Showing papers on "Bidding published in 2002"
TL;DR: In this article, a method to predict next-day electricity prices based on the ARIMA methodology is presented, which is used to analyze time series and have been mainly used for load forecasting, due to their accuracy and mathematical soundness.
Abstract: Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.
1,080 citations
TL;DR: This article studied the second-price auctions run by eBay and Amazon and found that the fraction of bids submitted in the closing seconds of the auction is substantially larger in eBay than in Amazon, and more experience causes bidders to bid later on eBay but earlier on Amazon.
Abstract: Auctions on the Internet provide a new source of data on how bidding is in uenced by the detailed rules of the auction. Here we study the second-price auctions run by eBay and Amazon, in which a bidder submits a reservation price and has this (maximum) price used to bid for him by proxy. That is, a bidder can submit his reservation price (called a proxy bid) early in the auction and have the resulting bid register as the minimum increment above the previous high bid. As subsequent reservation prices are submitted, the bid rises by the minimum increment until the second-highest submitted reservation price is exceeded. Hence, an early bid with a reservation price higher than any other submitted during the auction will win the auction and pay only the minimum increment above the second-highest submitted reservation price. eBay and Amazon use different rules for ending an auction. Auctions on eBay have a xed end time (a “hard close”), while auctions on Amazon, which operate under otherwise similar rules, are automatically extended if necessary past the scheduled end time until ten minutes have passed without a bid. These different rules give bidders more reason to bid late on eBay than on Amazon.We nd that this is re ected in the auction data: the fraction of bids submitted in the closing seconds of the auction is substantially larger in eBay than in Amazon, and more experience causes bidders to bid later on eBay, but earlier on Amazon. Last-minute bidding, a practice called “sniping,” arises despite advice from both auctioneers and sellers in eBay that bidders should simply submit their maximum willingness to pay, once, early in the auction. For example, eBay instructs bidders on the simple economics of second-price auctions, using an example of a winning early bid. They discuss last-minute bids on a page explaining that they will not accept complaints about sniping, as follows:
1,040 citations
TL;DR: A review of the scientific literature on the market for corporate control can be found in this article, where the authors argue that corporate control is best viewed as an arena in which managerial teams compete for the rights to manage corporate resources.
Abstract: This paper reviews much of the scientific literature on the market for corporate control. The evidence indicates that corporate takeovers generate positive gains, that target firm shareholders benefit, and that bidding firm shareholders do not lose. The gains created by corporate takeovers do not appear to come from the creation of market power. With the exception of actions that exclude potential bidders, it is difficult to find managerial actions related to corporate control that harm shareholders. Finally, we argue the market for corporate control is best viewed as an arena in which managerial teams compete for the rights to manage corporate resources.
851 citations
TL;DR: In this article, the authors provide two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models, which are explained and checked against each other.
Abstract: In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.
807 citations
Posted Content•
TL;DR: In this article, the authors show that large bidders have an incentive to reduce demand in order to pay less for their winnings, which creates an inefficiency in multi-unit auctions.
Abstract: Auctions typically involve the sale of many related goods. The FCC spectrum auctions and the Treasury debt auctions are examples. With conventional auction designs, large bidders have an incentive to reduce demand in order to pay less for their winnings. This incentive creates an inefficiency in multi-unit auctions. Large bidders reduce demand for additional units and so sometimes lose to smaller bidders with lower values. We demonstrate this inefficiency in several auction settings: flat demand and downward-sloping demand, independent private values and correlated values, and uniform pricing and pay-your-bid pricing. We also establish that the ranking of the uniform-price and pay-your-bid auctions is ambiguous. We show how a Vickrey auction avoids this inefficiency and how the Vickrey auction can be implemented with a simultaneous, ascending-bid design (Ausubel 1997). Bidding behavior in the FCC spectrum auctions illustrates the incentives for demand reduction and the associated inefficiency.
704 citations
TL;DR: An auction of carbon permits is the best way to achieve domestic carbon caps designed to limit global climate change as mentioned in this paper, which allows reduced tax distortions, provides more flexibility in distribution of costs, provides greater incentives for innovation, and reduces the need for politically contentious arguments over the allocation of rents.
399 citations
Patent•
01 Feb 2002TL;DR: In this paper, a system and method for enabling information providers using a computer network such as the Internet to influence a position for a search listing within a search result list generated by an Internet search engine is presented.
Abstract: A system and method for enabling information providers using a computer network such as the Internet to influence a position for a search listing within a search result list generated by an Internet search engine. A database stores accounts for the network information providers. Each account contains contact and billing information for a network information provider. In addition, each account contains at least one search listing having at least three components: a description, a search term comprising one or more keywords, and a bid amount. The network information provider may add, delete, or modify a search listing after authenticated login. A search term relevant to the content of the web site or other information source to be listed is first selected. A search listing includes the search term and a description. A bidding process occurs when the network information provider enters a new bid amount for a search listing. The system and method then compares the bid amount with all other bid amounts for the same search term, and generates a rank value for all search listings having that search term. The rank value determines where the listing will appear on the search results list page that is generated in response to a query of the search term by a searcher.
337 citations
TL;DR: In this paper, the authors discuss the auctions in the UK, Netherlands, Germany, Italy, Austria, Switzerland, Belgium, Greece and Denmark, and discuss the sequencing of the auctions.
320 citations
TL;DR: In this article, the optimal bidding strategy of a price-taker producer is obtained by estimating the probability density functions of next-day hourly market clearing prices, which are then used to formulate a self-scheduling profit maximization problem.
Abstract: This paper provides a framework to obtain the optimal bidding strategy of a price-taker producer. An appropriate forecasting tool is used to estimate the probability density functions of next-day hourly market-clearing prices. This probabilistic information is used to formulate a self-scheduling profit maximization problem that is solved taking advantage of its particular structure. The solution of this problem allows deriving a simple yet informed bidding rule. Results from a realistic case study are discussed in detail.
309 citations
TL;DR: In this paper, the design of a competitive market for reactive power ancillary services is presented, and the reactive power market is settled on uniform price auction, using a compromise programming approach based on a modified optimal power flow model.
Abstract: This paper presents the design of a competitive market for reactive power ancillary services. Generator reactive power capability characteristics are used to analyze the reactive power costs and subsequently to construct a bidding framework. The reactive power market is settled on uniform price auction, using a compromise programming approach based on a modified optimal power flow model. The paper examines market power issues in these markets and identifies locations where strategic market power advantages are present that need to be removed through investments in reactive power devices.
260 citations
TL;DR: The Seller's optimal bidding and Buyers' optimal contracting strategies in a von Stackelberg game with the Seller as the leader are derived and it is shown that Buyer's optimal reservation level follows an index that combines the Seller's reservation and execution cost.
TL;DR: In this article, the authors examined person-to-person transactions within the Internet market known as eBay and found that eBay transactions exhibit characteristics similar to transactions in more conventional markets, namely, prices are higher when there is less quantity supplied (when fewer of the items are available the same day), prices are lower during periods of lower demand (times less likely to have high traffic), and sellers with higher shipping and handling costs receive lower prices, and sellers failing to provide information about shipping/handling fees (i.e., larger information asymmetries) receive fewer bids.
Abstract: I. INTRODUCTION Without transmission of credible information, asymmetries may lead to underproduction of goods or even market failure. Reputation mitigates inefficiencies associated with information asymmetries by providing an informative signal of quality. (1) The difficulty in quantifying reputation means that few studies can analyze empirically the role of reputation in markets. Our analysis of a quantified, market-observed measure of reputation provides direct evidence of the effect of a seller's reputation on the terms of a onetime real-world transaction, thereby contributing empirical support to a fundamental economic principle. This study empirically examines person-to-person transactions within the Internet market known as eBay. In this virtual market of unseen participants and products, buyers and sellers face the risks of repudiation, as the counter party may deny the agreement after the fact. Buyers assume risks associated with lack of seller integrity and asymmetric information about the particular product, as the buyer is typically required to send payment before the seller ships the product. In addition, for many eBay transactions, the cost of enforcing a contract is high relative to the transaction's value, resulting in a practical absence of legal enforcement. (2) By providing a history of trade execution information, eBay benefits market participants by reducing information asymmetries while achieving substantial transaction cost economies. Market participants relate personal experiences, which eBay uses to calculate a numerical reputation measure for each user. Market participants, in turn, can use this reputation measure to assess counter party risk and adjust bidding behavior accordingly. In a sample of 460 auctions held between January 1998 and July 1998, we find a positive relation between prices and eBay's reputation measure. Higher-reputation sellers experience higher auction prices, ceteris paribus. Our findings suggest that repeat players are rewarded for building reputation. Consistent with the belief that the high-reputation seller's value of future transactions outweighs the value of taking advantage of the buyer in the current transaction, buyers are willing to pay more to a higher-reputation seller. This article contributes to the literature not only by providing quantitative support of long-accepted reputation theories but also by illustrating the use of nontraditional markets as a natural laboratory for experiments. This article is an example of how a newly formed electronic market can provide the elements necessary for analytical research. We find that eBay transactions for this item exhibit characteristics similar to transactions in more conventional markets, namely (1) prices are higher when there is less quantity supplied (when fewer of the items are available the same day), (2) prices are lower during periods of lower demand (times less likely to have high traffic), (3) sellers with higher shipping and handling costs receive lower prices, and (4) sellers failing to provide information about shipping and handling fees (i.e., larger information asymmetries) receive fewer bids. Dramatic innovations in online market structure and increasing availability of online market data should enable researchers to examine directly other traditionally non-quantifiable economic ideas. This article is organized as follows. Section II describes how reputation can be used to facilitate transactions in the presence of asymmetric information. Section III describes the eBay market, summarizes the listing and bidding processes, and discusses the reputation mechanism for this market. Section IV presents the price and reputation descriptive statistics associated with a consistently auctioned item and reports the empirical findings of how this item's highest bid price varies with the level of the seller's reputation. We conclude in section V with a discussion of eBay's continued attempts to add value to the market through recent structural changes. …
TL;DR: The authors assess empirically the effects of the winner's curse which, in common-value auctions, counsels more conservative bidding as the number of competitors increases and find that median procurement costs rise as competition intensifies.
Abstract: We assess empirically the effects of the winner's curse which, in common-value auctions, counsels more conservative bidding as the number of competitors increases. First, we construct an econometric model of an auction in which bidders' preferences have both common- and private-value components, and propose a new monotone quantile approach which facilitates estimation of this model. Second, we estimate the model using bids from procurement auctions held by the State of New Jersey. For a large subset of these auctions, we find that median procurement costs rise as competition intensifies. In this setting, then, asymmetric information overturns the common economic wisdom that more competition is always desirable.
10 Dec 2002
TL;DR: A method using both neural networks (NNs) and fuzzy-c-means (FCM) is presented for forecasting LMPs and it was found that the proposed neural networks were capable of forecasting L MP values efficiently.
Abstract: Bidding competition is one of the main transaction approaches in deregulated electricity markets. Locational marginal prices (LMPs) resulting from bidding competition represent electricity values at nodes or in areas. A method using both neural networks (NNs) and fuzzy-c-means (FCM) is presented for forecasting LMPs. The recurrent neural network (RNN) was addressed and the traditional NN-based on a backpropagation algorithm was also investigated for comparison. The FCM helped classify the load levels into three clusters. Individual RNNs according to three load clusters were developed for forecasting LMPs. These RNNs were trained/ validated and tested with historical data from the PJM (Pennsylvania, New Jersey, and Maryland) power system. It was found that the proposed neural networks were capable of forecasting LMP values efficiently.
01 Nov 2002
TL;DR: This paper presents eMediator, an electronic commerce server prototype that demonstrates ways in which algorithmic support and game–theoretic incentive engineering can jointly improve the efficiency of e–commerce.
Abstract: This paper presents eMediator, an electronic commerce server prototype that demonstrates ways in which algorithmic support and game–theoretic incentive engineering can jointly improve the efficiency of e–commerce. eAuctionHouse, the configurable auction server, includes a variety of generalized combinatorial auctions and exchanges, pricing schemes, bidding languages, mobile agents, and user support for choosing an auction type. We introduce two new logical bidding languages for combinatorial markets: the XOR bidding language and the OR–of–XORs bidding language. Unlike the traditional OR bidding language, these are fully expressive. They therefore enable the use of the Clarke–Groves pricing mechanism for motivating the bidders to bid truthfully. eAuctionHouse also supports supply/demand curve bidding. eCommitter, the leveled commitment contract optimizer, determines the optimal contract price and decommitting penalties for a variety of leveled commitment contracting mechanisms, taking into account that rational agents will decommit strategically in Nash equilibrium. It also determines the optimal decommitting strategies for any given leveled commitment contract. eExchangeHouse, the safe exchange planner, enables unenforced anonymous exchanges by dividing the exchange into chunks and sequencing those chunks to be delivered safely in alternation between the buyer and the seller.
TL;DR: In this article, a unified framework for optimizing energy and reserve bidding strategies under a deregulated market is presented, where the hourly MCPs and reserve prices are modeled as discrete random variables, whose probability mass functions are predicted with a classification based neural network approach.
Abstract: In the deregulated power industry, a generation company (GenCo) sells energy and ancillary services primarily through bidding at a daily market. Developing effective strategies to optimize hourly bid curves for a hydrothermal power system to maximize profits becomes one of the most important tasks of a GenCo. This paper presents a unified framework for optimizing energy and reserve bidding strategies under a deregulated market. In view of high volatilities of market clearing prices (MCP), the hourly MCPs and reserve prices are modeled as discrete random variables, whose probability mass functions are predicted with a classification based neural network approach. The mean-variance method is applied to manage bidding risks, where a risk penalty term related to MCP and reserve price variances is added to the objective function. To avoid buying too much power from the market at high prices, a GenCo may also require covering at least a certain percentage of its own customer load. This self-scheduling requirement is modeled similar to the system demand in traditional unit commitment problems. The formulation is a stochastic mixed-integer optimization with a separable structure. An optimization based algorithm combining Lagrangian relaxation and stochastic dynamic programming is presented to optimize bids for both energy and reserve markets. Numerical testing based on an 11-unit system in New England market shows that the method can significantly reduce profit variances and thus reduce bidding risks. Near-optimal energy and reserve bid curves are obtained in 4-5 minutes on a 600 Hz Pentium III PC, efficient for daily use.
TL;DR: In this paper, an algorithm that allows a market participant to maximize its individual welfare in electricity spot markets is presented, and the use of the algorithm in determining market equilibrium points, called Nash equilibria, is demonstrated.
Abstract: An algorithm that allows a market participant to maximize its individual welfare in electricity spot markets is presented. The use of the algorithm in determining market equilibrium points, called Nash equilibria, is demonstrated. The start of the algorithm is a spot market model that uses the optimal power flow (OPF), with a full representation of the transmission system and inclusion of consumer bidding. The algorithm utilizes price and dispatch sensitivities, available from the Hessian matrix and gradient of the OPF, to help determine an optimal change in an individual's bid. The algorithm is shown to be successful in determining local welfare maxima, and the prospects for scaling the algorithm up to realistically sized systems are very good. Nash equilibria are investigated assuming all participants attempt to maximize their individual welfare. This is done by iteratively solving the individual welfare maximization algorithm until all individuals stop modifying their bids.
TL;DR: In this paper, the design of seller-optimal auctions when winning bidders can attempt to resell the good is investigated, and a sufficient and necessary condition for sincere bidding given the possibility of resale is found.
Abstract: This paper investigates the design of seller-optimal auctions when winning bidders can attempt to resell the good. In that case, the optimal allocation characterized by Myerson (1981) cannot be achieved without resale. I find a sufficient and necessary condition for sincere bidding given the possibility of resale. In two-bidder cases, I prove that the Myerson allocation can be achieved under standard conditions supplemented with two assumptions. With three or more bidders, achieving the Myerson allocation is more difficult. I prove that it can be implemented in special cases. In those cases, the Myerson allocation is generated through a sequence of resale auctions, each optimally chosen by a reseller.
28 Jul 2002
TL;DR: The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies, suggesting that trading in online markets is a viable domain for highly autonomous agents.
Abstract: The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies. Based on a challenging market scenario in the domain of travel shopping, the competition presents agents with difficult issues in bidding strategy, market prediction, and resource allocation. Entrants in 2001 demonstrated substantial progress over the prior year, with the overall level of competence exhibited suggesting that trading in online markets is a viable domain for highly autonomous agents.
TL;DR: In this article, the authors examined bidding strategies in a bilateral market in which generating companies submit bids to loads and derived necessary and sufficient conditions of Nash equilibrium bidding strategy based on a generic generating cost matrix and the loads' willingness to pay vector.
Abstract: This paper examines bidding strategies in a bilateral market in which generating companies submit bids to loads. A load accepts electricity delivery from the generator with the lowest bid at its bid price as long as this price is not higher than the load's willingness to pay. Necessary and sufficient conditions of Nash equilibrium (NE) bidding strategy are derived based on a generic generating cost matrix and the loads' willingness to pay vector. The study shows that in any NE, efficient allocation is achieved. Furthermore, all Nash equilibria are revenue equivalent for the generators. Based on the necessary and sufficient conditions, this problem is formulated as an optimal assignment problem. Network optimization techniques are applied to calculate NE bid prices for the generators.
15 Jul 2002
TL;DR: A general framework in which real-time Dynamic Programming (DP) can be used to formulate agent bidding strategies in a broad class of auctions characterized by sequential bidding and continuous clearing, and suggests that this algorithm may offer the best performance of any published CDA bidding strategy.
Abstract: We develop a general framework in which real-time Dynamic Programming (DP) can be used to formulate agent bidding strategies in a broad class of auctions characterized by sequential bidding and continuous clearing. In this framework, states are represented primarily by an agent's holdings, and transition probabilities are estimated from the market event history, along the lines of the "belief function" approach of Gjerstad and Dickhaut [7]. We use the belief function, combined with a forecast of how it changes over time, as an approximate state-transition model in the DP formulation. The DP is then solved from scratch each time the agent has an opportunity to bid. The resulting algorithm optimizes cumulative long-term discounted profitability, whereas most previous strategies such as Gjerstad-Dickhaut (GD) merely optimize immediate profits.We test our algorithm in a simplified model of a Continuous Double Auction (CDA). Our results show that the DP-based approach reproduces the behavior of GD for small discount parameter g, and is clearly superior for large values of g close to 1. We suggest that this algorithm may offer the best performance of any published CDA bidding strategy. The framework our algorithm provides is extensible and can accommodate many market and research aspects.
12 Feb 2002
TL;DR: In this paper, a M + 1-st price auction scheme using homomorphic encryption and mix-and-match technique was proposed, which offers secrecy of bidding price and public verifiability.
Abstract: This paper provides a M + 1-st price auction scheme using homomorphic encryption and the mix and match technique; it offers secrecy of bidding price and public verifiability. Our scheme has low round communication complexity: 1 round from each bidder to auctioneer in bidding and log p rounds from auctioneer to trusted authority in opening when prices are selected from p prefixed choices.
TL;DR: In this article, the authors consider a setting where several privately informed agents bid for a price and all bidders bear a cost of bidding that is an increasing function of their bids, and moreover, bids may be capped.
Abstract: We study contests where several privately informed agents bid for a price. All bidders bear a cost of bidding that is an increasing function of their bids, and, moreover, bids may be capped. We show that, regardless of the number of bidders, if agents have linear or concave cost functions then setting a bid cap is not profitable for a designer who wishes to maximize the average bid. On the other hand, if agents have convex cost functions (i.e. an increasing marginal cost) then affectively capping the bids is profitable for a designer facing a sufficiently large number of bidders.
TL;DR: In this article, the authors analyzed a dataset of 400 Swedish Treasury auctions and found that bidders vary their prices, bid dispersion, and the quantity demanded in response to increased uncertainty at the time of bidding.
Abstract: We analyze a unique data set on multiunit auctions, which contains the actual demand schedules of the bidders as well as the auction awards in over 400 Swedish Treasury auctions. First, we document that bidders vary their prices, bid dispersion, and the quantity demanded in response to increased uncertainty at the time of bidding. Second, we find that bid shading can be explained by a winner’s curse–driven model in which each bidder submits only one bid, despite the fact that the bidders in our data set use much richer bidding strategies. Third, we explore the extent to which the received theories of multiunit auctions are able to offer insights into the bidder behavior we observe. Our empirical evidence is consistent with some of the predictions of the models of auctions that emphasize private information, the winner’s curse and the champion’s plague. While the models of multiunit auctions serve as useful guideposts, our empirical findings also point to several new areas of research in multiunit auctions...
TL;DR: Managers should make auction sites simple and accessible, develop and encourage involvement with their sites, and recognize that individuals who have a high affinity for computers and readily accept technology comprise a lucrative target market.
Abstract: On-line auction sites like eBay are very popular but have not been thoroughly researched, particularly with respect to bidding behavior and the reasons why consumers use auction sites. Drawing upon three different disciplines, this paper shows that propensity to bid in on-line auctions is influenced by acceptance of technology, involvement with auctions, and affinity for computers. In light of this, managers should make auction sites simple and accessible, develop and encourage involvement with their sites, and recognize that individuals who have a high affinity for computers and readily accept technology comprise a lucrative target market.
TL;DR: In this article, the optimal purchase allocation problem for dual electric power markets and demand bid generation is discussed, where the price volatility is explicitly considered in purchase allocation problems and the sequential nature is modeled by conditional stochastic characteristics.
Abstract: The purchase allocation problem is one of the most important problems faced by an electric energy service provider under new market environments. The optimal purchase allocation problem for dual electric power markets and demand bid generation are discussed in this paper. The price volatility is explicitly considered in purchase allocation problems and the sequential nature is modeled by conditional stochastic characteristics. An analytical solution for the optimal allocation is derived with given demand and statistical characteristics of the market prices. The method for generating demand bids for purchaser based on market allocation and price forecasting is then developed. The numerical simulations for market allocation and bid generation are demonstrated based on the actual data of the U.S. California power market.
TL;DR: In this article, the authors discuss the manner in which late bids are caused both by sophisticated, strategic reasoning and by irrationality and inexperience, the interaction of late bidding with incremental bidding, and the relation between market design and artificial agent design.
Abstract: Many bidders in eBay use bidding strategies that involve late bids, incremental bids, or both Based on field evidence, we discuss the manner in which late bids are caused both by sophisticated, strategic reasoning and by irrationality and inexperience; the interaction of late bidding with incremental bidding; and the relation between market design and artificial agent design
TL;DR: In this article, the authors examine how extensively bidders signaled each other with retaliating bids and code bids in the DEF-block PCS spectrum auction and find that only a small fraction of the participating bidder commonly used retaliating or code bids.
Abstract: This paper describes the bid signaling that occurred in many of the FCC spectrum auctions. Bidders in these auctions bid on numerous spectrum licenses simultaneously, with bidding remaining open on all licenses until no bidder is willing to raise the bid on any license. Simultaneous open bidding allows bidders to send messages to their rivals, telling them on which licenses to bid and which to avoid. This “code bidding” occurs when one bidder tags the last few digits of its bid with the market number of a related license. We examine how extensively bidders signaled each other with retaliating bids and code bids in the DEF-block PCS spectrum auction. We find that only a small fraction of the bidders commonly used retaliating bids and code bids. These bidders won more than 40% of the spectrum for sale and paid significantly less for their overall winnings.
TL;DR: It is shown how the procedure eliminates envy by compensating envious players, establishes envy-freeness with minimal resources, and demonstrates its application to a wide class of fair-division problems.
Abstract: We develop a procedure for implementing an efficient and envy-free allocation of m objects among n individuals with the possibility of monetary side-payments, assuming that players have quasi–linear utility functions. The procedure eliminates envy by compensating envious players. It is fully descriptive and says explicitly which compensations should be made, and in what order. Moreover, it is simple enough to be carried out without computer support. We formally characterize the properties of the procedure, show how it establishes envy-freeness with minimal resources, and demonstrate its application to a wide class of fair-division problems.