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We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

We propose an oversampling technique to increase the true positive rate (sensitivity) in classifying imbalanced datasets (i.e., those with a value for the target variable that occurs with a small frequency) and hence boost the overall performance measurements such as balanced accuracy, G-mean and area under the receiver operating characteristic (ROC) curve, AUC. This oversampling method is based on the idea of applying the Synthetic Minority Oversampling Technique (SMOTE) on only a selective portion of the dataset instead of the entire dataset. We demonstrate the effectiveness of our oversampling method with four real and simulated datasets generated from three models.

Adopting the concept of sustainable development is connected with the necessity of redefining marketing strategies and, as a consequence, should also be reflected in the policies adopted for the individual tools. In this chapter, the sustainability marketing mix is considered with regard to five instruments: product, price, place, promotion and people (5P). The aim of the chapter is to investigate the current state of knowledge in this matter as well as the scope for the implementation of the sustainability marketing concept in small and medium-sized enterprises (SME) in the food and drink sector from an international perspective. The international approach has been adopted to try and find out whether in more highly developed countries sustainability marketing activities are comprehensive and include all the marketing tools; and, on the other hand, whether in less-developed markets sustainability marketing activities are limited to the tools for which the concept of sustainable development can theoretically be implemented the most easily, namely, promotional activities and those targeted at a company’s own employees.

Each of the analysed instruments was described from two angles: in terms of the results obtained for the whole research sample, indicating the countries whose respondents had the highest and lowest values for the specific variables defining each marketing mix instrument and in terms of a comparison of two groups of countries, indicating similarities and differences in the opinions of managers on the use of marketing mix instruments in a sustainable way. The chapter concludes with the results obtained through factor analysis, which made it possible to identify the ways in which SME managers in the food and drink sector define the individual sustainability marketing tools.

In recent years, social scientists have begun exploring the neurological foundations of behavior in an attempt to gain a more complete understanding of decision-making in the realms of both politics and economics (see Cacioppo & Viser, 2003; Fowler & Schreiber, 2008; McDermott, 2009; Caplin & Schotter, 2008).

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Estimating parameters of constrained optimization models in a consistent way requires a different set of methods than what is available in a typical econometric toolkit. We identify three complications likely to arise in this context, and suggest solutions to those complications: (i) the bi-level programming character, (ii) ill-posedness, and (iii) derivation of estimator properties. The solutions suggested involve a combination of numerical techniques and utilization of out-of-sample information through Bayesian techniques. The proposed framework is also suitable for typical empirical problems arising in trade analysis such as the estimation of trade equilibrium models and data balancing exercises.

Kalecki's 1968 paper on Marx's Reproduction Schemes aimed, starting from Marxian Schemes, to build an analytical bridge to the modern theories of Effective Demand and Growth. Kalecki accomplished his task modifying the structure of Marxian Schemes, reinterpreting them in terms of vertically integrated sectors, and this sidesteps Marx's analysis of the monetary intersectoral transaction. This chapter tries to show that the impossibility of implementing the intersectoral monetary transaction is not simply due to monetary technicalities, as held by Kalecki, but has crucial implications regarding Say's Law. Putting aside Marx's problem, Kalecki puts aside the true meaning of Marx's unsuccessful analysis: that an economy obeying Say's Law cannot function; as it were, Marx's Impossibility Theorem on Say's Law.

Many Information Theoretic Measures have been proposed for a quantitative assessment of causality relationships. While Gouriéroux, Monfort, and Renault (1987) had introduced the so-called “Kullback Causality Measures,” extending Geweke’s (1982) work in the context of Gaussian VAR processes, Schreiber (2000) has set a special focus on Granger causality and dubbed the same measure “transfer entropy.” Both papers measure causality in the context of Markov processes. One contribution of this paper is to set the focus on the interplay between measurement of (non)-markovianity and measurement of Granger causality. Both of them can be framed in terms of prediction: how much is the forecast accuracy deteriorated when forgetting some relevant conditioning information? In this paper we argue that this common feature between (non)-markovianity and Granger causality has led people to overestimate the amount of causality because what they consider as a causality measure may also convey a measure of the amount of (non)-markovianity. We set a special focus on the design of measures that properly disentangle these two components. Furthermore, this disentangling leads us to revisit the equivalence between the Sims and Granger concepts of noncausality and the log-likelihood ratio tests for each of them. We argue that Granger causality implies testing for non-nested hypotheses.