Systemics and emergence for Architecture

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

2. PART 1 p. 38 1. 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 2

3. PART 2 p. 32 3. 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 3

4. PART 3 p. 26 5. 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 6.2.1 Language and process of thinking 6.2.2 From computing to learning 4

5. PART 4 p. 15 7. 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 5

6. PART 5 p. 29 8. 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 6

7. PART 6 p. 23 9. 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 7

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

9. 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. 9

10. Elements characteristics and characteristics of generated systems. 10

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). 11

12. Design systems or model a phenomenon • as a system • It is possible to distinguish between two conceptual cases: • 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. • 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. 12

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. 13

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. 14

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. 15

16. 16

17. Examples of properties of composing interacting elements and acquired by generated systems: 17

18. 1.2 Introductory history • System intended as device • Control Theory, Automata, Systems Theory and • Cybernetics • The Watt’s centrifugal regulator: 18

19. The “single loop” 19

20. The “double loop” 20

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

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. 22

23. Examples of negative feedback to control a system are: • 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), • hormonal regulation, and • temperature regulation in animals. 23

24. Examples of positive feedback to control a system are: • 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, • lactation involves positive feedback so that the more the baby suckles, the more milk is produced, • in stock exchange when the more stakeholders sell and the more they sell. 24

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 (1854-1912) dx(t)/dt = f(x(t)) 25

26. A dynamical system is based on two kinds of information: • One dealing with the representation of the system’s state and information about the system itself, i.e., dx(t)/dt; • 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. 26

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. 27

28. 2. Systemics or General • System Theory • In general, systems may be established or modelled as such by considering • 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!!) 28

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. 29

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. 30

31. b) A processof 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. 31

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). 32

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. 2.1 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 Q1 , Q2 , . . . , Qn , whose instantaneous values specify the state of the system. dQ1 / dt = f1 (Q1, Q2, …, Qn) dQ2 / dt = f2 (Q1, Q2, …, Qn) …………………………. dQn / dt = fn (Q1, Q2, …, Qn) where Q suitable state variable. 34

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. 35

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. 36

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. 37

38. For instance, when considering agents establishing collective behaviour like a flock, we focus on variables such as: • Mx, number of elements having maximum distance at a given • point in time; • Mn, number of elements having the minimum distance at a • given point in time; • Nk number of elements having same value of variables such • as: N1= number of elements having same distance from the • nearest neighbour, N2= number of elements having same • speed and N3  = number of elements having same direction • over time. 38

39. 2.2 The concept of emergence Phase transition Self-Organisation Emergence (Cruchtfield, Baas): computational and phenomenological 39

40. 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. 40

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. 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. 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. 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). 44

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. 45

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. 46

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 47

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. 48

49. 3.1 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. 49

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. 50