1. History, Theory, and Philosophy of Science (In SMAC + RT) 7th smester -Fall 2005 Institute of Media Technology and Engineering Science Aalborg University Copenhagen 4 th Module The paradigms of complexity Luis E. Bruni

2. What is the paradigm of Modernity? Modernity  from ~1450 to ? Scientific Rationalism  1600 Mechanicism  1600 Materialism  1700 Positivism  1800 Reductionism

3. Asymptotic knowledge grow Total Knowledge

4. Paradigms of complexity In the 1900’s  alternatives to the reductionist-positivistic epistemologies. Technological evolution  produces a perception of increasing complexity and interactive synergies. Frontier disciplines  cognitive sciences, evolutive sciences, systemic thinking, philosophy of science, experimental epistemology, cybernetics.

5. The cybernetic conferences (1946-1953) Cybernetics Cognitive sciences Information Theory Systems Theory The common denominator  the notion of ”complexity” The first conference  ”The Feedback Mechanism and Circular Causal Systems in Biology and the Social Sciences”

6. Coevolutionary paradigms Coevolution of  Cybernetics, Information Theory, Cognitive Sciences and Systems Theory  multiple research programs: Game Theory Artifitial Intelligence Communication Sciences Neurosciences Complex Systems Artifitial Life Caos theory etc, etc, etc… Applications in mechanical, electronical, biological and social systems.

7. Transdisciplinarity was born The advances in the new disciplines were marked by many travels back and forth  between machine, organisms, man, and society. From the machine to the living organism  transferring from one to the other the ideas and concepts  e.g. feedback and finality  opening the way for automation and computers. The vocabularies of engineering and physiology started to be used interchangeably  the basics of a common language and concepts from cybernetics, system theory, cognitive science, etc. was created  e.g. learning, regulation, adaptation, self-organization, perception, memory, emergence, feedback, attractors, agency. The need to make machines imitate certain functions of living organisms  contributed to the speeding up of progress in the understanding of cerebral mechanisms.

8. The second industrial revolution A good deal of the exponential growth in technological developments and the mass production of devices and systems communication and computation  was directly influenced by the Macy’s Cybernetic Conferences. Norbert Wiener (1940’s)  predicted a second industrial revolution  centered on communication, control, computation, information and organization. The future  more interest in concepts than in hardware  Second Order Cybernetics

9. Definitions of Cybernetics Greek  kubernetes  pilot, or rudder, steer. The word was first used by Plato  cybernetics  "the art of steering" or "the art of government ". Ampère  "the study of ways of governing." One of the very first cybernetics mechanisms  invented by James Watt and Matthew Boulton (1788)  a governor, or ball regulator  to control the speed of the steam engine.

10. The modern definition The word cybernetics was re-coined by Norbert Wiener in 1948. A philosophical definition  Louis Couffignal (1958)  "the art of assuring efficiency of action. " Cybernetics  the same root as government  the art of managing and directing highly complex systems.

11. Cybernetics Cybernetics  from the Greek word kybernetes  steersman  "the art of steering" Norbert Wierner  mathematician and founder of cybernetics  the science of communication and control in the animal and the machine  and in individual human beings and social systems. Claude Shannon  information theory  designed to optimize the transmission of information through communication channels. Cybernetics proposes a revolution  with respect to the linear, mechanistic models of traditional Newtonian science. In classical science  every process is determined solely by its cause  a factor residing in the past  however, the behavior of living organisms is typically teleonomic  oriented towards a future state  which does not exist as yet.

12. The new concepts The simplest example of such a circular mechanism is feedback  engineering control systems. The simplest application of negative feedback for self-maintenance is homeostasis  Walter Cannon (1932)  American physiologist  homeostasis from two Greek words meaning to remain the same  resistance to change. A homeostatic system  an open system that maintains its structure and functions by means of a multiplicity of dynamic equilibriums rigorously controlled by interdependent regulation mechanisms. The non-linear interaction between the homeostatic or goal-directed system and its environment results in a relation of control of the system over the perturbations coming from the environment.

13. Feedback The first cybernetic conference (1946)  ”The Feedback Mechanism and Circular Causal Systems in Biology and the Social Sciences”

14. Positive and negative feedback

15. Systems Theory "General System Theory”  biologist Ludwig von Bertalanffy. Systems Theory  the transdisciplinary study of the abstract organization of phenomena, independent of their substance, type, or spatial or temporal scale of existence. It investigates both the principles common to all complex entities, and the (usually mathematical) models which can be used to describe them. Applications  as diverse as engineering, computing, biology, ecology, management, social sciences etc.

16. The analytical vs. the synthetic approach The systems approach  distinguishes itself from the more traditional analytic approach (reductionism)  it emphasizes the interactions and connectedness of the different components of a system. However  the systems approach  integrates the analytic and the synthetic method  encompassing both holism and reductionism. The systems approach  in principle considers all types of systems  in practice it focuses on the more complex, adaptive, self-regulating systems  what we might call "cybernetic" systems.

17. Open vs. closed systems von Bertalanffy  noted that all systems studied by physicists are closed  they do not interact with the outside world  it is possible to calculate future states with perfect accuracy. Organisms are open systems  they interact with other systems outside of themselves  they cannot survive without continuously exchanging matter, energy and information with their environment.

18. System boundaries The inside and the outside of a system  system and environment  boundaries. The environment  consists of other systems interacting with their environments. Systems are structured hierarchically Hierarchical systems and subsystems  sometimes fuzzy or sharp boundaries  interfaces, borders frontiers.

19. System boundaries

20. Black and white boxes If we can see the system's internal processes  we might call it a "white box". The black box view is not restricted to situations where we don't know what happens inside the system. In many cases  we can easily see what happens in the system  yet we prefer to ignore these internal details. This has to do with our selection of boundaries and hierarchical levels.

21. Black and white boxes

22. Hierarchical and emergent systems According to the analytic approach  reductionism  the low level view is all you need. Example  in medicine  the body is a whole  the state of your mind affects the state of your stomach  which in turn affects the state of your mind These interactions are not simple, linear cause and effect relations  but complex networks of interdependencies  maintaining the organism in good health  These interactions function at the level of the whole  they are meaningless at the level of an individual organ or cell.

23. Emergence Emergence  the arising of patterns, structures, or properties that do not seem adequately explained by referring only to the system’s pre- existing components and their interaction. The new global patterns or properties are radically novel with respect to the pre-existing components. The emergent patterns seem to be: unpredictable nondeducible from the components irreducible to those components.

24. Emergence and reductionism Each ‘level’ of complexity of nature involves new interactions and relationships between the component parts which cannot be inferred simply by taking the system to pieces. Yet ontological reductionism  implies that even if higher order properties are emergent they remain secondary to lower-order ones. The lower the order the greater the primacy  it seems as if only lower order explanations can be ‘truly’ scientific.

25. Downward causality Reductionism  the laws governing the parts determine or cause the behavior of the whole  "upward causality"  from the lowest level to the higher ones. In emergent systems  the laws governing the whole also constrain or "cause" the behavior of the parts. When we say that the whole is more than the sum of its parts, the "more" refers to the higher level laws, which make the parts function in a way that does not follow from the lower level laws.

26. Hierarchical organisation Hierarchy  A partially-ordered structure of entities in which every entity but one is successor to at least one other entity; and every entity except the basic entities is a predecessor to at least one other entity. Although each level in a hierarchy has its own laws, these laws are often similar. The same type of organization can be found in systems belonging to different levels  structural and functional organization. The mechanistic paradigm  seeks universality by reducing everything to its material constituents. The systemic paradigm  seeks universality by ignoring the concrete material out of which systems are made  so that their abstract organization comes into focus.

27. Structure and function Structure  the complex of concurrent relations among a set of objects with the number of objects more numerous than the ordinality of the relations connecting them  the actual relations which hold between the components which integrate a concrete entity in a given space. Function  The normal or characteristic action of a system of entities, generally in time  a notion that arises in the description made by the observer of the components of a machine or system in reference to an encompassing entity  which may be the whole system or part of it and whose states constitute the goal that the changes in the components are to bring about. Thus we find similar structures and functions for different systems, independent of the particular domain in which the system exists  physical, chemical, biological, cognitive (mental), social or cultural.

28. Towards the mental sphere New concepts useful not only in biology and engineering but also for a transdisciplinary synthesis in human sciences. Cognitivism  ideas from cybernetics, computarized models of cognitive processes, information theory  a challenge to academic psychology. Kenneth Craik (1944)  what kind of machine is the human being situated between an information output and a gun?  an analogy between human mind and servomechanism. Welford (1947)  the first model of mental function in terms of information flux  cognitivism.

29. Cognitivism Behaviorism  the observable behavior of organisms (humans, animals) resulting from exposure to different stimuli. Cognitivism  mental processes the primary object of study  model the mental processes. Knowledge  symbolic, mental constructions in the minds of individuals. The development of computers with a strict "input - processing - output architechture" from the 1960s and up till today certainly have inspired these "information-processing" views of mental processes  "information- processing ideas".

30. Critics to cognitive science 1. The emotion challenge  cognitive science neglects the important role of emotions in human thinking. 2. The consciousness  challenge  cognitive science ignores the importance of consciousness in human thinking. 3. The world challenge  cognitive science disregards the significant role of physical environments in human thinking  the context. 4. The body challenge  cognitive science neglects the contribution of the body to human thought and action. 5. The social challenge  human thought is inherently social in ways that cognitive science ignores. 6. The dynamical systems challenge  the mind is a dynamical system, not a computational system. 7. The mathematics challenge  mathematical results show that human thinking cannot be computational in the standard sense, so the brain must operate differently, perhaps as a quantum computer.

31. Cognitive science and philosophy of science Interesting methodological questions: What is the nature of representation? What role do computational models play in the development of cognitive theories? What is the relation among apparently competing accounts of mind involving symbolic processing, neural networks, and dynamical systems? What is the relation among the various fields of cognitive science such as psychology, linguistics, and neuroscience? Are psychological phenomena subject to reductionist explanations via neuroscience?

32. "Sciences of complexity" "Sciences of complexity"  studying self-organization and heterogeneous networks of interacting actors or entities. Some recent fashionable approaches have their roots in ideas proposed by cyberneticians many decades ago  e.g. artificial intelligence, neural networks, complex systems, human-machine interfaces, self-organization theories, systems therapy, etc. Most of the fundamental concepts of these approaches were already formulated by cyberneticians  Norbert Wiener, W. Ross Ashby, Ludwig von Bertalanffy, Heinz von Foerster, John von Neumann, Warren McCulloch, Gregory Bateson among many others in the 1940's through 1960's. Few practitioners in these disciplines seem to be aware that many of their concepts and methods were proposed or used by cyberneticians since many years.

33. Difficulties of defining complexity Many specific definitions are only applicable to a very restricted domain  computer algorithms or genomes  very technical  meaningless in other domains. Latin  complexus  "entwined", "twisted together". In order to have a complex system you need two or more components  joined in such a way that it is difficult to separate them. Oxford Dictionary  something is "complex" if it is "made of (usually several) closely connected parts". A basic duality  between parts which are at the same time distinct and connected.

34. Unpredictability, non-linearity, chaos Intuitively  a system would be more complex if more parts could be distinguished, and if more connections between them existed. Complexity  can be characterized by lack of symmetry or "symmetry breaking"  by the fact that no part or aspect of a complex entity can provide sufficient information to actually or statistically predict the properties of the others parts.

36. The analytical (reductionistic) stream Focuses in investigating parts. It emerges from traditions of experimental science where a narrow enough focus is chosen in order to pose hypotheses, collect data, and design critical tests to reject invalid hypotheses. Because of its experimental base, the chosen scale typically has to be small in space and short in time  how can we design experiments to test the safety of a GMO in a given ecosystem?

37. The integrative stream Knowledge of the system is always incomplete. Surprise is inevitable. There will rarely be unanimity of agreement among peers - only an increasingly credible line of tested argument. Not only is the science incomplete  the system itself is a moving target  evolving because of the impacts of management and the progressive expansion of the scale of human influences on the Biosphere.

38. We can distinguish between: Complex systems Complicated systems Simple systems.

39. A simple system A system is “simple”  if it can be adequately captured using a single perspective or description and by a standard (e.g. analytical) model providing a satisfactory description or general solution through routine operations Examples  ideal gases, mechanical motion.

40. A ”complicated” system A system is “complicated”  when it cannot be satisfactorily captured trough the application of a standard model, although it is possible to improve the description or the solution through approximations, computations, or simulations However  a complicated system can still be characterized by using a single perspective Examples  a system of many billiard balls in movement, cellular automata, the pattern of communications in a large switchboard.

41. Complex vs. complicated Gallopín et.al.  the basic criterion to separate “complex” from complicated  is the need to use two or more irreducible perspectives or descriptions in order to characterize the system. Complex systems share with complicated ones the property of not being capturable through the application of a generic model through routine operations. Complex systems  different conceptions  complexity is not an automatic outcome of increasing the number of elements and/or relations in a system.

42. Some attributes that distinguish complex systems 1. Multiplicity of legitimate perspectives 2. Non-linearity 3. Emergence 4. Self-organization 5. Multiplicity of scales 6. Irreducible uncertainty

43. Attributes of complex systems (I) 1. Multiplicity of legitimate perspectives  it is difficult to understand an adaptive system without also considering its context. 2.Non-linearity  many relations between the constitutive elements are non linear  the magnitude of the effects is not proportional to the magnitude of the causes  a very rich repertoire of behaviour (e.g. chaotic behaviour, multi- stability because of the existence of alternative steady-states, runaway processes, etc.).

44. Attributes of complex systems (II) 3. Emergence  "the whole is more than the sum of its parts"  a systemic property, implying that the properties of the parts can be understood only within the context of the larger whole and that the whole cannot be analysed (only) in terms of its parts  true novelty can emerge from the interactions between the elements of the system. 4.Self-organization  the phenomenon by which interacting components cooperate to produce large-scale coordinated structures and behaviour.

45. Attributes of complex systems (III) 5. Multiplicity of scales  many complex systems are hierarchic  each element of the system is a subsystem of a smaller-order system, and the system itself is a subsystem of a larger order “supra-system”  there is strong coupling between the different levels  the system must be analysed or managed at more the one scale simultaneously. But systems at different scale levels have different sorts of interactions, and also different characteristic rates of change  it is impossible to have a unique, correct, all- encompassing perspective on a system at even one systems level.

46. Attributes of complex systems (IV) 6. Irreducible uncertainty  many sources of uncertainty arise in complex systems: Epistemological  some of them are reducible with more data and additional research, such as the uncertainty due to random processes (amenable to statistical or probabilistic analysis)  or that due to ignorance (because of lack of data or inappropriate data sets, incompleteness in the definition of the system and its boundaries, incomplete or inadequate understanding of the system). Fundamental (ontological)  irreducible uncertainty may arise from non-linear processes (e.g. chaotic behaviour), in the processes of self- organization and through the existence of purposeful behaviour including different actors or agents each with their own goal  entails a different conception of causality.

47. From simple to complex While some of the above attributes exhibited by complex systems can be displayed by some complicated, and even simple systems (such as non-linearity, or uncertainty)  the point is that any complex system is likely to have all of them.

48. Implications of systemic complexity for scientific research 1. Fluctuations can drive averages. Micro fluctuations (external or internal to the system) can, in certain circumstances, lead to drastic restructurations at the macro level  demonstrated for a number of physical, chemical, and biological cases by Ilya Prigogine. Example  drug testing  usually considered statistically low-risk  with an average of less than one person in a thousand dying or suffering irreversible damage. If the system is “Prigoginean”  a perturbation can amplify itself so as to change the average values  for example, synergetic factors  in which case  attempts to deal statistically with those situations are unsatisfactory not only socially but also scientifically  the “side-effects” can be unpredictable and more important than the intended effects or benefits from the drug.

49. Implications of systemic complexity for scientific research Scientific research about complex systems  may have to deal with a compounding of complexity at different levels  the interplay between the factors across the different levels and layers adds to the complexity intrinsic to each of the layers.

50. Implications for scientific research Attention to the complex systems properties presents difficulties for established conventions of scientific practice and expert advice within the scientific community. Knowledge in the sense of insight and understanding is absolutely not synonymous with capacity for predictions. Equally, awareness of risks is not synonymous with capacity to intervene to reduce or control the risks.

51. Increased control? Many will argue that this is not new  that ignorance and incompleteness of knowledge have always been admitted within the scientific project. Gallopín et.al.  There has been, in the past (and is still widespread today), an important ideological process that has protected science practice from having to address deeply this feature of inherent uncontrollability. Any uncontrolled change effects are interpreted as symptoms of the imperfection in the current knowledge and/or its application, with the presumption that more knowledge will reduce uncertainties, increase capacity for control, and permit the remedying of past mistakes.