Suchergebnisse

AbstractHumankind requires social responsibility (SR) via its global bodies United Nations (in UN Global Compact) and International Standards Organization (in ISO 26000), for humankind to avoid global crises and extinction caused by Social irresponsibility (SI), resulting from un‐systemic behavior, i.e., over‐specialization and poor holism that interdisciplinary creative cooperation should replace, which pioneers of Systems Theory (Bertalanffy) and Cybernetics (Wiener) required and practiced several decades ago. Ports, providing logistic services, present potential SR or SI. The now‐a‐days technology causes more ports' SI than SR by waste, air, water, and land pollution. SI is nearly unavoidable, if process is not handled with systemic behavior (SB), including a (requisitely) holistic approach of any process participants. Authors present a stepwise regression statistical modeling research on a port, applying systems approach in a mathematical way, to study a chance of port's SB leading to SR rather than SI damaging local people. SR and SB are difficult to measure; they prevent problems and costs. SI causes measurable problems and costs to indirectly express (lacking) SR and SB.

Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Dossier: The European Migrant Crisis -- Abstract -- 1.1. Introduction -- 1.2. A Short Reminder about the Past and Present of the Western Balkans - Conflicts, Policy, and Interests -- 1.3. The Intelligence Community and the Migrant Crisis -- 1.4. The Intelligence Community and the Challenges of the Current Migrant Crisis -- References -- Chapter 2 -- The Intelligence and Security Challenges of the European Migrant Crisis: Insight into an Innovative Forecasting Model -- Abstract -- 2.1. Introduction -- 2.2. ARIMA Intervention Modeling -- 2.3. Background and Problem Statement -- 2.3.1. Collection of Migrant Flow Time Series Data -- 2.3.2. Driving Mechanisms of the Smuggling Networks -- 2.4. The Conceptual Framework of the Research -- 2.4.1. The Conceptual Framework of Intervention Modeling With the CL Procedure and Estimation of the ARIMA-Intervention Model -- 2.4.2. Framework for Generating Future Forecasts with a Monte-Carlo Scenario-Playing Mechanism -- 2.4.3. The Process of Applying the Random Generators to Produce Different Future Scenarios -- 2.4.4. Concept of Computation of the Average Values of the Future Scenarios -- 2.4.5. Concept Of Estimation of Probability Distributions Over All Scenarios for Each Future Time Point and the Principle of Calculation of the Likelihood of Future Scenarios -- 2.5. Applied Methodology and Modeling Design -- 2.5.1. Basic ARIMA Time Series Models -- 2.5.2. ARIMA-Intervention Models -- 2.5.3. Estimation of Probability Density Functions and Calculation of Probabilities -- 2.6. Model Estimation Results and Analysis of Outliers -- 2.6.1. Identification of the Model Structure and Parameter Estimation -- 2.6.2. Model Validation -- 2.6.3. Analysis of Cumulative Intervention Effects