The history of Systems Intelligence reflects one of the most significant intellectual transformations in modern scientific thought. Although the expression Systems Intelligence is comparatively recent, the principles upon which it is founded have evolved through more than a century of interdisciplinary enquiry into the nature of complexity, organisation, adaptation and intelligent behaviour. Unlike traditional conceptions of intelligence, which have generally concentrated upon the cognitive capabilities of individuals, Systems Intelligence recognises that intelligence frequently emerges through the interactions of interconnected components operating within dynamic environments. This perspective represents a profound departure from reductionist traditions that sought to explain complex phenomena by analysing their constituent parts in isolation. Instead, Systems Intelligence proposes that meaningful understanding depends upon recognising relationships, feedback, emergence and continual adaptation, thereby providing a richer framework through which intelligence may be understood across biological, technological, organisational and societal domains. As Artificial Intelligence increasingly permeates every aspect of modern life, this broader conception of intelligence has assumed growing importance, offering an intellectual foundation capable of explaining not only how intelligent systems function internally but also how they interact with one another within increasingly sophisticated socio-technical ecosystems.
Beyond Reductionism: Systems Theory and Cybernetics
The intellectual origins of Systems Intelligence can be traced to the gradual dissatisfaction with reductionism that emerged during the late nineteenth and early twentieth centuries. Classical scientific enquiry had achieved extraordinary success through the decomposition of complex phenomena into smaller, measurable components. This analytical methodology transformed physics, chemistry and engineering, establishing principles that underpinned the Industrial Revolution and much of modern technological progress. Nevertheless, researchers increasingly encountered phenomena that resisted explanation through reductionist methods alone. Living organisms exhibited behaviours that could not be understood solely through the examination of individual cells. Economic systems displayed patterns that exceeded the behaviour of individual market participants. Organisations frequently succeeded or failed for reasons that extended beyond the competence of individual employees. Such observations encouraged scientists to seek new theoretical frameworks capable of explaining complexity through relationships rather than isolated entities.
One of the earliest and most influential contributions arose through the work of the Austrian biologist Ludwig von Bertalanffy, whose General Systems Theory fundamentally altered scientific perspectives concerning organisation and complexity. Bertalanffy argued that many natural and artificial systems share common organisational characteristics regardless of their physical composition or disciplinary context. Biological organisms, social institutions, technological infrastructures and ecological environments all exhibit patterns of organisation, hierarchy, interaction and adaptation that transcend their individual domains. His work established the principle that universal systems concepts could provide a common language across scientific disciplines, thereby encouraging collaboration between fields that had previously developed in relative isolation. General Systems Theory did not simply introduce a new analytical methodology; it proposed an entirely different way of understanding reality, one in which relationships frequently possess greater explanatory power than individual components.
Running parallel to these developments was the emergence of cybernetics under the leadership of Norbert Wiener. Whereas Bertalanffy concentrated upon organisational principles, Wiener examined communication, regulation and control within biological and mechanical systems. Cybernetics demonstrated that intelligent behaviour depends fundamentally upon feedback, whereby systems continually compare their current state with desired objectives and modify their behaviour accordingly. Feedback transformed scientific understanding because it revealed that adaptation arises through continuous interaction between a system and its environment rather than through static design alone. This insight profoundly influenced subsequent developments in engineering, robotics, computer science, neuroscience and eventually Artificial Intelligence. Modern autonomous systems continue to depend upon cybernetic principles established during the middle decades of the twentieth century, illustrating the enduring significance of Wiener's contribution.
Post-War Systems Research, Organisational Learning and Complexity
The decades following the Second World War witnessed rapid expansion in systems-based research as increasingly complex technological and organisational challenges demanded broader analytical perspectives. Jay Forrester's development of System Dynamics represented a particularly important milestone by providing computational methods capable of modelling industrial, urban and environmental systems through interacting feedback loops and accumulative processes. His simulations demonstrated that many apparently irrational organisational outcomes arise not from poor decision-making but from structural characteristics embedded within the systems themselves. Delays between action and consequence, reinforcing feedback, balancing mechanisms and resource constraints frequently generate behaviours that appear counterintuitive when viewed through conventional linear analysis. Forrester's work therefore reinforced the emerging recognition that effective intelligence requires understanding systems as dynamic wholes rather than collections of independent variables.
The intellectual influence of systems research expanded further through organisational theory during the latter decades of the twentieth century. Peter Senge's conception of the learning organisation transformed systems thinking into a practical philosophy of leadership and management by arguing that sustainable organisational success depends upon the ability to perceive relationships, encourage collective learning and adapt continuously to changing circumstances. Organisations were increasingly understood not as rigid bureaucratic structures but as living systems characterised by continual interaction between people, technology, knowledge and external environments. Senge's work established systems thinking as a central component of strategic management while simultaneously demonstrating that intelligence exists not solely within individuals but also within organisations capable of learning collectively through experience and reflection.
Simultaneously, developments within complexity science fundamentally expanded understanding of how intelligent behaviour emerges within adaptive systems. Researchers investigating ecosystems, biological evolution, financial markets and social networks observed that remarkably sophisticated behaviour frequently arises without centralised planning or hierarchical control. John Holland's work on complex adaptive systems demonstrated that relatively simple agents following local rules may collectively produce highly organised global behaviour through continual interaction and adaptation. Stuart Kauffman's investigations into self-organisation suggested that complexity itself possesses intrinsic organisational tendencies, while Brian Arthur's research into increasing returns challenged conventional assumptions concerning economic equilibrium. Collectively, these developments shifted scientific attention away from static optimisation towards emergence, adaptation and continual evolution, principles that now occupy central positions within Systems Intelligence.
By the beginning of the twenty-first century, these diverse intellectual traditions had begun to converge around an increasingly comprehensive understanding of intelligence itself. Rather than interpreting intelligence exclusively through cognitive psychology or computational performance, researchers increasingly recognised that intelligence must also encompass the capacity to perceive complexity, understand relationships, anticipate systemic consequences and influence adaptive environments responsibly. The rapid expansion of Artificial Intelligence accelerated this convergence. Early Artificial Intelligence research frequently concentrated upon isolated algorithms designed to solve narrowly defined problems. Contemporary Artificial Intelligence, however, increasingly operates through interconnected computational ecosystems comprising cloud infrastructure, distributed learning systems, autonomous agents, sensor networks, human oversight and continuous data exchange. The performance of such environments depends not merely upon individual models but upon the quality of interaction between multiple intelligent components. Systems Intelligence therefore provides an increasingly essential framework for understanding how Artificial Intelligence functions within complex organisational and societal systems rather than as an isolated technological capability.
This convergence has profound implications for the future scientific study of intelligence. Systems Intelligence no longer represents simply an extension of systems thinking, nor merely an application of complexity science to organisational management. Instead, it is progressively emerging as an independent scientific perspective capable of integrating insights drawn from engineering, behavioural science, economics, biology, computer science and Artificial Intelligence into a unified understanding of intelligent behaviour. The defining characteristic of this perspective is its recognition that intelligence is fundamentally relational rather than isolated. Intelligent action depends upon understanding interactions, recognising dependencies, anticipating consequences and adapting continuously within environments characterised by uncertainty and continual change. As the complexity of modern technological and organisational systems continues to increase, Systems Intelligence is likely to assume an increasingly central role in shaping both scientific enquiry and practical decision-making, establishing the intellectual foundations for a broader science of intelligence suited to the interconnected realities of the twenty-first century.
Contemporary Artificial Intelligence, Networks and Resilience
The opening decades of the twenty-first century have witnessed the gradual transition of Systems Intelligence from an interdisciplinary perspective into an emerging scientific discipline with increasingly broad relevance across research, government, industry and Artificial Intelligence. This transition has been driven less by the creation of entirely new theories than by the unprecedented complexity of the environments within which modern societies now operate. Globalisation, digital transformation, interconnected infrastructure, autonomous technologies, climate uncertainty and rapidly expanding computational capability have collectively created systems whose behaviour can no longer be understood through conventional analytical methods alone. As organisations have become more digitally integrated, intelligence has increasingly been recognised as the capacity to understand relationships rather than isolated variables, to anticipate systemic behaviour rather than immediate outcomes and to influence adaptive environments whose future states are continually evolving. Systems Intelligence therefore represents not simply another branch of intelligence research but an intellectual response to the increasing interconnectedness of the modern world.
Perhaps no field has accelerated the development of Systems Intelligence more profoundly than Artificial Intelligence itself. During its formative decades, Artificial Intelligence largely concentrated upon solving narrowly defined computational problems. Early expert systems, symbolic reasoning engines and rule-based decision frameworks operated within carefully constrained environments where objectives, inputs and outputs were explicitly defined. Although these systems achieved remarkable successes, they generally functioned as isolated computational entities whose behaviour could be understood independently of wider organisational contexts. The emergence of machine learning, deep learning and large-scale foundation models fundamentally altered this landscape. Modern Artificial Intelligence systems rarely exist in isolation; they operate within extensive computational ecosystems comprising cloud platforms, distributed databases, autonomous software agents, application programming interfaces, sensor networks and continuous human interaction. Consequently, the effectiveness of Artificial Intelligence increasingly depends not solely upon algorithmic performance but upon the quality of relationships that exist throughout these broader technological environments.
This transformation has encouraged researchers to reconsider the meaning of intelligence itself. Whereas earlier approaches frequently measured intelligence according to computational capability, predictive accuracy or reasoning performance, contemporary research increasingly recognises that intelligent behaviour must also include contextual awareness, adaptability and systemic understanding. Artificial Intelligence systems are expected not only to generate accurate outputs but also to interact responsibly with other technologies, comply with regulatory requirements, accommodate human values and remain resilient under changing operational conditions. These requirements extend beyond conventional computer science into organisational behaviour, ethics, governance and systems engineering. Systems Intelligence therefore provides the conceptual framework capable of integrating these diverse considerations into a coherent understanding of intelligent technological ecosystems.
The emergence of network science has further reinforced this transition. During recent decades researchers have demonstrated that many natural and artificial systems exhibit remarkably similar structural characteristics despite their apparent diversity. Social networks, biological organisms, transportation infrastructure, financial markets, communication systems and digital platforms all display patterns of connectivity governed by common mathematical and organisational principles. Nodes, relationships, clustering, resilience, diffusion and connectivity have become central concepts for understanding how information, influence and resources move throughout complex systems. Systems Intelligence naturally extends these observations by recognising that intelligent behaviour frequently depends upon understanding the structure of relationships rather than simply analysing individual components. Intelligence therefore becomes inseparable from the architecture of the networks within which it operates.
Resilience engineering has emerged as another important influence upon the evolution of Systems Intelligence. Traditional engineering frequently concentrated upon preventing failure through increasingly robust design. While this remains an essential objective, modern critical infrastructure operates within environments characterised by continual uncertainty, evolving threats and unpredictable interactions. Energy systems, transportation networks, healthcare services, financial institutions and national digital infrastructure must continue functioning despite cyber attacks, natural disasters, supply chain disruption and geopolitical instability. Systems Intelligence contributes to resilience by encouraging continuous adaptation rather than static optimisation. Instead of assuming that stability can be guaranteed indefinitely, it recognises that resilient systems continually learn, reorganise and respond to changing environmental conditions. This adaptive philosophy increasingly underpins contemporary approaches to national resilience, business continuity and critical infrastructure protection.
Parallel developments have occurred within organisational science. Modern enterprises increasingly resemble complex adaptive systems composed of employees, customers, digital technologies, regulatory frameworks, suppliers and international partnerships. Organisational success depends less upon rigid hierarchical control than upon effective coordination across these interconnected relationships. Consequently, leadership itself is being redefined through the principles of Systems Intelligence. Effective leaders are increasingly expected to recognise systemic interdependencies, encourage organisational learning, anticipate long-term consequences and cultivate resilience across entire institutional ecosystems rather than optimising isolated departments. This represents a significant departure from earlier management philosophies, reflecting the growing recognition that organisational intelligence emerges collectively through interaction rather than residing exclusively within individual decision-makers.
Distributed Intelligence, Digital Twins and Sustainability
The future trajectories of Systems Intelligence are likely to be shaped by several converging scientific developments. Among the most significant is the emergence of distributed Artificial Intelligence comprising multiple specialised intelligent agents cooperating across shared computational environments. Rather than constructing ever larger monolithic models, researchers are increasingly exploring architectures in which numerous specialised systems collaborate to achieve complex objectives through communication, negotiation and coordinated decision-making. Such environments bear a striking resemblance to biological ecosystems, social organisations and economic markets, all of which exhibit intelligence through distributed interaction rather than centralised control. Systems Intelligence is therefore likely to provide the theoretical foundation for understanding these increasingly sophisticated computational societies.
Equally significant is the continuing development of digital twins. Originally conceived as virtual representations of physical assets, digital twins are rapidly evolving into comprehensive computational models capable of representing organisations, industrial facilities, transportation systems, healthcare networks and even entire cities. These continuously updated environments enable researchers and decision-makers to simulate alternative futures, evaluate strategic interventions and anticipate systemic risks before implementing real-world decisions. As Artificial Intelligence becomes integrated into these virtual environments, Systems Intelligence will become essential for ensuring that simulations accurately represent the relationships, feedback processes and adaptive behaviours that characterise real-world systems. Digital twins may therefore become practical laboratories in which Systems Intelligence is continually developed, tested and refined.
Another emerging trajectory concerns the convergence of Systems Intelligence with sustainability science. Climate change, biodiversity loss, resource scarcity and environmental resilience cannot be addressed through isolated technical interventions because each involves highly interconnected ecological, economic and political systems. Future research is therefore increasingly likely to combine Systems Intelligence with environmental modelling, Earth system science and sustainable development policy. This integration will enable governments and international organisations to evaluate long-term systemic consequences more effectively while balancing economic development with environmental stewardship. The growing emphasis upon sustainable development suggests that Systems Intelligence will become an increasingly important component of international governance throughout the coming decades.
Advances in computational capability will likewise influence the future evolution of the discipline. Continuous streams of information generated through satellites, connected devices, intelligent sensors and cloud computing now provide unprecedented opportunities to observe complex systems in real time. Rather than relying upon periodic reports or historical datasets, future Systems Intelligence will increasingly operate through continuously evolving computational representations capable of detecting emerging patterns as they develop. This transition from retrospective analysis towards predictive and adaptive intelligence represents one of the most important scientific developments of the coming decades. Decision-making will increasingly become anticipatory rather than reactive, supported by intelligent systems capable of recognising subtle changes across highly interconnected environments before those changes become fully visible through conventional observation.
Towards a Comprehensive Science of Intelligence
Perhaps the most profound future trajectory, however, concerns the gradual emergence of Systems Intelligence as a comprehensive science of intelligence itself. Historically, psychology, neuroscience, computer science, organisational theory and systems science have each investigated intelligence from relatively independent perspectives. Contemporary research increasingly suggests that these disciplines describe different manifestations of common underlying principles involving adaptation, emergence, feedback, learning and relational organisation. Systems Intelligence provides the possibility of integrating these perspectives into a unified conceptual framework capable of explaining intelligent behaviour wherever complex adaptive systems exist. Such an intellectual synthesis would represent one of the most significant developments in modern science, extending the study of intelligence beyond human cognition and Artificial Intelligence towards a universal understanding of intelligent organisation across biological, technological and societal systems.
The future of Systems Intelligence is therefore unlikely to be defined simply by the expansion of a single academic discipline. Rather, it appears destined to become an integrative framework through which multiple scientific traditions converge around a common understanding of complexity, adaptation and intelligent behaviour. As Artificial Intelligence becomes increasingly embedded within every aspect of economic activity, public administration and scientific research, Systems Intelligence is likely to evolve from an emerging field of study into one of the foundational intellectual disciplines of the twenty-first century, shaping not only how intelligent systems are designed but also how intelligence itself is understood.
From Intellectual Foundations to a Mature Discipline
If the twentieth century established the intellectual foundations of Systems Intelligence, the coming decades are likely to determine whether it matures into one of the defining scientific disciplines of the twenty-first century. The increasing convergence of Artificial Intelligence, computational science, organisational theory, systems engineering, behavioural science and complexity research is creating conditions in which traditional disciplinary boundaries are becoming progressively less relevant. Contemporary scientific challenges rarely exist within a single field of enquiry. Instead, they emerge through the interaction of technological, economic, environmental and social systems whose behaviour is characterised by continual adaptation and uncertainty. Systems Intelligence offers a conceptual framework capable of integrating these diverse domains into a coherent understanding of intelligent behaviour, suggesting that its future development will be driven not by disciplinary specialisation but by intellectual convergence.
Systemic Artificial Intelligence and Autonomous Societies
One of the most important trajectories concerns the transformation of Artificial Intelligence from isolated computational capability towards systemic intelligence. Early generations of Artificial Intelligence were evaluated primarily according to their ability to perform specific cognitive tasks such as classification, prediction or logical reasoning. Contemporary systems increasingly function as participants within much larger digital ecosystems composed of autonomous software agents, cloud infrastructure, distributed knowledge repositories, robotic platforms, sensor networks and human operators. The effectiveness of these environments depends less upon the performance of any individual model than upon the quality of coordination between numerous interconnected components. Future Artificial Intelligence will therefore require increasingly sophisticated forms of Systems Intelligence capable of orchestrating cooperation, resolving conflicts, allocating resources and adapting continuously to changing operational conditions. Intelligence will become an emergent characteristic of entire computational ecosystems rather than an attribute of individual algorithms.
Closely associated with this evolution is the emergence of intelligent autonomous societies comprising both human and machine participants. Smart cities, autonomous transportation systems, intelligent healthcare networks, adaptive manufacturing environments and digitally integrated governments all represent examples of complex socio-technical systems within which millions of independent decisions interact continuously. Such environments cannot be governed effectively through conventional command-and-control approaches because their behaviour changes constantly in response to new information, environmental conditions and human activity. Systems Intelligence therefore provides an essential intellectual foundation for understanding how these increasingly sophisticated ecosystems may remain resilient, trustworthy and aligned with societal objectives. Rather than replacing human judgement, Artificial Intelligence will increasingly augment collective decision-making by operating within governance frameworks informed by Systems Intelligence.
Scientific Discovery, Computational Modelling and Education
Another important trajectory concerns the growing relationship between Systems Intelligence and scientific discovery itself. Scientific research has traditionally advanced through increasing specialisation, with disciplines developing highly sophisticated methods for investigating increasingly narrow questions. While this process has generated remarkable progress, it has also created fragmentation that sometimes obscures relationships between apparently unrelated fields. Systems Intelligence encourages movement in the opposite direction by identifying common principles underlying diverse forms of complexity. Feedback, emergence, adaptation, resilience, self-organisation and network behaviour are increasingly recognised across biology, economics, engineering, neuroscience and Artificial Intelligence. Future scientific enquiry is therefore likely to place greater emphasis upon identifying universal principles governing intelligent systems irrespective of their physical implementation. Such developments may ultimately contribute towards a unified science of intelligence extending across natural, technological and organisational domains.
The future of Systems Intelligence will also be shaped by advances in computational modelling. The increasing sophistication of digital twins, high-performance computing and continuous simulation will enable researchers to examine entire organisational, industrial and societal systems with levels of fidelity previously considered unattainable. Rather than analysing isolated datasets, future investigators will increasingly explore living computational representations that evolve continuously alongside their physical counterparts. These environments will permit strategic decisions to be evaluated under multiple future scenarios before implementation, substantially reducing uncertainty within complex planning processes. Systems Intelligence will provide the theoretical framework necessary for interpreting these simulations, ensuring that relationships, dependencies and adaptive behaviours remain central to decision-making rather than becoming obscured by computational complexity alone.
Education and professional development are likewise likely to undergo significant transformation. Much of contemporary education remains organised around individual disciplines despite the increasingly interdisciplinary nature of professional practice. Engineers, economists, healthcare professionals, computer scientists, environmental researchers and public administrators increasingly confront common systemic challenges requiring integrated perspectives rather than isolated expertise. Systems Intelligence therefore has the potential to reshape higher education by encouraging curricula that combine analytical reasoning with systems thinking, organisational understanding, ethical reflection and interdisciplinary collaboration. Graduates equipped with Systems Intelligence will be expected not merely to possess specialist knowledge but also to understand how that knowledge interacts with broader technological, organisational and societal systems.
Governance, Public Policy and Economic Transformation
Equally significant is the contribution that Systems Intelligence is expected to make to governance and public policy. Governments throughout the world are confronted by increasingly interconnected challenges including demographic change, climate resilience, cyber security, healthcare provision, infrastructure modernisation and economic transformation. These issues cannot be addressed independently because interventions within one domain frequently produce unintended consequences elsewhere. Systems Intelligence encourages policymakers to evaluate public policy as an integrated system rather than a collection of isolated initiatives. Such an approach supports more coherent long-term planning while strengthening resilience against future uncertainty. It also aligns closely with emerging approaches to responsible Artificial Intelligence, which increasingly recognise that trustworthy technology depends upon institutional governance, regulatory coordination and human oversight as much as technical excellence.
The economic implications are equally substantial. Organisations capable of applying Systems Intelligence are likely to possess significant strategic advantages within increasingly volatile markets. They will be better equipped to recognise emerging opportunities, anticipate systemic risks, coordinate innovation across organisational boundaries and adapt more rapidly to technological disruption. Competitive advantage will increasingly derive not simply from superior products or services but from the capacity to understand and influence complex ecosystems comprising customers, suppliers, regulators, technologies and global markets. Systems Intelligence therefore represents not merely an academic construct but an important source of organisational capability likely to influence economic competitiveness throughout the coming decades.
Scientific Challenges and Human-System Relationships
Despite these promising developments, important scientific challenges remain. Systems Intelligence has yet to acquire universally accepted theoretical boundaries, standardised methodologies or comprehensive measurement frameworks. Much contemporary research continues to draw upon concepts originating within systems science, complexity theory, cybernetics and organisational learning without fully integrating these perspectives into a single coherent discipline. Future scholarship will therefore need to establish clearer conceptual foundations while developing rigorous empirical methods capable of evaluating Systems Intelligence across diverse contexts. Such work will require sustained collaboration between psychologists, engineers, computer scientists, economists, philosophers, organisational researchers and Artificial Intelligence specialists. The inherently interdisciplinary character of Systems Intelligence represents one of its greatest strengths, yet it also presents significant challenges concerning terminology, methodology and theoretical integration.
Nevertheless, the historical trajectory examined throughout this paper suggests that Systems Intelligence is progressing towards increasing scientific maturity. From its intellectual origins in General Systems Theory and cybernetics through its development within complexity science, organisational learning and Artificial Intelligence, the discipline has consistently evolved in response to growing recognition that intelligence cannot be understood adequately through isolated analysis alone. Instead, intelligence emerges through interaction, adaptation and continual engagement with complex environments. This insight has become progressively more significant as digital transformation has interconnected virtually every aspect of modern society.
Ultimately, the future of Systems Intelligence will be determined not simply by advances in Artificial Intelligence or computational technology but by humanity's capacity to understand complexity itself. The defining challenges of the twenty-first century are fundamentally systemic in nature, involving relationships between technology, society, economics, governance and the natural environment that cannot be separated without losing essential understanding. Systems Intelligence therefore represents more than an emerging research field. It offers a comprehensive intellectual perspective through which intelligence may be understood as the capacity to perceive relationships, anticipate systemic consequences, adapt responsibly to continual change and influence complex systems for the collective benefit of society. As scientific disciplines continue to converge and Artificial Intelligence becomes increasingly embedded within every dimension of human activity, Systems Intelligence appears destined to become one of the foundational concepts upon which the next generation of intelligence science will be built.
Bibliography
- Arthur, W\.B. (2021) Foundations of Complexity Economics. Oxford: Oxford University Press.
- Bertalanffy, L. von (1968) General System Theory: Foundations, Development, Applications. New York: George Braziller.
- Capra, F. and Luisi, P.L. (2014) The Systems View of Life: A Unifying Vision. Cambridge: Cambridge University Press.
- Forrester, J.W. (1961) Industrial Dynamics. Cambridge, MA: MIT Press.
- Holland, J.H. (1995) Hidden Order: How Adaptation Builds Complexity. Reading, MA: Addison-Wesley.
- Kauffman, S.A. (1993) The Origins of Order: Self-Organization and Selection in Evolution. Oxford: Oxford University Press.
- Meadows, D.H. (2008) Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing.
- Mitchell, M. (2009) Complexity: A Guided Tour. Oxford: Oxford University Press.
- Senge, P.M. (2006) The Fifth Discipline: The Art and Practice of the Learning Organization. Revised edn. London: Random House Business Books.
- Simon, H.A. (1996) The Sciences of the Artificial. 3rd edn. Cambridge, MA: MIT Press.
- Sterman, J.D. (2000) Business Dynamics: Systems Thinking and Modelling for a Complex World. Boston: McGraw-Hill.
- Wiener, N. (1948) Cybernetics: Or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press.