The Personal Data Economy, the Corporatist State Model, and a Global Framework for an Emergent Classification of Social, Political, and Economic Power

In the exponential advancement of information and communication technology, it is observable that a multitude of associated political and economic transformations have characterised the twenty-first century. Principally, the abundance of personal data has become an ever more essential component for societal improvement and civic management in the modern world. Secondly, governments and transnational corporations have understood the relevance of digital information and are involved in negotiations that will determine the dimensions of international commerce in personal data. The personal data economy is typically defined as the network of individual consumers, private companies, and governmental institutions that facilitate the commercial exchange of personal information. It is arguable that personal data has become the central component in an emergent network of political and economic architectures. For example, a number of transnational corporations have used their unprecedented access to personal information to dominate new sectors of the global economy. Furthermore, the personal data economy has necessitated the creation of innovative policy programmes in an endeavour to effectively govern the commercial exchange of personal information. Evidently, the availability of personal information has become an important component of the global economy. My research will critically examine and evaluate the personal data economy as a global framework for an emergent classification of power that is dependent on the perpetual extraction of personal information. Furthermore, the research will investigate how the global political and economic establishment and the associated neoliberal ideology have influenced the development the personal data economy and the global transformations that can be attributed to the national and international commerce in personal information. The research will contribute to an understanding of the personal data economy as a global framework for contemporary power relations and the associated social, political, and economic implications for a world that is increasingly dependent on digital technology. Furthermore, the research will contribute to the production of interdisciplinary knowledge for the development of innovative policy programmes for the effective regulation of the commercial exchange of personal information.
Research Questions:
Does the personal data economy function as a global framework for the advancement of an emergent classification of power? How does the dependency on the extraction and examination of personal information contribute to a distinctive classification of power?
Is it permissible to consider the personal data economy as an extension of the political and economic establishment? Do the principles of neoliberal market capitalism determine the parameters of the personal data economy?
What is the foremost method to establish an innovative policy programme to effectively govern the personal data economy? What are the central components of a policy programme that is involved in the governance of the personal data economy?
Research Methodology
The thesis will use a multimethod framework to facilitate the integration of investigative research procedures, supported by an extensive review of the established literature and empirical data collected from multiple sources. The project will be inherently interdisciplinary, with the implementation of methods from the social sciences, computer science, and applied mathematics. To expand the potential of research, I will implement the Scikit-Learn software package of Python programming to build a machine learning model. It will use supervised learning for numerical encoding unprocessed information and unsupervised learning to identify patterns in large quantities of empirical data. I will use pipelines and visualisation features to build a model that can efficiently handle unprocessed data and accurately demonstrate findings with graphics.