Bidding In Auctions Research Articles

Spatial Coordination and Joint Bidding in Conservation Auctions

Spatial coordination of land use change is pivotal in agri-environmental policy to improve the delivery of environmental goods. This paper implements a laboratory experiment to study spatial coordination in a conservation auction. In addition to letting individual producers bid competitively against each other to supply environmental goods, we ask whether opportunities for joint bidding can enhance spatial coordination in the auction cost-effectively. Auction performance depends on the nature of incentives for individual bids; in particular, whether an agglomeration bonus is offered for individual bids. With an individual bonus in place, joint bidding gives no improvement in either environmental benefits procured or cost-effectiveness. Absent an individual bonus, joint bidding improves environmental performance but can decrease cost-effectiveness. Further, across both individual and joint bidding treatments, the average environmental benefits, degree of spatial coordination, and cost-effectiveness are greater, and amount of seller markups lower, with multiple-round bidding compared to single-round bidding.

Resource allocation mechanisms for maximizing provider’s revenue in infrastructure as a service (IaaS) cloud

Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution. The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans. We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon. First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services. The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot. Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity.

Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts

AbstractThis paper uses non‐linear methodologies to follow the synchronously reported relationship between the Nordic/Baltic electric daily spot auction prices and geographical relevant wind forecasts in MWh from early 2013 to 2020. It is a well‐known market (auctions) microstructure fact that the daily wind forecasts are information available to the market before the daily auction bid deadline at 11 a.m. The main objective is therefore to establish conditional and marginal step ahead spot price density forecast using a stochastic representation of the lagged, synchronously reported and stationary spot price and wind forecast movements. Using an upward expansion path applying the Schwarz (Bayesian information criterion [BIC]) criterion and a battery of residual test statistics, an optimal maximum likelihood process density is suggested. The optimal specification reports a significant negative covariance between the daily price and wind forecast movements. Conditional on bivariate lags from the SNP information and using the known market information for wind forecast movements at t1, the paper establishes one‐step‐ahead bivariate and marginal day‐ahead spot price movement densities. The result shows that wind forecasts significantly influence the synchronously reported spot price densities (means and volatilities). The paper reports day‐ahead bivariate and marginal densities for spot price movements conditional on several very plausible price and wind forecast movements. The paper suggests day‐ahead spot price predictions from conditional and synchronously reported wind forecasts movements. The information should increase market participants spot market insight and consequently make spot price predictions more accurate and the confidence interval considerably narrower.

Asymmetric Cost Pass‐Through in Multi‐Unit Procurement Auctions: An Experimental Approach*

We explore input cost pass‐through in multi‐unit procurement auctions. Whether cost shocks are private versus common across firms has important implications for discriminatory, but not uniform price auctions. We provide evidence of asymmetric pass‐through of private cost shocks in discriminatory auctions. Unlike uniform price auctions, revenue‐maximizing bidders in discriminatory auctions ‘pad’ bids close to the expected clearing price for units with costs below that price, but they do not bid below cost on higher cost units. Therefore, if costs are higher than expected, the clearing price rises and if costs are lower than expected, the clearing price remains high.

Preference for bid time in hybrid auctioned IPOs: Evidence from China

Using comprehensive bidding data of 783 Chinese IPOs from June 2009 to November 2012, we investigate institutions’ preference for bid time in hybrid auctions. We find that (1) earlier bids are associated with higher pricing errors, which is more pronounced in IPOs with greater uncertainty; (2) the bid prices of earlier bids tend to be aggressively high; (3) earlier bids are associated with fewer bid shares and allocations; (4) bidders submitting earlier bids (hereafter, earlier bidders) have inferior profits; and (5) earlier bidders show persistence in bid time. Overall, the empirical results remain robust after controlling for unobserved bidder-underwriter relationships and indicate that earlier bidders tend to be less informed. Our findings reveal and highlight the effect of bid time on identifying informative bids in hybrid auctions.

Incentive-Compatible Learning of Reserve Prices for Repeated Auctions

How can an auctioneer optimize revenue by learning the reserve prices from the bids in the previous auctions? How should the long-term incentives and strategic behavior of the bidders be taken into account? Motivated in part by applications in online advertising, in “Incentive-Compatible Learning of Reserve Prices for Repeated Auctions,” Kanoria and Nazerzadeh investigate these questions. They show that if a seller attempts to dynamically update a common reserve price using the bidding history, buyers will shade their bids, which can hurt the revenue. However, when there is more than one buyer, using personalized reserve prices, the auctioneer can achieve a near-optimal revenue. In their proposed mechanism, the personal reserve price for each buyer is determined using the historical bids of other buyers.

An experimental study of VCG mechanism for multi-unit auctions: competing with machine bidders

This paper complements the main experimental result reported in Takahashi et al. (Evolut Inst Econ Rev 16:357–374, 2019) to a deeper understanding of subjects’ bidding behavior under the VCG mechanism. In the experiment, there are two types of appearance of information about bidders’ valuations of the item given to them and the bids they are asked to submit: one is unit valuations and the unit bids themselves (Appearance 1) and the other is unit valuations and the unit bids multiplied by the number of units (Appearance 2). For subjects who compete with truth-telling machine bidders in multi-unit auctions, we confirmed that in Appearance 1, they choose truth-telling bids more frequently, and efficient allocations are observed more frequently, as compared to the situation where they compete with human bidders. This result suggests a possibility that in Appearance 1, subjects learn their dominant strategy not by practicing with other subjects but by practicing with machine bidders in experiments for multi-unit auctions, although the item allocation and payment determination under the VCG mechanism is never intuitively understandable to the subjects.

Price impacts of signalling in Chinese residential land auctions

Auctions are used by local Chinese governments to raise revenue while releasing land for development. Because developers reveal their valuation of parcels through winning, bids for land use rights in subsequent auctions may be influenced by outcomes of earlier government sales. A log linear hedonic regression analysis of more than 900 residential land use rights auctions in Chengdu over eight years reveals that top developers’ winning bids signal other developers who adjust their bids in subsequent auctions. Developers appear to believe that the top companies possess superior knowledge through research, experience, or insider information. The results are higher government revenue and rising land prices, which can lead to lessened competition among housing developers, higher house prices, and affordability problems in neighbourhoods where the top developers buy land.

Discrete Signal Processing with Set Functions

Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite set N. They are fundamental and ubiquitous in many application domains and have been used, for example, to formally describe or quantify loss functions for semantic image segmentation, the informativeness of sensors in sensor networks the utility of sets of items in recommender systems, cooperative games in game theory, or bidders in combinatorial auctions. In particular, the subclass of submodular functions occurs in many optimization and machine learning problems. In this paper, we derive discrete-set signal processing (SP), a novel shift-invariant linear signal processing framework for set functions. Discrete-set SP considers different notions of shift obtained from set union and difference operations. For each shift it provides associated notions of shift-invariant filters, convolution, Fourier transform, and frequency response. We provide intuition for our framework using the concept of generalized coverage function that we define, identify multivariate mutual information as a special case of a discrete-set spectrum, and motivate frequency ordering. Our work brings a new set of tools for analyzing and processing set functions, and, in particular, for dealing with their exponential nature. We show two prototypical applications and experiments: compression in submodular function optimization and sampling for preference elicitation in combinatorial auctions.

Blockchains, Front-Running, and Candle Auctions

Blockchain implementations of auctions have to deal with the problem of front-running: block production happens at discrete intervals, and anyone can inspect and react to the incoming bids before they are written on chain. The presence of smart contracts among bidders, a hallmark of automated blockchain environments, renders current cryptographic solutions infeasible and essentially precludes static auctions. Moreover, their transparent nature makes smart contracts susceptible to shill bidding in strategy-proof auctions more generally. As a remedy, this paper considers an ascending auction with a random ending time --- a so-called candle auction. Time is discrete and in every round, the bidders bid sequentially and in a fixed order. The bidder with the highest bid at the end of the decisive round wins the auction and pays her bid. We show that an appropriately chosen ending time distribution mitigates the main problems of front-running: low revenue, reduced utility when being front-run, and limited efficiency.

Uninformed Bidding in Sequential Auctions

We consider a private value auction with one-sided incomplete information in which two objects are sold in a sequence of two second-price auctions. Buyers have multi-unit demands and both are asymmetrically informed at the ex-ante stage of the game. One buyer perfectly knows his type and the other is uninformed about his own type. We con-sider interim information acquisition for the uninformed buyer and derive an asymmetric equilibrium which is shown to produce a declining price sequence across both sales. The supermartingale property of the price sequence stems from the uninformed buyer’s incen-tives to gather private information which leads to an aggressive bidding at the first-stage auction.

The effect of public knowledge on highway procurement auction bidding

The effect of public knowledge on highway procurement auction bidding

Transactive Energy Management of PV-Based EV Integrated Parking Lots

Electric vehicles (EVs) are a viable alternative for a sustainable mode of transportation. A huge penetration of EVs in future will lead to increasing demand in commercial parking lots equipped with charging systems. Hence, this research area is being pursued meaningfully in recent years. This article proposes a transactive energy management system (EMS) for commercial parking lots equipped with EV charging system and rooftop PV system. Initially, the EMS is optimized with the objective of balancing charging demand with supply. To make the EMS more realistic, factors such as battery degradation cost and photovoltaic levelized cost of energy are considered. At the later stage, the EMS of each parking lot communicates the energy requirement and excess energy to the local trading agent to initiate the proposed transactive energy transaction mechanism. The double-sided auction bidding mechanism is price flexible and based on the valuation of the energy requirement of parking lots. For a system with six parking lots and 25 EVs in each parking lot, the proposed scheme results in cost savings in the range of 2% to 7% for different cases of fixed and variable feed-in tariff. The uncertainty analysis shows that the cost savings vary from a minimum of 2.41% to maximum 12.09% with an average of 6.11%. The case studies demonstrate the potential economic benefit of the proposed scheme.

Competition in a fish auction: The case of Atlantic cod in Northern Norway

This study investigates the role of bidder numbers in fish auctions, a neglected area of past research and one that is of key importance for optimal use of limited marine resources. The study of a Norwegian auction for Atlantic cod shows that prices increase when two or more bidders participate in the auction. Holding other variables such as lot size, quality grade, and seasonality constant, price increases of 4.51 %, 6.47 %, 7.18 %, and 9.88 % in auctions with two, three, four, and five plus bidders were found, compared to auctions with only one bidder. The increasing prices following from higher bidder numbers also indicate that fishers should be incentivized to provide high-quality fish at the right time of importance for optimal resource use. Increased competition also means that, over time, winning bids will be placed by the buyers that are the most capable of adding value and earning profits, which also contributes to optimal resource use. Findings also show to what extent factors such as lot size, quality grade, fishing method (bottom trawl, longline, Danish seine), and seasonality influence the probability of two or more participating bidders in auctions, which should be highly relevant information for fishers and policy makers.

An Overview on Game Theory and Its Application

This research paper looks into Game Theory while immensely addressing the historical background of this theory. The document also gives an overview and definition of relevant terminologies related to this theory like a game, Nash equilibrium, and dominance which form the basis of the theory concept. It also focuses on mixed strategies, extensive games with both perfect and imperfect information, auction bidding, and their relevant practical application of the concept as applied in the field of economics.

Strategy-Proof Spectrum Allocation Among Multiple Operators in Wireless Networks

The exponentially increasing demand for data necessitates efficient spectrum allocation among operators in wireless networks. In this paper, we address the spectrum allocation problem among non-cooperative operators via auctions. The classical Vickrey-Clarke-Groves (VCG) approach provides the framework for a strategy-proof and social welfare maximizing auction. However, the VCG mechanism has high computational complexity, which makes it infeasible for practical implementation. In this work, we propose sealed bid auction mechanisms for spectrum allocation, which are computationally tractable. These can be used for spectrum allocation by performing auctions at shorter intervals to cater to the dynamic load variation in the network. We establish that the proposed algorithm is strategy-proof for the uniform demand scenario. Furthermore, for non-uniform demand, we propose an algorithm that satisfies weak strategy-proofness. Here, we also consider non-linear increase in the marginal valuations with demand. Simulation results are also presented to exhibit the performance comparison of the proposed algorithms with VCG and other existing mechanisms.

Multi-unit multiple bid auctions in balancing markets: An agent-based Q-learning approach

There is an ongoing debate on the appropriate auction design for competitive electricity balancing markets. Uniform (UPA) and discriminatory price auctions (DPA), the prevalent designs in use today, are assumed to have different properties with regard to prices and efficiencies. These properties cannot be thoroughly described using analytical methods due to the complex strategy space in repeated multi-unit multiple bid auctions. Therefore, using an agent-based Q-learning model, we simulate the strategic bidding behaviour in these auctions under a variety of market conditions. We find that UPAs lead to higher prices in all analysed market settings. This is mainly due to the fact that players engage in bid shading more aggressively. Moreover, small players in UPAs learn to free ride on the price setting of large players and earn higher profits per unit of capacity owned, while they are disadvantaged in DPAs. UPAs also generally feature higher efficiencies, but there are exceptions to this observation. If demand is varying and players are provided with additional information about scarcity in the market, market prices increase only in case asymmetric players are present.

Hedging renewable energy investments with Bitcoin mining

Renewable energy sources such as wind power are increasing their share of the world energy matrix. In Brazil, the regulator promotes reverse bid auctions where the winner agrees to begin production a number of years ahead under a long-term contract. If a wind farm project chooses to anticipate construction, it can sell its energy in the short-term market but becomes subject to electricity price volatility. In order to create incentives for early investment, we propose that wind farm investors can hedge electricity price risk by simultaneously investing in a cryptocurrency mining facility that uses electricity as input to produce newly minted Bitcoins. As electricity and Bitcoin prices are uncorrelated, the ability to switch between these outputs allows the wind farm to maximize revenues and minimize losses. We develop a numerical application under the real options approach to determine the financial impact of the investment in a Bitcoin facility for the wind energy producer that will allow it to optimally switch outputs depending on the relative future prices of electricity and Bitcoins. The short-term energy price and Bitcoin price/mining-difficulty ratio are modeled as distinct stochastic diffusion processes. The results indicate that the option to switch outputs significantly increases the generator's revenue while simultaneously decreasing the risk of anticipating the construction. These findings, which can also be applied to other renewable energy sources, may be of interest to both the energy generator as well as the system regulator as it creates an incentive for early investment in sustainable and renewable energy sources.

Creating an educational computer game “Modeling bid auction in the electricity market”

The goal of the game created by the authors is to study the mechanisms of the electricity market and to shape students’ economic knowledge. In the course of the game, abilities are formed to analyze the current situation on the electric power industry market, to plan and predict the development of events one step further in order to achieve their goals. And within the framework of difficult situations, make the choice of the most effective ways to solve economic problems.

The Cross-Entropy Method for the Winner Determination Problem in Combinatorial Auctions

The combinatorial auction is one of the important methods used for multi-item auctions, and the solution to the winner determination problem (WDP) is the key factor in the widespread application of combinatorial auctions. This paper explores the use of the cross-entropy method to solve the WDP, which is an NP problem. The performance of the proposed approach is evaluated on the basis of two well-known benchmark test cases. The experimental results show that, compared with the genetic algorithm and the particle swarm optimization algorithm, the cross-entropy（CE）method has the advantages of a higher success rate and a shorter time for solving the WDP. Therefore, the CE method provides a high-quality solution for the effective solution of the problem of determining winning bids in combined auctions.