1、外文翻译 原文 1 Matching Models for Preference-sensitive Group Purchasing Matching buyers and sellers is one of the most fundamental problems in economics and market design. An interesting variant of the matching problem arises when self-interested buyers come together in order to induce sellers to offer
2、quantity or volume discounts, as is common in buying consortia, and more recently in the consumer group couponing space (e.g., Groupon).We consider a general model of this problem in which a group or buying consortium is faced with volume discount offers from multiple vendors, but group members have
3、 distinct preferences for different vendor offerings. Unlike some recent formulations of matching games that involve quantity discounts, the combination of varying preferences and discounts can render the core of the matching game empty, in both the transferable and nontransferable utility sense. Th
4、us, instead of coalitional stability, we propose several forms of Nash stability under various epistemic and transfer/payment assumptions. We investigate the computation of buyer-welfare maximizing matchings and show the existence of transfers (subsidized prices) of a particularly desirable form tha
5、t support stable matchings. We also study a nontransferable utility model, showing that stable matchings exist； and we develop a variant of the problem in which buyers provide a simple preference ordering over “deals” rather than specific valuationsa model that is especially attractive in the consum
6、er spacewhich also admits stable matchings. Computational experiments demonstrate the efficacy and value of our approach. Categories and Subject Descriptors: I.2.11 Distributed Artificial Intelligence: Multiagent Systems； J.4 Computer Applications: Social and Behavioral SciencesEconomics General Ter
7、ms: Algorithms, Economics, Theory Additional Key Words and Phrases: stable matching, preferences, demand aggregation, group purchasing,volume discounts, daily deals, cooperative games. 1. INTRODUCTION Matching buyers and sellers is one of the most fundamental problems in economics anddeal” providers
8、 like Groupon and Living Social (and services that aggregate such deals) has propelled group discounts into the public consciousness.Group buying and demand aggregation has been studied from several perspectives, and many models have been proposed for their analysis. However, we consider a vital ing
9、redient of group buying that has received insufficient attention in the literature, namely, the fact that buyers often have distinct preferences for the offerings of different vendors. Most matching models with volume discounts assume that vendor offerings are indistinguishable to buyers, which sign
10、ificantly limits their applicability. For instance,suppose two buyers X and Y are (jointly) comparing the offers of two vendors or some item: A offers a price of 10 for one unit, but a discounted price of 8 if both buy from him； and B offers a single price of 9 per unit. If A and B are indistinguish
11、able, X and Y should cooperate and buy from A. But suppose X prefers B (with valuation 11.5) to A (valuation 10). In this case, X would prefer to stick with B unless Y offers some payment to switch vendors (Y would gladly share some of her generated surplus with X for this purpose). Without the abil
12、ity to express preferences over vendors, “group buying” would not emerge even in this trivial example. market design. A wide variety of models and mechanisms have been developed that reflect different assumptions about the demands, valuations/preferences, and knowledge of the market participants and
13、 their ability to cooperate. Each leads to its own computational challenges when developing algorithms for computing stable (core) matchings,Nash equilibria, clearing prices or other solution concepts. In this paper, we address the problem of cooperative group buying, in which a group of buyers coor
14、dinate their purchases to realize volume discounts, mitigate demand risk, or reduce inventory costs. Group buying has long been used for corporate procurement,via industry-specific buying consortia or broadly based group purchasingorganizations (GPOs) Chen and Roma 2010. The advent of the Internet,
15、in particular,has helped businesses with no prior affiliation more easily aggregate their demandAnand and Aron 2003. Consumer-oriented group purchasing has also been greatly facilitatedby the web； and the recent popularity of volume-based couponing and “dailydeal” providers like Groupon and Living S
16、ocial (and services that aggregate such deals)has propelled group discounts into the public consciousness.Group buying and demand aggregation has been studied from several perspectives,and many models have been proposed for their analysis. However, we consider a vital ingredient of group buying that
17、 has received insufficient attention in the literature,namely, the fact that buyers often have distinct preferences for the offerings of different vendors. Most matching models with volume discounts assume that vendor offerings are indistinguishable to buyers, which significantly limits their applic
18、ability. For instance,suppose two buyers X and Y are (jointly) comparing the offers of two vendors for some item: A offers a price of 10 for one unit, but a discounted price of 8 if both buy from him； and B offers a single price of 9 per unit. If A and B are indistinguishable, X and Y should coopera
19、te and buy from A. But suppose X prefers B (with valuation 11.5) to A (valuation 10). In this case, X would prefer to stick with B unless Y offers some payment to switch vendors (Y would gladly share some of her generated surplus with X for this purpose). Without the ability to express preferences o
20、ver vendors, “group buying” would not emerge even in this trivial example.While matching becomes much more subtle in such models, assigning buyers to vendors in a way that triggers volume discounts, while remaining sensitive to buyer preferences, offers flexibility and efficiency gains that greatly
21、enhance the appeal of group buying. Consider a group of businesses or buyers working with a GPO to procure supplies within a specific product category (e.g., manufacturing materials, packaging, transportation, payroll services, etc.). The GPO is able to negotiate volume discounts from a handful of s
22、uppliers or vendors, possibly with multiple discount thresholds. Buyers generally have different valuations for the offerings of different vendors (e.g., buyers may have slightly different manufacturing specifications； or may prefer the contract, payment or delivery terms of certain vendors). A suit
23、able matching of buyers to vendors must trade off these preferences with the triggered discount prices.The same issues arise in consumer domains. Suppose a daily deal aggregator creates a “marketplace” for some product category, say, spas. Multiple spas offer deals that only trigger if a certain qua
24、ntity is sold. Buyers are faced with a dilemma: they may want only one item, but are uncertain about which deal will trigger. If they only offer to buy (i.e., conditionally purchase) their most preferred spa, they may not get any deal if their preferred deal does not trigger. But if they offer on mu
25、ltiple spas to hedge that risk, they run the opposite risk of obtaining more items than they want. A matching model that allows consumers to specify preferences for items relative to their discounted prices provides flexibility that benefits both consumers and retailers.Our model. In broad strokes,
26、our model assumes a set of vendors offering products (e.g., within a specific product category). Interacting with some GPO or informal buying group, vendors offer (possibly multiple) volume discounts that trigger if the group collectively buys in a certain quantity. We assume these are proposed or negotiated in
