Significance of Affective Sciences and Machine Intelligence to Decipher Complexity Rooting in Urban Sciences

Significance of Affective Sciences and Machine Intelligence to Decipher Complexity Rooting in Urban Sciences

Alok Bhushan Mukherjee, Akhouri Pramod Krishna, Nilanchal Patel
Copyright: © 2020 |Pages: 16
DOI: 10.4018/978-1-5225-9276-1.ch027
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Abstract

An urban system is a complex system. There are many factors which significantly influences the different aspects of it. The influencing factors possess different characteristics as they may be environmental, economical, socio-political or cognitive factors. It is not feasible to characterize an urban system with deterministic approach. Therefore there is a need of study on computational frameworks that can investigate cities from a system's perspective. This kind of study may help in devising different ways that can handle uncertainty and randomness of an urban system efficiently and effectively. Therefore the primary objective of this work is to highlight the significance of affective sciences in urban studies. In addition, how machine intelligence techniques can enable a system to control and monitor the randomness of a city is explained. Finally the utility of machine intelligence technique in deciphering the complexity of way finding is conceptually demonstrated.
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1. Introduction

This chapter aims to explain the significance of affective sciences in deciphering the complexity of an urban system. In addition, the pivotal role which machine intelligence techniques are capable of in characterizing an urban system is described. The proposed chapter begins with a note on urban system outlining its various facets. Then “affective science” is explained in detail, and how the idea of affective science is relevant in functioning of an urban system is outlined. Later, the proposed chapter details the applicability of machine intelligence techniques in studying an urban system under the realm of affective sciences. Having provided a detailed description on the significance of machine intelligence techniques, and their utility in understanding a complex system; a research problem on wayfinding using a machine intelligent technique and decision tree is demonstrated. This chapter ends with concluding remarks, and suggestions that can be incorporated to strengthen the present study.

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