Auction Outcomes Research Articles

A flexible combinatorial auction bidding language for supplier selection and order allocation in a supply chain with price sensitive demand

This study addresses the multi-item multi-sourcing supplier selection and order allocation problem in a Combinatorial Auction (CA) framework. First, we propose a new flexible procurement CA bidding language, then prove that it can convey the same information as existing bidding languages while allowing more efficient auction outcomes and reduced computational complexity. Suppliers submit combinatorial bids to a single manufacturer. Each bid carries information about the supplier’s cost structure and potential discounts. The manufacturer purchases the items and assembles one or more product types that experience price-sensitive demand rates by end consumers. We use the realistic logit function to represent the price-sensitive demand rates of finished products. A Mixed Integer Non-Linear Programming (MINLP) model is developed to help the manufacturer make optimal supplier selection, order allocation, and pricing decisions under the proposed bidding language. The MINLP model considers purchasing, transportation, ordering, administrative, holding, and manufacturing costs. These considerations make the results of this study realistic with the potential to be used in practical applications. Also, we derive a set of necessary conditions that must exist in at least one optimal solution to the problem. These conditions can be utilized to design efficient solution mechanisms for this difficult problem in the future. Finally, an extensive numerical analysis is performed to illustrate the bidding language and the key results.

Fragmented property rights and their risks on foreclosed housing: a qualitative comparative analysis based on judicial auctions in China

During China’s transition towards land and housing marketization in the 1980s to 1990s, a series of legal and policy arrangements led to the separation of land ownership from land-use rights, followed by the separation of housing ownership from land-use rights. This has institutionally fragmented the housing property rights, allowing properties to be financialized as collaterals. Consequently, the fragmentation of housing property rights inflicts substantial risks on judicial auctions of foreclosed housing. As China’s real estate sector is grappling with a mounting debt crisis, it becomes imperative to enhance the efficiency of judicial auctions to address bank debts and stabilize the housing financial system. By employing the cs-QCA method with a dataset comprising 136 cases of foreclosed properties, this study reveals the critical role of various configurations of property-rights risks in determining the outcome of judicial auctions. Additionally, it identifies the importance of credible commitments offered by the courts in mitigating property-rights risks. The research expects to provide practical and theoretical insights for China’s courts and financial institutions to manage foreclosed properties effectively.

Profit mechanisms in penny auctions: Unraveling the impact of sunk costs

This study provides a distinctive perspective on the intricate dynamics of penny auctions, which are characterized by incremental bid costs and potential sunk costs, constituting a significant area of research in existing literature. Our research takes a unique approach by specifically delving into the profit mechanisms inherent in penny auctions, with a focused examination on the profound impact of the sunkcost fallacy on bidder behavior. To achieve this, we employ a custom model built upon the structural elements proposed in previous works, aiming to scrutinize the dynamics of bid costs and bid increments. In the course of our investigation, we uncover counterintuitive strategies employed by bidders that have a substantial impact on auctioneer profitability. This revelation contributes to a deeper understanding of bidder decision-making processes and their consequential influence on auction outcomes. By shedding light on these nuanced aspects, our study provides valuable insights that extend the current understanding of the intricate dynamics governing penny auctions. The implications of our findings have relevance not only for academic researchers seeking to expand their knowledge in this domain but also for industry professionals involved in auction platforms, offering practical considerations for optimizing profitability in this unique auction format.

A Comprehensive Exploration of Machine Learning Models for Predicting Online Auction Prices

The transformative impact of traditional commerce by online marketplaces is exemplified through eBay, a global platform that facilitates diverse transactions via auctions. In this research, the dynamics of eBay auctions, crucial for buyers, sellers, and researchers, are delved into. The central inquiry revolves around the key factors shaping auction outcomes, examining bid behaviors and types. The study leverages a robust dataset from eBay, meticulously curated to encompass auction identifiers, bid details, pricing information, auction types, and temporal aspects. A comprehensive approach involves data preprocessing, ensuring reliability by addressing missing values and outliers. Rigorous exploration and validation validate the dataset's integrity. Machine Learning Techniques, including MLP, SVR, Linear Regression, Extra Trees, and Gradient Boosting, form the analytical backbone. Model evaluation reveals top-performing candidates, such as MLP Regressor (0.8084), SVR (0.8210), and Linear Regression (0.8173), exhibiting superior accuracy and reliability. These models are identified for adoption in future work, emphasizing nuanced predictions in eBay auctions. This research contributes to understanding online auction dynamics, offering practical insights for eBay users and the broader e-commerce community. The models identified pave the way for enhanced predictive capabilities and continuous refinement in deciphering factors influencing auction outcomes.

Ways to enhance the operations of interregional territorial departments of the Antimonopoly Committee of Ukraine: Ensuring unity in safeguarding economic competition

The article provides a thorough analysis of the structure and functions of the territorial branches of the Antimonopoly Committee of Ukraine, focusing on their decisive impact on regional control over compliance with legislation on economic competition protection. The key role of the Committee’s Chairman is highlighted, emphasizing the importance of the branches’ functionality in shaping and implementing competitive policies.The article proposes specific recommendations to enhance the effectiveness and compliance with legislation of interregional territorial branches. The authors identify shortcomings and propose solutions to issues related to the application of legislation on economic competition protection. The article emphasizes the crucial importance of adhering to the Methodology and calls for the use of a unified practice among interregional territorial branches.Specific proposals for decisions by the administrative boards of territorial branches are discussed, addressing the correction of discrepancies in the Methodology, strengthening control, and improving case review procedures. The significance of regular updates and improvements to the methodology of territorial branches’ work is emphasized to prevent unjust decisions and deficiencies in conducting investigations.In the context of anticompetitive cases, the article highlights the necessity of considering specific circumstances and ensuring proper control over the review, evidence collection, and justification of decisions to improve the quality and objectivity of antimonopoly control in Ukraine.In the analysis of cases related to violations of anticompetitive concerted actions affecting auction outcomes, the Antimonopoly Committee can identify the absence of necessary documents, such as bank guarantees, provided by auction participants. The primary goal is to identify facts of competition violations. However, it is crucial to avoid general conclusions regarding the submission of all necessary documents, as this area falls under legislation on public procurement and is not within the scope of the Antimonopoly Committee’s investigation or authority.

Exploring the influence of Brazilian project cancellation mechanisms on new wind power generation

The use of wind power has gained prominence on the world stage, especially in Brazil. In addition to generating energy free of emissions, wind technology is the second cheapest generation option in Brazil. At the same time, it has contributed to reducing hydrological vulnerability caused by climate variation. Although wind power has experienced a steady expansion in the last decade, recent poor outcomes of wind auctions produced uncertainty about the future of this technology in the country. This paper analyses the influence of contract mechanisms used in Brazilian energy auctions for the growth of wind generation. To do this, a scenario analysis based on a hybrid simulation model is used, combining agent-based modelling (ABM) and system dynamics (SD). From the hybrid simulation, the effect of the wind project cancellation policy is analysed, on the one hand, and different ceiling prices for competing sources in the auctions are assessed, on the other. Moreover, the simulation model shows how hydro conditions affect reserve energy auctions.

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.

Estimating damages from bidding rings in first-price auctions

Bidding rings typically coordinate to rig auctions and keep prices low. Despite bid rigging being pervasive, measuring its damages (i.e., the revenue loss suffered by the auctioneer) is a challenge for antitrust authorities. Indeed, most of the methods to quantify damages compare outcomes of auctions affected by the collusive behavior with unaffected auctions, requiring data that is hard to obtain. We propose a model-based method to estimate damages. Its main advantages are that only information on affected auctions is required and that the underlying assumptions of the economic model are explicit, so they can be challenged and eventually modified for damage reassessment. In a Monte Carlo exercise, we show that our methodology performs well in moderate-size samples. We apply our method to data from the Ohio milk cartel and estimate damages similar to those found in previous studies, even when we discard information from non-affected markets.

The impact of competition, trust and capital on renewable energy auction outcomes in sub-Saharan Africa: Analysing auctions in South Africa, Zambia and Namibia

Africa is short of power, despite having abundant renewable energy resources. Over the past decade, renewable energy auctions have emerged as an effective mechanism to competitively procure utility-scale private power projects. This paper identifies the elements contributing to efficient price and effective project realisation outcomes through comparative case studies in South Africa, Zambia and Namibia. The analysis combined existing literature and theory on those elements that contribute to success in Independent Power Projects (IPPs), with that on renewable energy auction design and implementation. The application of an integrated analytical framework shows that the introduction of renewable energy auctions in Africa provides an essential programmatic element which connects existing country and project level factors and is crucial in achieving superior project realisation and price outcomes, compared to projects procured through direct negotiation or feed-in tariffs.Auction price outcomes are mainly determined by factors that act as auction signals, barriers and incentives primarily geared towards increasing competition in the procurement process and lowering projects' cost of capital. The case studies also show that project realisation is determined by bidder quality, project preparation levels, investor commitment, constraints (revenue stream and political economy) and post-award support. These are influenced by factors from all three levels – country, project and programme – that act as auction signals, barriers, incentives and support measures. Factors influencing bidders’ trust in the auctioning authority and auction process are particularly important signals influencing both project prices and realisation rates.

Assessing the design and the outcome of the new renewable electricity auctions in Spain

Similarly to other countries in the EU, Spain has very ambitious renewable electricity targets for the 2030 timeframe. According to its National Climate and Energy Plan, 74% of electricity will have to be generated from renewable electricity sources. This means that around 60 GW of new renewable electricity capacity (mostly wind and solar PV) will have to be deployed in the next decade, which will certainly be a challenge. Auctions are the main instrument to reach those targets. A radically different design of the auctions was adopted in 2020 and already four auctions have been conducted with the new design. Based on an analysis of official data and secondary sources, the aim of this article is to assess the main design features and the outcome of those auctions. The design of the auction is considered to be appropriate. However, the ex-ante effectiveness of the auction has decreased over the auction rounds. Similarly, support cost efficiency was very high in the first auction and decreased over auction rounds (higher bid prices) due to the higher costs of materials, logistics and financing. Finally, technological and actor diversity have been low in most auctions.

Trustworthy sealed-bid auction with low communication cost atop blockchain

Auction, widely used by firms and governments, has facilitated trillions of dollars in the world economy. However, when price is the only factor determining the winner, how to guarantee the trustworthiness of the auction outcomes, and at the same time preserve the privacy of losing bidders is a challenging issue. In this paper, to address this issue, we present a sealed-bid auction protocol with privacy preserving and public verifiability atop blockchain. No bidder is able to repudiate or change any bidding to outbid others and all public information comes with proof that even observers are able to validate the auction. Proofs post along the way to validate biddings and identify the winner are in the form of group elements with communication complexity O(log⁡(nm)), n,m denoting the upper bound bit length of the bidding price and the number of bidders respectively. The logarithmic complexity significantly reduces the communication cost and makes it practical for large-scale auction. Moreover, our protocol can be easily extended to any type of ordinal-price sealed-bid auction such as second-price with privacy preserving. We provide detailed security proofs or analysis of the required security goals. The evaluation of time cost in off-chain operations and gas cost in on-chain functions demonstrate the practicality of our protocol in real-world applications.

Quantifying technology skewness in European multi-technology auctions and the effect of design elements and other driving factors

Multi-technology auctions are a popular instrument to support renewable electricity in the European Union. While they increase competition, some technologies could dominate, which might be an issue for some countries’ power system reliability. Using statistical methods, I analyse how balanced or skewed European multi-technology auctions are and investigate driving factors. I show that 80% of all multi-technology auction rounds from 2011 to 2020 were skewed, strongly or exclusively favouring one technology. None of the investigated design elements and general context factors can explain this. Instead, specific auction-external context factors may better explain the observed skewness. Furthermore, the aggregated outcome across all rounds, years, and countries is relatively balanced because the rounds are differently skewed. This could be coincidental and change in the future if outcomes remain strongly skewed. Policymakers may consider shifting to technology-specific auctions that target single technologies, particularly if they cannot manage the risks of skewed auction outcomes. Thereby, they promote a diverse and targeted deployment of renewables.

Adverse inclusion of asymmetric advertisers in position auctions

Product listing platforms commonly use generalized second-price auctions to select competing advertisers for limited ad positions. However, when advertisers are asymmetric, position auctions may confound the post-auction competition structure and thus endogenize the bidders’ values of the ad positions. We build an analytical model to examine the impact of position auctions on an asymmetric market structure, which consists of a mass marketer and two specialized advertisers of heterogeneous quality efficiencies. The advertisers bid for two ad slots and then compete for the market in price and quality. We find that the asymmetric market structure may increase the uncertainty of the auction outcomes, which then may induce the advertisers to underbid using a conservative strategy profile in the locally-envy free equilibrium. Consequently, the auction outcome may adversely include the less-efficient specialized advertiser. This result is stronger than the position paradox in the classic auction literature, as the advertiser with a competitive advantage may be driven out and obtain zero profit.

A Comprehensive Exploration of Machine Learning Models for Predicting Online Auction Prices

The transformative impact of traditional commerce by online marketplaces is exemplified through eBay, a global platform that facilitates diverse transactions via auctions. In this research, the dynamics of eBay auctions, crucial for buyers, sellers, and researchers, are delved into. The central inquiry revolves around the key factors shaping auction outcomes, examining bid behaviors and types. The study leverages a robust dataset from eBay, meticulously curated to encompass auction identifiers, bid details, pricing information, auction types, and temporal aspects. A comprehensive approach involves data preprocessing, ensuring reliability by addressing missing values and outliers. Rigorous exploration and validation validate the dataset's integrity. Machine Learning Techniques, including MLP, SVR, Linear Regression, Extra Trees, and Gradient Boosting, form the analytical backbone. Model evaluation reveals top-performing candidates, such as MLP Regressor (0.8084), SVR (0.8210), and Linear Regression (0.8173), exhibiting superior accuracy and reliability. These models are identified for adoption in future work, emphasizing nuanced predictions in eBay auctions. This research contributes to understanding online auction dynamics, offering practical insights for eBay users and the broader e-commerce community. The models identified pave the way for enhanced predictive capabilities and continuous refinement in deciphering factors influencing auction outcomes.

Loss aversion in sequential auctions

I analyze sequential auctions with expectations‐based loss‐averse bidders who have independent private values and unit demand. Equilibrium bids are history dependent and subject to a “discouragement effect”: the higher is the winning bid in the current round, the less aggressive are the bids of the remaining bidders in the next round. Moreover, because they experience a loss in each round in which they fail to obtain an object, bidders are willing to pay a premium to win sooner rather than later. This desire to win earlier leads prices to decline in equilibrium. I also show how various disclosure policies regarding the outcome of earlier auctions affect equilibrium bids, and that sequential and simultaneous auctions are neither bidder‐payoff equivalent nor revenue equivalent.

An empirical test of auction methods in the primary market of sovereign debt

ABSTRACT There has been a long debate as to which of the most extended systems, the uniform system or the discriminatory price system, is the most appropriate for central banks and debt management offices issuing sovereign bonds. The purpose of this paper is to shed light on this question. The main variables to explore the auction mechanisms are the price difference between the primary and secondary markets (price spread) and the coverage ratio. Subsequently, to determine patterns for clarifying which auction system is optimal, we propose a new model based on the price spread and a valuation model for financial options that covers both negative and positive spreads. We conclude that the auction method and the number of primary dealers are relevant parameters in the auction outcome. Finally, the results of the new model are the cornerstone for identifying detailed patterns followed by top-performing countries.

Entry in first-price auctions with signaling

We study the optimal entry fee in a symmetric private value first-price auction with signaling, in which the participation decisions and the auction outcome are used by an outside observer to infer the bidders’ types. We show that this auction has a unique fully separating equilibrium bidding function. When the bidders’ sensibility for the signaling concern is sufficiently strong, the expected revenue maximizing entry fee is the maximal fee that guarantees full participation. The larger is the bidder’s sensibility, the higher is the optimal participation.

Competition in online land lease auctions in Ukraine: Reduced-form estimation

An innovative pilot project to facilitate the transparent transfer of rental rights for publicly owned agricultural land via an ascending-price online auction was launched in Ukraine in October 2018. This paper analyses publicly disclosed auction data and investigates how competition, auction design characteristics, and farmland-specific properties influenced the auction outcomes. This information is factored into the probability of the plot being rented (i.e. auction success) and the size of the winning bid (i.e. rental rates). The analysis was conducted using an independent private values framework, employing a mixed-effects model with sample selection. Estimation results confirmed that a higher number of bidders and more active bidding lead to a significantly greater probability of auction success and higher rental rates.

Distinguishing incentive from selection effects in auction-determined contracts

This paper develops a novel approach to estimate how contract and principal–agent characteristics influence a post-auction outcome when the matching between agents and principals derives from an auction process. We propose a control-function approach to account jointly for the endogeneity of contracts and matching. This consists of, first, estimating the primitives of an interdependent values auction model – which is shown to be non-parametrically identified from the bidding data – second, constructing control functions based on the distribution of the unobserved private signals conditional on the auction outcome. A Monte Carlo study shows that our augmented outcome equation corrects the endogeneity biases well, even in small samples. We apply our methodology to a labor market application and estimate the effect of sports players’ auction-determined wages on their individual performances. We also use our structural estimates to evaluate the strength of matching inefficiencies and assess counterfactual reservation wage policies.

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.