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@article{166400, author = {Dr. Sugata Sen and Aditi Chakraborty}, title = {Reduction of Incentive Incompatibility in Regional Planning: An analysis through Big Data Analytics}, journal = {International Journal of Innovative Research in Technology}, year = {2024}, volume = {11}, number = {2}, pages = {656-658}, issn = {2349-6002}, url = {https://ijirt.org/article?manuscript=166400}, abstract = {Over time it has been observed that decentralized planning is an improvement over the traditional planning procedures due to its participatory nature. Participation of the beneficiaries in the planning procedure is the backbone of this type of planning. But the experiences of different countries have shown that the beneficiaries are not becoming interested to attach themselves with the process of planning. One of the main reasons behind this non-participation may be attributed to the existence of incentive incompatibility. It is observed that due to the presence of incentive incompatibility the decentralized planning procedure is becoming futile in delivering the expected output. Here the Big Data Analytics can play an important role. The network architecture of Big Data Analytics is very much similar to the decentralized planning structure. The client server relationship under the Big Data Analytics can be used to explain the planner beneficiary relationship. At the same time the processing technique under the Big Data Analytic is also very much similar with the decentralized planning. Thus this work wants to show how the use of Big Data Analytics can successfully implement the participation of beneficiaries into the planning system and reduce the problem of incentive incompatibility. This work uses the tenets of Big Data Analytics to decompose the beneficiary planner relationship. Such that it takes into consideration HADOOP frame work to execute this decomposition. More specifically here the use of HDFS has been substantiated. It is observed that Big Data Analytics not only matches the structure of the decentralized planning, it also helps to draw the optimum output easily. So the use of Big Data Analytics will usher a new era in the field of participatory planning. }, keywords = {}, month = {July}, }
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