Complex Adaptive System Model Research Articles

The Robustness and Vulnerability of a Complex Adaptive System With Co-Evolving Agent Behavior and Local Structure

Agents' irrational behavior would lead to local configuration of complex adaptive system percolation. The corresponding critical point is key to making decisions for improving the system or keeping the system from collapsing. The authors construct a complex adaptive system model where agent behavior and its local configuration co-evolve. This model shows, when an arbitrary agent and its neighbors change their strategy and local interactive configuration, how the properties of percolation critical point of this system would emerge under random attack and intentional attack. It is shown that the system is robust if it is attacked randomly, and there are always at least two large components keeping the system connected. However, if the system is attacked intentionally, the result is more interesting. The system is robust if the deleting probability is smaller than a certain critical probability c0, but the system is vulnerable if the probability is larger than c0. Furthermore, the critical probability c0 is determined by the agent payoff, system structure, and noise.

Irruption Theory: A Novel Conceptualization of the Enactive Account of Motivated Activity.

Cognitive science is lacking conceptual tools to describe how an agent's motivations, as such, can play a role in the generation of its behavior. The enactive approach has made progress by developing a relaxed naturalism, and by placing normativity at the core of life and mind; all cognitive activity is a kind of motivated activity. It has rejected representational architectures, especially their reification of the role of normativity into localized "value" functions, in favor of accounts that appeal to system-level properties of the organism. However, these accounts push the problem of reification to a higher level of description, given that the efficacy of agent-level normativity is completely identified with the efficacy of non-normative system-level activity, while assuming operational equivalency. To allow normativity to have its own efficacy, a new kind of nonreductive theory is proposed: irruption theory. The concept of irruption is introduced to indirectly operationalize an agent's motivated involvement in its activity, specifically in terms of a corresponding underdetermination of its states by their material basis. This implies that irruptions are associated with increased unpredictability of (neuro)physiological activity, and they should, hence, be quantifiable in terms of information-theoretic entropy. Accordingly, evidence that action, cognition, and consciousness are linked to higher levels of neural entropy can be interpreted as indicating higher levels of motivated agential involvement. Counterintuitively, irruptions do not stand in contrast to adaptive behavior. Rather, as indicated by artificial life models of complex adaptive systems, bursts of arbitrary changes in neural activity can facilitate the self-organization of adaptivity. Irruption theory therefore, makes it intelligible how an agent's motivations, as such, can make effective differences to their behavior, without requiring the agent to be able to directly control their body's neurophysiological processes.

The green-restructuring of clusters: investigating a biocluster's transition using a complex adaptive system model

ABSTRACT Bioclusters’ promise of helping achieving sustainable bioeconomies has invoked great interest among policymakers and academia. However, bioclusters are not intrinsically sustainable. If they are to fulfil their promise, bioclusters must undergo green-restructuring. While cluster-research has elaborated on green regional development, we need more clarity on how clusters transition to normatively desired states; we need more evidence of how green-restructuring unfolds. In this study, we conduct a longitudinal analysis to demonstrate how a biocluster green-restructures through the interactions of agency, regional and industrial structures, and phenomena at (supra-)national levels. To execute this analysis, we created a novel cluster-evolution framework that treats clusters, and the regional innovation system and sectoral systems of innovation that contain the cluster, as complex adaptive systems. We applied this framework to study the greening of the Basque pulp-and paper-biocluster, over four phases between 1986 and 2019. Our analysis helped us discover patterns of agency, structural dynamics, and of agency-structure interactions and how supra-regional phenomena shaped structures and agency over the four phases. Based on our findings, we recommend policymakers encourage not only green-tech entrepreneurs, but also institutional-entrepreneurs and place-leaders who can help shape both (supra-)regional and industrial structures.

Scaling up breastfeeding in England through the Becoming Breastfeeding Friendly initiative (BBF).

Breastfeeding is the most accessible and cost-effective activity available to public health and has been shown to be one of the most effective preventive measures mothers can take to protect their children's health. Despite the well-documented benefits, the UK has one of the lowest breastfeeding rates in the world. The Becoming Breastfeeding Friendly (BBF) toolkit was developed through highly structured technical and academic collaboration, led by Yale University. It provides an evidence-based process to help countries assess their breastfeeding status and readiness to scale up, and identifies concrete measures countries can take to sustainably increase breastfeeding rates, based on data-driven recommendations. BBF is grounded in the Breastfeeding Gear Model complex adaptive systems framework which is made up of eight simultaneous conditions that sustain breastfeeding. In 2018, a committee of multi-agency stakeholders implemented the BBF process in England, collecting evidence to score the 'gear' components of England's breastfeeding environment against 54 benchmarks. The Training and Programme Delivery gear received the highest score, attributable to existing learning outcomes for health professionals and practitioners, peer supporters and specialist services, although there is a need for greater coordination and integration. The lowest scores were given for Promotion and Coordination, Goals and Monitoring due to the lack of a dedicated national strategy for breastfeeding and poor sharing of localised strategies and programmes. The process generated clear recommendations highlighting the need for more robust routine infant feeding data collection and reporting, and the necessity for strengthening leadership, monitoring and oversight to scale up and sustain breastfeeding.

Exploring a market-like organizational system’s performance by considering the efficiency/effectiveness trade-off in micro-level exchanges

PurposeThe study explores how a market-like organizational system realizes efficient and/or effective development by investigating the efficiency/effectiveness trade-off in micro-level exchanges.Design/methodology/approachThe study is motivated by two principles: reciprocity and similarity. Reciprocal benefits drive exchanges. Accordingly, two agents for a potential exchange should have different resources. However, differences in resources usually cause lack of trust, which hinders the efficient occurrence of exchanges. Alternatively, if two parties have similar resource positions, they can conduct an exchange efficiently. Nevertheless, the similarity makes the exchange less effective. Therefore, an efficiency/effectiveness trade-off exists in micro-level exchanges. To understand how different focuses on the efficiency/effectiveness trade-off shape the macro-level performance, the author develops a complex adaptive systems model for computer simulations.FindingsThe author finds that an efficiency-focus institution facilitates a market-like organizational system's rapid emergence but hinders the system's effective development.Research limitations/implicationsThe study develops a model of how a dyadic exchange happens (or not) between any two parties in a competitive and uncertain environment and how the micro-level exchanges aggregate, suggesting one specific way to understand the micro-to-macro process of a market-like organizational system's economic dynamism. Future research is expected to improve the model with different contingencies.Practical implicationsThe study's findings suggest that the efficiency-focus institution and the effectiveness-focus institution should be used at different times in a market-like organizational system's development process.Originality/valueThe study investigates the macro-level consequences building upon the micro-level efficiency/effectiveness trade-off.

Macroscopic-microscopic coupled water-energy nexus simulation and behavior optimization under uncertainty

Investigating the nexus of water and energy is important for understanding their complex interrelationships and promoting sustainable development. This study used a complex adaptive system model to explore the interaction mechanism of the water–energy nexus at the regional scale and generate behavior-optimization schemes for industries and individuals under uncertainty. This model was integrated with a system dynamics model and an agent-based model, as well as a fuzzy optimization approach that considered uncertainty in agents' behavior. A novel index of the water–energy nexus was constructed to express its characteristics. This framework was applied to Tianjin, China, to evaluate water–energy outputs considering the interaction between micro- and macrolevel systems. The results revealed the following: 1) Water and energy consumption showed a slowly increasing trend, whereby energy and water consumption will reach, respectively, 29.66 × 108 m3 and 101.80 × 106 tons of coal equivalent (tce) by 2030. 2) A low degree of satisfaction among agents will lead to larger amounts of water-related energy and energy-related water from household use and efficiency investment by enterprise. A high degree of satisfaction is key to achieving high energy-related water-consumption intensity in the early stage; that dominance will turn into a low degree of satisfaction in the late stage. 3) Aggressive behavior and willingness among agents will have a greater effect on the water–energy nexus in the early stage. Conservative behavior, meanwhile, will have considerable potential to control the development of the water–energy nexus in the future.

Complex adaptive systems, agent-based modeling and supply chain network management: A systematic literature review

In recent decades, researchers are turning to the potential of ABMs to study complex phenomena. Due to intrinsic interconnections, structural interactions and inter-dependencies, individual variations, and communications of various components, supply chain network should be accordingly treated as a complex adaptive system. ABM is dominant tool exploring the emergent behavior of supply chain network with numerous interactive agents. This paper aims to conduct a systematic literature review on the agent-based modeling in the concepts of supply chain and various fields of research. Using reputable databases, combining intended keywords and applying filters based on restrictions and indicators, a total of 123 relevant articles are selected from the valid journals and conferences in year 2010-2019, and 17 subjects in association with supply chain management and 23 subjects related to other fields are presented. Moreover, a brief history and the definition of the three basic areas including complex systems, complex adaptive system and agent-based modeling are provided. The main objective is to provide a perspective based on agent-based modeling and complex adaptive systems for researchers in different sciences and especially supply chain researchers, who are not sufficiently familiar with the philosophy and applications of these approaches.

Capturing Continuous Economic Development from a Bottom-Up Exchange Process a Complex Adaptive Systems Model

How do an economic system’s elementary components aggregate and affect the macro-level performance? How do changes to these micro-level components change the macro-level development of economic vitality? Prior research has not fully addressed these important questions. Built on the broad framework of complex adaptive systems, this research presents an agent-based model of economic exchanges that captures a bottom-up process with three interrelated elements: agents that comprise basic entities of actions, economic exchanges that represent the interactions and mutual adaptions of the agents, and an environment that structures the agents’ economic exchanges. We have developed a computer simulation program to quantitatively explore the macro-level performance of economic exchanges along with the variation of three elementary components. The experiments in this study have several findings of that indicate ways to sustain the economic system’s prosperity through managing the system’s micro-level components.

Mapping validity and validation in modelling for interdisciplinary research

Complex Adaptive Systems (CAS) is an interdisciplinary and dynamic modelling approach for the study of today’s global challenges. It is used for the explanation, description, and prediction of behaviours of system components and the system at large. To understand and assess the quality of research in which CAS models are designed and used, a thorough understanding of the meanings of ‘validity’ from social science research methodology and ‘validation’ from simulation modelling is needed. In this paper, we first describe the modelling process. Then, we analyse the concepts ‘validity’ and ‘validation’ as used in a set of research methodology textbooks and a set of modelling textbooks. We present one single map that integrates validity as characteristic of the model input, the modelling process, model validation, and the validity of the model built. The map is illustrated by means of one example. The terminology proposed in the map allows to describe and distinguish between the validity of primary research used for input in the model, how the quality of the modelling depends on structural and behavioural validation, and, how the assessment of the validity of the model is informed by these types of validation plus research with independent data.

Emergent behaviour in a system of industrial plants detected via manifold learning

The efficiency behaviour of an industrial plant, part of a huge international structure of plants, is modelled as an emergent phenomenon in a complex adaptive system. The study is based on real in-service data obtained from an industrial production line monitoring system. Models of complex adaptive systems and some modern manifold learning methods are introduced in a unified formalism. The emergent behaviour is efficiently described in this setup.

A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile

BackgroundThere is a strong spatial correlation between demographics and chronic diseases in urban areas. Thus, most of the public policies aimed at improving prevention plans and optimizing the allocation of resources in health networks should be designed specifically for the socioeconomic reality of the population. One way to tackle this challenge is by exploring within a small geographical area the spatial patterns that link the sociodemographic attributes that characterize a community, its risk of suffering chronic diseases, and the accessibility of health treatment. Due to the inherent complexity of cities, soft clustering methods are recommended to find fuzzy spatial patterns. Our main motivation is to provide health planners with valuable spatial information to support decision-making. For the case study, we chose to investigate diabetes in Santiago, Chile.MethodsTo deal with spatiality, we combine two statistical techniques: spatial microsimulation and a self-organizing map (SOM). Spatial microsimulation allows spatial disaggregation of health indicators data to a small area level. In turn, SOM, unlike classical clustering methods, incorporates a learning component through neural networks, which makes it more appropriate to model complex adaptive systems, such as cities. Thus, while spatial microsimulation generates the data for the analysis, the SOM method finds the relevant socio-economic clusters. We selected age, sex, income, prevalence of diabetes, distance to public health services, and type of health insurance as input variables. We used public surveys as input data.ResultsWe found four significant spatial clusters representing 75 percent of the whole population in Santiago. Two clusters correspond to people with low educational levels, low income, high accessibility to public health services, and a high prevalence of diabetes. However, one presents a significantly higher level of diabetes than the other. The second pair of clusters is made up of people with high educational levels, high income, and low prevalence of diabetes. What differentiates both clusters is accessibility to health centers. The average distance to the health centers of one group almost doubles that of the other.ConclusionsIn this study, we combined two statistical techniques: spatial microsimulation and selforganising maps to explore the relationship between diabetes and socio-demographics in Santiago, Chile. The results have allowed us to corroborate the importance of the spatial factor in the analysis of chronic diseases as a way of suggesting differentiated solutions to spatially explicit problems. SOM turned out to be a good choice to deal with fuzzy health and socioeconomic data. The method explored and uncovered valuable spatial patterns for health decision-making. In turn, spatial microsimulation.

Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome.

Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. We extend our previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and we explore possible beneficial effects of inter-cluster inhibitory connections. We conclude that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process.

Informing theoretical development of salutogenic, asset-based health improvement to reduce syndemics among gay, bisexual and other men who have sex with men: Empirical evidence from secondary analysis of multi-national, online cross-sectional surveys.

Globally, gay, bisexual and other men who have sex with men (GBMSM) experience an increased burden of poor sexual, mental and physical health. Syndemics theory provides a framework to understand comorbidities and health among marginalised populations. Syndemics theory attempts to account for the social, environmental, and other structural contexts that are driving and/or sustaining simultaneous multiple negative health outcomes, but has been widely critiqued. In this paper, we conceptualise a new framework to counter syndemics by assessing the key theoretical mechanisms by which pathogenic social context variables relate to ill-health. Subsequently, we examine how salutogenic, assets-based approaches to health improvement could function among GBMSM across diverse national contexts. Comparative quantitative secondary analysis of data on syndemics and community assets are presented from two international, online, cross-sectional surveys of GBMSM (SMMASH2 in Scotland, Wales, Northern Ireland and the Republic of Ireland and Sex Now in Canada). Negative sexual, mental and physical health outcomes were clustered as hypothesised, providing evidence of the syndemic. We found that syndemic ill-health was associated with social isolation and the experience of stigma and discrimination, but this varied across national contexts. Moreover, while some of our measures of community assets appeared to have a protective effect on syndemic ill-health, others did not. These results present an important step forward in our understanding of syndemic ill-health and provide new insights into how to intervene to reduce it. They point to a theoretical mechanism through which salutogenic approaches to health improvement could function and provide new strategies for working with communities to understand the proposed processes of change that are required. To move forward, we suggest conceptualising syndemics within a complex adaptive systems model, which enables consideration of the development, sustainment and resilience to syndemics both within individuals and at the population-level.

A Complex Adaptive Systems Approach to the Relationship between Personality and Social Division

AbstractAlthough psychological differences appear to be a factor in political and social division, there are few process‐based accounts of the relationship between political orientation and personality. We present a complex adaptive systems model with biological differences at the micro level and sociocultural structures at the macro level, both of which exert a bidirectional influence on personality at the meso level. This provides the foundation for a process‐based model in which social interactions act as feedback loops that encourage the development of biases at the biological level, accentuating individual differences while driving collective adaptation to changing environments. Cyclical phases in adaptive processes are then associated with emergent political values. Therefore, political and social division is an inevitable feature of an adaptive society and results from interactions between individuals responding to stress. Using this model, we are able to account for both congruence and incongruence between personality and political orientation.

Modeling of Complex Information Processes in the Oversaturated Media Environment (to the article by A.P. Sukhodolov, I.V. Anokhov, V.A. Marenko Informational Impulse-Wave Interaction between the Media and Society)

The article discusses promising areas of modeling informational interaction between the media and the society from the standpoint of the Paradigm of Complexity. Increasing complexity of the society’s media system, turbulence in the media environment, transformation of the functions of traditional media, changing methods of information production and consumption, and development of Web 2.0 technologies are critical challenges for media theorists. Basing on the theory of behavioral economics, the authors prove that in the context of a significant growth of information volume, most people demonstrate a shift of thinking from System 1 to System 2. System 1 is a sub-system of the brain responsible for making rational, well-considered and balanced decisions, whereas System 2 is the area of automatic quick, spontaneous decision-making. This shift results in the dominance of populism, post-truth and fake news in the media space. The authors show that Web 2.0 technologies, which help people, regardless their occupation, to generate info-content, extends the boundaries of journalism and media. This, particularly, leads to development of citizen journalism and the increasing role of social communities (networks) as isolated media-systems that fragment the media space. The article substantiates that traditional media’s abilities to manipulate mass consciousness in the current supersofisticated media environment are extremely limited. The authors put forward a hypothesis about a radical change in the media’s key role, namely, a transition from content production to creating a semantic context. The methodological reflections presented in the article may serve as new impulses to building up synergetic, nonequilibrium, evolutionary and other models of complex adaptive media systems.

Towards a complex model of disaster behaviour.

This paper outlines why a move towards a complex adaptive systems model of behaviour is required if the goal is to generate better understanding of how individuals and groups interact with their environment in a disaster setting. To accomplish this objective, a bridge must be built between the broader social sciences and behavioural economics to incorporate discipline-specific insights that are needed to move towards complexity. This is only possible through a deeper understanding of behaviour and how the environment in which they occur can influence actions. It is then that one can counteract the poor behavioural predictions, flawed policies based on myth, inefficient design, and suboptimal outcomes that have flourished in the absence of a complex adaptive systems model. This paper provides a conceptual framework that draws on concepts from across the natural and social sciences, such as behavioural economics, endocrinology, psychology, sociobiology, and sociology in order to build an interactive theory of disaster behaviour.

Applying the Early Adopters Model to Organizations Undergoing Technological Innovation Process

This article examines the relevance of the Early Adapter to the process of technological innovation assimilation in a contemporary organization environment._In the 21st century, an organization that wishes to survive in face of the flood of changes in customer requirements, technological innovation, and changing environment must assimilate technological innovation as an ongoing routine by way of carrying out an intensive process of digital transformation. Studies have shown that the digital transformation process affects the employees. It may require additional work effort as well as re-learning, and even change in status. Consequently, resistance may rise. This might affect the success and the effectiveness of the digital transformation in the organization. Therefore, it requires the support of management and the cooperation of the employees, while planning a realistic, concise, and coordinated timetable for the process. The Diffusion of Innovations model (DOI) argues that workers with a positive propensity to technology are more likely to become the early adopters of the digital transformation. According to the Complex Adaptive Systems model (CAS) in organizations, employees who act as fractals (namely, work in a functional autonomy) are prone to encourage technological innovation and utilize resources effectively and share knowledge and solutions tailored to customer needs. _This study examined the relationship between workers function as fractals in an organization with CAS characteristics, and the following traits: development of positive attitudes towards technology, ready to use, and involvement in the assimilation process. According to the DOI model, these are the characteristics of early adopters. Such employees are the most significant contributors to the effectiveness of digital transformation. An empirical study was conducted among 270 subjects who worked in four different organizations in different capacities. The results of the study show that there is a significant relationship between working as an early adopter and working as a fractal in a CAS organization. It also shows that the dimensions “ready to use” and “employee involvement in the process” were the most significant, and exhibited strong and meaningful relationship. The conclusions of the study indicate that the transformation of the organization into CAS, as well as the development of workers as fractals, will encourage the employees to become early adaptors, hence contribute to an efficient and effective process of digital transformation, and to effectively handling disruptive innovation.

A review of agent-based modeling (ABM) concepts and some of its main applications in management science

We live in a very complex world where we face complex phenomena such as social norms and new technologies. To deal with such phenomena, social scientists often use reductionism approach where they reduce them to some lower-lever variables and model the relationships among them through a scheme of equations. This approach that is called equation based modeling (EBM) has some basic weaknesses in modeling real complex systems so that assumptions such as unbounded rationality and perfect information are strongly emphasized while adaptability and evolutionary nature of all engaged agents along with network effects go unaddressed. In tackling deficiencies of reductionism, the complex adaptive system (CAS) framework has been proven very influential in the past two decades. In contrast to reductionism, under CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive. As the most powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practitioners. ABMs show how agents’ simple behavioral rules and their local interactions at micro-scale can generate surprisingly complex patterns at macro-scale. Despite a growing number of ABM publications, those researchers unfamiliar with it have to study a number of works to understand (1) why and what of ABM, (2) its differences with EBM (3) its main functionalities in scientific studies and (4) some of its applications in management science. So, this paper’s major contribution is to help researchers particularly those unfamiliar with ABM to get insights regarding its philosophy and use and gain a big picture of it.

Becoming Breastfeeding Friendly Index: Development and application for scaling-up breastfeeding programmes globally.

Global efforts to further improve exclusive breastfeeding rates have not been successful, in part because effective scaling-up frameworks and roadmaps have not been developed. The Becoming Breastfeeding Friendly (BBF) toolbox includes an evidence-based index, the BBF Index (BBFI), to guide the development and tracking of large scale, well-coordinated, multisector national breastfeeding promotion programmes. This paper describes the development of the BBFI, which is grounded in the Breastfeeding Gear Model complex adaptive systems framework. The BBFI was developed by the BBF Steering Committee in collaboration with a high-level Technical Advisory Group following the Delphi consensus methodology. Key benchmarks and definitions were informed by evidence-based health, nutrition, and newborn survival initiatives identified from the academic and grey literature. The BBFI consists of 8 gears (54 benchmarks): Advocacy (4); Political Will (3); Legislation and Policies (10); Funding and Resources (4); Training and Program Delivery (17); Promotion (3); Research and Evaluation (10); and Coordination, Goals, and Monitoring (3). Scores are generated for 8 gear scores plus a total country score to gauge the scaling-up enabling environment. The BBFI provides an evidence-based index to assist countries in (a) assessing their readiness to scale up breastfeeding programmes and (b) tracking scaling-up progress.

Virtual organizational design laboratory: Agent-based modeling of the co-evolution of social service delivery networks with population dynamics

This paper extends the concept of biological co-evolution to explain the performance and survival of one type of service organizations. It proposes that service delivery network design would benefit from complex adaptive systems (CAS) modeling approaches to recreate organizational phenomena driven by the interaction of the organization with its operating environment. This approach, paired with experimental design methods can serve as a virtual laboratory.We take the case of social service delivery (SSD) organizations that are structured as nonprofit organizations providing humanitarian assistance and relief services. These organizations often serve different geographical regions, thus, racial composition, migration patterns, and wealth of the populations served are factors that vary between locations. SSDs operate through service nodes (i.e., field offices, chapters, branches) in a network configuration. Therefore, the managerial decision of where to locate the field offices is an important one. An agent-based model to recreate agents’ interactions as proxies of those exchanges occurring in real SSD settings is used. A series of validation experiments instill confidence that our model can be used as a virtual research laboratory. This paper contributes to the field of organizational design by testing a model able to recreate different policies that combined with different operating conditions impact the network over time and space. In addition, it provides experimental insights on what type of network configuration might provide a higher number of services delivered over time across the service network. The results can inform those defining the service system architecture looking to achieve SSD's goals considering the demographics of the markets served.