Observing the Spontaneous Breakdown of Unitarity

During the past decade, the experimental development of being able to create ever larger and heavier quantum superpositions has brought the discussion of the connection between microscopic quantum mechanics and macroscopic classical physics back to the forefront of physical research. Under equilibrium conditions this connection is in fact well understood in terms of the mechanism of spontaneous symmetry breaking, while the emergence of classical dynamics can be described within an ensemble averaged description in terms of decoherence. The remaining realm of individual-state quantum dynamics in the thermodynamic limit was addressed in a recent paper proposing that the unitarity of quantum mechanical time evolution in macroscopic objects may be susceptible to a spontaneous breakdown. Here we will discuss the implications of this theory of spontaneous unitarity breaking for the modern experiments involving truly macroscopic Schrodinger cat states.Comment: 4 pages, no figure

Hedonic price models and indices based on boosting applied to the Dutch housing market

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables.We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance with up to 40% compared with Linear Regression. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price.Finally, we construct hedonic price indices and compare these to the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam.data mining;machine learning;gradient boosting;housing;hedonic price models;hedonic price index

Choosing Attribute Weights for Item Dissimilarity using Clikstream Data with an Application to a Product Catalog Map

In content- and knowledge-based recommender systems often a measure of (dis)similarity between items is used. Frequently, this measure is based on the attributes of the items. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present an approach to determine attribute weights in a dissimilarity measure using clickstream data of an e-commerce website. Counted is how many times products are sold and based on this a Poisson regression model is estimated. Estimates of this model are then used to determine the attribute weights in the dissimilarity measure. We show an application of this approach on a product catalog of MP3 players provided by Compare Group, owner of the Dutch price comparison site http://www.vergelijk.nl, and show how the dissimilarity measure can be used to improve 2D product catalog visualizations.dissimilarity measure;attribute weights;clickstream data;comparison

Boosting the accuracy of hedonic pricing models

Hedonic pricing models attempt to model a relationship between object attributes andthe object's price. Traditional hedonic pricing models are often parametric models that sufferfrom misspecification. In this paper we create these models by means of boosted CARTmodels. The method is explained in detail and applied to various datasets. Empirically,we find substantial reduction of errors on out-of-sample data for two out of three datasetscompared with a stepwise linear regression model. We interpret the boosted models by partialdependence plots and relative importance plots. This reveals some interesting nonlinearitiesand differences in attribute importance across the model types.pricing;marketing;data mining;conjoint analysis;ensemble learning;gradient boosting;hedonic pricing

Modeling brand choice using boosted and stacked neural networks

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand choice problem, as far as we know. Ensemble methods generate a number of models for the same problem using any base method and combine the outcomes of these different models. It is well known that in many cases the predictive performance of ensemble methods significantly exceeds the predictive performance of the their base methods. In this report we use boosting and stacking of neural networks and apply this to a scanner dataset that is a benchmark dataset in the marketing literature. Using these methods, we find a significant improvement in predictive performance on this dataset.

Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.algorithms;probabilistic latent semantic analysis;hybrid recommender systems;recommender systems

Competitive Implications of Interfirm Mobility

This paper examines the competitive consequences of interfirm mobility. Because the loss of key members (defined as top decision makers) to competing firms may amount to a replication of a firm’s higher-order routines, we investigate the conditions under which interfirm mobility triggers transfer of routines across organizational boundaries. We examine membership lists pertinent to the Dutch accounting industry to study key member exits and firm dissolutions over the period 1880–1986. We exploit information on the type of membership migration (individual versus collective) and the competitive saliency of the destination firm as inferred from the recipient status (incumbent versus start-up) and its geographic location (same versus different province). The dissolution risk is highest when collective interfirm mobility results in a new venture within the same geographic area. The theoretical implications of this study are discussed

Spatial and Temporal Heterogeneity in Founding Patterns

A growing body of literature suggests that populations of organizations are not homogeneous, but instead comprise distinct subentities. Firms are highly dependent on their immediate institutional and competitive environments. The present paper further explores this issue by focusing on the spatial and temporal sources of industry heterogeneity. Our goal is threefold. First, we explore founding rates as a function of spatial density, arguing that density-dependent processes occur along a geographic gradient ranging from proximate, to neighboring, to more distant contexts. Second, we show how multiple, local evolutionary clocks shape such entrepreneurial activity. Third, we provide evidence on how diffusion processes are directly affected by social contagion, with new organizational forms spreading through movements of individuals. Results from data on the Dutch accounting industry corroborate these patterns of heterogeneity