Raw Conceptual Overview

Systems thinking is based on the idea that a system is an integrated whole composed of interconnected parts, with properties that cannot be attributed to individual parts alone. Systems thinking contrasts with the Cartesian paradigm, which assumes that the behavior of the whole can be understood from the properties of its parts. The composition, structure, and organization of a system define its identity at any given moment.

Ludwig von Bertalanffy formalized modern systems thinking through his General Systems Theory (GST) in the early 20th century, emphasizing the transfer of principles across different fields.

Bertalanffy argued that the laws of classical physics, based on assumptions of closed systems and equilibrium dynamics, did not apply to biological systems, which are open systems capable of maintaining ordered steady states under non-equilibrium conditions.

In management, systems thinking challenged the Newtonian paradigm and initially focused on designing organizations as controllable systems. Later developments shifted towards ideas of co-evolution and unintended consequences of management interventions.

Cybernetics, autopoiesis (Maturana and Varela), and dissipative systems (Prigogine) were influential in the evolution of ideas about the management and organization of systems, focusing on control mechanisms, self-organization, and the dynamics of organizational transformation, respectively.

Cybernetics, coined by Norbert Weiner, is the science of control and communication in animals and machines. It developed during World War II and was further explored in the Macy meetings.

Cybernetics contributed to management science by conceptualizing feedback loops between system components as regulating mechanisms for system performance. The behavior of a system depends on the cumulative effect of the links between its components. Early management science based on cybernetic principles focused on exploiting negative feedback loops for self-regulation and stability. The importance of positive feedback mechanisms gained attention in the 1990s.?

Jay Forrester's System Dynamics and Stafford Beer's Viable Systems Model are two prominent developments derived from the cybernetic movement in management. System Dynamics models define an enterprise in terms of the structure of feedback loops underpinning its dynamic behavior. It focuses on long-term patterns and internal organizing structures of closed information loops in controlling and regulating the enterprise's behavior. System Dynamics tools enable the representation of causal structures of problems in terms of stocks, flows, and feedback loops. These tools help decision-makers explore the potential unintended consequences of their interventions.

The predictive power of System Dynamics simulations is limited by the extent to which a persistent set of feedback mechanisms and their causal effects can be defined for the lifetime of the model. The assumption of structural stability is crucial for the model's accuracy and usefulness.

The VSM originated in the 1950s and was conceived by Stafford Beer as a generic blueprint for the organizing structure of any autonomous system. According to the VSM, any organization can be defined as a set of systems nested within systems, embodying a recursive organizing structure. The generic VSM template comprises five necessary and sufficient subsystems that are interactively involved in maintaining the identity of an organism or organization independently of other such organisms within a shared environment. The five subsystems are labelled as Systems 1–5 and take care of the primary function of the organization, information and communication, governance, environmental monitoring, policy, and strategy. The VSM template is replicated at all levels of detail within the nested structure, displaying a fractal, self-similar architecture. An organization is considered viable if and only if it has this specified inter-related set of management functions embodied recursively at all levels of organization. Any deficiency in the subsystems compromises the organization's viability.

The VSM has been widely used for organizational diagnosis and design, unifying its application at all scales to define management structures for maintaining a cohesive organizational structure and identity.

The VSM and System Dynamics conform to a design worldview based on assumptions of structural stability, where desired behaviors of complex systems can be brought about in a largely deterministic manner by management interventions on feedback loops.

This view has been criticized for not taking into account the non-rational behavior of human actors and the emergent aspects of collective behaviors.

Systems Engineering emerged as an analytic approach to dealing with complexity, based on the definition of systems in terms of hierarchical structures and modular organization. The technical and management challenge in Systems Engineering lay in partitioning projects, systems, and development work without losing the holistic view of the system, while the conceptual challenge was in defining boundaries and interfaces to preserve the integrity of the reassembled whole.

Systems Engineering and Software Engineering focused on the internal consistency of modularized systems, using techniques for modularized description, design, and development of system components.?

Peter Checkland's Soft Systems Methodology (SSM) grew out of his critique of the way in which systems engineering methods neglected the human dimension of the context within which systems were conceived and used. SSM focuses on the often-contested question of what the 'right' system should be, addressing human purpose and value-based perceptions. In SSM, the problem situation is viewed as a human activity system with multiple stakeholders having different perceptions about the system and its purpose. SSM engages each stakeholder in explicitly defining the problem situation, the transformation it must undergo to achieve a more desirable state, and the activities required to deliver the transformation. The different stakeholder 'models' of transformation are then fed into a collective debate and discussion to arrive at a decision that is systemically desirable and culturally feasible.

While SSM pointed to the importance of human and social values and perceptions in decision-making and its outcomes, it remains within the design paradigm, focusing on specifying and designing the 'right' system intervention to achieve a desired state of affairs.

There was a growing concern about the unintended and unforeseen consequences of planned management interventions, accompanied by a questioning of the popularity of centralized, hierarchical management control. Herbert Simon's concept of bounded rationality highlighted the limitations in managers' informational and cognitive scope and capacity to make optimal decisions in complex situations. Henry Mintzberg's concept of emergent strategy pointed to the contextual complexity for strategic action, suggesting that actual strategies emerge from the dynamics of interaction between the organization and its environment.

The organizational behavior literature showed a growing interest in the role of self-organizing groups and front-line inventiveness in enabling transformation and innovation while maintaining organizational integrity in dynamic competitive contexts. The rapid adoption of the Internet and related technological advances in the 1990s highlighted the networked nature of society and economics, characterized by increased informational complexity and scope for greater uncertainty and unpredictability. The global interconnectedness and network dynamics made it difficult to define the requisite system boundary and parameters of structural stability within the deterministic design paradigm. These developments generated interest among management scholars in the 'new' science of complex systems, which enabled the formalization of ideas of adaptation, emergence, self-organization, and transformation.

Complex adaptive systems adapt and evolve through interactions with dynamic environments. Adaptation at the macro level is characterized by emergence and self-organization based on the local adaptive behavior of the system's constituents.

Prigogine's work on dissipative structures demonstrated that energy input to an open system with many interacting components, operating far from equilibrium, can give rise to a higher level of order.

Haken's Synergetics showed the self-organization of an incoherent mixture of lightwaves into a coherent laser light, and this mechanism was later used by Beer in his formulation of Syntegrity for team-based problem-solving.

Eigen's hypercycles suggested that the origins of life may lie in interacting autocatalytic cycles that evolved by passing through instabilities and creating successively higher levels of organization.

These self-organizing systems are characterized by stable states far from equilibrium, amplification processes through positive feedback loops, breakdown of stable states through instabilities leading to new forms of organization, continual flow of energy/matter, and mathematical description using nonlinear equations. The impossibility of prediction distinguishes complex adaptive systems from chaotic systems, which are deterministic but sensitive to initial conditions. The scientific study of open systems led to the science of complexity, dealing with systems that can undergo spontaneous, symmetry-breaking transformations with new emergent features, capabilities, and processes. These ideas suggested a novel paradigm for the organization of complex social systems, where local interactions result in the emergence of coherent collective behavior without central coordination or individual sight of the whole problem space.

Adaptation, evolution, and co-evolution in complex systems, focusing on the interplay between micro-diversity at the elemental level and the selection operated by the collective dynamic.

Simulations using von Neumann's cellular automata contributed to the development of ideas about competition, dominant designs, disruptive technologies, and organizational adaptation. "Evolutionary drive" is proposed as the underlying mechanism that describes the change and transformation of complex systems through the continuous interplay of processes that create micro-diversity and the selection operated by the collective dynamic. Co-evolution is seen as an ongoing process, not reaching an optimal outcome, but enabling the system to respond to undefined changes in the environment.

Successful organizations require mechanisms that continuously create internal micro-diversity of ideas, practices, schemata, and routines, driving an evolving, emergent system characterized by qualitative, structural change. In competitive markets, differential performance is defined by customers and investors, while within organizations, evolutionary change requires deliberate reinforcement of what works well and discouragement of what works less well.

Managers face the challenge of deciding how much diversity to support within the organization for the sake of an unknowable future. The dynamics and coupling of the internal organization and the environment complicate the situation, with the shape of the competitive landscape changing as individual organizations make their moves.

Evolutionary dynamics can be demonstrated through computer programs that consider populations with different rates of randomness in character space, showing the relative success of different rates of exploration. The irreducible uncertainty of open-ended co-evolution in social systems means that micro-diversity is essential for adapting to an unknown future, and complex systems models can help explore the consequences of different practices, values, and beliefs.

Advances in mathematics, such as statistical mechanics, dynamical systems theory, and fractal geometry, have contributed to the study of complex systems. Cellular automata and ABMs are the most prevalent modelling approaches for complex social systems, with early examples including Thomas Schelling's segregation model and Robert Axelrod's model for competing strategies in the Prisoner's Dilemma.

ABMs have been used to examine phenomena at various scales, from societal issues to organizational effectiveness and social networks, with applications in strategic, operational, and organizational domains.

The diffusion of ABMs has been accelerated by the availability of specialized modelling software and the establishment of special interest groups and journals focusing on their use in the social sciences. Learning multi-agent models, where agents can learn and adapt over time, are considered true complex systems models, as they open the system to new ideas and decision behaviours. ABMs with learning agents can be used to study competitive dynamics, considering the ability to outcompete similar organizations or discover new niches that escape competition. The openness of an organization to its environment underlines the importance of the 'fit' between an organization and its environment, requiring the organization to actively transform itself over time. The dynamic perspective on competition shifts the emphasis from creating maximally efficient operations to developing adaptive capacity, recognizing the reality of openness and the need for continuous learning and adaptation. While there is no single definitive solution to the problem of balancing efficiency and diversity to deal with the changing competitive landscape, ABMs allow for the exploration of possible futures that may evolve from a set of endowments and actions attributed to agents.