Unsupervised neural models for country and political risk analysis

Abstract

Abstract. This interdisciplinary research project focuses on relevant applications of Knowledge Discovery and Artificial Neural Networks in order to identify and analyse levels of country, business and political risk. Its main goal is to help business decision-makers understand the dynamics within the emerging market countries in which they operate. Most of the neural models applied in this study are defined within the framework of unsupervised learning. They are based on Exploratory Projection Pursuit, Topology Preserving Maps and Curvilinear Component Analysis. Two interesting real data sets are analysed to empirically probe the robustness of these models. The first case study describes information from a significant sample of Spanish Multinational Enterprises (MNEs). It analyses data pertaining to such aspects as decisions over the location of subsidiary enterprises in various regions across the world, the importance accorded to such decisions and the driving forces behind them.Through a projection-based analysis, this study reveals a range of different reasons underlying the internationalization strategies of Spanish MNEs and the different goals they pursue. It may be concluded that projection connectionist techniques are of immense assistance in the process of identifying the internationalization strategies of Spanish MNEs, their underlying motives and the goals they pursue. The second case study covers several risk categories that include task policy, security, and political stability among others, and it tracks the scores of different countries all over the world.Interesting conclusions are drawn from the application of several business intelligence solutions based on neural projection models, which support data analysis in the context of country and political risk analysis.

Analysis of Knowledge Management in Industrial Sectors by Means of Neural Models

“…The solution proposed in this research applies an unsupervised neural model called Cooperative Maximum Likelihood Hebbian Learning (CMLHL) [4]. It is based on Maximum Likelihood Hebbian Learning (MLHL) [4], and introduces the application of lateral connections [4] derived from the Rectified Gaussian Distribution [9]. This connectionist model has been chosen because it reduces the data dimensionality while preserving the topology in the original data set.…”

“…where: η is the learning rate, τ is the "strength" of the lateral connections, b the bias parameter, p a parameter related to the energy function and A a symmetric matrix used to modify the response to the data [4]. The effect of this matrix is based on the relation between the distances separating the output neurons.…”

“…To do that, present research proposes the application of neural projection models [1,4,5] (described in Sect. 2) to generate intuitive visualizations of KM data by reducing their dimensionality.…”

Unified Knowledge Based Economy neural forecasting map

“…The SOM is considered to be a feasible tool for aggregating multiple related variables to visualize and monitor the evolution of economic conditions over time. However, Herrero et al [11] argued that SOM results were inconsistent and inferior compared to other techniques such as PCA when they were applied to assess and project the political risk for nations. Hence, in this study we suggest using a FFBNN model to forecast the KBE indicators, and to combine the forecasted results using PCA and SOM.…”

“…Hadavandi et al [12] proposed the use of Stepwise Regression to select key factors in their genetic fuzzy and ANN forecasting model. Herrero et al [11] used three different methods, including Cooperative Maximum-Likelihood Hebbian Learning, PCA, SOM and Curveilinear Component Analysis (CCA) to select the most appropriate variables to forecast the political risk for most of the world's nations.…”