On the emergent “Quantum” theory in complex adaptive systems

Supporting interoperability in complex adaptive enterprise systems: A domain specific language approach

Complex Adaptive Systems (CAS) are ubiquitous and composed of many interacting “agents” that exhibit independent properties and behaviors that function together with their environment to produce emergent properties. Emergence and emergent properties cannot be predicted by isolated understanding of these interacting agents/components; but can be demonstrated by observing the outcomes associated with dynamically changing interacting components of a CAS. Obviously, evolution also plays a key role in driving emergence as a defining feature of biological CAS. Biological CAS are highly heterogeneous and complex, both within and across broad scales of time and space. Biological CAS are also non-linear which means that predicting outcomes is difficult. Moreover, it is impossible to “fix” a CAS, rather the identification of leverage points that can alter the trajectory of the system to achieve desired outcomes is a more logical approach. Until recently, systems of such high dimensionality were not sufficiently tractable to understand and apply CAS principles to a disease as complex as cancer. However, recent progress in the development of advanced technologies such as computation, machine learning, artificial intelligence, and modeling portend a day when cancer will be viewed and managed as a CAS. These “big data” tools offer new and innovative opportunities to mine, manage, manipulate, model and simulate cancer to derive the information needed to manage it as a CAS. In terms of applying these principles at least one approach to treating cancer, immunotherapy, suggests that achieving a future state where cancer is viewed through the lens of CAS is well underway. Immunotherapy represents a paradigm shift in cancer therapy in that it targets the immune system, which is a quintessential CAS. When immunotherapy is successful the outcome is a “homeostatic reset” of what is an extraordinarily complex interaction between cancer and the immune system. Together these two complex systems comprise a CAS that promises to re-define how we treat and prevent cancer. A variety of immunotherapeutics (dominated by checkpoint inhibitors) have produced durable responses (possible cures) in a few patients against some cancers. These agents essentially block signals that the tumor employs to keep the immune system from recognizing and killing the cancer. However, the interaction of cancer and the immune system is a dynamic CAS that will ultimately require a detailed understanding of the cellular and microenvironmental changes that occur in patients in response to specific immunotherapeutic interventions. The challenges we face are significant including: identifying responders/non-responders; determining doses; predicting and controlling toxicities; developing rational combinations; and creating new targeted systems-based therapies. Fortunately, many of these challenges can be met by defining the “states” produced by some of the defining alterations observed in responsive and non-responsive patients including “omics” alterations, types of immune cells, temporal relationships, immune activation, humoral factors, etc. Although early, models and platforms to describe, annotate, model and simulate these systems alterations are emerging. In the past several years, we have developed a modeling platform that permits the study of the immune system and its interaction with cancer. “Cell Studio” is an immune-modeling engine that seeks to examine cancer and the immune response as a dynamic CAS by using real world data on the immune system and cancer to develop and inform computational models. Cell Studio permits the user to conduct in silico experiments of defined time and complexity. It combines agent-based and mathematical modeling approaches to capture multiscale dynamics within the immune system. The engine permits user creation of multiple different types of immune cells each with different classes of properties including different collections of cell surface receptors at different concentrations and affinities as well as the capacity to release and respond to cytokines. Multiple compartments corresponding to different body niches (e.g. lymph node, tumor environment) can be created. Mathematical models govern phenomena such as diffusion and cell tracking of cytokine gradients. As a CAS, based on a finite number of “rules” the system is self-organizing and can display emergent properties. User defined therapeutic interventions such as drug administration can be incorporated to assess the system’s response. Cell Studio is implemented using a gaming platform so that the in silico experiments can be visualized in 3D - in real time if desired. This permits researchers to perform experiments similar to those done using biologic model systems and visualizing the results. Like most video gaming platforms, different user views, overviews, individual cell movement, etc. are available and real-time as well as cumulative statistical outputs are captured and displayed. Unlike biologic model systems the simulations can be time reversed to identify, visualize, and manipulate key events. Experimentation using the Cell Studio modeling engine shows that it can recapitulate the longitudinal events in biologic model systems. Additionally, it can recover “immunophenotypes” observed in human studies of immunotherapy in cancer. It is anticipated that the in silico modeling can augment current biologic modeling strategies - especially since it can be run with numbers of replicates of virtual experiments that are not practical with biologic model systems. Additionally, it promises to assist in the rationale to develop combinatorial interventions hitting multiple immune targets and in understanding factors that modulate successful outcomes. In summary, the implications of viewing, studying and developing strategic approaches to fundamentally understand the cancer - immune system CAS are profound. Cell Studio is a next generation novel and powerful approach to analyze and model specific components of this dynamic and integrated CAS for the benefit of patients. Citation Format: Anna D. Barker, Kenneth Buetow. Viewing cancer as a complex adaptive system and managing immunotherapy as “homeostatic reset” [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY36-03.

This chapter introduces Complex Adaptive Systems Thinking (CAST) into the domain of Intellectual Capital (IC). CAST is based on the theories of Complex Adaptive System (CAS) and Systems Thinking (ST). It argues that the CAST, combined with Intelligence Base offers a potentially more holistic approach to managing the Intellectual Capital of an organization. Furthermore, the authors extend this IC management with additional dimensions proper to a social entity such as an organization. New organizational design methods are needed and the capability approach is such a method that supports IC in virtual and real organizations. The characteristics of Intellectual Capital are discussed in the iterative process of inquiry and the Cynefin Framework, guaranteeing a holistic view on the organization and its environment.

This paper will explore the concept of resilience from its roots in ecology to the application of this ecological concept of resilience to social and community resilience in the context of climate change. In this context, resilience is seen as a property of complex adaptive communities rather than of individuals. This paper will explore how this ecological concept of resilience has been taken up both by climate adaptation research and by the Transition Town movement. This ecological concept of resilience is at odds with the individualism of both psychological and economic approaches to resilience in relation to climate change.

PurposeThe aim is to investigate the state of complex adaptive system (CAS) theory in the organizational theory literature and to provide a map for future studies of CAS theory.Design/methodology/approachAbstracts were searched via electronic database and a range of recently published (1996‐2004) books and articles were identified that contained a relatively concise description of CAS. Content analysis is used to deconstruct the CAS descriptions into “component concepts.” Those concepts are analyzed from multiple viewpoints.FindingsThere is no single, shared, sense of CAS theory. Differing understandings of CAS theory are identified based on “expert version” and “most popularly identified concepts.” Also, differences and similarities are identified between an “academic” version of CAS and a version developed by those who are influenced by both academic learning and practical experience.Research limitations/implicationsStudy is limited to concise definitions of CAS, so could be improved by including more lengthy conversations. Additionally, study is limited to organizational theory, so may be less applicable in other disciplines.Practical implicationsWhen working within a CAS framework, academics should specify their CAS perspective to improve clarity of their work. When using a CAS framework to study organizations, researchers should include a comprehensive suite of concepts. Though not described in depth, no effective application of CAS for organizational change were found.Originality/valueFor those who study CAS theory and theory of theory, this paper provides an important benchmark by identifying a bifurcation in the evolution of CAS theory.

Classical forest management has worked out a series of forest regulation methods with the aim of obtaining the “fully regulated” forest. Considering the forest as a complex biological adaptive system means overcoming the reductionist and mechanist paradigm, and entails a shift towards a systemic approach in silviculture and forest management. The aim of this work is to discuss the objectives and theoretical assumptions of classical forest management methods in the light of the new systemic paradigm. I conclude that managing forests as complex adaptive systems and sustaining their ability to adapt to future changes is possible only if there is also a change in forest management methods so that they are consistent with the new theoretical approach.

Purpose– The purpose of this study is to evaluate whether the global policy on sustainability, United Nations Global Compact (UNGC), is in alignment with the complexity of the sustainability landscape utilizing complex adaptive system (CAS) theory and theory of change.Design/methodology/approach– An original Complex Adaptive Policy System (CAPS) framework is used as a qualitative instrument with a constant comparison of 11 CAS themes in analyzing 117 UNGC speeches listed on the Global Compact Web site.Findings– Although this study is intended as a preliminary study, the findings raise important questions regarding the long-term impact of the Global Compact as a global policy on sustainability.Research limitations/implications– The limitations of the study include the preliminary study design and limited source of information. Future research should include a comprehensive evaluation of the UNGC to yield specific recommendations for aligning policy with the landscape.Originality/value– The study offers an original systems framework to evaluate public and private organizational polices on sustainability.

Complexity science suggests that our current health care delivery system acts as a complex adaptive system (CAS). Such systems represent a dynamic and flexible network of individuals who can coevolve with their ever changing environment. The CAS performance fluctuates and its members' interactions continuously change over time in response to the stress generated by its surrounding environment. This paper will review the challenges of intervening and introducing a planned change into a complex adaptive health care delivery system. We explore the role of the "reflective adaptive process" in developing delivery interventions and suggest different evaluation methodologies to study the impact of such interventions on the performance of the entire system. We finally describe the implementation of a new program, the Aging Brain Care Medical Home as a case study of our proposed evaluation process.

Complex Adaptive Socio-Technical Systems The Role of Socio-Technical Networks in New Product Development

Classic management and systems theory seek to maintain homeostasis and thus are inadequate in describing the dynamics in managing continual change. Accordingly, some theorists posit complex adaptive systems (CAS) theory and others, the improvisation metaphor, as new ways to re-conceive the design processes and the theory we use to understand contemporary organizations and the open-processional change processes within them. Yet, we lack robust empirical data in support of this theory. This research study investigates a regional theatre company: an organization that has learned how to manage continuous change because continuous change is part of its goal and necessary to its survival. The research findings make four contributions. First, in combining the CAS with the improvisation metaphor, it delineates a theoretic framework that more accurately describes the dynamics of open-processional change in a non-equilibrium system. Second, it sets forth the ontology and underlying logics that differentiate this CAS-improvisation theoretic framework from classic management and systems theory. Third, it grounds this theoretic framework in empirical data and shows how the company members adopt its underlying logics in embracing turbulence as creative force. Fourth, it presents the company's organizational design and its members' practices as a living heuristic to illuminate how an open-processional change model works.

Complex Adaptive Systems, for our purposes, are social systems that that evolve and display new, emergent properties, and self-organizing behavior of their components; they are based on a reasonably stable infrastructure, on the satisfaction of the most basic needs, and flexible, frequent, and open communication and interaction. Complex Adaptive Systems may be based on a few, simple rules, but can yield complex and unpredictable outcomes. The ‘Hole in the Wall’ project is an interesting case in point in the design of spaces for complex adaptive systems, or complex adaptive networks. In this project, touch screen computers were literally put in ‘holes in walls’ in places where unschooled children congregated. The children were given no instructions on how to use the computers, or what to do with them, but with startling results: the children soon taught themselves how to use the computers and the Internet, and much more (Mitra, 2003).

Complex Adaptive Systems, for our purposes, are social systems that that evolve and display new, emergent properties, and self-organizing behavior of their components; they are based on a reasonably stable infrastructure, on the satisfaction of the most basic needs, and flexible, frequent, and open communication and interaction. Complex Adaptive Systems may be based on a few, simple rules, but can yield complex and unpredictable outcomes. The ‘Hole in the Wall’ project is an interesting case in point in the design of spaces for complex adaptive systems, or complex adaptive networks. In this project, touch screen computers were literally put in ‘holes in walls’ in places where unschooled children congregated. The children were given no instructions on how to use the computers, or what to do with them, but with startling results: the children soon taught themselves how to use the computers and the Internet, and much more (Mitra, 2003).

Complex adaptive systems (CAS) have been identified as being hard to comprehend, composed of multiple interacting components acting interdependently with overlapping functions aimed at adapting to external/environmental forces. The current theoretical model utilized the natural functions of teams, viewing teams as a complex adaptive system, to develop the structure of the theory of complex adaptive team systems (CATS). The CATS model was formulated around the components of complexity theory (interactions, nonlinearity, interdependency, heterogeneity, complex systems, emergence, self-organizing, and adaptability) to show its utility across multiple domains (the role of leadership, organizational learning, organizational change, collective cognitive structures, innovation, cross-business-unit collaborations). In theorizing the CATS model, a new level of analysis was implemented, the interactions between agents as a move toward emergence in complex systems. The CATS model ultimately provides a model for organizations/institutions to drive knowledge creation and innovation while operating in today's complexity.

BackgroundIrrational use of antibiotics is proving to be a major concern to the health systems globally. This results in antibiotics resistance and increases health care costs. In Iran, despite many years of research, appreciable efforts, and policymaking to avoid irrational use of antibiotics, yet indicators show suboptimal use of antibiotics, pointing to an urgent need for adopting alternative approaches to further understand the problem and to offer new solutions. Applying the Complex Adaptive Systems (CAS) theory, to explore and research health systems and their challenges has become popular. Therefore, this study aimed to better understand the complexity of the irrational use of antibiotics in Iran and to propose potential solutions.MethodThis research utilized a CAS observatory tool to qualitatively collect and analyse data. Twenty interviews and two Focus Group discussions were conducted. The data was enriched with policy document reviews to fully understand the system. MAXQDA software was used to organize and analyze the data.ResultWe could identify several diverse and heterogeneous, yet highly interdependent agents operating at different levels in the antibiotics use system in Iran. The network structure and its adaptive emergent behavior, information flow, governing rules, feedback and values of the system, and the way they interact were identified. The findings described antibiotics use as emergent behavior that is formed by an interplay of many factors and agents over time. According to this study, insufficient and ineffective interaction and information flow regarding antibiotics between agents are among key causes of irrational antibiotics use in Iran. Results showed that effective rules to minimize irrational use of antibiotics are missing or can be easily disobeyed. The gaps and weaknesses of the system which need redesigning or modification were recognized as well.ConclusionThe study suggests re-engineering the system by implementing several system-level changes including establishing strong, timely, and effective interactions between identified stakeholders, which facilitate information flow and provision of on-time feedback, and create win-win rules in a participatory manner with stakeholders and the distributed control system.

PurposeComplex systems adapt to survive, but little comparative literature exists on various approaches. Adaptive complex systems are generic, this referring to propositions concerning their bounded instability, adaptability and viability. Two classes of adaptive complex system theories exist: hard and soft. Hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and softer theories, which we refer to as Viable Systems Theories (VSTs), that includes Management Cybernetics at one extreme and Humanism at the other. This paper has a dual purpose distributed across two parts. In part 1 the purpose was to identify the conditions for the complementarity of the two classes of theory. In part 2 the two the purpose is to explore (in part using Agency Theory) the two classes of theory and their proposed complexity continuum.Design/methodology/approachExplanation is provided for the anticipation of behaviour cross-disciplinary fields of theory dealing with adaptive complex systems. A comparative exploration of the theories is undertaken to elicit concepts relevant to a complexity continuum. These explain how agency behaviour can be anticipated under uncertainty. Also included is a philosophical exploration of the complexity continuum, expressing it in terms of a graduated set of philosophical positions that are differentiated in terms of objects and subjects. These are then related to hard and softer theories in the continuum. Agency theory is then introduced as a framework able to comparatively connect the theories on this continuum, from theories of complexity to viable system theories, and how harmony theories can develop.FindingsAnticipation is explained in terms of an agency’s meso-space occupied by a regulatory framework, and it is shown that hard and softer theory are equivalent in this. From a philosophical perspective, the hard-soft continuum is definable in terms of objectivity and subjectivity, but there are equivalences to the external and internal worlds of an agency. A fifth philosophical position of critical realism is shown to be representative of harmony theory in which internal and external worlds can be related. Agency theory is also shown to be able to operate as a harmony paradigm, as it can explore external behaviour of an agent using a hard theory perspective together with an agent’s internal cultural and cognitive-affect causes.Originality/valueThere are very few comparative explorations of the relationship between hard and soft approaches in the field of complexity and even fewer that draw in the notion of harmony. There is also little pragmatic illustration of a harmony paradigm in action within the context of complexity.