THE EFFECT OF RISK AND AUTONOMY ON INDEPENDENT HOG PRODUCERS' CONTRACTING DECISIONS

The introduction of vertical coordination in the hog industry has provided producers with new business arrangements for raising hogs. While some researchers have elicited utility functions for hog producers on the basis of income risk, none have addressed autonomy, a factor which appears to be important in business arrangement selection for independent family hog operations. In this study, a method is developed for eliciting a multi-attribute function with attributes of income and autonomy. Utility functions are elicited for a group of Minnesota farrow-to-finish hog producers. For these producers, autonomy dominated risk as the most important attribute in business arrangement selection.Livestock Production/Industries,

Review of bayesian analysis in additive hazards model

In Survival Analysis, the focus of interest is a time $T^*$ until the occurrence of some event. A set of explanatory variables (denoted by a vector $Z$) is considered to analyze if there is a relationship between any of them and $T^*$. Accordingly, the ``hazard function´´ is defined: [ lambda(t,z) := lim_{Delta downarrow 0} rac{P[Tleq t+ Delta ert T >t,Z=z]}{Delta} .] Several models are defined based on this, as is the case of the additive model (among others). Bayesian techniques allow to incorporate previous knowledge or presumption information about the parameters into the model. This area grows extensively since the computationally techniques increase, giving rise to powerful Markov Chain Monte Carlo (MCMC) methods, which allow to generate random samples from the desired distributions. The purpose of this article is to offer a summary of the research developed in Bayesian techniques to approach the additive hazard models.Fil: Alvarez, Enrique Ernesto. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Fisicomatemática; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Riddick, Maximiliano Luis. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector $w^* \in \mathbb{R}^d$ that represents the "qualities" of the $d$ items being compared; this class of models includes the two most widely used parametric models--the Bradley-Terry-Luce (BTL) and the Thurstone models. Working within a standard minimax framework, we provide tight upper and lower bounds on the optimal error in estimating the quality score vector $w^*$ under this class of models. The bounds depend on the topology of the comparison graph induced by the subset of pairs being compared via its Laplacian spectrum. Thus, in settings where the subset of pairs may be chosen, our results provide principled guidelines for making this choice. Finally, we compare these error rates to those under cardinal measurement models and show that the error rates in the ordinal and cardinal settings have identical scalings apart from constant pre-factors.Comment: 39 pages, 5 figures. Significant extension of arXiv:1406.661

Value-Focused Thinking in the Presence of Weight Ambiguity: A Solution Technique Using Monte Carlo Simulation

When a Decision Maker is asked to provide his or her preferences, the response represents a snapshot in time. While their preference structure elicited at any given moment may represent their revealed preferences at that point in time, it may change over time. These changing preferences over time represent ambiguity in the decision maker\u27s preferences. Other sources of ambiguity may exist. One weakness of many decision analysis techniques today is the inability to incorporate ambiguity into the basic decision model. The existence of the problem has been known and commented on for many years. This research addresses that problem. It begins with the basic approach and methodology developed by Ralph Keeney, Value-Focused Thinking (VFT). This methodology is then expanded to allow decision makers to specify not just constant weights to demonstrate their preferences, but an entire distribution. These distributions are then incorporated with the value of the attributes and the whole is simulated using Monte Carlo Simulation provided by Crystal Ball. The result of incorporating these weight distributions into the model, is an empirical distribution for the value of an alternative. The alternative distributions can be compared in a number of ways to provide insight to the decision maker

Estimating Optimal Recommendation Set Sizes for Individual Consumers

Online consumers must burrow through vast piles of product information to find the best match to their preferences. This has boosted the popularity of recommendation agents promising to decrease consumers\u27 search costs. Most recent work has focused on refining methods to find the best products for a consumer. The question of how many of these products the consumer actually wants to see, however, is largely unanswered. This paper develops a novel procedure based on signal detection theory to estimate the number of recommendable products. We compare it to a utility exchange approach that has not been empirically examined so far. The signal detection approach showed very good predictive validity in two laboratory experiments, clearly outperforming the utility exchange approach. Our theoretical findings, supported by the experimental evidence, indicate conceptual inconsistencies in the utility exchange approach. Our research offers significant implications for both theory and practice of modeling consumer choice behavior