Limitation of Sales Warranties as an Alternative to Intellectual Property Rights: An Empirical Analysis of IPhone Warranties’ Deterrent Impact on Consumers

Apple\u27s success with the Apple iPhone has brought with it certain problems. Its success has engendered a community that has attempted to circumvent Apple\u27s exclusive service agreement with AT&T. Unfortunately for Apple (and similarly situated manufacturers), intellectual property law allows consumers to alter their products so as to circumvent relationships that manufacturers may have with others. The patent and copyright law first sale doctrine allows consumers to manipulate a product after it is purchased. As a result, manufacturers are increasingly turning to alternatives to intellectual property to secure control over the device after the sale. One such alternative is the exclusion of warranty under Article 2 of the Uniform Commercial Code. This iBrief considers whether limitation of warranties have the deterrence effect manufacturers desire. Said differently, it considers whether manufacturers can use warranty limitations to prevent consumers from using their products in an unauthorized manner. The iBrief presents a behavioral model based on the Triandis model of planned behavior and enhances the model by accounting for likely and unlikely benefits and detriments. The model suggests that participants weigh the probability and magnitude of the detriment against the probability and magnitude of the beneficial impact when making the decision to engage in technological piracy. This model, considered with other empirical evidence, suggests that Apple\u27s warranty could be a stronger deterrent for consumers than civil liability. The iBrief concludes that manufacturers can better protect their post-sale expectation of profits by raising consumer awareness of their warranty\u27s quality and by raising awareness of the consequences for using the product in a way that is outside the terms of the consumers\u27 authorized use

On Optimal Input Design for Feed-forward Control

This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are analytically solved and fundamental properties of the optimal input signal solution are presented. The optimal input signal contains feed-forward control and depends of the noise model and transfer function of the system in a specific way. Next, we show how to apply the partial correlation approach to closed loop optimal experiment design to the general feed-forward problem. A framework for optimal input signal design for feed-forward control is presented and numerically evaluated on a temperature control problem

Identification of Stochastic Wiener Systems using Indirect Inference

We study identification of stochastic Wiener dynamic systems using so-called indirect inference. The main idea is to first fit an auxiliary model to the observed data and then in a second step, often by simulation, fit a more structured model to the estimated auxiliary model. This two-step procedure can be used when the direct maximum-likelihood estimate is difficult or intractable to compute. One such example is the identification of stochastic Wiener systems, i.e.,~linear dynamic systems with process noise where the output is measured using a non-linear sensor with additive measurement noise. It is in principle possible to evaluate the log-likelihood cost function using numerical integration, but the corresponding optimization problem can be quite intricate. This motivates studying consistent, but sub-optimal, identification methods for stochastic Wiener systems. We will consider indirect inference using the best linear approximation as an auxiliary model. We show that the key to obtain a reliable estimate is to use uncertainty weighting when fitting the stochastic Wiener model to the auxiliary model estimate. The main technical contribution of this paper is the corresponding asymptotic variance analysis. A numerical evaluation is presented based on a first-order finite impulse response system with a cubic non-linearity, for which certain illustrative analytic properties are derived.Comment: The 17th IFAC Symposium on System Identification, SYSID 2015, Beijing, China, October 19-21, 201

Minority Challenge of Majority Actions in a Close Corporation in Italy and the United States

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to estimate the change times and the mean values within the segments. The second case is when the mean value is constant, but the variance can change. The assumption is that the variance is piecewise constant in time, and we want to estimate change times and the variance values within the segments. To find solutions to these problems, we will study an l_1 regularized maximum likelihood method, related to the fused lasso method and l_1 trend filtering, where the parameters to be estimated are free to vary at each sample. To penalize variations in the estimated parameters, the $l_1$-norm of the time difference of the parameters is used as a regularization term. This idea is closely related to total variation denoising. The main contribution is that a convex formulation of this variance estimation problem, where the parametrization is based on the inverse of the variance, can be formulated as a certain $l_1$ mean estimation problem. This implies that results and methods for mean estimation can be applied to the challenging problem of variance segmentation/estimationQC 20140908</p