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Complex Adaptive Systems
Published in James E. Luckman, Olga Flory, Transforming Leader Paradigms, 2019
A complicated system has a leader and is controllable and predictable, whereas a complex adaptive system is leaderless, self-organizing, and adaptable and the outcome is emergent and not predictable. This does not imply that culture change happens without a leader, it needs leadership that will become apparent in Part 3, Action.
Identifying the main constructs for an interdisciplinary workplace management framework
Published in Vitalija Danivska, Rianne Appel-Meulenbroek, A Handbook of Management Theories and Models for Office Environments and Services, 2021
Vitalija Danivska, Rianne Appel-Meulenbroek, Susanne Colenberg
The ‘Aligning organisation and workplace strategies’ part of the framework puts workplace management forward as a complex adaptive system (CAS). Holland (2006, p. 1) defines complex adaptive systems as “systems that have a large number of components, often called agents, that interact and adapt or learn”. Main features of a CAS are that it has a high number of agents who make their own decisions on how to behave. Agents are seen as autonomous objects that have local knowledge and can be replaced without disrupting the whole system (Carmichael & Hadžikadić, 2019). They interact with each other, and the behaviour of the whole system cannot be predicted based on the behaviour of individual agents, thus requiring a holistic understanding of the system (Sullivan, 2011). In addition, agent behaviour changes depending on events taking place. Interactions between agents as well as other internal and external changes thus influence the system’s behaviour. Kämpf-Dern and Pfnür (2014) studied corporate real estate management (CREM) as an organisational management function and confirmed that it is a complex system composed of multiple various relationships and interactions. There are internal and external environments as well as inter- and intra-systems that affect CREM (Kämpf-Dern & Pfnür, 2014) on strategic, tactical, and operational levels. Moreover, within the workplace, there are multiple operational levels – physical space, digital environment, and social environment that interact together. All these components might influence the outcome or actions of other parts. Additionally, workplaces have many stakeholders coming both from internal and external environments, such as employees, management, investors, suppliers, and public bodies (Kämpf-Dern & Pfnür, 2014). All of them can be referred to as someone who has a relationship with a certain workplace (Tagliaro, 2018) and, thus, can be considered as system agents. In this manner, workplace has a loose hierarchy of replaceable agents.
Artificial intelligence and constructed-language emergence
Published in Sergio Barile, Raul Espejo, Igor Perko, Marialuisa Saviano, Francesco Caputo, Cybernetics and Systems, 2018
Diego Gonzalez-Rodriguez, Jose-Rodolfo Hernandez-Carrion
Finally, the paper introduces the notion of emergent languages and exposes the foundations of Self-Organized Linguistic Systems. SOLS rely on the theoretical framework of Complex Adaptive Systems in conjunction with the development of computational tools and simulations such as agent-based models.
Urban and architectural design from the perspective of flood resilience: framework development and case study of a Chinese university campus
Published in Journal of Asian Architecture and Building Engineering, 2023
Xu Ke, Wang Yang, Lin Misheng, Zhao Ranting
The focus of research on resilience in various disciplines has changed frequently, leading to fuzziness around the concept of flood resilience (Martin-Breen and Anderies 2011). Keating et al. (2017) highlighted that common challenges in resilience measurement frameworks are “(1) defining an appropriate scale of analysis both geographically and temporally [and] (2) identifying the potential end users and potential purposes”. Based on more than 50 years of interdisciplinary resilience research, three conceptual frameworks of resilience can be identified: engineering resilience, system resilience, and resilience based on complex adaptive systems (Martin-Breen and Anderies 2011). The concept of resilience of a complex adaptive system includes strategies to deal with flood disasters, moving from emphasising “resistance” to “recovery” to the ability to “living with water” (McClymont et al. 2020). Because this perspective emphasises the concepts of “disaster adaptation” and dynamic disaster response and focuses on longer-term resilience, the theory of complex adaptive systems may be the most appropriate conceptual framework given that flood disasters are becoming the new normal.
Evolution of community residents’ waste classification behavior based on multi-agent simulation
Published in Journal of the Air & Waste Management Association, 2022
Qing Yang, Mengyuan Luo, Xingxing Liu
Multi-agent system (MAS) has become one of the most popular methods for modeling real scenes and artificial social computing experiments because of its distribution, interaction and intelligence (Zhang et al. 2018). At present, it has been widely used in economics (Rano 2020), machine learning (Ehsan 2021), virus spreading (Castro et al. 2021), emergency management (Yang et al. 2020b) and other fields. Multi-agent modeling and simulation can simulate real or potential behavior in complex adaptive systems, while being able to adapt to changing environments and highly restore complex systems. The target attribute of the agent and the ability to perceive the external environment to make decisions independently are very suitable for the study of multi-agent behavior. In addition, Waste classification behavior is a spatial interaction simulation between individuals and groups, and the multi-agent model can more realistically reflect individual classification behavior and group classification results.
Coupling agent-based simulation and spatial optimization models to understand spatially complex and co-evolutionary behavior of cocaine trafficking networks and counterdrug interdiction
Published in IISE Transactions, 2022
Nicholas R. Magliocca, Ashleigh N. Price, Penelope C. Mitchell, Kevin M. Curtin, Matthew Hudnall, Kendra McSweeney
The persistence of transnational cocaine trafficking, its array of negative societal impacts, and the expansive spatial nature of the problem demand analysis and understanding of the phenomenon as a complex spatial and adaptive system (Magliocca et al., 2019). A complex adaptive system emerges and maintains a coherent form over time, and adapts and self-organizes in response to interactions among its internal components and environment (Holland, 1995; Choi et al., 2001). Complex adaptive systems become even more challenging to understand when causal dynamics and emergent behavior vary with spatial context and include long-distance spatial interactions (Manson, 2001; O’Sullivan, 2004). Since narco-traffickers’ primary adaptive response to counterdrug interdiction is to change trafficking route locations (Magliocca et al., 2019), a spatial dynamics perspective must be integrated with a complex adaptive systems approach. However, understanding adaptive behaviors requires the study of system dynamics over time and space at the level of system components (i.e., trafficking nodes), which is a daunting task for a phenomenon as expansive and dynamic as the co-evolution of transnational cocaine trafficking and counterdrug interdiction.