Exploring organizational theory and leadership through the framework of complexity and neural sciences (part 4)
Dear Readers, thank you for your continuous support and valuable feedback regarding the blog. The blog post we launch today is the 2:nd last part of the longer series where we’ve been exploring the value and insights which complexity sciences may have to offer in terms of leadership and organizational theory.
Complexity science is the scientific study of complex systems - the patterns of relationships within them, how they are sustained, how they self-organize and how outcomes emerge. Complexity science provides a valuable conceptual framework from which to consider not just the parts of the system but the interactions between them because when dealing with complex systems the way the parts interact is critical to how the whole system works. A complex system is one in which numerous independent elements continuously interact and spontaneously organize and reorganize themselves into more and more elaborate structures such that their overall or global behavior and outcomes cannot be easily explained in terms of interactions between the individual constituent elements. It can be thought to comprise multiple agents acting in parallel: while each agent follows a simple set of rules, the patterns in outcomes of the system can be novel and unpredictable.
One of the most defining characteristics of complex systems is that these are non-linear by nature. This means that when change occurs in complex systems it occurs in a non-linear fashion. Linear change is where there is a sequence of events that affect each other in order as they appear one after the other. In contrast, in non-linear change, one sees elements being changed by previous elements, but then in turn these changed elements affect the elements that are before it in the sequence. Because a complex system is non-linear this makes it fundamentally non-deterministic. It is impossible to anticipate or predict precisely the behavior of such systems even if we completely know the function of its constituents. A complex system has capability of self-organizing. Complex systems are also emergent in nature. Emergence is closely related to self-organization and it means that the overall behavior or outcome of the system emerges from the interaction of the parts. Emergent behavior can’t be traced back to the individual parts constructing the system. Emergence is the formation of complex but regular patterns from the interaction of the many simple parts of a system. A complex system has a dynamic structure which means it’s constantly reorganizing, redefining and/or restructuring itself to adapt to the changes in its environment and therefore it is impossible to study its properties by decomposing it into functionally stable parts. It is these aspects of complex systems that have been ignored not only by traditional scientific approaches to management but also by schools of thought related to system thinking. Complexity science considers aspects of systems and organizations that have been overlooked by traditional scientific approaches. By studying the patterns, relationships and interactions among the various parts of the system, complexity science offers a framework to understand the non-linear, uncontrollable and unpredictable aspects of organizations and their ability to adapt to ever-changing environments.
While exploring the concept of complex systems it’s beneficial to review Genetic Algorithms and Cellular Automata (CA). Genetic algorithms (GA) are computer algorithms commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. In this way GA’s demonstrate the process of natural selection. Cellular automata are decentralized spatially extended systems and idealized models of complex systems. These are large networks of simple components with limited local communication among components whereas there’s no central control. Their behavior resembles complex dynamics from simple rules. CAs and similar systems will also be increasingly important as modeling tools, as scientists’ probe more deeply into the behavior of natural systems composed of simple components with local communication and emergent collective properties such as self-organization. Cellular automata were developed in the 1940s by Stanislaw Ulam and John von Neumann to prove that self-reproduction is possible in machines and to further link biology and computation. This work was highly inspired by a mathematical model of computing known as a Turing machine invented by Alan Turing in 1936. Probably the most famous and influential one is the “Game of Life” published in 1970 by British mathematician John Conway. CAs can be viewed as dynamical systems, with different attractors fixed-point, periodic, chaotic, “edge of chaos”.
Our journey so far has helped us to understand the various human pictures and the dynamics of change. We’ve also been identifying different forms of organizations and how this influences our theories regarding leadership and organizational management. Another important aspect is the system dynamics and the notion of causality. Causality is the relationship between two correlated events such as a cause and its effect or a stimulus and its response. It refers to the "way of knowing" that one thing causes another or a way of understanding the human experience of physical nature (Stacey et al., 2000). It is the basis or the thinking behind the human understanding of any related set of events. The first causality is the efficient causality (x + y = z). In this case cause is viewed as the set of necessary and sufficient conditions for an effect. Most problems are analyzed and understood using this form of causality whereby a defined set of events are said to be the direct cause or reason for another event happening (Stacey et al., 2000, 2007). In this way of thinking it is assumed that the change takes place in a linear, predictable and controllable manner following a known set of “if then” rules. This is highly aligned with the basic assumptions behind behaviorist human picture but also the basis of how scientific management describes the change taking place in organizations. Within this view, individuals have a capability to choose their goals and actions by stepping outside a system they are a part of. In other words, an individual’s behavior is determined by their intentions which they choose without the influence of others. The underlying assumption here is that their thinking and behavior is not constrained by their environment. Stacey (Stacey, 2001) refers to this causal framework as the rationalist teleology.
Formative causality is another form of causality. In this case events are said to unfold their true nature as they mature to realize a given form in the future. In other words, this is the human experience of pattern, of the given sequence of changes in the form. The initial or first event is understood to be like a seed which later on grows into a tree as it matures. The seed or event in this case is thought to hold inside itself the final form (the tree or an organization) even before this final form is realized. Formative causality means that something ‘behaves’ based on its inherent design properties. People as well, from a biological perspective, physically grow to become people by this same causality. In this case organization and systems in general are thought to be self-organizing but at the same time a predictable and controllable way based on their pre-defined design and properties. It is meaningful to discuss social institutions as systems where the systemic structures lie – or they are thought of “as if” they laid – “outside” the interaction they produce. In other words, an individual is a victim of their mental models which determine how they react to their environment. This gives an idea that the overall system grows or stems according to a predefined set of plans and strategies (Stacey et al., 2007). Stacey (Stacey, 2001) refers to this causal framework as the “formative teleology”. This intellectual split creates the trap of dualistic causality. It is autonomous individuals who choose either to conform to the systemic structures or to “step outside” them for reflection from the outside.
Rationalistic causality is a method of knowing that regards reason as the chief domain and test of knowledge. It holds that, because reality itself has an inherently rational structure, there are truths, especially in logic, mathematics and physics but also in ethics and metaphysics that the intellect can grasp directly. According to this causality, all the truths of physical science and even history can in principle be discovered by pure thinking. Simply put, rational causality believes that behavior is caused and governed by individual choice. An individual is therefore seen as ‘autonomous or free spirit’ who is not restricted by the laws of nature. Reflecting the scientific management tradition but also the assumptions behind systems thinking, in organization the rationalist causality only applies to the leaders. It is only the leader who exercises the ‘freedom of autonomous choice’ in the act of choosing the goals and designing the rules that the members of the organization are to follow in order to achieve the ‘common’ goals. The other members of the organization are not understood to be autonomous individuals, but they are understood as rule following agents. This way of thinking also assumes that the leader can act as an external observer, who can step outside of the organization and assess the overall picture of the organization and by this design the strategy, mission and vision steering the future direction (trajectory) of an organization. Both scientific management and systems thinking are facing this paradox. In scientific management the organization is thought to change according to efficient causality. In systems thinking the organization is thought to change according to formative causality. But both of these theories see that managers are able to act according to rational causality. How can managers be operating by two different kinds of causalities? Neither of these theories have explained this in a satisfying way (Stacey et al., 2007).
Transformative causality is completely different from the other three because rather than having an already determined outcome, the future is constructed by the interactions of the present. In this theory of causality, the non-linear dynamics of the complex system and the replicating activity of the agents that make up the system are co-creating the evolution of patterns of behaviors that the system produces. This form of thinking is indeed the future way of understanding change in organizations and it is the basis for the complex responsive processes (Stacey et al., 2007).
Dominant mainstream thinking:
- We act into a knowable and predictable future
- We live in a linear world: if only all key variables are known and well understood, and assuming we have sufficient computing power, we can make accurate predictions about the future
- Paradoxes require resolution
- Change in organizations is a planned and designed process
- The purpose of our behavior is survival
- Findings from complexity sciences are used as directly transferable concepts
Complex responsive processes perspective:
- We act in primarily unknown future
- We live in an emergent world
- Change in an organization is a social, interactive process
- Many paradoxes remain irresolvable
- The purpose of our behavior is an expression of our identity
Making the effort to understand leadership and organizational dynamics from a complexity sciences perspective forces leaders and other organizational actors to challenge the semi-automatic sense making activity grounding to historical development of psychology and engineering sciences. Complex Responsive Processes perspective explains most of the current management tools as abstractions, means of legitimizing managerial control/position, and relieving leaders from the anxiety associated with "managing without knowing" and to feeling of "being out of control".
BlackSmith Consulting Oy, Juho Partanen
Chairman of the Board
+358 40 153 5606