Identifying the Components and Interrelationships of Smart Cities in Indonesia: Supporting Policymaking via Fuzzy Cognitive Systems

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. INDEX TERMS Cognitive computing, participatory modeling, public policy, systems for smart cities. I. INTRODUCTION The population is increasing not only around the world but also in Indonesia [1], [2]. This is paralleled with a tendency for people to cluster in cities, which are already central to Indonesia and are due to play an even more important role. According to Parasati, about 59.35% of people live in urban areas [3]. The share of the Indonesian population living in The associate editor coordinating the review of this manuscript and approving it for publication was Anna Visvizi. cities is predicted to reach 67.66% by 2025, and 82% by 2045. These dynamics have many consequences on economical and societal factors in cities [4]. In particular, the challenges that cities face will become even more pressing, calling for new infrastructures [5]. Challenges include waste management, traffic, and quality of life [6], [7]. Additional challenges come from changes in the age pyramid as the population gets older. Cities thus need new strategies [8], calling for innovative scenarios and solutions [9]. The concept of 'smart city' is one of the solutions.

Enhance the Uncertainty Modeling Ability of Fuzzy Grey Cognitive Maps by General Grey Number

“…areas, including decision-support [4], [5], system simulation [6], system prediction [7], smart city [8], intrusion detection [9], and industrial process control [10]- [13] etc. FCM assigns fuzzy weights to topological graphs, so it is also treated as a fuzzy-neural system [14].…”

A Cybernetic Participatory Approach for Whole-Systems Modelling and Analysis, with Application to Inclusive Economies

“…That is chiefly because it is relatively quick and easy to populate and parameterize from varied sources of qualitative knowledge [43] with flexibility in representation (as more components are added to the system) [44], modest time investment, and a degree of transparency to non-technical experts [22, Table 1]. Comparing to conceptual mapping techniques such as CLD, FCM allows more resolution on the nature of links [45] which can be used to quantify and analyze structural dynamics of the system across individual or groups of participants [46]. Comparing to quantitative participatory techniques such as agent-based simulations and system dynamics, it does not require a lot of empirical data and explicit systems knowledge [22, Table 2]thus making FCMs suitable for data-poor and multi-interest situations [47].…”

A framework for designing cognitive trajectory controllers using genetically evolved interval type‐2 fuzzy cognitive maps

“…9 A review of literature shows that, because of the abilities of FCMs such as fuzzy reasoning and high compatibility and flexibility, FCMs have been successfully used in various fields. [10][11][12][13][14][15][16][17][18] In conventional FCMs (CFCMs), the weights assigned to links remain fixed till the end of the computations (whether in classifying, predicting, control, or other applications). 2 In FCMs based on the opinions of experts, it has been common for experts to express opinions using type-1 (T1) fuzzy sets (FSs) and rules.…”