Complex Adaptive Systems, Autopoietic Machines, Unified Theory of Information Processing, Distributed Self-Managing Clouds and all that Jazz

Abstract:

Advances in our understanding of complex adaptive systems, information processing structures and the role of cognition in developing self-managing adaptive patterns are pointing to a new approach to evolve current cloud computing systems to next level of resiliency, and efficiency at scale. A new class of digital computing structures go beyond the limitations of current Church-Turing thesis boundaries, point to a non-Marcovian reasoning augmenting current deep learning models and push the evolution of cloud computing to enable intelligent self-managing information processing and communication systems.

Introduction:

Unified theories and resulting new technologies evolve from combining our understanding of multiple disciplines. This note claims that the next major breakthrough in IT will come from combining our understanding in these four areas:

1.      Cognitive behaviors from biological systems,

2.      Complex Adaptive Systems theory and practice,

3.      Church-Turing thesis boundaries and the limitations of current AI practices and

4.      New mathematics of named sets, knowledge structures, cognizing oracles and the structural machines.

Cognitive Behaviors:

All living beings exhibit sentience along with some form of intelligence and resilience. Sentience comes from the Latin sentient-, "feeling," and it describes things that are alive, able to feel and perceive, and show awareness or responsiveness. The degree of intelligence (the ability to acquire and apply knowledge and skills) and resilience (the capacity to recover quickly from non-deterministic difficulties without requiring a reboot) depend on the cognitive apparatuses the organism has developed. The cognitive apparatuses are built using information processing structures that exploit physical, chemical and biological processes, concerned with matter and energy, that transform their physical and kinetic states to establish a dynamic equilibrium between themselves and their environment using the principle of entropy minimization.

According to Maturna and Varela [1] “A cognitive system is a system whose organization defines a domain of interaction in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in the domain. Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with or without a nervous system.”

According to Wikipedia, "cognition is considered as the ability of adaptation in a certain environment.” That definition is not as strange as it seems at first glance: for example, one is considered to have a good knowledge of Mathematics if they can understand and subsequently solve a Mathematical problem. That is, one can recognize the mathematical entities, their interrelations and the procedures used to view other aspects of the relevant phenomena; all these, are the domain of Mathematics. And one with knowledge of that domain, is one adapted to that domain, for they can tweak the problems, the entities and the procedures within the certain domain.

Cognition emerges as a consequence of the continuous interaction between the components of the system and its environment. The continuous interaction triggers bilateral perturbations; perturbations are considered problems – therefore the system uses its functional differentiation procedures to come up with a solution (if it doesn't have one handy already through its memory). Gradually the system becomes "adapted" to its environment – that is it can confront the perturbations so as to survive. The resulting complexity of living systems is cognition produced by the history of bilateral perturbations within the system/environment schema.”

Cognition enables complex physical structures to evolve, adapt and become autopoietic. The term autopoiesis [1] refers to a system capable of reproducing and maintaining itself. “An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network.”

In biological systems, cognition is embodied in physical structures and is encoded in its cell that enables its functions, structure and fluctuation management. As Waldrop Mitchell points out in his book on complexity [2], “The DNA was actually the foreman in charge of construction. In effect, DNA was a kind of molecular -scale computer that directed how the cell was to build itself and repair itself and interact with the outside world.” Each cell can divide and differentiate itself into muscle cells, brain cells, liver cells, and all other kinds of cells that make up a new born. Each different type of cell corresponds to a different pattern of activated genes. In figure 1. We represent the embodied cognition in the human genome that that senses, models, reasons and manages both physical and mental structures (using the mind, brain, and body structures).

Figure 1: Physical and Mental Structures, their representation in the Digital Structures and Cognitive Modeling, Reasoning and self-Managing Patterns with Mind-Brain-Body Embodied Cognition

Cognition is thus the ability of complex adaptive systems to sense, model, represent, execute and adapt behaviors that allow the systems to manage themselves and their environment using physical and chemical processes to maximize their sustenance and survival using the resources effectively.

Complex Adaptive Systems Theory and Practice:

Evolution of self-maintenance patterns in complex adaptive systems (CAS) has been studied extensively and lessons learned have been successfully applied to understand and improve diverse domains [3-7] such as economics of stock market trading, modeling organizational change, supply-chain network dynamics, and examining global health governance. A CAS consists of a network of individual entities interacting with each other and its environment. Each entity exhibits a specific behavior and may be composed of subnetworks of entities providing a composed behavior. It takes energy to process information, sustain its structure and exhibit the intended behavior. Various systems adapt different strategies to use matter and energy to sustain order in the face of fluctuations caused by internal or external forces. The second law of thermodynamics comes into play because of matter and energy involvement which states that "there is no natural process the only result of which is to cool a heat reservoir and do external work." In more understandable terms, this law observes the fact that the useable energy in the universe is becoming less and less. Ultimately there would be no available energy left. Stemming from this fact we find that the most probable state for any natural system is one of disorder. All-natural systems degenerate when left to themselves.

An adaptive system refuses to be ‘left to itself’ and develops self-organizing patterns to reconfigure the structure to compensate for the deviations of behavior due to fluctuations. Thus function, structure, fluctuations, sensory perception, awareness and reconfiguration processes play key roles in the evolution of CAS.

Church-Turing thesis boundaries and the limitations of current AI practices:

The digital structures are made possible by the human mind and its implementation of cognitive representations of the physical world using physical structures such as computers, networks and storage devices. Current information technologies are implemented as physical structures implementing the models of the physical and mental structures using the digital genes and digital neurons [8-9]. They are modeled and managed by the human. Figure 2 shows the digital structures and their implementation using physical structures such as computer, networks and storage devices.

Figure 2: Digital Structures Modeling and Managing the Physical Structures

The physical and Cognitive structures are modeled as digital information processing structures and are used to establish sensors and actuators and manage the physical and mental worlds. Complex mathematical problems using digital information processing. Current enterprise business process automation and the Internet based services have all been made possible using both the digital genes and neurons.

Next, we discuss the limitations of current information technologies and taking the cues from the embodied cognition in biological systems, we propose infusion of cognitive behavior in digital computing structures to implement self-managing patterns that provide resiliency and efficiency at scale. However, [10-11], the limitation of current computing model is the inability of including the computing infrastructure itself in the model of the computing structure that models and manages the physical world.

In addition to the self-referential circularity of the Turing computing model, the Church-Turing thesis boundaries are challenged when rapid non-deterministic fluctuations drive the demand for resource readjustment in real-time without interrupting the service transactions in progress [10]. This is more pronounced in the case of distributed computing structures that are composed of concurrent and asynchronous functions contributing to a common goal as in the case of business process automation tasks. The information processing structure in this case utilizes software components executed in hardware components from multiple infrastructure providers with local management systems enabling the deployment, operation and maintenance of the computation workloads on their infrastructure in the form of a cloud or a datacenter. The information processing system, in effect, behaves as complex autonomous system and is prone to emergent behavior in the face of strong fluctuations. For example, when the system is subject to sudden fluctuations in the demand for computing resources or sudden decrease due to failure of some components, the system will experience severe deviation from its mission unless the resources are restored and the inconsistencies resulting from the local components being in different states are resolved.

Figure 3: Digital Computing Structures with Infused Cognition

Taking the cue from biological cognitive systems with self-managing patterns, we assert that the digital information processing structures must also become autonomous and predictive by extending their cognitive apparatus to include themselves and their behavior along with the information processing tasks at hand. Figure 3 shows the infusion of cognition into digital computing structures using the structural machine approach described in references [8-13]

Cognitive apparatuses that sense, model and monitor, reason and manage the digital computing structure allow encoding the information to deploy, monitor and manage the computing processes with local autonomy and global coordination. True intelligence involves generalizations from observations, creating models, deriving new insights from the models through reasoning. In addition, human intelligence also creates history and uses past behaviors and experience in making the decision. The cognitive overlay we propose in this paper provides a means to encode the information and the means to execute the processes required to understand the goals of the computational structure, available resources and the means to execute end to end deployment, monitoring and management to maintain homeostasis. In short, the new digital genome provides a means to create an autopoietic machine.

New mathematics of named sets, knowledge structures, cognizing oracles and the structural machines:

The theory of knowledge and the theory of information [12, 13] articulated by Professor Burgin states that information is encapsulated in named sets (objects), their attributes in the form of data and the intrinsic and ascribed knowledge of these objects in the form of relationships, algorithms and processes which, makeup the foundational blocks for information processing. Information processing structures utilize knowledge in the form of algorithms and processes that transform one state (determined by a set of data) of the object to another with a specific intent. Information structures and their evolution using knowledge and data determine the flow of information.

The use of the new mathematics detailing named sets, knowledge structures, structural machines, the theory of oracles and digital Information processing structures is discussed by Burgin and Mikkilineni [8, 11, 12 and 13]. The long and short of it is that cognitive behaviors can be infused into digital information processing structures to implement self-managing patterns. Figure 4 shows an architecture to infuse cognition and create a self-managing workload that provides self-healing, self-scaling, self-protection and self-reconfiguration to address fluctuations in demand for or the availability of computing resources in a distributed network of autonomous cloud resources with global supervision and mediation without disrupting the functional behavior of the digital information processing structure.

Figure 4: A cognition infused information processing architecture

The advantage of this architecture is that Professor Burgin has shown mathematically that it is more efficient, scalable and resilient than the current state of the art that suffers from Church-Turing thesis boundaries when fluctuations are rapid enough or strong enough to cause emergence. We do not want evolutionary surprise in our IT structures when we are delivering secure information services with well defined service assurance parameters such as response time. Treating current information processing structure as a complex adaptive system with infused cognitive behaviors to maintain assured service levels also addresses the shortcomings of self-referential circularity not moored to physical reality and CAP theorem issues related to consistency of dynamics in a distributed autonomous systems with asynchronous communication. In addition, it also points a way to implement strong AI augmenting the deep learning methods with non-Markovian reasoning as discussed in [9].

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Acknowledgement: I am grateful to many people who have contributed to my understanding and application of science and technology. I am indebted to Professor Mark Burgin and Professor Gordana Dodig Krnkovic for patiently educating me on the very subtle concepts that are profound and have great influence on science, technology and philosophy. Their writings have been an inspiration for me.

References

[1]    Maturana, Humberto R./Varela, Francisco J. (1980): Autopoiesis and Cognition. The Realization of the Living. Dordrecht: Reidel, p. 13.

[2]    Waldrop, W. Mitchell 1992 Complexity: The Emerging Science at the Edge of Order and Chaos. New York: Touchstone.

[3]    Yang, A. & Shan, Y. (eds) (2008) Intelligent Complex Adaptive Systems, IGI Publishing,Hershey, PA.

[4]    Arthur, W.B., Durlauf, S. & Lane, D. (eds.) (1997) The Economy as an Evolving Complex System. Addison-Wesley, Reading, MA.

[5]    Dooley, K., 1997. A complex adaptive systems model of organizational change. Non-linear Dynamics, Psychology and the Life Sciences 1, 69–97.

[6]    Choi, Thomas Y., Kevin J Dooley, and Manus Rungtusanatham (2001), "Supply Networks and Complex Adaptive Sysems: Control Versus Emergence, "Journal of Operations Management, Vol. 19, No. 3, pp. 351-66

[7]    Miller, J., & Page, S. (2007). Complex adaptive Systems: An introduction to computational models of social life, Princeton, NJ: Princeton University Press.

[8]    Mikkilineni, R.; Burgin, M. Structural Machines as Unconventional Knowledge Processors. Proceedings 2020, 47, 26.

[9]    Mikkilineni, R. Information Processing, Information Networking, Cognitive Apparatuses and Sentient Software Systems. Proceedings 2020, 47, 27.

[10]  Mikkilineni, R. Going beyond Church–Turing Thesis Boundaries: Digital Genes, Digital Neurons and the Future of AI. Proceedings 2020, 47, 15.

[11]  Burgin, M.; Eberbach, E.; Mikkilineni, R. Processing Information in the Clouds. Proceedings 2020, 47, 25.

[12]  Burgin, M. (2011). Information in the structure of the world, International Journal “Information Theories and Applications”, Vol. 18, Number 1.

[13]  Burgin, M. 2016. Theory of Knowledge: Structures and Processes; World Scientific Books: Singapore.