A methodology for improving computed individual regressions predictions.

This research proposes a methodology to improve computed individual prediction values provided by an existing regression model without having to change either its parameters or its architecture. In other words, we are interested in achieving more accurate results by adjusting the calculated regression prediction values, without modifying or rebuilding the original regression model. Our proposition is to adjust the regression prediction values using individual reliability estimates that indicate if a single regression prediction is likely to produce an error considered critical by the user of the regression. The proposed method was tested in three sets of experiments using three different types of data. The first set of experiments worked with synthetically produced data, the second with cross sectional data from the public data source UCI Machine Learning Repository and the third with time series data from ISO-NE (Independent System Operator in New England). The experiments with synthetic data were performed to verify how the method behaves in controlled situations. In this case, the outcomes of the experiments produced superior results with respect to predictions improvement for artificially produced cleaner datasets with progressive worsening with the addition of increased random elements. The experiments with real data extracted from UCI and ISO-NE were done to investigate the applicability of the methodology in the real world. The proposed method was able to improve regression prediction values by about 95% of the experiments with real data.

Hayek and complexity: coordination, evolution and methodology in social adaptive systems

The affinity between the work of the Austrian economist Friedrich A. Hayek and the approach of Complexity Economics is widely recognized by the literature. In spite of this, there still is a lack of studies that seek to analyze in depth the relationship between Hayek and complexity. This dissertation is a contribution to the filling of this large gap in the literature. In the first part of the work, we analyze the various periods in the development of Hayek\'s vision of complexity, showing that this vision is strongly present in his works on knowledge, competition, methodology, evolution, and spontaneous order. In the second part, we explore how Hayek was influenced by two of the main precursors of modern complexity theory - cybernetics and general system theory - from the time he was working on his book on theoretical psychology, The Sensory Order (1952), until the end of his intellectual career.