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Smart city article details
| Title | Identifying The Components And Interrelationships Of Smart Cities In Indonesia: Supporting Policymaking Via Fuzzy Cognitive System |
|---|---|
| ID_Doc | 30102 |
| Authors | Firmansyah, HS; Supangkat, SH; Arman, AA; Giabbanelli, PJ |
| Year | 2019 |
| Published | IEEE ACCESS, 7 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2019.2908622 |
| Abstract | Multiple Indonesian cities currently aim to qualify as "smart cities." Previous research on defining smart cities (e.g., the implementation-oriented maturity model) tends to focus on components over interrelationships, is challenging to apply to a specific context such as Indonesia, and offers limited support for policy-relevant questions. In this paper, we propose to address these shortcomings to support policymakers in identifying concrete action plans in Indonesia specifically. Our approach clarifies interrelationships for the context of use and supports structural (e.g., what aspects of a "smart city" are impacted by an intervention?) as well as what-if policy questions. We started with a systems' science approach to developing a cognitive map of the components and their interrelationships, as is increasingly done in participatory modeling and particularly in socio-ecological management. We transformed semi-structured interviews of 10 Indonesian experts into maps and assembled them to create the first comprehensive smart cities cognitive map for Indonesia, totaling 52 concepts and 98 relationships. While a cognitive map already provides support for decision-making (e.g., by identifying loops in the system), it is only conceptual and thus cannot form predictions. Consequently, we extended our cognitive map into a fuzzy cognitive map (FCM), whose inference abilities allow to examine the dynamic response of an indicator (e.g., "smart city") in response to different interventions. As fuzzy cognitive maps include the strengths of interrelationships but not the notion of time, future research may refine our model using system dynamics. This refinement would support policymakers in investigating when to conduct and/or evaluate an intervention. |
| Author Keywords | Cognitive computing; participatory modeling; public policy; systems for smart cities |
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