Presentation is loading. Please wait.

Presentation is loading. Please wait.

11 Systemics and emergence for Architecture In memory of Professor G. Ciribini Gianfranco Minati Italian Systems Society www.AIRS.itwww.AIRS.it doctoral.

Similar presentations


Presentation on theme: "11 Systemics and emergence for Architecture In memory of Professor G. Ciribini Gianfranco Minati Italian Systems Society www.AIRS.itwww.AIRS.it doctoral."— Presentation transcript:

1 11 Systemics and emergence for Architecture In memory of Professor G. Ciribini Gianfranco Minati Italian Systems Society doctoral lecturer on systems science, Polytechnic University of Milan, Department “Building Environment Sciences and Technology”

2 22 PART 1 p The concept of system 1.1 From sets to systems 1.2 Introductory history 2. Systemics or General System Theory: System as phenomenon of emergence 2.1 A formal introduction 2.2 The concept of emergence

3 33 PART 2 p Systemics 3.1 Mono-, multi-, inter- and trans- disciplinarity 3.2 Inter- and trans- disciplinary research 4. Systemic openness 4.1 From thermodynamic to logical openness 4.2 Logical Openness 4.3 Systemic models based on thermodynamic and logical openness 4.4 An example of logical openness in education 4.5 General comments on systemic closeness and openness

4 44 PART 3 p DYnamic uSAge of Models (DYSAM) 6. Logical inferences, language and process of thinking 6.1 Introduction to Deduction, Induction and Abduction 6.2 General Comments Language and process of thinking From computing to learning

5 55 PART 4 p Applications 7.1 Reductionism 7.2 The systemic level of description 7.3 Systemics only for science? 7.4 Emergence and conservation: the example of buildings as systems 7.5 Assuming the wrong model, i.e. at the unsuitable level of description 7.6 Design and emergence 7.7 Emergence of Architecture from social systems

6 66 PART 5 p Self-Architecture 8.1 Architecture as the design of suitable boundary conditions for emergence of social systems: meta- structures. 8.2 From acquired to structural properties: architecture as structural synthesis 8.3 From implicit, unexpressed properties to structural properties: architecture as design of new structures intended as representation, translations of social phenomena 8.4 The concept of Self-Architecture 8.5 Meta-elements and Meta-structures

7 77 PART 6 p Conclusions 9.1 Growth, Development and Sustainability 9. 2 Theoretical role of the observer, constructivism, levels of description 9.3 Falsification of Systemics 9.4 Successes and failures of Systemics

8 88 PART 1 1.The concept of system 1.1 From sets to systems Interaction as necessary condition Examples of sets, structured sets, systems, subsystems:

9 99 What are Systems? In the scientific literature a System has been defined in various ways. For instance as “A set of objects together with relationships between the objects and between their attributes” or “... a set of units with relationships among them”. A system has been intended as an entity having properties different from those of what are considered elements by the designer (for artificial systems) or by the observer (for natural systems). A set is an entity having a rule of belonging. A necessary and sufficient condition for the establishment of systems is that elements, as designed (for artificial systems) or represented (for natural systems) by the observer, interact in a suitable way.

10 10 Elements characteristics and characteristics of generated systems. Football players Weight, age Team Harmony, game strategy Cells Function Living being Behaviour Students Numbers School Collective ability to learn Brain components Configuration Memory, Intelligence Processing capabilities Single musician Instrument played Orchestra Polyphony Words Correct Grammar A poem, a book, a story Meaning Musical notes Correctness in the score Music Harmony Couple Synchronization of interests Family Emergence of roles Soldiers Single abilities Army Ability to apply military strategies Workers Quantity Corporation Value Animals Quantity, single behaviour Herds, swarms, flocks, packs Collective behaviour

11 11 Interaction We may assume, in short, that two or more elements interact when one’s behaviour affects the other’s as observed by the observer. Examples of such interactions are processes of mutual exchange of energy (e.g., collisions and magnetic fields, where vector fields exert a magnetic force on magnetic dipoles or moving electric charges), matter (e.g., economic interchange) or information (e.g., prey- predator).

12 12 Design systems or model a phenomenon as a system It is possible to distinguish between two conceptual cases: 1.Systems are considered in an objectivist way when they are artificially designed, i.e., we know the component parts and how they interact because they were designed that way. 2.Systems are considered in a constructivist way (as for natural systems which have not been artificially designed) when the observer decides to apply a level of description (i.e., partitioning and interactions) to those systems, as if they had been designed as such. In this case, the observer constructivistically models phenomena as systems, by assuming elements and interactions. When this level of description works for applications, it is often assumed to be the true one within the conceptual framework of a discovery, thus resuming an objectivist approach.

13 13 What are non-systems? Depending on the level of description and on the model adopted by the observer, an entity is not a system when its properties are states, considered as not necessarily being supported by a continuous process of interaction amongst its components. Systems are thus entities assumed to be continuously acquiring systemic properties. Non-systems are entities considered by the observer as possessing non-systemic properties. Only systems may acquire systemic properties, while systems and non-systems may possess non- systemic properties.

14 14 For instance, the property of a set of boids establishing a flock is continuously established and this continuity is considered as the coherence of the collective or coherent behaviour of boids. It should be stressed that systemic properties are not the result of interactions. Systems and their properties are established by the continuous interaction among elements (e.g., an electronic device acquiring a property when powered on, leading to interactions amongst the component elements) and not as a state.

15 15 States are non-systemic properties, i.e. properties of non-systems like a new colour obtained from mixing primary colours (e.g., Red-Green-Blue), and of entities possessing properties like weight, speed, the Avogadro number and age. When elements of a system stop to interact than the system degenerates into a set.

16 16 SetsStructured SetsSystemsSubsystems Build components Buildings as structures in engineering Building as processes Floors of building Students belonging to a specific school Students in alphabetical order or grouped by age SchoolClassrooms Cells available for experiments Cells per dimension, age, type, etc., Living beingsOrgans Casual wordsWords in alphabetical order A story, PoemChapters, Verses Electronic components listed by the designer Electronic components structured by the outline of an electronic circuit in an electronic device An electronic device assumes properties different from ones of components when interacting, i.e. when the board is powered Group of components classed by function such as power supply, regulators and decoders. Animals available for study Animals per age or illness Swarms, Schools and flocks Groups of puppies, animals in reproduction and parents

17 17 Examples of properties of composing interacting elements and acquired by generated systems: Properties of composing interacting elements Properties acquired by generated systems Build Components Physical properties Buildings Habitability and energy consuming Cells Function Living being Behaviour Words Grammatical correctness A poem, a story Meaning Electronic components Availability, dissipation Receiver Revelation, stability Football players Age, weight Team Game strategy Animals Quantity, single behaviour Herds, swarms, flocks, packs Collective behaviour

18 Introductory history  System intended as device Control Theory, Automata, Systems Theory and Cybernetics The Watt’s centrifugal regulator:

19 19 The “single loop”

20 20 The “double loop”

21 21 System Dynamics (SD) Introduced by Jay W. Forrester (1918 -) in 1961, in the book Industrial Dynamics. Networks of feedbacks A B

22 22 Feedback refers to the situation of X affecting Y and Y in turn affecting X perhaps through a chain of causes and effects. One cannot study the link between X and Y and, independently, the link between Y and X and predict how the system will behave. Only the study of the whole system as a feedback system will lead to correct results.

23 23 Examples of negative feedback to control a system are: a)thermostat control (when the temperature in a room reaches a certain upper limit the heating is switched off making the temperature to fall down. When the temperature drops to a lower limit, the heating is switched on), b) hormonal regulation, and c) temperature regulation in animals.

24 24 Examples of positive feedback to control a system are: a)contractions in childbirth: when a contraction occurs, the hormone oxytocin is released into the body, stimulating further contractions. This results in contractions increasing in amplitude and frequency, b)lactation involves positive feedback so that the more the baby suckles, the more milk is produced, c)in stock exchange when the more stakeholders sell and the more they sell.

25 25 Modelling Systems behaviour The theory of dynamical systems (to be not confused with System Dynamics) has been developed on the basis of researches implemented by J. H. Poincaré ( ) dx(t)/dt = f(x(t))

26 26 A dynamical system is based on two kinds of information: 1.One dealing with the representation of the system’s state and information about the system itself, i.e., dx(t)/dt; 2.The other specifies the dynamics of the system, through a rule describing its evolution over time, i.e., f(x(t)). Examples of models of this kind are those used to model simple systems such as the motion of the pendulum or the moon moving along its orbit, by using the equations of motion of classical mechanics.

27 27 In simple systems, like the pendulum, a state variable describes the microscopic behaviour of elementary components and may be sufficient to describe the behaviour of the entire system. In more complex systems macroscopic variables are assumed as state variables suitable for describing the system as a dynamical system using those variables like volume, temperature, number of components (prays and predators) in ecosystems.

28 28 2. Systemics or General System Theory In general, systems may be established or modelled as such by considering a)structure between elements (structure is a specification of organisation. Organisation is a network of relationships), and as b) phenomenon of self-organisation and emergence (not emergency!!)

29 29 Systems are established by: a) A structured functional way, when organisation is intended as a network of pre-established functional relationships which control the manners of interacting. Rules of interaction are either a) determined by following a design or b) constructivistically intended as such by the observer. In both cases they are sufficient conditions for establishing systems. Structured rules completely define the way in which elements interact, i.e., they define all the degrees of freedom possessed by interactions between elements at the specified level of description.

30 30 Examples of case a) include mechanical devices, such as machines, and electronic devices, such as circuits. Examples of non-designed systems, as in case b), are natural entities modelled as organised systems by the observer, such as organs performing given functions in living beings and eco-systems.

31 31 b) A process of self-organisation takes places when a structure or a change in structure is acquired or lost, as in phase transitions (e.g., ice-liquid-gas) due to environmental perturbations (e.g., change of temperature or pressure) and in collective phenomena. Examples of systems modelled in this way are flocks, swarms, industrial districts, lasers, ferromagnetic and superconducting systems.

32 32 Emergence deals with a generalisation of such processes by considering the process of hierarchically acquiring new properties as properties of systems of systems. Through processes of emergence systems acquire themselves or collectively (i.e., through systems of systems) new further systemic properties at different levels. Examples are given by the establishment of properties such as cognitive abilities in natural and artificial systems, collective learning abilities in social systems such as flocks, swarms, markets, firms, and functionalities in networks of computers (e.g., on the Internet).

33 33 Maria Bertalanffy (his wife) and Ervin Laszlo wrote the following considerations about the term General Systems Theory: "The original concept that is usually assumed to be expressed in the English term General System Theory was Allgemeine Systemtheorie (or Lehre). Now “Theorie” or Lehre, just as Wissenschaft, has a much broader meaning in German than the closest English words theory and science." The word Wissenschaft refers to any organized body of knowledge. The German word Theorie applies to any systematically presented set of concepts. They may be philosophical, empirical, axiomatic, etc. Bertalanffy’s reference to Allgemeine Systemtheorie should be interpreted by understanding a new perspective, a new way of doing science more than a proposal of a General System Theory in the dominion of science, i.e. a Theory of General Systems.

34 A formal introduction Within the second conceptual framework Ludwig von Bertalanffy (1901 – 1972), considered to be the father of General System Theory, described a system S, characterized by suitable macroscopic state variables Q 1, Q 2,..., Q n, whose instantaneous values specify the state of the system. dQ 1 / dt = f 1 (Q 1, Q 2, …, Q n ) dQ 2 / dt = f 2 (Q 1, Q 2, …, Q n ) …………………………. dQ n / dt = f n (Q 1, Q 2, …, Q n ) where Q suitable state variable.

35 35 State variables Macro, micro and meso state variables to model the system Microscopic state variables relate to a level of description focusing on components (designed or modelled as such) of a system. Examples are variables used by equations of motion of classical mechanics when modelling simple systems such as the motion of the pendulum or the moon moving along its orbit, and the Brownian motion.

36 36 Macroscopic state variables relate to a level of description focusing on the average effects of large number of microscopic variables such as when considering the movement of a billiard ball and ignoring its molecular description; density, volume or surface when considering thermodynamic phenomena and ignoring molecular description.

37 37 Mesoscopic state variables relate to a level of description focusing on variables intermediate between the two previous cases. At this level we consider variables reduced, i.e., considering more details, with reference to the macroscopic level, but without completely neglect all the degrees of freedom considered at the microscopic level.

38 38 For instance, when considering agents establishing collective behaviour like a flock, we focus on variables such as: M x, number of elements having maximum distance at a given point in time; M n, number of elements having the minimum distance at a given point in time; Nk number of elements having same value of variables such as: N 1 = number of elements having same distance from the nearest neighbour, N 2 = number of elements having same speed and N 3 = number of elements having same direction over time.

39 The concept of emergence Phase transition Self-Organisation Emergence (Cruchtfield, Baas): computational and phenomenological

40 40 1.Phase transitions relating to changes in structure, e.g., water-ice-vapour transition and ferromagnetism. A note on phase and state of matter Phases are sometimes confused with states of matter, more precisely thermodynamic states. For instance, two gases at different pressures are in different thermodynamic states, but at the same phase of matter. Two states are in the same phase if they can be transformed into one another with sample variations of thermodynamic properties.

41 41 Phases of matter In physics a phase is a region of space (a thermodynamic system), where physical properties of a material are essentially uniform, like having same density. A phase of a physical system may be defined as a region in the parameter space of the system's thermodynamic variables where the free energy is analytic.

42 42 Free energy In thermodynamics the term free energy relates to a physical variable such that: Its changes measure the minimum work the system can do; Its minimum values correspond to stable equilibrium states of the system. The free energy is, for instance, the total amount of energy, used or released during a chemical reaction. The term relates to the part of the total energy available for useful work and not dissipated in useless work, like random thermal motion. When a system undergoes changes, its free energy decreases.

43 43 Analytic In the region in the parameter space of the system's thermodynamic variables the free energy can be transformed in analytic way, i.e., transforming function is infinitely differentiable and can be described by a Taylor series. In correspondence, we may say that two states of a system are in the same phase when they can be transformed into each other with continuity, i.e., without discontinuity among thermodynamic properties. During a phase transition the free energy is non-analytic.

44 44 2.Processes of self organisations considered as phase transitions when a new acquired structure is dynamic and stable, i.e., repeated in a regular way. Examples are non- perturbed swarms, i.e., synchronised oscillators, established by suitable initial conditions, reaching stationary states in a non-perturbed way such as populations of synchronized fireflies and oscillating chemical reaction (Belousov-regular chromatic changes, Benard- convection cells roll in the same direction).

45 45 3.Processes of emergence may be understood as phase transitions when newly acquired dynamic structures coherently change over time. The process of emergence relates to changes in dynamic structures over time. This way of understanding processes of emergence is suitable for modelling collective behaviours of entities provided with cognitive systems allowing the collective system to process internal and external perturbations. The active role of the observer is fundamental detecting, representing and modelling emergent properties. Coherence is a property primarily generated by the cognitive system of the observer.

46 46 Examples of emergent properties are given by cognitive abilities in natural and artificial systems (behaviour), collective learning abilities in social systems such as flocks, swarms, markets, firms and functionalities in networks of computers (e.g., in Internet), adopting variable non- regular behaviour as in the presence of any suitable environmental condition, but displaying the same property to the observer.

47 47 PART 2 3. SYSTEMICS Difference between possessing from acquiring (systemic) properties The concepts of property, level of description and the role of the observer

48 48 Systemics This term is used to denote a corpus of systemic concepts, extension of systemic principles by using, for instance, analogies and metaphors. Systemic Approach This expression is used to denote the general methodological aspects of Systemics, considering, for instance, identification of components, interactions and relationships (structure), levels of description, processes of emergence and role of the observer. General System Theory This expression has been introduced in the literature to refer to the theoretical usage of systemic properties considered within different disciplinary contexts (inter- disciplinarity) and per se in general (trans-disciplinarity). Current research identifies it with the Theory of Emergence, i.e. acquisitions of properties. Systems Theory This expression, often inappropriately used as shorthand for General Systems Theory, relates to First-order cybernetics and Systems Engineering for applications such as Control systems and Automata.

49 Mono-, multi-, inter-, and trans- disciplinarity Mono-disciplinarity The basilar idea is that a single discipline may deal with any kind of problems. Or any problem may be formulated as problem of a single discipline. It is a typical reductionistic approach, e.g., social problems are economical, psychological problems are neurological, etc.

50 50 Multi-disciplinarity Multi-disciplinarity relates to the use of different disciplines to deal with the same problem like psychology or economy or laws or organisation to deal with a managerial problem occurring in corporations and to evaluate post-occupancy problems.

51 51 Inter-disciplinarity Inter-disciplinarity takes place when problems and approaches of one discipline are used by another (for instance, when models of physics are used in economics and economic problems are represented as physical models, e.g., collective behaviour to represent markets). Contrary to Multi-disciplinarity, Inter-disciplinarity is not a usage of different disciplines, but a theoretical issue consisting of formulating a disciplinary problem by using the models of another discipline. Inter-disciplinarity also occurs in education when teaching one discipline by using another (for instance, teaching history while dealing with geography, mathematics with physics, and medicine with chemistry).

52 52 Inter-disciplinarity deals with the study of the same systemic properties in different disciplines (e.g., openness, adaptability and chaos in physics, economics, biology and psychology). Inter-disciplinarity is about dealing with concepts, approaches, theoretical issues, and models suitable for usage within different disciplinary contexts. A usual approach is based on transposing variables when applying the same model to different disciplinary cases.

53 53 An example The Lotka-Volterra model describes interactions between two species by using those two state variables. If we consider the two state variables x, the density of prey individuals, and y, the density of predators, the explicit form of the model is: dx/dt = ax - cxy dy/dt = - bx + cxy Where: a is the intrinsic rate of prey population increase; b the predator mortality rate; c denotes both predation rate coefficient and the reproduction rate of predators per prey eaten.

54 54 The Lotka Volterra system may be used to model systems where processes of competition occur, like in finance, by changing meaning of variables and parameters.

55 55 Examples of issues in interdisciplinary research are: 1.'How models used in physics may be used in the social sciences', 2.'How models describing processes of biological aggregation may be used to model socio-economic processes', 3.'When Game Theory is sufficient to model decision-making processes and when the cognitivist view must be adopted'. Generic rather than general usage of inter- disciplinarity occurs when using, for instance, metaphors and analogies instead of models. In this case conclusions reached have limited values of robustness and reliability.

56 56 Analogy is reasoning or explaining from parallel cases. "Analogies prove nothing that is true" wrote Sigmund Freud, "but they can make one feel more at home.“ An example of analogy is “horses are to past societies as cars are to modern societies”. Metaphors claim for a limited level of total identification. An example of metaphor is the flux of time.

57 57 Trans-disciplinarity The term Trans-disciplinarity is widely used, but with no clear, unequivocal or generally accepted definition. Jean Piaget probably first used the term on the occasion of the workshop "L'interdisciplinarité - Problèmes d'enseignement et de recherche dans les universités", Nice (France), September 7-12, There are different international institutions devoted to research on this subject mostly focusing upon humanistic interpretations.

58 58 For our purposes we will use this term in a very precise way. We consider Trans-disciplinarity to arise when systemic properties are studied per se, i.e., considered in general as properties of models and representations without any reference to specific disciplinary cases. Trans-disciplinarity also studies the relations between systemic properties, e.g., models of dissipation, equilibrium, openness, adaptability and chaos, and their relationships (Fig. 1).

59 59 Examples of issues in trans-disciplinary research are: 1.‘Is it possible to formulate a theory about the relationship between systemic properties?' 2.‘How can processes of emergence in systems be induced?’ 3. 'How, in general, can systemic properties be induced or regulated?' 4.'Is it possible to identify a general way to measure systemic properties?' 5.'Using mathematics for modelling is a way to represent systemic properties. Are there other equivalent ways of representing the same systemic properties?'.

60 60 Trans-disciplinarity When systemic properties are considered per se, in general, i.e. without considering specific disciplinary fields, and in relation between them. Inter-disciplinarity Chaoticity is a systemic property, i.e. valid in different disciplinary fields, with the same modeling and simulation. Inter-disciplinarity Openness is a systemic property, i.e. valid in different disciplinary fields, having the same modeling and simulation. 3.2 Inter- and trans- disciplinary research Phy---C---sics Bio---H---logy Che--A---mistry Eco--O---nomics Psy---T---chology Soc---I----iology Met--C---ereology Phy---O ---sics Cog---P ---itive sciences Geo---E ---logy Ele--- N ---ctronics Lin--- N ---guistics Mus---E ---ics Ant--- S ---ropology Env--- S ---ironmental sciences

61 61 Generic, Metaphorical, Analogue and General

62 62 4. Systemic openness 4.1 From thermodynamic to logical openness Closed systems Systems are considered closed when isolated from the environment. Systems may be closed to matter/energy flows (autarkic), closed to information flows (independent), closed to organization.

63 63 Thermodynamic open systems Thermodynamic openness relates to the ability of systems to have permeable boundaries. Examples of thermodynamically open systems are: internal combustion engine, dissipative structures, e.g., water whirlpool and living systems, ecosystems and electronic devices. A conceptual way to close a system is to incorporate its environment. Logical Openness relates to the variety of simultaneous ways to model a system.

64 Logical Openness The concept of logical openness relates to the constructivist role of the observer generating n-levels of modelling by assuming n different levels of description, representing one level through another, modelling a strategy to move amongst them, and considering simultaneously more than one level.

65 65 The concept of level of description in making representations, in short, relates to: 1) the disciplinary knowledge of the observer when dealing with a phenomenon. For instance, the crying of a human being may be represented as physical, chemical, biological and psychological process; 2) the cognitive model adopted by the observer; 3) the nature and quantity of variables, relations and interactions and the scaling used in general by the observer to represent the phenomena observed.

66 66 The classic reductionistic approach is based on considering a unique level of description, able to represent and deal with any kind of problem. This often corresponds to the usage of a standardized approaches, tools, and remedies. The systemic approach is based on considering different, interacting levels of descriptions, by multi- modelling problems (e.g. environmental problems, for instance, are simultaneously physical, chemical, biological, economical and social; industrial projects are simultaneously related to some specific disciplinary field –such as electronic, mechanic, chemical, etc-, economical, legal, and social).

67 Systemic models based on thermodynamic and logical openness Only thermodynamic open systems are able to be logically open because openness is a systemic property to be sustained by using energy. Logically Closed Model or Logical Closeness A model may be defined as logically closed when: a)a formal description of the relationships between all the state variables is available in the model’s equations; b) an explicit and complete description of the interactions system-environment is available; c) all possible asymptotic states and structural features are derivable in a unique way from the knowledge contained in a) and b).

68 68 Logically closed modelling relates to rigid and foreseeable input processing modalities. In contrast, logically open modelling relates to such a description of the system that it’s impossible to know, in principle, how the input-output will be processed. In this case it’s impossible to know the asymptotic states (if any) of the system. An example is given by a computer program playing a game with a player. Logical open modelling or logical openness may be introduced on the basis of violation of one of the three criteria a), b), c), previously introduced to describe logical closed modelling.

69 69 The more interesting from a theoretical point of view is the violation of the second criterion, with reference to the availability of a model carried out by the observer on the basis of his/her knowledge and goals and characterized by its ability to explain and foresee the evolution of the system. In this context, the logical openness corresponds to the fact that system-environment interactions cannot be explicitly, completely and uniquely described. This is the case mentioned above when the observer has n-different levels of descriptions c orresponding to n-different models.

70 An example of logical openness in education It is possible to consider hierarchies of logically open models based on suitable openness levels. An examples of a hierarchy of this kind, within the context of social systems and with reference, for instance, to education and to cognitive processing of information, may be the following.

71 71 Level of openness 1. It corresponds to the classic thermodynamic level where matter and energy are able to cross system’s border. At this level to close a system is sufficient to consider a larger system containing the original system and the other interacting systems (like the environment). By making reference to systems able to send and receive information, this may be the case where systems are able to send and receive signals, but not to attribute or process meaning. An example is given when two or more people may physically exchange words with no common understanding because they speak different languages. In the same ways computers may physically exchange messages between each other but having not the software able to process them.

72 72 Level of Openness 2. At this level the meaning of messages is assumed to be identical and constant between sender and receiver. The process of interacting is assumed to be context- independent. This is the classical approach based on objectivism. Examples are rules, instructions, and formal language for programming.

73 73 Level of Openness 3. At this level the process of interacting is assumed to be context-sensitive with reference to the sending/receiving systems. Each system generates a model of the other having learning capabilities and the communication process is activated between models. Examples are the interactions between teacher and student, seller and buyer, physician and patient, user and information systems able to process users profiles. It is also what usually goes on between corresponding agents via electronic mail in the Internet whom never met in person.

74 74 Level of Openness 4. At this level during the communication process the systems exchange not only messages, but also information about their context: the process of interacting is assumed to be context-sensitive with reference to the sending/receiving agents and to their environment. Messages are semantically processed with continuous reciprocal modelling of systems and of their context. A typical example occurs when two agents are negotiating in different times, having the possibility of influencing their contexts.

75 75 Level of Openness 5. At this level the system may decide which of the previous level of openness to adopt depending on a strategy and on contextual evaluation. The possibility to dynamically decide which level of openness to adopt may be realized as the highest level of openness. Each level of openness includes the possibility to assume the previous one. We underline that at this level a system may decide to stop, to degenerate into a set, i.e., suicide, but not to start.

76 76 Other examples are also given when interaction among people takes place by using different kinds of technologies, by allowing 1.One way interaction with no model of the receiver or real time feedback; for instance a book writer; 2.One way interaction with no model of the receiver but with real time feedback; for instance a theater actor; 3.Two ways interaction with no model of the receiver; for instance selling by telephone/TV or Internet; 4.Two ways interaction with a model of the receiver; for instance private direct selling; 5.Two ways interaction with a model of the receiver and of its context; for instance business marketing through sales managers; etc.

77 77 Levels of opennessRelated levels of closeness 1.Thermodynamic level: crossing of matter-energy trough borders of the system No crossing of matter/energy trough borders of the system 2. Meaning assumed identical between sender and receiver Crossing of matter/energy trough borders of the system, but no common meaning between sender and receiver 3. Interacting systems produce mutual context-sensitive models: systems have learning capabilities Meaning assumed identical between sender and receiver, but the systems do not produce mutual context-sensitive models and have not learning capabilities 4. Interactive systems produce dynamic mutual context- sensitive models: systems have learning capabilities Interacting systems produce mutual, but not dynamic context- sensitive models: systems have learning capabilities 5. The system may continuously decide which level to use in interacting Interactive systems produce dynamic mutual context-sensitive models, systems have learning capabilities, but they cannot decide which level to use in interacting

78 General comments on systemic closeness and openness In Logical Openness the problem of finding the ultimate reality and the best representation becomes a non effective strategy. The strategy looking for effectiveness is based on considering how it is more convenient to think that something is rather than trying to find out how something really is. The second case is just a particular vase of the first one.

79 79 CLOSED SYSTEMSLOGICAL OPEN SYTEMS PassiveActive Context insensitiveContext sensitive Non learningLearning Object orientedProcess oriented Non flexibleFlexible Fixed rules, variable parametersChanging rules Contradiction avoidersUsing contradictions at higher level of description Mono or non-dynamic strategiesMulti- dynamic strategies (DYSAM, chapter 5) DeductiveInductive and abductive (constructivism) ObjectivisticNon objectivistic Observer as generator of relativism Observer as part of the system and generator of cognitive existence

80 80 PART 3 5. DYnamic uSAge of Models (DYSAM) The concept of DYSAM relates to situations in which the dynamical adoption of properties by the system is such that any single model is, in principle, unsuitable to model such dynamics, because single models are assumed to model a specific system.

81 81 The DYSAM approach was introduced to deal with the dynamical emergent properties of complex systems, i.e., when 1.the system to be studied is so complex (processes of emergence occur within it) that we cannot, in principle, describe it using a single or a sequence of models, refinement of the preceding one, and 2.the process of emergence gives rise to the dynamic establishment of different systems, Multiple-systems (MSs) and Collective Beings (CBs) introduced later. Dynamic models model dynamical properties of a specific phenomenon, while DYSAM models change over time, i.e., the dynamic acquisition of different, emergent properties and properties of MSs and CBs as well.

82 82 DYSAM is based on approaches already considered in the literature having the common strategy of not looking for a unique, optimum solution like, for instance, the a) Bayesian method, e.g., what is the probability of a hypothesis given the occurrence of an event? b) Pierce’s abduction, hypothesis inventing process, i.e., because B is true probably A is also true, since if A were true the truth of B would be obvious; c) Machine Learning, e.g. in Neural Networks; d) Ensemble Learning, combining an uncorrelated collection of learning systems all trained in the same task, and e) Evolutionary Game Theory, emerging of cooperative/competitive strategies.

83 83 Approaches DYSAM-like are used, for instance, a) in generic medicine when testing multiple pharmacological treatments to cope with an illness not exactly diagnosed or dealing with unexpected side effects and simultaneously considering the psychological, biological and chemical level of description; b) when modelling biological systems, like the brain, as quantistic or not; c) for the use of surviving resources in damaged systems (i.e., in case of disabilities managing balancing and compensation); and d) for learning the use of the five sensory modalities in the evolutionary age for children not having the purpose to choose the best one, but to use all of them together.

84 84 Multiple Systems A MS is a set of systems established by the same elements interacting in different ways, i.e., having multiple simultaneous or dynamical roles. The role of single systems in a MS must be not confused with that of subsystems related to different functions within the same system. Within the conceptual framework of MS concurrent/cooperative effects of different interactions affecting the same elements perturb the effects of single interactions. Moreover, the action of concurrent interactions may be neither simultaneous nor regular. The same interacting components may establish different systems through organization or emergence and at different times (i.e., simultaneously or dynamically).

85 85 Examples of MSs in systems engineering include networked interacting computer systems performing cooperative tasks, as well as the Internet, and electricity networks (an unfortunate emergent property is the black-out) where different systems play different roles in continuously new, emerging usages (e.g., market of telephone traffic).

86 86 Collective Beings CBs are particular MSs established by agents possessing a (natural or artificial) cognitive system. In CBs the multiple belonging is active, i.e., decided by the component autonomous agents. In the process of emergence of CBs agents interact by simultaneously or dynamically using, in the model constructivistically designed by the observer, different cognitive models.

87 87 Examples are Human Social Systems where (a) agents may simultaneously belong to different systems (e.g., behave as components of families, workplaces, traffic systems, as buyers, of a mobile telephone network). Simultaneously is not only related to time, but also to agent behaviour, considering their simultaneous belonging, and their roles in other systems; and (b) agents may dynamically give rise to different systems, such as temporary communities (e.g., audience, queues, passengers on a bus), at different times and without considering multiple belonging.

88 88 Modelling social systems has been based on considering families, corporations, cities, hospitals, schools, and so on, as subsystems. We postulate the effectiveness of also considering them as CBs. The management of the multiple systems of a CB by considering them as subsystems is a source of serious managerial problems. Moreover, subsystems are functional, i.e., specialised components in an organised system. Managerial problems occur when failing to consider that in the case of MSs and CBs which are considered as subsystems are dynamically established by the same elements.

89 89 Management of properties acquired by MSs and CBs should focus on multiple roles and related processes of acquisition. The various multiple roles taken on by a subsystem within a system must be not confused with the multiple roles assumed by autonomous agents when making emergent a new system.

90 90 In sum what is DYSAM? In order to model acquisition of subsequent emergent properties, MSs and CBs we need different partial representations related to each component system. Systemic properties, interdisciplinarity, transdisciplinarity, and different levels of description are resources for multiple representing and modelling. DYSAM is a methodology, an approach.

91 91 In short, the main components of DYSAM are: 1. a repertoire of different possible models of the same system; 2. a strategy for selecting, on the basis of general and momentary goals, the available knowledge and the context, the models to be used (and eventually integrated) to model the system considered from simultaneous different approaches. Such a strategy is not only variable, based, for instance, on learning (and not optimisation only), but on modelling interactions between the adopted models. Moreover, not only is the strategy variable, but evolutionary as it varies with the evolution of the interactions between the observer and the system.

92 92 LR MSDBGDM IC (t) SIM (t) DYSAM may be implemented in different ways, such as where  DB data base of models connected by a trained Neural Network;  IC contextual information;  GDM dynamic manager of models;  LR Levels of representations;  MS model to be used for the simulation or for taking decisions;  SIM simulation or decision.

93 93 6. Logical inferences, language and process of thinking 6.1 Introduction to Deduction, Induction and Abduction Deduction Induction Abduction

94 94 Deduction Deduction is a kind of inference (the process of making inferences may be understood as generating conclusions from premises), starting from the necessary premises: the latter contain everything necessary to reach the conclusion. Therefore, in a valid deduction, the conclusion cannot be false if all premises are true. In the case of deduction the most widely used rule is the so-called Modus Ponens.

95 95 In it one starts from the application of a general rule (R): x--->y, which is expected to be true if premises are true. When a particular case (C) holds, the resulting conclusion (Res) is obtained because the rule (R) and (C)---->Res is applied. Here is an example: All the pieces in this box are black, - rule (R). Those pieces come from this box, - case (C). Therefore those pieces are black, - result (Res).

96 96 Induction Induction is an inference, which from a finite number of particular cases leads to another case or to a general conclusion. For instance, if from a bird watch the passage of only black ravens has been observed, then it is possible to induce that the next raven detected will be black or that all ravens are black.

97 97 In the case of the induction of a rule or of a result (Res) from a set of configurations (C n ) of elements, we start from the observations: C--->Res, C’--->Res, C’’--->Res, …, and then we assume valid the general rule C any--->Res. An example is: Those pieces come from this box, - case (C). Those pieces are red, - (Res). All the pieces in this box are red, - (R).

98 98 Abduction In the case of abduction a reasoning of this kind is adopted: The starting point is a collection of data D; The hypothesis H, if true, could explain D; No other hypothesis can explain D better than H; Then H is probably true. There is a hypothesis inventing process that may be even viewed as a selection among the most suitable ones for explaining D.

99 99 With abduction a process of clustering is carried out, grouping together variables that are most probably related (or, more precisely, that it is suitable to think they are): "Because B is true probably A is also true, since if A were true the truth of B would be obvious”. Charles S. Peirce defines his concept of abduction in the following way: "Abduction is the process of forming an explanatory hypothesis. It is the only logical operation which introduces any new idea” (Peirce, 1998).

100 100 Paraphrasing Foerster, there is no information, or anomalies in the environment. If a given phenomenon looks strange, this means that the theoretical framework used to interpret this phenomenon is inappropriate. This cognitive process of reformulation of the model is labelled abduction, and its aim is to “normalize” anomalies.

101 General Comments Language and process of thinking Edward Sapir ( ) Sapir-Whorf hypothesis

102 102 The point relates the question: how learning involves language and how language influences learning? As introduced by Vigotsky: “The relationship between thought and word is not a thing but a process, a continual movement back and forth from thought to word and from word to thought:.... thought is not merely expressed in words; it comes into existence through them." This view was successively elaborated and formulated as the celebrated Sapir-Whorf hypothesis – now accepted in the weaker sense. In this context we give only a general idea of the approach.

103 103 The general, ‘strong’ (the so-called ‘weaker’ versions are mentioned below), idea introduced by this approach is that what we can think is enabled by the language that we use for representing, hypothesizing, designing, rejecting, and so on. If we do not have the language to say it, it doesn’t exist for us. There are many approaches for dealing with the ideas introduced by the Sapir-Whorf hypothesis. The versions of these approaches are briefly summarized below: 1.Strong hypothesis—language determines thinking; 2.Weak hypothesis—language influences perception and thinking; 3.Weakest hypothesis—language only influences memory.

104 From computing to learning We only mention a distinction between symbolic and sub-symbolic computation. Symbolic processing is performed by using explicit, hierarchically arranged, rules to serially process symbols.

105 105 A connessionist system performs sub-symbolic computation having a model, a description, provided with not explicit rules and symbols but with connections among elements. The effect of an input value is computed through weights assigned to connections and to functions synthesizing signals coming from various connections to output elements. Reference is to the well-established technology of Neural Networks inspired by network of connection among brain neurons.

106 106 Connectionist systems perform parallel information processing of sub-symbols, by using statistical properties and not logical, explicit rules. While symbolic computation is assumed to be able to present a result y from an input x to a function f y = f(x) connectionistic models are able to learn from an input x and an output how to compute it in sub- symbolic way.

107 107 PART 4 7. Applications Applications of Systemics are based on Designing a system or Modelling a phenomenon as such, when the observer notes effectiveness of this approach. In both cases the designer/observer must identify 1) the components, partitioning the system, and the interactions among them, and 2) the systemic properties differing from those of the components.

108 108 Then a suitable model of the system must be formulated at an effective level of description, i.e., by considering 1)the nature and the quantity of variables, microscopic, macroscopic or meso-scopic as introduced later, 2) the relations and interactions, and 3) the scaling used to represent the phenomena observed, i.e., choose the order of magnitude.

109 109 The model must explain a) in a symbolic or b) sub- symbolic way) the establishment of systemic properties and allow the designer/observer to act on them, e.g., to regulate, start, stop, combine, etc. them. a) - the system is artificial, e.g., electronic system; - the system is natural but modelled by assuming to know the suitable symbolic level of description, e.g., solar system. b) The system is not suitably represented by using a symbolic level of description. Other approaches are available like sub-symbolic representation like in Neural Networks:

110 110

111 111 The Russian computer scientist Mikhail Moiseevich Bongard proposed a method of creating an adequate language to model a system, the language in which the creation of the system could be described (Bongard M., 1970, Pattern Recognition. Spartan Books, New York.)

112 Reductionism The reductionistic approach is based on assuming that actions on components will produce the same effects on systemic properties, i.e., on assuming linear relationships between the properties of components and the systemic properties. The reductionistic approach also assumes that the same level of description assumed for components works properly also for acquired systemic properties, and that a unique level of description should work for all acquired systemic properties acquired in time, be this level of description based on details (microscopic) or general (macroscopic). Reductionism relates to a wrong level of description.

113 The systemic level of description The systemic approach is based on considering different, interacting levels of descriptions, by multi- modelling problems (e.g. environmental problems, for instance, are simultaneously physical, chemical, biological, economical and social; industrial projects are simultaneously related to some specific disciplinary field –such as electronic, mechanic, chemical, etc-, economical, legal, and social).

114 114 Macroscopic (no reference to magnitude) Microscopic (no reference to magnitude) Mesoscopic (no reference to magnitude) Systemic models

115 115 Microscopic state variables relate to a level of description focusing on components (designed or modelled as such) of a system. Examples are built components having well defined and stable properties, to be replaced in case of malfunction or degradation with time without affecting the systemic properties of interest for the observer/designer, like functionalities, e.g., windowing, doors, and entrance-exit and engineering properties like stability.

116 116 Macroscopic state variables relate to a level of description focusing on the average effects of large numbers of microscopic variables such as when considering habitability, energy management, urban and landscape role. Buildings are intended as systems having acquired properties not reducible to those of components. This is similar to the effect we experience with music and painting. Single notes and painting details are necessary conditions for the emergence of the acquired properties realised at macroscopic level.

117 117 Mesoscopic state variables relate to a level of description focusing on variables intermediate between the two previous cases. At this level we consider variables reduced, i.e., considering more details, with reference to the macroscopic level, but without completely neglecting all degrees of freedom considered at the microscopic level. I think that a typical case relates to Architectural Beauty and Cultural Heritage. In both cases microscopic variables are considered as important details to be considered harmonic at macroscopic level. The observer continuously changes levels of descriptions moving from details and functionalities to global, harmonic aspects enjoying the non-linear rebuilding from details.

118 118 Systemic models are necessary to deal with complex systems and with their acquired properties. They are necessary to manage acquired properties. This is for a vary large number of cases, such as in education, economics, sociology, medicine, biology, physics, …, and architecture. Systemic models are necessary to design actions at macroscopic levels, i.e., affecting acquired emergent properties, like maintenance, dealing with changed environmental conditions, due to Post-Occupancy Evaluations and Building Performance Evaluation.

119 Systemics only for science? In science and engineering we introduced quantitative models, but in a trans-disciplinary way the same approach may be used by multi-modelling when different levels of descriptions relate to different disciplinary approaches like in medicine we deal with chemical, biological, pharmaceutical and psychological levels and in architecture we deal with engineering, chemical properties of materials, energy, light, psychology, etc.

120 Emergence and conservation: the example of buildings as systems processing input and not only reacting i.e., when processes of emergence occur Buildings as systems processing inputs and internal changes of configurations (non linear substitutability of components) affecting emergent acquired properties. Models of the processing may be both symbolic (engineering), related to emergent properties (sub- symbolic) and qualitative related to qualitative emergent properties (beauty) as introduced later.

121 121 The system to be considered in conservation is an open system modelling the environment and interactions. CONSERVAZIONE PROGRAMMATA, è di necessità rivolta prima che verso singoli beni, verso l'ambiente che li contiene e dal quale provengono tutte le possibili cause del loro deterioramento. Il suo obiettivo è pertanto il controllo di tali cause, per rallentare quanto più possibile la velocità dei processi di deterioramento, intervenendo, in pari tempo e se necessario, con trattamenti manutentivi appropriati ai vari tipi di materiali. Giovanni Urbani, "Piano Pilota per la conservazione dei beni culturali in Umbria", 1976.

122 122 The case of the Basilica di San Gaudenzio, Novara ( ). Cracks in the vaults were restored by using structural approaches, i.e., by assuming they were built up by using structural engineering. Result was that cracks widened. As Bongard said, in order to restore a system, i.e., a process acquiring emergent properties, we have to know how it has been established and try to reproduce it. In this case we can’t fix emergent properties acquired though antique building by just applying new techniques. Piantanida e V. Borasi, Conseguenze del tipo di committenza sulla qualità della manutenzione come guida e controllo per la conservazione di monumenti: il caso del complesso di San Gaudenzio a Novara, in Ripensare alla manutenzione. Ricerche, progettazione, materiali, tecniche per la cura del costruito, atti del convegno di Bressanone, Venezia 1999,

123 Assuming the wrong model, i.e. at the unsuitable level of description For instance, in antiquity columns were built by segments to be more easily transported and superimposed. In case of external inputs, e.g., mechanical (earthquakes) or thermal, components had the degree of freedom to move in order to adjust by micro-changes with no or limited effects on the global system. Substitution of a column build in this way with one built as a whole, compact piece induces imbalances in the building reducing its ability to properly process perturbations. We have other examples, like making structural maintenance by assuming current models to ancient domes. The more maintenance is performed in this way and worst the situation becomes.

124 Design and emergence In a systemic conceptual framework designing should focus not only on functionalities and properties, but also on processes induced by usages invented by social systems and related acquisition of new emergent properties irreducible to functionalities and originating properties. New properties cannot be foreseen, but detected and modelled by a continuous Dynamic Usage of Models, context-sensitive and focused on the observer. The designer should also design how to dynamically observe and manage his/her creation. This asks for a new systemic awareness and ethical concerns from architecture having a key role in designing and inducing emergent processes in social systems.

125 Emergence of Architecture from social systems A new line of research is studying architecture as the design of suitable boundary conditions influencing emergence of behaviour in human social systems. This vision may help to clarify the role of architecture in materializing structures leading to emergent social properties.

126 126 PART 5 8. Self-Architecture In the conceptual framework of the theory of emergence and second-order cybernetics focusing on the theoretical role of the observer generator of cognitive existence, architecture may be intended as the self-design by a social system of boundary conditions suitable to keep or to make emergent what are considered important aspects by the social system itself.

127 127 Self-design relates to the transformation of emergent social properties, e.g., life styles and customs, into structural constrains, aiming to acquire the same properties as structural and no longer as emergent ones as in the previous examples.

128 128 What is Architecture? The etymology of the word comes from the Greek arkitecton and the Latin architectura, identifying an activity that “nascitur ex fabrica et ratiocinatione” - it comes from practice and ratiocination- Vitruvius, De Architectura, I, 1 – 25 BC Definitions of Architecture from Di Battista (2006). Di Battista, V. (2006), Towards a Systemic Approach to Architecture, In: In: Systemics of Emergence: Applications and Development (G. Minati, E. Pessa and M. Abram, eds.), Springer, New York, pp ).

129 129 Functional definition, proposed by Francesco Milizia ( ) “architecture may be called twin-sister to agriculture, since to hunger, for which man gave himself to agriculture, must be also connected the need for a shelter, whence architecture came.” (1781, Principii di Architecttura Civile, Milano) Building definition, proposed by Eugène-Emmanuel Viollet-le-Duc ( ) “Construire, pour l’architecte, c’est employer les matériaux en raison de leu qualités et de leur nature propre, avec l’idée préconçue de satisfaire à un besoin par les moyens les plus simplex et les plus solides.” ( , Entretiens, Paris)

130 130 Poetic definition, proposed by Le Corbusier ( ) “l’architecture est le jeu savant, correcte et magnifique des volumes assembles sous le soleil.” (1929, Oeuvre Complete, Zurich) Technologic definition, proposed by Le Corbusier ( ) “the Parthenon is a product of selection applied to a standard. Architecture acts on standards. Standards are a matter of logic, of analysis, of painstaking study; they are established upon well set problem. Research definitively settles the standard.” (1929, Oeuvre Complete, Zurich)

131 131 Meta-systemic definition, proposed by William Morris ( ) “all the signs that mankind leaves on the Earth except pure desert.” (1881, The prospect of architecture in civilization, London) Multiple definitions, proposed by Bruno Zevi ( ) “the art of space to be distinguished among cultural, psychological and symbolic definitions” “functional and technical definitions” and “linguistic definitions.” (1958, entry “Architettura” in the Enciclopedia Universale dell’Arte, Firenze)

132 Architecture as the design of suitable boundary conditions for emergence of social systems Human settlements are the product of human societies, they are mostly built and developed by a huge number of unconsciously interacting acts over a long time, rather than by purposely designed single acts. Such a vision generates the idea of implicit (or sub- symbolic) project that relies upon the systemic approach (Di Battista, 1988; De Matteis, 1995).

133 133 This project is implicit because it is self-generated by the random summation of many different and distinct needs and intentions, continuously carried out by undefined and changing subjects. It gets carried through in a totally unpredictable way – as it comes to goals, time, conditions and outcomes. It is this project anyway, by chaotic summations which are nevertheless continuous over time, that transforms and/or preserves all built environments. G. De Matteis, Progetto implicito (Franco Angeli, Milano, 1995) V. Di Battista, Recuperare, 36, (Peg, Milano, 1988)

134 134 The concept of boundary conditions is used in mathematics. Dealing with differential equations, the boundary value problem is given by a differential equation and a set of additional restraints, called the boundary conditions. We will generalize the concept of boundary condition to the degrees of freedom or constraints given by structures, e.g., geometrical and topological properties of living space as shaped by architectural design, to interacting agents establishing collective behaviour.

135 135 Examples of boundary conditions affecting collective behaviours and inducing emergence of social behavioural properties are: 1.Number of entries and exits for flats; 2.Central role assumed by some functional areas in flats, like the kitchen coming from the age where it was the warmest place; 3.Available living surface inducing residence for singles or families; 4.Shapes of roads inducing properties of traffic;

136 Number of baths per inhabitants; 6. Form walls and topology usually fit with roles; 7. Availability of sidewalks inducing or preventing pedestrian traffic; 8. Lighting making living styles possible; 9. Stairs, e.g., stairs with one handrail and with two handrails; width allowing usages; 10. Internal facilities (private, inducing consuming, e.g. shopping centres) rather than external (public)

137 137 The structural aspects of architecture, specifically materials used to build, stability, shapes, dimensions, illumination, acoustic properties and energy consumption have functional effects on those who behave in the structured space. Autonomous systems also cognitively represent the space in which they live, and because of that, they become inhabitants. As a result, they not only respect the boundary conditions from a functional point of view, but also cognitively process and use the representations they have of the space structured by the boundary conditions to adapt their behavior.

138 138 This is why there are different architectures for different ages and social systems. Architects have the power and responsibility to set the boundary conditions, because they make the plans and models that organize the space for the inhabitants.

139 139 Self-Architecture as specification of the concept of implicit project “Architecture organizes and represents the settlement system; it interprets, materializes, interacts with and confirms the references of cognitive systems, and projects (foresees) and builds coherent occurrences (steadiness, confirmation) and incoherent occurrences (emergence) in the settlement itself. “ [1]. [1] Di Battista, Valerio, (1988), “La concezione sistemica e prestazionale nel progetto di recupero”, Recuperare, n. 35, pp

140 140 “Architecture operates in the interactions between mankind and natural environment with coherent actions (communication; consistent changes; confirmation of symbols and meaning) and incoherent actions (casual changes, inconsistent changes, new symbols and meanings). Coherent actions are usually controlled by rules and laws that guarantee stability to the system (conditions of identity and acknowledged values); incoherent actions generally derive from a break in the cognitive references (breaking the paradigm) or from the action of implicit projects” [1] [1] Di Battista, Valerio, (1988), “La concezione sistemica e prestazionale nel progetto di recupero”, Recuperare, n. 35, pp

141 141 “These are the result of multiple actions by different subjects who operate all together without any or with very weak connections and have different – sometimes conflicting – interests, knowledge, codes, objectives. Implicit projects always act in the crack and gaps of a rule system; they often succeed, according to the freedom allowed by the settlement system....

142 142 Perhaps, the possible virtuous connections of this project, in its probable ways of organization and representation, could identify, today, the boundaries of architecture that, with or without architects, encompass “the whole of artifacts and signs that establish and define the human settlement”. Di Battista, V. (2006), Towards a Systemic Approach to Architecture, In: In: Systemics of Emergence: Applications and Development (G. Minati, E. Pessa and M. Abram, eds.), Springer, New York, p. 398.

143 143 “In the open system of the built environment and in the continuous flow of human settlements that inhabit places, there are many reasons, emotions, needs, all of which are constantly operating everywhere in order to transform, preserve, infill, promote or remove things. These intentional actions, every day, change and/or confirm the different levels of our landscape and built environment. This flow records the continuous variation of the complex connexions between people and places....

144 144 “This flow records the continuous variation of the complex connexions between people and places. This flow represents and produces the implicit project that all built environments carry out to update uses, values, conditions and meaning of their places. No single project, either modern or contemporary, has ever been and will ever be so powerful as to direct the physical effects and the meanings brought about by the implicit project.” Di Battista, 2008, Environment and Architecture – A paradigm shift, In: Processes of emergence of systems and systemic properties. Towards a general theory of emergence (Minati G., Pessa E. and Abram M., eds.), World Scientific, Singapore, pp ).

145 From acquired to structural properties: architecture as structural synthesis Some examples are architectures of dwellings intended first as materialization of ways of housing and then inducing them; architectures of hospitals intended first as materialization of therapeutic and medical approaches and then inducing them; and architectures of schools intended first as materialization of ways of considering knowledge, i.e., disciplinary fragmentation, and then inducing it. Other examples are the shape of roads influencing traffic and the number of entrances-exits or surface of a flat influencing inhabitants’ social behaviour.

146 From implicit, unexpressed properties to structural properties: architecture as design of new structures intended as representation, translations of social phenomena Moreover architecture does not only materialise and transform acquired emergent properties of social systems into structural constraints, but it is also inducing new emergent properties when introducing innovative ways of structuring space. Examples are vertical constructions, e.g., skyscrapers, underground constructions and cities.

147 The concept of Self-Architecture Self-architecture relates to the transformation of emergent acquired social properties into structures able to play the role of constraints suitable to make properties to be functionally established. Self-architecture relates to the transformation of implicit, still unexpressed cultural properties of social systems into meta-structures able to confirm and induce emergence of coherent behavioural properties. The process of self-architecture related to the global interdisciplinary coherence between different simultaneous aspects of social systems like one related to language, music, literature, religion and science.

148 148 Self-architecture also represents evolutionary processes when temporary incoherence allow social systems to restructure and reach new equilibrium. In the hands of the architecture design there is temporary syntheses representing coherences and incoherence of the social system. In a trans-disciplinary view this happens in any discipline. Architecture designs concrete constraints. Other disciplines and cognitive constraints.

149 Meta-elements and Meta-structures Meta-elements are introduced as sets of time- ordered values in a discrete temporal representation adopted by suitable mesoscopic state variables describing global, collective aspects of the system under study. The properties of meta-elements are expected to represent aspects of a more general and, consequently, more suitable level of description for collective behavioural phenomena.

150 150 Examples of mesoscopic variables in collective behaviours like flocks and swarms are: Mx, number of elements having the maximum distance at a given point in time; Mn, number of elements having the minimum distance at a given point in time; N 1 (t) number of elements having the same distance from their nearest neighbour at a given point in time; N 2 (t) number of elements having the same speed at a given point in time; N 3 (t) number of elements having the same direction at a given point in time.

151 151 Examples of mesoscopic variables in architectural systems are: Mx, number of inhabitant agents having the maximum distance at a given point in time; Mn, number of inhabitant agents having the minimum distance at a given point in time; N 1 (t) number of inhabitant agents having the same distance from their nearest neighbour at a given point in time; N 2 (t) number of inhabitant agents opening a door at a given point in time; N 3 (t) number of inhabitant agents using stairs at a given point in time;

152 152 N 4 (t) number of inhabitant agents opening a window at a given point in time; N 5 (t) number of inhabitant agents in the same room at a given point in time; N 6 (t) number of inhabitant agents using the elevator (s) at a given point in time.

153 153 Values assumed by mesoscopic variables and parameters used to define them, such as speed, distance and direction, establish Meta-Elements. Meta-Structures are given by mathematical properties of Meta-Elements, sets of values. They may relate, for instance, to regular changing of values, their distribution and repeatability.

154 154 Meta-structures are assumed to represent the properties of meta-elements and their possible relationships. Meta-structures may be intended as degrees of freedom of elements at a more general level of description, i.e., those of meta-elements, able to indirectly influence the behaviour of agents described at a lower level of description and producing collective phenomena by non-linearly completing a partial structure. Minati, G., (2008), New Approaches for Modelling Emergence of Collective Phenomena - The Meta-structures project, Polimetrica, Milan. Open Access Publication ew-approaches-for-modelling-emergence-of-collective-phenomena- (gianfranco-minati) ew-approaches-for-modelling-emergence-of-collective-phenomena- (gianfranco-minati)

155 155 Agent-based models (ABM) available in the literature may simulate pre-occupancy issues by considering constraints, i.e., boundary conditions, and interacting agents with specific characteristics -microscopic-. Meta-Structural analysis in Architecture is a possible alternative approach using values assumed by mesoscopic variables to deal with pre-occupancy evaluation and use simulations non only to certify functionalities, but to detect possible processes of emergence of acquired properties within the inhabitant social system -mesoscopic-.

156 156 PART 6 9. Conclusions

157 Growth, Development and Sustainability Growth Quantitative process of increasing like: linear, e.g., y = ax + b factorial, e.g., y = x! exponential, e.g., y = e x and logistic, from Minati, G. 2009, The Dynamic Usage of Models (DYSAM) as a theoretically-based phenomenological tool for managing complexity and as a research framework, In: Cybernetics and Systems Theory in Management: Tools, Views, and Advancements, (Steven E. Wallis. Ed.),IGI Global, PA, US, pp

158 158 logistic curve Introduced by the Belgian mathematician P. Verhulst ( ) for the study of population growth It represents changing from an increasing growing to a decreasing growing

159 159 We may consider a logistic curve as the place where the following kinds of events occur with sequential continuity: a – pre-existing services and products are offered by using new solutions; When the goal is to produce more efficiently using more advanced organizational approaches and/or technologies which are already available;

160 160 b – new services and products are offered by using new solutions; When the goal is to produce new products or offer new services using new organizational approaches and/or new technologies;

161 161 c – new services and products are offered by using available solutions; When established production systems and/or organizational approaches are used to produce new products and/or offer new services i.e., innovation using what is already available;

162 162 d – new production systems and organizational approaches are used in old ways; When new products and new services are used without taking full advantage of their potential: traditional activities but using new technologies;

163 163 e – pre-existing services and products are offered produced by using pre-existing solutions; Massive use of well-established technologies, looking only for high levels of production and mass markets. We may consider the case when point e is reached. One non-systemic solution is to try to move the asymptote up the more as possible. This is the case for mass markets when trying to artificially improve consumption. This is a very expensive strategy subtracting resources to the establishment of new markets.

164 164 Development a) Development as harmonic processes of growth b) Development as subsequent processes of growth c) Development as acquired emergent property of a system of coherent processes of growths.

165 165 From Sustainability of growths to sustainability of developments Sustainability of what? The concept of sustainability is a particularly fitting one for describing processes occurring over time with reference to available resources and their reproduction rate. This was the conceptual content of the message from the Club of Rome in 1972, when the book "Limits to Growth" was published as the first report of the Club of Rome. Focus was placed upon the process of growth with reference to population, use of resources and pollution as a consequence.

166 166 Sustainability relates to maintain and support processes considered necessary, irreplaceable. Moreover this conceptual framework excludes appearance of processes of innovation typical of development. The concept of sustainability should be suitably reformulated with reference to development.

167 167 How can one sustain emergence of a property acquired by a complex system, such as profitability, competitiveness, ability to innovate and regenerate? How to sustain health intended as emergent property continuously acquired rather than possessed ? How to sustain life intended as property of matter continuously acquired ?

168 168 Sustainability in Architecture It usually relates to environmentally-conscious design techniques. It relates, for instance, to energy consuming and management, usage of sustainable building materials, i.e., having limited or no ecological impact allowing recycle, suitable water usage and waste production. That’s what is intended for green architecture.

169 169 This approach is often considered for single systems like hospitals, schools and residential buildings in the non systemic idea that a system of green buildings should only be green itself. However, systems of different buildings may induce emergence of social acquired properties having not such green characteristics. Examples are given by induced traffic, global waste management, centralization of services, i.e., shopping and market centers.

170 170 Besides, green aspects relate to engineering and processes of growing with no reference to induced social properties acquired by inhabitant systems. We experience a difference similar to growth and development vs. green buildings and processes of acquired social properties.

171 171 Sustainability and openness Is s ustainability only for non-complex systems, i.e. system where no processes of emergence occur? Sustainability only for closed system? Sustainability of processes of emergence In urbanism, town-planning, the problem is how to sustain, keep coherence of development, i.e., identity of an urban area in the Cybernetic Self-Design considered by Self-Architecture.

172 Theoretical role of the observer, constructivism, and levels of description Architecture reduced to structures, with no usage? That is mono-disciplinary architecture. “Architecture as “the set of human artefacts and signs that establish and denote mankind’s settlement system” (Di Battista, 2006)…architecture always represents the settlement that generates it, under all circumstances and regardless of any artistic intention.”

173 173 Therefore, our artefacts shape and mark places for a long time; moreover, they come from the past continuously reflecting changes occurring in the settlement and in the built environment. All this means that architecture often outlives its generating system, becoming a heritage to the following ones, thus acting as memory – an identity condition linking people and places to their past systems.

174 Falsification of Systemics The falsification principle was introduced, in opposition to the verification principle, by the “Vienna circle”. According to K. R. Popper ( ), the main exponent of an approach based upon falsifying, any scientific system cannot be selected once and for all but it must be possible to confute it through experience. The success of a critical confuting experiment is sufficient to refute, to invalidate, the hypothesis forming the basis of a scientific theory.

175 175 A scientist introducing a scientific theory should also introduce a falsifying experiment that, if occurring, falsifies the theory itself one and forever. The Falsification of Systemics can be considered equivalent to the possibility of finding systemic properties as properties of non-systems.

176 Successes and failures of Systemics After the pioneering works of Bertalanffy the concept of system reached scientific status in an interdisciplinary context. Moreover, the concept of system and systemic problems had been studied disciplinarily. Inter-disciplinary research focused on transpositions of models from one discipline to another by changing meaning of variables.

177 177 The study of systemic properties per se, like emergence, self-organisation, collective behaviours, and openness by using approaches like Synergetics and meta-structures, has been very limited. The reason is that such problems are more easily approachable in single disciplines and then generisable, rather than studied in abstract as in trans-disciplinary framework.

178 178 On the other side the systemic view has been misunderstood as focusing on 1. popularization, 2. making no attention to details, 3. allowing to ignore disciplinary knowledge, 4. using analogies, 5. metaphors. The main success of Systemic is to keep the trans- disciplinary level in an age of focus on reduced interdisciplinary, i.e., usage of models in different disciplines.

179 179 In Architecture, intended as language of space, we have the fantastic opportunity to transform a discipline into a trans-disciplinary approach having the power to induce processes of emergence in social systems.


Download ppt "11 Systemics and emergence for Architecture In memory of Professor G. Ciribini Gianfranco Minati Italian Systems Society www.AIRS.itwww.AIRS.it doctoral."

Similar presentations


Ads by Google