30649473
Joint Bidding in the Name-Your-Own-Price Channel: A Strategic Analysis
In this paper, we study the name-your-own-price (NYOP) channel. We examine theoretically and empirically whether asking consumers to place a joint bid for multiple items, rather than bid one item at a time as practiced today, can increase NYOP retailers' profits. Relatedly, we also examine whether allowing consumers to self-select whether to place a joint bid or itemwise bids increases retailers' profits and consumers' surplus. We construct a dynamic model that incorporates both demand uncertainty and supply uncertainty to address these issues. Our theoretical analysis identifies the conditions under which joint bidding can increase both NYOP retailers' profits and consumers' surplus. We find that some consumers might bid more for the very same items when they place joint bids. The increase in bid amount is related to the fact that joint bidding reduces the chance of mismatch between NYOP retailers' costs and consumer bids. We conducted a laboratory study to assess the descriptive validity of some of the model predictions, because there are no field data on joint bidding in the NYOP channel. The results of the study are directionally consistent with our theory.
15127807
Pricing of Information Products on Online Servers: Issues, Models, and Analysis
Online information servers that provide access to diverse databases where users can search for, browse through, and download the information they need have been rapidly increasing in number in the past few years. Online vendors have traditionally charged users for information on the based on the length of the time they were connected to the databases. With hardware and software advances, many online servers have recently started changing their pricing strategies to search-based and/or subscription-fee pricing. This paper examines the various issues involved in pricing these information products, and presents an economic approach to analyze conditions under which the various pricing schemes may prove optimal for the online servers. Our results show that the variation in consumer expertise and valuation of information affects the choice of a pricing strategy by the server. We present general conditions under which subscription-fee pricing is optimal even when consumer demand is inelastic. We also find that, given the cost structures characterizing the market, undifferentiated online servers can compete and coexist in the market each making positive profits. We show that in a competitive setting an increase in costs of online servers can sometimes benefit them by enabling them to differentiate themselves. Our results offer insights into the trends in pricing strategies and may provide an explanation as to why many servers may persist with connect-time strategies.
29558431
Learning Languages in a Union
In inductive inference, a machine is given words in a language and the machine is said to identify the language if it correctly names the language. In this paper we study classes of languages where the unions of up to a fixed number (n say) of languages from the class are identifiable. We distinguish between two different scenarios: in one scenario, the learner need only to name the language which results from the union; in the other, the learner must individually name the languages which make up the union (we say that the unioned language is discerningly identified). We define three kinds of identification criteria based on this and by the use of some naturally occurring classes of languages, demonstrate that the inferring power of each of these identification criterion decreases as we increase the number of languages allowed in the union, thus resulting in an infinite hierarchy for each identification criterion. A comparison between the different identification criteria also yielded similar hierarchies. We show that for each n, there exists a class of disjoint languages where all unions of up to n languages from this class can be discerningly identified, but there is no learner which identifies every union of n+1 languages from this class. We give sufficient conditions for classes of languages where the unions can be discerningly identified. We also present language classes which are complete with respect to weak reduction (in terms of intrinsic complexity) for our identification criteria.
42624577
Iterative learning from positive data and negative counterexamples
A model for learning in the limit is defined where a (so-called iterative) learner gets all positive examples from the target language, tests every new conjecture with a teacher (oracle) if it is a subset of the target language (and if it is not, then it receives a negative counterexample), and uses only limited long-term memory (incorporated in conjectures). Three variants of this model are compared: when a learner receives least negative counterexamples, the ones whose size is bounded by the maximum size of input seen so far, and arbitrary ones. A surprising result is that sometimes absence of bounded counterexamples can help an iterative learner whereas arbitrary counterexamples are useless. We also compare our learnability model with other relevant models of learnability in the limit, study how our model works for indexed classes of recursive languages, and show that learners in our model can work in non-U-shaped way—never abandoning the first right conjecture. © 2007 Elsevier Inc. All rights reserved.
46696695
Variations on U-Shaped Learning
274701
Variations on U-shaped learning
The paper deals with the following problem: is returning to wrong conjectures necessary to achieve full power of algorithmic learning? Returning to wrong conjectures complements the paradigm of U-shaped learning when a learner returns to old correct conjectures. We explore our problem for classical models of learning in the limit from positive data: explanatory learning (when a learner stabilizes in the limit on a correct grammar) and behaviourally correct learning (when a learner stabilizes in the limit on a sequence of correct grammars representing the target concept). In both cases we show that returning to wrong conjectures is necessary to achieve full learning power. In contrast, one can modify learners (without losing learning power) such that they never show inverted U-shaped learning behaviour, that is, never return to old wrong conjecture with a correct conjecture in-between. Furthermore, one can also modify a learner (without losing learning power) such that it does not return to old "overinclusive" conjectures containing non-elements of the target language. We also consider our problem in the context of vacillatory learning (when a learner stabilizes on a finite number of correct grammars) and show that each of the following four constraints is restrictive (that is, reduces learning power): the learner does not return to old wrong conjectures; the learner is not inverted U-shaped; the learner does not return to old overinclusive conjectures; the learner does not return to old overgeneralizing conjectures. We also show that learners that are consistent with the input seen so far can be made decisive: on any text, they do not return to any old conjectures--wrong or right.
45395448
Learning by Switching Type of Information
The present work is dedicated to the study of modes of datapresentation between text and informant within the framework of inductive inference. The model is such that the learner requests sequences of positive and negativ e data and the relations between the various formalizations in dependence on the number of switches between positive and negative data is investigated. In particular it is shown that there is a proper hierarchy of the notions of learning from standard text, in the basic switching model, in the newtext switching model and in the restart switching model. The last one of these turns out to be equivalent to the standard notion of learning from informant.