The market economy deals with many interacting agents such as buyers and sellers who are autonomous intelligent agents pursuing their own interests. One such multi-agent system (MAS) that plays an important role in auctions is the combinatorial auctioning system (CAS). We use this framework to define our concept of fairness in terms of what we call as "basic fairness" and "extended fairness". The assumptions of quasilinear preferences and dominant strategies are taken into consideration while explaining fairness. We give an algorithm to ensure fairness in a CAS using a Generalized Vickrey Auction (GVA). We use an algorithm of Sandholm to achieve optimality. Basic and extended fairness are then analyzed according to the dominant strategy solution concept.
The auction mechanism proposed by Biggart @cite_14 provides an economic sociology perspective. There, fairness can mean different things for bidders and auctioneer. The auctioneer may consider a process fair which in fact only gives him the maximum revenue, whereas the bidders may consider a process fair which only gives the auctioneer the least return on all items. The most important consideration overall is to sustain the community's faith in the fairness of the process. This does not mean that buyers and sellers cannot press their advantage, but they are allowed to do so only insofar as the community as a whole considers their actions appropriate and acceptable.
@cite_14
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We study selling one indivisible object to multiple potential buyers. Depending on the objective of the seller, different selling mechanisms are desirable. The Vickrey auction with a truthful reserve price is optimal when the objective is efficiency (i.e., allocating the object to the party who values it the most). The Myerson auction is optimal when the objective is the seller's expected utility. These two objectives are generally in conflict, and cannot be maximized with one mechanism. In many real-world settings---such as privatization and competing electronic marketplaces---it is not clear that the objective should be either efficiency or seller's expected utility. Typically, one of these objectives should weigh more than the other, but both are important. We account for importance of both objectives by designing a new deterministic auction mechanism that maximizes expected social welfare subject to a minimum constraint on the seller's expected utility. This way the seller can expect to do well enough for himself, while maintaining the attractive properties of the mechanism. We study the dynamic mechanism design problem of a seller who repeatedly sells independent items to a buyer with private values. In this setting, the seller could potentially extract the entire buyer surplus by running efficient auctions and charging an upfront participation fee at the beginning of the horizon. In some markets, such as internet advertising, participation fees are not practical since buyers expect to inspect items before purchasing them. This motivates us to study the design of dynamic mechanisms under successively more stringent requirements that capture the implicit business constraints of these markets. We first consider a periodic individual rationality constraint, which limits the mechanism to charge at most the buyer's value in each period. While this prevents large upfront participation fees, the seller can still design mechanisms that spread a participation fee across the first few auctions. These mechanisms have the unappealing feature that they provide close-to-zero buyer utility in earlier auctions in exchange for higher utility in later auctions. To address this problem, we introduce a martingale utility constraint, which imposes the requirement that from the perspective of the buyer, the next item's expected utility is equal to the present one's. Our main result is providing a dynamic auction satisfying martingale utility and periodic individual rationality whose loss in profit with respect to first-best (full extraction of buyer surplus) is optimal up to polylogarithmic factors. The proposed mechanism is a dynamic two-tier auction with a hard floor and a soft floor that allocates the item whenever the buyer's bid is above the hard floor and charges the minimum of the bid and the soft floor. Motivated by sponsored search auctions with hard budget constraints given by the advertisers, we study multi-unit auctions of a single item. An important example is a sponsored result slot for a keyword, with many units representing its inventory in a month, say. In this single-item multi-unit auction, each bidder has a private value for each unit, and a private budget which is the total amount of money she can spend in the auction. A recent impossibility result [, FOCS’08] precludes the existence of a truthful mechanism with Paretooptimal allocations in this important setting. We propose Sort-Cut, a mechanism which does the next best thing from the auctioneer’s point of view, that we term semi-truthful. While we are unable to give a complete characterization of equilibria for our mechanism, we prove that some equilibrium of the proposed mechanism optimizes the revenue over all Pareto-optimal mechanisms, and that this equilibrium is the unique one resulting from a natural rational bidding strategy (where every losing bidder bids at least her true value). Perhaps even more significantly, we show that the revenue of every equilibrium of our mechanism differs by at most the budget of one bidder from the optimum revenue (under some mild assumptions). We study the problem of maximizing revenue for auctions with multiple units of a good where bidders have hard budget constraints, first considered in [2]. The revenue obtained by an auction is compared with the optimal omniscient auction had the auctioneer known the private information of all the bidders, as in competitive analysis [7]. We show that the revenue of the optimal omniscient auction that sells items at many different prices is within a factor of 2 of the optimal omniscient auction that sells all the items at a single price, implying that our results will carry over to multiple price auctions. We give the first auction for this problem, to the best of our knowledge, that is known to obtain a constant fraction of the optimal revenue when the bidder dominance (the ratio between the maximum contribution of a single bidder in the optimal solution and the revenue of that optimal solution) is large (as high as 1 2). Our auction is also shown to remain truthful if canceled upon not meeting certain criteria. On the negative side, we show that no auction can achieve a guarantee of 1 2-e the revenue of the optimal omniscient multi-price auction. Finally, if the bidder dominance is known in advance and is less than 1 5.828, we give an auction mechanism that raises a large constant fraction of the optimal revenue when the bidder dominance is large and is asymptotically close to the optimal omniscient auction as the bidder dominance decreases. We discuss the relevance of these results for related applications.