Auction Model Research Articles

Real Time Auction Models for Programmatic Advertising Efficiency

The rapid evolution of programmatic advertising has necessitated the development of real-time auction models to enhance efficiency and optimize ad spend. This study explores the dynamics of real-time auctions within programmatic advertising, focusing on the mechanisms that drive bid strategies and pricing models. By analyzing the interplay between advertisers, publishers, and demand-side platforms (DSPs), we identify the factors influencing bidding behavior and auction outcomes. We propose a novel framework that integrates machine learning algorithms to predict bid values based on historical data and contextual parameters, aiming to improve decision-making processes in real-time environments. Furthermore, this research examines the impact of auction transparency and competition on advertising effectiveness, revealing how different auction formats—such as second-price and first-price auctions—affect bidder strategies and overall campaign performance. We employ empirical analysis using data from various programmatic platforms to validate our model, demonstrating significant improvements in cost efficiency and ad placement outcomes. Ultimately, our findings contribute to a deeper understanding of real-time auction dynamics in programmatic advertising, providing actionable insights for marketers seeking to enhance campaign efficiency. By leveraging advanced analytics and real-time data, advertisers can better navigate the complexities of programmatic ecosystems, resulting in optimized advertising strategies that maximize return on investment (ROI) while effectively engaging target audiences.

A single-channel single-winner auction model for homogeneous channel allocation in CRNs

Cognitive radio (CR) is a novel intelligent technology which enables opportunistic access to temporarily unused licensed frequency bands. A key functionality of CR is to distribute free channels efficiently amongst Secondary Users (SUs) boosting spectrum usage to assist the escalating wireless applications world wide. In this context, this paper introduces a channel allocation mechanism which enables SUs (CR enabled unlicensed users) to dynamically access unused spectrum bands to fulfill their spectrum needs. We model the channel allocation problem as a sealed-bid single-sided auction which primarily aims at maximizing the overall spectrum utilization. Market based spectrum auctions in CR networks motivate licensed users to participate and lease their under utilized radio resources to gain monetary benefits. Sequential bidding is applied to this model for auctioning homogeneous channels, which reduces communication overhead. Bid submission takes into account two major CR constraints, namely, dynamics in spectrum opportunities and differences in channel availability time, which on incorporation provide disruption free data transmission to the SUs. We reduce resource wastage in this model by performing multiple auction rounds. Application of second price auction determines winning bidders and their respective payments to auctioneer. The design of our auction mechanism is supported with the proofs of truthfulness and individually rational properties. Furthermore, experimental results indicate that our model outperforms an existing auction method. Spectrum utilization values show 22 to 75% improvement in our model with changing number of SUs, and 23 to 93% improvement in our model with changing number of channels.

Research on wireless spectrum allocation and utility evaluation of cross-domain multi-slot radio

The existing spectrum use authorization adopts the large-period and large-area allocation mode, and users exclusively use the right to use the spectrum, resulting in the idle spectrum of multiple time slots and regions and a low utilization rate. This paper proposes a cross-domain, multi-slot Wireless Spectrum Allocation Mechanism (CMWSA) from a fine-grained spatiotemporal perspective. The bilateral spectrum auction model includes the buyer’s location, the start and end time, and the interference radius in the auction information, and the multi-spectrum buyer virtual grouping algorithm is designed. By classifying the spectrum and constructing the interference matrix, a cross-domain, multi-slot, spatiotemporal reuse Wireless Spectrum auction mechanism is proposed to improve the utilization of the idle spectrum and increase users’ satisfaction. Simulation results show that CMWSA can improve the spectrum utilization rate of secondary users, meet the diverse needs of secondary users, and improve the spectrum reuse rate.

Scheduling and Securing Drone Charging System Using Particle Swarm Optimization and Blockchain Technology

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones’ services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones’ battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm’s efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.

Policy implications of spatially differentiated renewable energy promotion: A multi-level scenario analysis of onshore wind auctioning in Germany

Auctions have been introduced in many countries as a useful alternative for renewable energy support. However, they often lead to a high concentration of renewable energy power plants at productive sites, at the expense of other, less favorable sites. This paper studies the impact of alternative renewable energy auction schemes on the promotion of renewable energy in Germany, using a novel multi-level approach—analyzing auctioning on the national, north/south, and federal states level, respectively. First, using a GIS tool, the onshore wind potential is studied at a high regional and temporal resolution. The results of this analysis show a considerable untapped onshore wind potential in the southern federal states of Germany. Second, an onshore wind power auction model is developed by using a system dynamics approach. Finally, the policy support payments are compared for different auction designs by using an electricity market simulation model. The findings suggest that more bidders from the southern federal states could win in the regional auctions. Detailed spatial analysis reveals a trade-off between balanced diversity of bidders and the average auction price. We conclude that regional auctioning can indeed lead to significant support payment savings, and should thus be considered in renewable energy support policy design.

A Study on the Mechanism and Pricing of Drainage Rights Trading Based on the Bilateral Call Auction Model and Wealth Utility Function

In the context of the high incidence of flooding disputes among neighboring subjects in the basin, exploring the drainage rights trading mechanism and clarifying the pricing method of drainage rights are new ideas to manage flooding disasters. In this study, the trading mechanism of drainage rights is constructed using a bilateral call auction model, in which the price constraint, the control total quantity constraint, the clearing rule, and the matching rule are explained. In addition, we adopt the wealth utility concept to construct a function, apply the social welfare function to associate the wealth utility functions of both sides of the transaction, and draw on the environmental Gini coefficient concept to construct a pricing model for drainage rights transactions based on fairness and efficiency. Finally, we conduct an arithmetic simulation of the drainage rights trading mechanism and transaction pricing for China’s Jiangsu section of the Huaihe River Basin. The study’s results show that the trading mechanism model can give the optimal trading scheme when multiple subjects are trading drainage rights. This fills the gap in existing studies and can lead to efficient trading of drainage rights. At the same time, the pricing model can give the optimal transaction price that considers efficiency and fairness, providing a more practical pricing theory for drainage rights trading.

A low-cost communication security scheme of UAV based on multi-round reverse auction model

In recent years, the security of wireless communication has received more and more attention. However, with the increasing requirements for security performance in the process of wireless communication, the cost of improving security also increases. Therefore, how to improve the security of wireless communication while reducing costs has become a serious challenge. Aiming at the security problems in the UAV-assisted wireless communication link based on the premise of low cost and high profit, this paper studies how to motivate non-offloading users to send jamming power to interfere with eavesdropping UAVs, so efficiently and economically to improve the security performance of wireless communication link. Firstly, the relationship between the security performance of the communication link and the interference power is revealed through the theoretical model. Then, we propose a scenario consisting of a legitimate UAV, a ground user group, and an eavesdrop UAV. In this scenario, we divide the ground users into offloading users and non-offloading users. At the same time, the cost functions for offloading users and utility functions for non-offloading users are defined, and non-offloading users are encouraged to help to offload users unload data safely to a legitimate UAV by obtaining rewards for offloading users. Considering the selfishness of users, a reverse auction algorithm based on multi-user groups is proposed to achieve the optimal mapping between non-offloading users and offloading users. Finally, the effectiveness of the proposed scheme is verified by a large number of simulations. The research results show that the scheme innovative designs a method for users to cooperate to interfere with eavesdroppers, which can reduce costs and improve utility while maximizing the improvement of wireless communication link security.

Strategic Analysis of Dollar Auction

Shubik invented the dollar auction in 1971, which is a straightforward but practical auction model for elucidating the motivations and process of conflict escalation. Later on, several researchers used this paradigm to conduct the study on a variety of subjects. Waniek, for example, investigated the relationship between spitefulness and dollar auction in-depth and presented the optimal decision under different scenarios. In this paper, I simplified his results and merged them into simpler forms. Some proposals that may be advantageous to our economic environment are provided based on the performance.

The influence of state politics on solar energy auction results

In this paper, we analyze the impact of policy-induced opportunity costs and restrictions on the outcomes of auctions to subsidize ground-mounted photovoltaic (PV) systems in Germany since 2015. First, we examine the incidence of opportunity cost reductions for some suppliers in a simple theoretical auction model. Second, we descriptively study the volumes and prices in PV auctions in Germany. Finally, we analyze a quasi-experiment: the opening of auctions for farmland in disadvantaged areas in some German states from 2017. We show that policy-induced opportunity cost reductions lead to greater success in PV auctions only in the state of Bavaria, which has more generous quantity restrictions and is generally expected to be more supportive of solar power suppliers after 2017. As a result, there is an interregional redistribution in favor of a single state that enables and administratively effectively implements support for disadvantaged areas.

An improved pricing approach based on combinatorial auction method in cloud environment

Cloud pricing models are essential in the context of the increasing availability of cloud service providers across the Internet and the willingness of users to use high-quality and affordable services. The double-sided combinatorial auction model has been used extensively due to its inherent characteristics. This paper presents a cost-effective pricing method that considers both the forward market and spot market. Moreover, resource requests are workflow or hybrid to support scalability and improve the number of federal cloud response requests. The evaluation results indicate more profit for both buyers and vendors simultaneously. Furthermore, the proposed algorithm reduces the waiting time and the number of pending demands, increasing users' satisfaction relative to cloud services.

Tax Advantages and Imperfect Competition in Auctions for Municipal Bonds

AbstractWe study the interaction between tax advantages for municipal bonds and the market structure of auctions for these bonds. We show that this interaction can limit a bidder’s ability to extract information rents and is a crucial determinant of state and local governments’ borrowing costs. Reduced-form estimates show that increasing the tax advantage by 3 pp lowers mean borrowing costs by 9–10$\%$. We estimate a structural auction model to measure markups and to illustrate and quantify how the interaction between tax policy and bidder strategic behaviour determines the impact of tax advantages on municipal borrowing costs. We use the estimated model to evaluate the efficiency of Obama and Trump administration policies that limit the tax advantage for municipal bonds. Because reductions in the tax advantage inflate bidder markups and depress competition, the resulting increase in municipal borrowing costs more than offsets the tax savings to the government. Finally, we use the model to analyse a recent non-tax regulation that affects entry into municipal bond auctions.

A Note on University Admission Tests: Simple Theory and Empirical Analysis

Abstract University admission mechanisms are often quite complex. This paper examines one effect of their design on the students’ incentives to exert effort in preparation for the test. We adapt a multi-unit all-pay model of auction to draw the conclusion that abler students work harder: this conclusion is in line with the behaviour of a sample of students who apply for admission to the Greek university system with the complex rules newly introduced in 2013.

A Bayesian game-enhanced auction model for federated cloud services using blockchain

Industrial applications often require federated cloud services from multiple providers to improve reliability and flexibility. Traditional selection methods through auctions usually involve a centralized auctioneer to coordinate the auction procedure. Blockchain and smart contracts provide a decentralized mechanism to automate the cloud auction process; however, existing solutions fail in the selection of the most suitable providers and the violation detection of the signed auction agreements, which are also known as service-level agreements (SLAs). To tackle these problems, we propose an integrated auction model using Bayesian game theory and blockchain techniques. The proposed model is enhanced with two Bayesian Nash Equilibriums (BNEs); the first BNE enables the selection of cost-effective providers to construct the federated cloud services, while the second BNE ensures consistent and trustworthy monitoring of federated SLAs. Moreover, a timed message submission (TMS) algorithm is proposed to protect the auction privacy during the message submission phase. This paper validates the equilibrium results of two BNEs and implements the proposed model on the Ethereum blockchain. The analytical and experimental results demonstrate the feasibility, trustworthiness, and cost-effectiveness of our model.

An Innovative Methodology for Network Latency Detection Based on IoT Centered Blockchain Transactions

The new IoT apps will not be able to inspire people to utilize them and may ultimately lose all their potential if an interoperable and trustworthy ecosystem is not provided. IoT has its extra security difficulties such as information storage, administration, privacy concerns, and authentication. The presently deployed IoT apps have encountered various security and privacy assaults globally. Due to being less secure and low powered, the IoT devices present a simple entryway to the adversaries to obtain access to the corporate networks, leading to giving easy control over all of the data of the users. The objective of this doctorate proposed work will be to solve the security associated difficulties in multiple IoT domains like the e-commerce, vehicular ad hoc networks (VANET), mobile ad hoc networks (MANET), and Internet of Drones (IoD). The proposed study focuses on the development of a distributed framework for IoT based on blockchain. The framework includes the usage of Ethereum-based smart contracts and auction models to increase the income and QoS for both the seller and the buyer and the development of a DAG chain-based distributed framework for parking lot allocation in a network of automobiles. The suggested model includes the requirement of obtaining agreement among the nodes with probability one in such a circumstance. The suggested model demonstrates to be predictable as typical voting-based consensus protocols like Practical Byzantine Fault Tolerance (PBFT) and at the same time can accommodate a high number of nodes even in an asynchronous setting. Research on Byzantine fault-tolerant systems has been ongoing for more than four decades, and although the solutions were shown to be feasible early on, they remained unworkable for a long time. With PBFT, the first feasible solution was provided in 1999, and this sparked fresh research that has resulted in unique applications that are still being developed today employing this technology. Despite the fact that the safety and liveness properties of PBFT-type protocols have been thoroughly investigated, when it comes to practical performance, only empirical results—often obtained in artificial environments—are known, and imperfections in the communication channels are not explicitly considered. It is our goal in this paper to propose the first performance model for PBFT that takes into account the effect of unreliable channels as well as the usage of alternative transport protocols across those channels. We also performed a large number of simulations to test the model and acquire a better understanding of the influence of different deployment factors on the total transaction timeframe.

It ain’t where you’re from, it’s where you’re at: Hiring origins, firm heterogeneity, and wages

Sequential auction models of labor market competition predict that the wages required to successfully poach a worker from a rival employer will depend on the productivities of both the poached and poaching firms. We develop a theoretically grounded extension of the two-way fixed effects model of Abowd et al. (1999) in which log hiring wages are comprised of a worker fixed effect, a fixed effect for the “destination” firm hiring the worker, and a fixed effect for the “origin” firm, or labor market state, from which the worker was hired. This specification is shown to nest the reduced form for hiring wages delivered by semi-parametric formulations of the canonical sequential auction model of Postel-Vinay and Robin (2002b) and its generalization in Bagger et al. (2014). Fitting the model to Italian social security records, origin effects are found to explain only 0.7% of the variance of hiring wages among job movers, while destination effects explain more than 23% of the variance. Across firms, destination effects are more than 13 times as variable as origin effects. Interpreted through the lens of Bagger et al. (2014)’s model, this finding requires that workers possess implausibly strong bargaining strength. Studying a cohort of workers entering the Italian labor market in 2005, we find that differences in origin effects yield essentially no contribution to the evolution of the gender gap in hiring wages, while differences in destination effects explain the majority of the gap at the time of labor market entry. These results suggest that where a worker is hired from tends to be relatively inconsequential for their wages in comparison to where they are currently employed.

Sovereign Debt Auctions in Turbulent Times

We use a model of multiunit discriminatory auctions with asymmetrically informed risk-averse bidders to analyze Mexican sovereign bond auctions during periods of macroeconomic stress. We argue that the discriminatory protocol provides insurance benefits to the government in bad times because it allows for uninformed bids above the marginal price to be executed at the bid price. Uninformed investors are willing to make such bids if the inframarginal risk premium is large enough to offset the winner's curse. In crisis periods, we infer 1 p.p. lower borrowing costs in the worst states of the world, but 2.2 p.p. higher average costs.

A self-organized criticality participative pricing mechanism for selling zero-marginal cost products

In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's “correct” sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be designed, in particular ones that consider consumer preferences and reality. Here we introduce an auction model where buyers continuously place bids and the challenge is to decide whether or not to accept them. The model does not include a deadline for placing bids, and exhibits self-organized criticality; it presents a critical price from which a bid is accepted with probability one, and avalanches of sales above this value are observed. This model is especially interesting for startup companies interested in profit as well as making the product known on the market.

Last minute only bidding is implausible in eBay sealed bid type-of-auctions

In recent years internet auctions have attracted much attention. This paper discusses the "last minute bidding" (sniping) phenomenon, first investigated by Roth and Ockenfels (American Economic Review 92, 1093-1103) in eBay, fixed-end, auctions. Unlike standard auctions where each offer is processed by the auctioneer due to system traffic, and connection time, in eBay auctions very late bids may be left unprocessed. In the paper we consider a simple two-players, two-periods sealed-bid auction model, inspired by eBay auctions, with private values and complete information. The main difference with respect to e-Bay auctions is given by the first period. Indeed, players can submit at most one bid per period. In the first stage bids are processed with certainty by the system while in the second stage, with positive probability, bids may not be processed due to system congestion. Unlike eBay we show how in this model last minute, that is second stage only, bidding may have low plausibility, as it can be a Nash equilibrium only under very specific circumstances and is never a unique best reply. Intuitively this is because the first stage is sealed-bid, with at most one offer per bidder, and players have no guarantee that they could outbid the opponent in the second period, once after the first round they realise that their bid is not the best.

Last-mile drone delivery combinatorial double auction model using multi-objective evolutionary algorithms

This study proposes a combinatorial double auction bi-objective winner determination problem for last-mile delivery using drone. Prior studies are limited on solving mixed integer model, which are not efficient for large-scale scenario. However, this is not practical in real cases as the computation time to obtain the solution is longer due to number of combinations of packages and participants anticipated in the last-mile delivery platform. Four multi-objective evolutionary algorithms (MOEAs) with the decomposed winner determination problem model are experimented. This study is able to yield Pareto optimal solutions from multiple runs of mixed linear integer programming (MILP) using different objectives weights in the model. Unmanned aerial vehicle, or drone, has potential to reduce cost and save time for last-mile logistic operations. The result positively shows MOEAs are more efficient than MILP in yielding a set of feasible solutions for undertaking complex winner determination problem models. The percentage of improvement in terms of time spent identifying the best option is almost 100%. This is likely an unprecedented research in drone where combinatorial double auction is applied to complex drone delivery services and MOEAs are used to solve the associated winner determination problem model.

An efficient, open-bid procurement auction for small-scale electricity markets

Despite early calls to investigate the application of Vickrey auctions in electricity markets, academic literature still provides little insight into the matter. Similarly, despite general auction-theoretic literature discussing various advantages of employing revealed rather than sealed bidding procedures, publications on electricity market auctions rarely even mention differences between these opposing features. This paper marries these neglected features into a new auction model for electricity markets. We propose a novel open-bid, descending-clock auction capable of handling inter-bidder dependencies such as thermal line limits. A novel set of termination criteria ensures that our proposed auction facing markets with power balance and generator capacity constraints and thermal line limits, preserves truthful bidding as a dominant strategy, which, if employed by all bidders, allocates supply efficiently constituting an ex-post perfect equilibrium. Furthermore, we discuss known disadvantages of Vickrey–Clarke–Groves type auctions in the context of our proposed auction applied in electricity markets.