threads of the algorithm, using different aggregation patterns. We evaluate the performance of the algorithm via numerical tests.

A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms.

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.

Merger simulation in second-score auctions: A nested logit model

We consider merger simulation based on the second-score auction (SSA) model and the “flat logit” assumption, and extend it to a more general “nested logit” assumption. We apply our framework to the 2012 acquisition of PowerReviews by Bazaarvoice and show that using flat logit instead of nested logit can potentially bias the predicted price effect from the merger substantially.

Efficiency of Public Procurement Function in Moroocco

Public procurement, involving the public sector’s purchase of goods, services, or works from the pri-vate sector, accounts for 13-20% of global GDP (The World Bank, 2020). In Morocco, this sector rep-resents 17% of GDP (The World Bank, 2014) and is a crucial source of business for SMEs, which make up 95% of all businesses (Augier, Castel & El Malki, 2019). The efficiency of public procure-ment is a central issue in research due to its strategic, economic, and managerial impact (Kakwezi & Nyeko, 2019). While past research often focused on practical assessments (Flynn & Davis, 2014) and overlooked the market structure (KLEMPERER, 1999), based on a renewal of the game auction model for first price sealed bid auctions, along with both a qualitative and quantitative empirical research , this paper examines Moroccan public procurement efficiency from a market-based, game auction model perspective. It demonstrates that Morocco’s predominant open tender procedure exhibits pat-terns of an efficient game auction model with a Bayesian Nash equilibrium expression , suggesting opportunities for maintaining and improving the open tender meachnism itself and the surrounding procurement mechanisms.

О МОДЕЛИРОВАНИИ ЗАКУПКИ ОБРАЗОВАТЕЛЬНОГО ОБОРУДОВАНИЯ И БЮДЖЕТНОЙ КОНКУРЕНЦИИ ШКОЛ

Educational establishments regularly acquire administrative and pedagogical equipment to ensure a quality educational process. In the context of limited budgets and competition among institutions, optimal resource allocation and efficient procurement management become crucial. This study builds upon approaches by applying game theory to model procurement in the educational sector. The first part extends single-stage auction models of procurement to account for heterogeneous participant information. Acquired knowledge enables participants to reduce uncertainty in procurement outcomes and outmaneuver stronger market participants with more favorable offers. Specifically, an optimal strategy is identified for the scenario where only one player possesses information, along with a quantitative order of neutralizing the undesired advantage through additional player inclusion. The second part of the study examines mechanisms of long-term budgetary competition among schools. Well-known Rubinstein, Baron, and Ferejohn game theory models are employed to investigate this setup. A substantive analogy between competition and multi-stage negotiations is drawn, and key parameters determining competitive outcomes are delineated. The purpose of the work is to evaluate the applicability of game theory models. Materials and methods. For modeling, the mathematical apparatus of dynamic games is used, including methods of probabilistic modeling, game theory and optimization. The determining criterion for the decision of the players is the greatest utility, calculated solely based on available information. Results of the modeling are performed analytically through optimization, considering the discounting of the utility function over time. Results. Novelty of work is coupled with domain interpretation of game theory models. Modeling allows predicting agents behavior under the regulation of the procurement market and the definition of their budgeting. Conclusion. The analysis of models of game theory in relation to the field of education is accomplished. It is shown that the degree of awareness of the players about the value functions can significantly influence their decisions and the results of procurement.

An iterative auction-based method for multi energy trading in a microgrid considering renewable energy uncertainties

Due to the significant strides made in renewable energy generating units, they are now an appealing choice for supplying energy with various benefits. Furthermore, the aggregation of different energy generators and resources and the rise of energy conversion facilities, like combined heat and power (CHP), have paved the way for a new category of multi-energy prosumers. This research presents an innovative model for a local energy market structure consisting of an aggregator, prosumers, electricity, gas, and heat utility grids, and consumers. The interplay between these entities is handled as an auction model with proposed bidding strategies in which each player participates to maximize their own objective function. The numerical analysis reveals the impact of the pricing strategy on the prosumers' willingness to provide more energy during peak demand periods. Furthermore, the study highlights the potential for mutually beneficial collaborations between prosumers, which leads to reduced energy costs.

FoodBlock: A secure and cost‐optimal framework for online food ordering using blockchain

AbstractThe internet has drastically changed trade and interpersonal interactions since the introduction of the pandemic. Food delivery services have expanded significantly in recent years. These services link customers with restaurants using the internet, enabling quick delivery of mobile meals. But as these businesses grow, dealing with problems like fraud and inefficiencies that may lower the quality of their services is more crucial than ever. This article lists the drawbacks of the existing food delivery services and recommends how to fix them. A blockchain‐based architecture is also used to increase security and reliability, and an auction process encourages fairness and transparency for both riders and restaurants. We have developed an auction model where the riders can quote the price they estimate for a particular delivery. Additionally, a machine‐predicted bid is also used to prevent the case where the riders would try to exploit the restaurants. This method leads to a better way of implementing online food delivery services, safeguarding the interests of customers, riders, and restaurants.

A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies

This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies.

On joint distribution of bids in some k-th price auction model

In the era of increasingly widespread use of electronic communication tools, constant growth of e-commerce turnover is hardly surprising. One of its mechanisms are online auctions whose turnover, on a global scale, is enormous. This paper proposes a stochastic model of the value of subsequent actual bids appearing in the course of a dynamic internet k-th price auction. On this basis, it was possible to determine unconditional joint probability distributions, as well as univariate marginal distributions of successive bids appearing in the course of the auction type under consideration.

CCP auction design

Central counterparties (CCPs) are systemically important. When a clearing member defaults, the CCP sells the defaulted portfolio to surviving members in an auction, and losses, if any, are partly absorbed by a cash pool prefunded by the surviving members. We propose a tractable auction model that incorporates this salient feature. We find that “juniorization” – the CCP first uses prefunded cash of members who submit bad bids – increases the auction price. However, too aggressive juniorization can reduce members' total profit, presenting a misalignment between the CCP's objective and that of the members. A wider customer participation can increase both the auction price and the total profit of members and customers.

Improving Farmers’ Income on Online Agri-Platforms: Evidence from the Field

Problem definition: To improve the welfare of smallholder farmers, multiple countries (e.g., Ethiopia and India) have launched online agri-platforms to transform traditional markets. However, there is still mixed evidence regarding the impact of these platforms and, more generally, how they can be leveraged to enable more efficient agricultural supply chains and markets. This paper describes work conducted in close collaboration with the state government of Karnataka, India, to design, implement, and assess the impact of a new two-stage auction on the state’s online agri-platform, the United Market Platform (UMP). Methodology/results: To ensure implementability and protect farmers’ revenue, the auction design is guided by practical operational considerations, as well as semistructured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By the end of May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase (corresponding to a 55%–94% profit gain) for over 10,000 farmers who traded in the treatment market. Managerial implications: The results from this paper offer tangible lessons on how innovative price discovery mechanisms could be enabled by online agri-platforms in resource-constrained environments. Importantly, the success of these designs critically depends on careful considerations of systemic operational and behavioral factors that affect trades in the physical markets. History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: Financial support from the Tata Center for Technology and Design at MIT and the National Science Foundation [Grant 1452875] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0437 .

Resource sharing for collaborative edge learning: A privacy-aware incentive mechanism combined with demand prediction

Edge intelligence benefits from ubiquitous resource capabilities by numerous edge devices. However, interest concerns and privacy issues caused by the openness of the edge network make smart devices reluctant to participate in collaborative edge learning. This work proposes a privacy-aware incentive mechanism based on combinatorial double auction (PIMCDA) to facilitate idle resource sharing of diversified edge devices. First, we establish an auction model based on blockchain to form a trusted sharing market in a multi-resource binding manner. Then, we propose a two-stage auction solution combined with differential privacy: The privacy-aware winner selection stage matches the optimal winner candidate to the task that is the closest in probability to maximize the revenue of resource providers with the obfuscated location information. The probabilistic pricing decision stage is designed based on a uniform pricing method and exponential mechanism, which can ensure bid privacy from inference attacks. Furthermore, to ensure the effectiveness of PIMCDA for long-term participation, we design a supply and demand balance mechanism with a learning-based resource prediction method. Theoretical and simulation analysis demonstrate that the proposed mechanism achieves privacy-preserving in location and bid information while ensuring effective incentive properties. Our approach effectively motivates the sharing of multiple resources in edge computing.

EWA‐DA‐based resource allocation method for 5G networks

AbstractIn 5G networks, most D2D work focuses on optimizing system throughput without addressing economic constraints. This article encourages users to exchange resources and apply honest bidding tactics to attain high auction efficiency. This study describes an experience‐weighted attraction double auction (EWA‐DA) technique for allocating resources in 5G D2D networks. The suggested method classifies mobile users into seller cellular users (SCU) and buyer cellular users' (BCU). The SCU transmits the beacon packet, and the nearby cellular users create a D2D group in response to the beacon considered BCU. Then, the bid values of the buyer and seller cellular users are transmitted to the base station side. The BS uses an experience‐weighted attraction‐learning model to obtain accurate bidding prices. Lastly, an ask‐to‐mean‐value double auction model was utilized instead of accurate bid values. Extensive simulation results demonstrate that the suggested strategy delivers excellent auction efficiency and fair earnings for both seller and buyer users. In addition, we identified that the participating users accomplished truthful bidding and used fewer resource blocks than existing techniques.

Procurement of last-mile delivery capacity: a reverse auction mechanism considering logistics service quality

ABSTRACT The increasing demand for last-mile delivery has created a challenge for urban consolidation centers (UCCs). Due to UCC’s limited delivery capacity, it has to purchase spare capacity from carriers. In the procurement, the UCC cannot observe the carriers’ efforts, resulting in low-quality logistics services. Thus, we propose a reverse auction mechanism considering logistics service quality (LSQ) for the UCC’s procurement of last-mile delivery capacity. First, we construct the benchmark of the mechanism by introducing the LSQ in last-mile delivery and presenting a reverse auction. Then, we develop an auction model with ideas of ensuring the UCC’s incentive and awarding business to the carriers who deliver high-quality services. The case study shows that the proposed mechanism is an efficient procurement tool for the UCC with a guarantee of LSQ, and the UCC will benefit from the proposed mechanism by significant total cost saving as well as effective use of resources.

The allocative efficiency of construction land quota in rural China: a perspective of bidders' behavior and regret psychology.

The construction land quota pricing mechanism with cost plus pricing method is not sufficient to reflect its intrinsic value. This diminishes the willingness of farmers to voluntarily reclaim abandoned residential and other rural construction land, leading to suboptimal efficiency in rural land utilization and an excessive squandering of rural land resources. Thus, a sequential auction model with two stages for complementary goods was constructed, which considered the synergic characteristics between the land and quota. Further, regret psychology of bidder was considered in the case of winning or losing. A rational pricing mechanism has been developed to allocate construction land quotas, aiming to enhance farmers' motivation to the vacant homesteads of reclamation and revitalizing the stock of rural construction land. The results revealed that the regret psychology in the case of winning would decrease the transaction price of the quota, i.e., the greater the perceived regret in the case of winning, the more significant the reduction in the bidding price offered. Moreover, the regret psychology in the event of losing/failure would increase the transaction price of quota. Furthermore, publishing only the winner's price after the auction leads to the highest price of the quota offered by the bidder. In contrast, publishing only the loser's bidding price leads to the lowest transaction price of the quota offered by the bidder. In addition, the fee for delayed construction would increase the bidding price of the construction land quota. Therefore, local governments should consider announcing only the winner of price after the quota auction has ended. In addition, imposing a fee for delayed construction would enhance the transaction price of land quota, increase farmers' revenue from land reclamation, and incentivize farmers to reclaim unused rural land.

Deconvolution from two order statistics

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The identification result confirms a hypothesis by Athey and Haile (2002) for a symmetric ascending auction model with unobserved heterogeneity. Extensions allow for heterogeneous measurement errors, broadening the applicability to additional empirical settings, including asymmetric auctions and wage offer models. We adapt an existing simulated sieve estimator and illustrate its performance in finite samples.