An international review of cultural consumption research

Despite the effects of the crisis, several studies show that there has been an increase in cultural production in all the most important western countries over the last twenty years. Nevertheless, the dimensions of the flows of demand are changing: the lowering of the threshold of perceived accessibility to the cultural contents on offer is resulting in new population segments using them. The modalities of cultural product consumption are also changing, and are increasingly influenced by the direct involvement of the consumer in the creative processes. On the other side, the competition to conquer consumersÕ free time has intensified because more figures are now involved, both from the cultural industry and outside. The cultural offer has multiplied and become more differentiated. But while this consumption is changing dimensions and modality, a gap is emerging in the information and knowledge of cultural consumption behaviour, mainly due to a lack of innovative official statistical measurements. The present paper wants to understand how academic literature reacted to the need for information on cultural consumption, that became widespread during 2000. Our main objective is to offer an initial overview of scientific literature of the fist decade of the twenty-first century, while trying to understand the future research trends. The analysis showed that great attention is still dedicated to the segmentation of cultural demand, but the analysis of motivations underlying cultural consumption is significantly acquiring more importance. Moreover, we identified vast research areas in which cultural consumption has only been partially studied, such as: social consumption, studies on individual businesses, methodological triangulation, and the operative implications for business management.Cultural consumption; Marketing research; Segmentation; Motivations

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author

Consumption of the Performing Arts from a Supply-Side Perspective: Searching for Artistic Benefit

This paper focuses on the artistic benefit for the audience in arts and culture consumption. This benefit is defined as the feeling of being part of the artistic process and it is generated by participatory dynamics and value co-creation. The artistic benefit interacts with other benefits (functional, symbolic, emotional, social) that are involved in the performing arts experience. Its analysis, from a supply-side view, could help to advance arts and culture marketing into a service-dominant logic. An empirical study involving two phases has been designed. First, a qualitative, explorative and item-generation investigation was carried out. A quantitative study was then undertaken to streamline the item-generation process and evaluate the validity of the identified semantic dimensions. The analysis identified the artistic benefit as composed by three different factor, that we labelled: breaking, participatory and dialogic. The results confirm the relevance of the topic as a means of shedding light on the audience experience in arts consumption

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing and for Graphical Process Unit (GPU) parallel computing. For the GPU implementation we use the Matlab parallel computing toolbox and show how to use General Purposes GPU computing almost effortless. This GPU implementation comes with a speed up of the execution time up to seventy times compared to a standard CPU Matlab implementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version, through some simulation experiments and empirical application

A fully coupled computational fluid dynamics – agent-based model of atherosclerotic plaque development: Multiscale modeling framework and parameter sensitivity analysis

Background: Peripheral Artery Disease (PAD) is an atherosclerotic disorder that leads to impaired lumen patency through intimal hyperplasia and the build-up of plaques, mainly localized in areas of disturbed flow. Computational models can provide valuable insights in the pathogenesis of atherosclerosis and act as a predictive tool to optimize current interventional techniques. Our hypothesis is that a reliable predictive model must include the atherosclerosis development history. Accordingly, we developed a multiscale modeling framework of atherosclerosis that replicates the hemodynamic-driven arterial wall remodeling and plaque formation. Methods: The framework was based on the coupling of Computational Fluid Dynamics (CFD) simulations with an Agent-Based Model (ABM). The CFD simulation computed the hemodynamics in a 3D artery model, while 2D ABMs simulated cell, Extracellular Matrix (ECM) and lipid dynamics in multiple vessel cross-sections. A sensitivity analysis was also performed to evaluate the oscillation of the ABM output to variations in the inputs and to identify the most influencing ABM parameters. Results: Our multiscale model qualitatively replicated both the physiologic and pathologic arterial configuration, capturing histological-like features. The ABM outputs were mostly driven by cell and ECM dynamics, largely affecting the lumen area. A subset of parameters was found to affect the final lipid core size, without influencing cell/ECM or lumen area trends. Conclusion: The fully coupled CFD-ABM framework described atherosclerotic morphological and compositional changes triggered by a disturbed hemodynamics

Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data

Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices

Bayesian Calibration of Generalized Pools of Predictive Distributions

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to derive a combined and calibrated density function using random calibration functionals and random combination weights. In particular, it compares the application of linear, harmonic and logarithmic pooling in the Bayesian combination approach. The three combination schemes, i.e., linear, harmonic and logarithmic, are studied in simulation examples with multimodal densities and an empirical application with a large database of stock data. All of the experiments show that in a beta mixture calibration framework, the three combination schemes are substantially equivalent, achieving calibration, and no clear preference for one of them appears. The financial application shows that the linear pooling together with beta mixture calibration achieves the best results in terms of calibrated forecast