1,721,385 research outputs found
Selling substitute goods to loss-averse consumers: limited availability, bargains, and rip-offs
Full text link© 2016, The RAND Corporation. This article derives the optimal pricing and product-availability strategies for a retailer selling two substitute goods to loss-averse consumers and shows that limited-availability sales manipulate consumers into an ex ante unfavorable purchase. The seller maximizes profits by raising the consumers' reference point through a tempting discount on a good available only in limited supply (the bargain), and cashing in with a high price on the other (the rip-off), which consumers buy if the bargain is not available to reduce their disappointment. The seller might prefer to offer a deal on the more valuable product, using it as a bait
Loss Aversion and Competition in Vickrey Auctions: Money Ain't No Good
Full text linkA key prediction of expectations-based reference-dependent preferences and loss aversion in second-price auctions with private values is that the number of bidders should affect bids in auctions for real objects but not in auctions with induced monetary values. In order to test this distinctive comparative statics prediction, we develop an experiment where subjects bid in multiple auctions for real objects as well as auctions with induced values, each time facing a different number of rivals. Our results are broadly consistent with expectations-based reference-dependent preferences and loss aversion. We find that in real-object auctions bids decline with the intensity of competition whereas in induced-value auctions, instead, bids do not vary with the intensity of competition. Our results suggest that bidders may behave differently in real-object auctions than in induced-value ones, casting some doubt on the extent to which findings from induced-value laboratory experiments can be transferred to the field
Bait and ditch: Consumer naïveté and salesforce incentives
Full text linkWe analyze a model of price competition between a transparent retailer and a deceptive one in a market where a fraction of consumers is naïve. The transparent retailer is an independent shop managed by its owner. The deceptive retailer belongs to a chain and is operated by a manager. The two retailers sell an identical base product, but the deceptive one also offers an add-on. Rational consumers never consider buying the add-on while naïve ones can be “talked” into buying it. By offering the manager a contract that pushes him to never sell the base good without the add-on, the chain can induce an equilibrium in which both retailers obtain more-than-competitive profits. The equilibrium features price dispersion and market segmentation, with the deceptive retailer targeting only naïve consumers whereas the transparent retailer serves only rational ones
Time series prediction using random weights fuzzy neural networks
No full textIn this paper, we introduce Random Weights Fuzzy Neural Networks as a suitable tool for solving prediction problems. The generalization capability of these randomized fuzzy neural networks is exploited in order to estimate accurately the sample be predicted from a multidimensional input. The latter is obtained by applying an embedding technique to the time series, which selects only the meaningful past samples to be used for prediction. We tested the proposed approach on real-world time series pertaining to the application context of power delivery. We proved the efficacy of the proposed approach by comparing its forecasting accuracy with respect to other prediction systems based on well-known data-driven regression models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multi-label classification with imbalanced classes by fuzzy deep neural networks
No full textMulti-label classification is an advantageous technique for managing uncertainty in classification problems where each data instance is associated with several labels simultaneously. Such situations are frequent in real-world scenarios, where decisions rely on imprecise or noisy data and adaptable classification methods are preferred. However, the problem of class imbalance represents a common characteristic of several multi-label datasets, in which the distribution of samples and their corresponding labels is non-uniform across the data space. In this paper, we propose a multi-label classification approach utilizing fuzzy logic in order to deal with the class imbalance problem. To eliminate the need for an expert to determine the logical rules of inference, deep neural networks are adopted, which have proven to be exceptionally effective for such problems. By combining both fuzzy inference systems and deep neural networks, the strengths and weaknesses of each approach can be mitigated. As a further development, a symbolic representation of time series is put in place to reduce data dimensionality and speed up the training procedure. This allows for more flexibility in model application, in particular with respect to time constraints arising from the causality of observed time series. Tests carried out on a multi-label classification dataset related to the current and voltage profiles of several household appliances show that the proposed model outperforms four baseline models for time series classification
Data-driven fault detection and diagnosis: research and applications for HVAC systems in buildings
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