Smart City 4.0 from the Perspective of Open Innovation

The purpose of a Smart City is to solve its inherent problems while simultaneously reducing its expenditure and improving its quality of life. Through the 4th Industrial Revolution technology, the advantages of Smart City are estimated to overcome the city’s expenses with city platformization. While a city traditionally is the subject of creation and not consumption, a Smart City currently is the key industry in generating more than 60% of its GDP in value creation from a production viewpoint. Moreover, with the expansion of online-offline convergence, cities can grow without limitation on its size, where connectivity and innovation determine the inclination of the city’s benefit-cost curve. As a city platform is responsible for connectivity, its value drastically increases through the 4th Industrial Revolution’s O2O (online to offline convergence) platform. When a city reflects on its own as a Digital Twin in the Cloud and when complete information becomes accessible through citizen’s participation through smartphones (Edge), Self-organization takes place, an ideal linkage between the city and citizens. Cities go through the self-organizing process of complex adaptive systems like the human brain. This research proposes a future model of a “Self-organizing City,” and suggests implementing the Smart City model based on the Smart City Tech-Socio Model in implementing strategies.

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Multiscale Modeling in Smart Cities: A Survey on applications, Current Trends, and Challenges

10.20944/preprints202106.0008.v1 ◽

2021 ◽

Author(s):

Asif Khan ◽

Khursheed Aurangzeb ◽

Sheraz Aslam ◽

Musaed Alhussein

Keyword(s):

Multiscale Modeling ◽

Complex Adaptive Systems ◽

Smart City ◽

Adaptive Systems ◽

Complex Adaptive System ◽

Smart Cities ◽

Health Issues ◽

Complex Adaptive ◽

Key Issues ◽

City Model

Megacities are complex systems facing the challenges of overpopulation, poor urban design and planning, poor mobility and public transport, poor governance, climate change issues, poor sewerage and water infrastructure, waste and health issues, and unemployment. Smart cities have emerged to address these challenges by making the best use of space and resources for the benefit of citizens. A smart city model views the city as a complex adaptive system consisting of services, resources, and citizens that learn through interaction and change in both the spatial and temporal domains. The characteristics of dynamic development and complexity are key issues for city planners that require a new systematic and modeling approach. Multiscale modeling (MM) is an approach that can be used to better understand complex adaptive systems. The MM aims to solve complex problems at different scales, i.e., micro, meso, and macro, to improve system efficiency and mitigate computational complexity and cost. In this paper, we present an overview of MM in smart cities. First, this study discusses megacities, their current challenges, and their emergence to smart cities. Then, we discuss the need of MM in smart cities and its emerging applications. Finally, the study highlights current challenges and future directions related to MM in smart cities, which provide a roadmap for the optimized operation of smart city systems.

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Building Complex Adaptive Systems

Applications of Complex Adaptive Systems ◽

10.4018/978-1-59904-962-5.ch009 ◽

2008 ◽

pp. 229-256 ◽

Cited By ~ 5

Author(s):

Jan Sudeikat ◽

Wolfgang Renz

Keyword(s):

Complex Adaptive Systems ◽

Adaptive Systems ◽

Software Systems ◽

Computational Techniques ◽

Multi Agent Systems ◽

Software Applications ◽

Agent Oriented Software Engineering ◽

Complex Adaptive ◽

Multi Agent ◽

Self Organizing

Agent Oriented Software-Engineering (AOSE) proposes the design of distributed software systems as collections of autonomous and pro-active actors, so-called agents. Since software applications results from agent interplay in Multi-Agent Systems (MAS), this design approach facilitates the construction of software applications that exhibit self-organizing and emergent dynamics. In this chapter, we examine the relation between self-organizing MAS and Complex Adaptive Systems (CAS), highlighting the resulting challenges for engineering approaches. We argue that AOSE developers need to be aware of the possible causes of complex system dynamics, which result from underlying feedback loops. In this respect current approaches to develop SO-MAS are analyzed, leading to a novel classification scheme of typically applied computational techniques. To relieve development efforts and bridge the gap between top-down engineering and bottom-up emerging phenomena, we discuss how multi-level analysis, so-called mesoscopic modeling, can be used to comprehend MAS dynamics and guide agent design, respectively iterative redesign.

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Evolutionary and Coevolutionary Theories

Theories of Communication Networks ◽

10.1093/oso/9780195160369.003.0016 ◽

2003 ◽

Author(s):

Peter R. Monge ◽

Noshir Contractor

Keyword(s):

Communication Networks ◽

Evolutionary Theory ◽

Complex Adaptive Systems ◽

Adaptive Systems ◽

Organizational Boundaries ◽

Complex Adaptive ◽

Quasi Experimental ◽

Emergent Structure ◽

Sociocultural Processes ◽

Self Organizing

Chapter 3 discussed the emergence of communication networks from the perspective of complexity theory. Specifically, we described complexity as a network of agents, each with a set of attributes, who follow rules of interaction, which produces emergent structure. Complexity arose from the fact that there were numerous agents with extensive relations. Some complex systems but by no means all, we argued, were self-organizing, meaning that they created and sustained internal structure in response to the flow of matter and energy around them. Some readers, particularly those with some familiarity with the complex adaptive systems literature, may have noticed that the discussion of complexity in chapter 3 did not include processes of adaptation, evolution, or coevolution. The reason for this is that it is possible to view these as theoretical mechanisms that operate in at least some complex, self-organizing systems, though not necessarily all. Thus, we have chosen to treat adaptation and the coevolutionary perspective as theoretical mechanisms in the same manner as the other theoretical mechanisms we have examined in chapters 5 through 8. In the present chapter we examine adaptive and coevolutionary processes as the basis for building MTML models of emergent communication networks that form the basis for organizational populations and communities. Modern interest in evolutionary theory as a basis for studying human social processes can be traced to the work of Amos Hawley (1950, 1968, 1986). Much of the interest in applying this perspective to studying organizational structures is credited to Donald Campbell (1965, 1974). Over much of his professional life Campbell explored the application of evolutionary theory to a wide array of sociocultural processes, including organizations (Baum & McKelvey, 1999). Campbell is perhaps best known throughout the social sciences for his work on experimental and quasi-experimental design (Campbell and Stanley, 1966; Cook & Campbell, 1980) and multimethod triangulation (Campbell & Fiske, 1959). Nonetheless, McKelvey and Baum (1999) point to Campbell’s enormous influence in organizational science via the early work of Aldrich (1972) on organizational boundaries, Weick’s (1979) formulation of an evolutionary model of organizing, Hannan and Freeman’s (1977, 1983) development of population ecology theory (and inertial theory), McKelvey’s (1982) work on organizational taxonomies, and Nelson and Winter’s (1982) evolutionary theory of economics.

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A complex systems perspective of news recommender systems: Guiding emergent outcomes with feedback models

PLoS ONE ◽

10.1371/journal.pone.0245096 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245096

Author(s):

Shankar Prawesh ◽

Balaji Padmanabhan

Keyword(s):

Recommender Systems ◽

Complex Adaptive Systems ◽

Adaptive Systems ◽

Online News ◽

Emergent Properties ◽

Feedback Model ◽

Complex Adaptive ◽

Systems Framework ◽

The Impact ◽

Self Organizing

Algorithms are increasingly making decisions regarding what news articles should be shown to online users. In recent times, unhealthy outcomes from these systems have been highlighted including their vulnerability to amplifying small differences and offering less choice to readers. In this paper we present and study a new class of feedback models that exhibit a variety of self-organizing behaviors. In addition to showing important emergent properties, our model generalizes the popular “top-N news recommender systems” in a manner that provides media managers a mechanism to guide the emergent outcomes to mitigate potentially unhealthy outcomes driven by the self-organizing dynamics. We use complex adaptive systems framework to model the popularity evolution of news articles. In particular, we use agent-based simulation to model a reader’s behavior at the microscopic level and study the impact of various simulation hyperparameters on overall emergent phenomena. This simulation exercise enables us to show how the feedback model can be used as an alternative recommender to conventional top-N systems. Finally, we present a design framework for multi-objective evolutionary optimization that enables recommendation systems to co-evolve with the changing online news readership landscape.

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