Kesra Nermend Book order

The book introduces innovative quantitative methods for automating objective decision-making, focusing on multidimensional comparative analysis (MCA) and multi-criteria decision-making analysis (MCDA). It elaborates on various criteria types and presents the author's unique preference vector method (PVM) and vector measure construction method (VMCM) for addressing multi-criteria challenges. Each method is detailed with step-by-step explanations and practical examples, primarily applied within economics and management, making it a valuable resource for practitioners in these fields.

This volume aims to provide insights into the latest developments in experimental economics, stemming from the 2015 Computational Methods in Experimental Economics (CMEE) conference. It features research and analysis on economic experiments in management science, decision theory, game theory, marketing, and political science. The focus is on utilizing various computer methods to equip researchers with diverse tools in experimental economics, a rapidly evolving field. Modern experimental economics necessitates an integration of knowledge across economic sciences, computer science, psychology, and neuroscience. Recent studies encompass both laboratory and field experiments, with findings aimed at testing and enhancing economic theories. Researchers primarily rely on established methods and computer tools to support their experiments. However, methods such as artificial intelligence, computer simulation, and computer graphics are underrepresented in current studies, with many experimenters overlooking their potential. The conference and the accompanying papers seek to facilitate an exchange of experiences and promote collaborative efforts to encourage a shift in this trend.

Focusing on regional development analysis, the book explores the limitations of traditional forecasting and taxonomic methods, particularly standard linear ordering techniques that can yield unreliable comparisons. It highlights the growing popularity of vector-autoregressive models and aims to establish a new methodological framework using vector calculus. This framework seeks to enhance the accuracy of data interpretation in regional analysis, supported by advanced computer systems, ensuring that the insights reflect reality more effectively.