Research Article Developing Statistical Optimization Models for Urban Competitiveness Index: Under the Boundaries of Econophysics Approach Cem Ça˘ grı D¨ onmez 1 and Abdulkadir Atalan 1,2 1 Department of Industrial Engineering, Marmara University, Istanbul, Turkey 2 Department of Mechanical Engineering, Bayburt University, Bayburt, Turkey Correspondence should be addressed to Cem Ça˘ grı D¨ onmez; cem.donmez@marmara.edu.tr Received 30 August 2019; Accepted 8 October 2019; Published 20 November 2019 Guest Editor: Marco Locurcio Copyright © 2019 Cem Ça˘ grı D¨ onmez and Abdulkadir Atalan. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e purpose of this research was to establish the urban competitiveness index (UCI) by using the statistical optimization method for the econophysics approach. With this technique, economic data regarding urban areas and the factors affecting UCI have been determined. e research covers 30 urban centres located in 15 countries worldwide. Urban centres with the gross domestic product per capita of $10,000 or more were taken into consideration. e significant levels of the factors were determined with the statistical optimization method, and optimum values were calculated with the developed optimization models. Re-index values were calculated and compared with the results of PricewaterhouseCoopers and World Economic Forum. According to the results, the high UCI value of these locations depends not only on economic data but also on high values of social factors. us, those locations are becoming the centre of attraction for investments and capital with increasing competitiveness. 1. Introduction During the last forty years, new developments in the dy- namics of the urban system in physics, the main incentive has come from mathematicians and physicists mainly adaptive cognitive systems in the economical perspective who started applying their models of emerging properties to engineering then to social sciences. which would give a global framework for explaining systems dynamics in a wide range of fields of knowledge [1]. Economic development research has been moving to data-driven approaches within the methodology of natural science, statistical physics, and complexity sciences [2–4], which makes it possible to in- troduce new metrics that surpass the traditional economic measures in revealing current economic status and pre- dicting future economic growth, with applications to eco- nomic development [5, 6], trading behaviour [7], poverty [8, 9], inequality [10, 11], unemployment [12, 13], and in- dustrial structure [5, 14]. Economists and physicists have also introduced a variety of nonmonetary metrics to quantitatively assess the country’s economic diversity and competitiveness by measuring intangible assets of the eco- nomic system [15, 16], allowing for quantifying the econ- omies’ hidden potential for future development [17, 18] in near real time and at low cost [19]. Many economic methods have been used for the analysis of urban economy problems. One of these methods is the econophysics approach. Econophysics was first introduced in India as a word, “Economics” and “Physics,” a conference on statistical physics in 1995 [20, 21]. “Econophysics” is a new discipline insufficiently rooted in economic theory and empirical observation. Another peculiarity of new complex system theory is to focus on the emergence of properties at a macrolevel resulting from the interactions between indi- vidual behaviour at a microlevel [1]. Although this term deals with the relations between economics, physics, mathematics, and finance, it basically takes into account the interaction of theories of physics with the economy [20]. is method, which is generally recommended for macro- economics, is intended to be used for microstructures of Hindawi Complexity Volume 2019, Article ID 4053970, 11 pages https://doi.org/10.1155/2019/4053970