Under the Going Global partnership collaboration program of the British Council, following the trans-national education as part of the New Education Policy (NEP) of the Government of India, the partner universities are developing a multi-disciplinary module as part of the 'Strategic Networks and Computations' Project.
This module on “Strategic Networks and Computation” is being developed by the Economics faculties Dr. Rajnish Kumar and Dr. Sonali SenGupta from QUB and Prof. Manjit Das from Bodoland University, Mathematics faculty Prof. Surajit Borkotokey from Dibrugarh university, and Computer Science faculty Prof. Nityananda Sarma from Tezpur University.
Study of complex network is an emerging field. Development of multidisciplinary courses to understand how such complex network systems function is important. The proposed course builds on the problems of strategic networks in social and economic issues.
The Indian students, under exchange programs to QUB, experienced a gap with the UK system, mainly because the Indian education encourages early specialization. Therefore, students from Computer Science (CSc) or Mathematics background fail to interpret necessary social and economic issues in their fields. This gap can be mapped through the multidisciplinary course that has components of game theory, strategic networks, and computations. Beneficiaries are future researchers, prospective practitioners in the R&D organizations, and students. The proposed course is multidisciplinary in nature that builds on the emerging and very pertinent theme of strategic networks and computing. It builds on the strategic formation and description of networks bearing insights from cooperative and non-cooperative game theory and their applications in Data Science, Machine Learning, and Artificial Intelligence. In view of the rapidly changing and inter-disciplinary nature of the employability avenues in the global market, students will be required to simultaneously possess proficiency in multiple disciplines and therefore, the proposed course will be a good fit for such requirements. Modern data science includes a large chunk of network studies and over a period of time, it is expected to engage almost all the disciplines of mathematical and social sciences for its development. However, with the advent of machine learning algorithms, a visibly wide gap between their theoretical foundations and their applications is noticed. This needs to be introspected through the strategic consideration of the formation and description of networks responsible for such complex mechanisms. The course will provide the students with the much-needed theoretical framework behind those mechanisms. This will be effective in avoiding the catastrophic effects feared to be periled by the uncontrolled and over-usage of ML and AI techniques in the long run. The central concepts in the course are fundamental and accessible, but they are dispersed across the research literature of the many different fields contributing to the topic.