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Choice Probabilities (choice + probability)
Selected AbstractsPARTIAL IDENTIFICATION OF COUNTERFACTUAL CHOICE PROBABILITIES,INTERNATIONAL ECONOMIC REVIEW, Issue 4 2007Charles F. Manski This article shows how to predict counterfactual discrete choice behavior when the presumed behavioral model partially identifies choice probabilities. The simple, general approach uses observable choice probabilities to partially infer the distribution of types in the population and then applies the results to predict behavior in unrealized choice settings. Two illustrative applications are given. One assumes only that persons have strict preferences. The other assumes strict preferences and utility functions that are linear in attribute bundles, with no restrictions on the shape of the distribution of preference parameters. [source] Discrete choice and stochastic utility maximizationTHE ECONOMETRICS JOURNAL, Issue 1 2003Ruud H. Koning Discrete choice models are usually derived from the assumption of random utility maximization. We consider the reverse problem, whether choice probabilities are consistent with maximization of random utilities. This leads to tests that consider the variation of these choice probabilities with the average utilities of the alternatives. By restricting the range of the average utilities we obtain a sequence of tests with fewer maintained assumptions. In an empirical application, even the test with the fewest maintained assumptions rejects the hypothesis of random utility maximization. [source] Carrier-sense-assisted adaptive learning MAC protocols for distributed wireless LANsINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 7 2005P. Nicopolitidis Abstract A Carrier-sense-assisted adaptive learning MAC protocol for wireless LANs, capable of operating efficiently in bursty traffic wireless networks with unreliable channel feedback, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. At each station, the learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol utilizes carrier sensing in order to reduce the collisions that are caused by different decisions at the various mobile stations due to the unreliable channel feedback. Simulation results show satisfactory performance of the proposed protocol compared to similar MAC protocols. Copyright © 2005 John Wiley & Sons, Ltd. [source] Increasing sales by introducing non-salable itemsMANAGERIAL AND DECISION ECONOMICS, Issue 8 2006Kobi Kriesler Rationality implies that adding ,irrelevant' and, in particular, inferior alternatives to the opportunity set cannot increase the choice probability of some other alternative. In this study, we propose a novel approach that can rationalize an intended addition of such alternatives because it strictly increases the choice probability of some existing alternative. The driving force behind the existence and extent of such an increase is the random nature of individual preferences, that implies intransitivity, and the random nature of the applied choice procedures. We study the case of a firm interested in increasing the sales of some of its existing products by introducing a new and inferior (non-salable) product. Our main results focus on the feasibility and potential advantage of a successful such strategy. We first establish necessary and sufficient conditions for an increase in the sale probability and then derive the maximal possible absolute and relative increase in this probability, when the firm has extremely limited information on the characteristics of the consumers. We then derive analogous results, assuming that the existing line of products consists of just two items and that the firm has accurate information on the consumers' stochastic preferences over the existing products. These later results are illustrated using some experimental evidence. The applicability of the approach is finally briefly discussed in the context of branding policy. Copyright © 2006 John Wiley & Sons, Ltd. [source] | ||||||||||