Systemic 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”
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PART 1 pp. 39
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
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PART 2
pp. 33
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
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PART 3 pp. 27
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
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PART 4 pp. 24
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
7.8 Social fields
7.9 Architecture and social fields
7.10 Coherence
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PART 5 pp. 30
8. Self-Architecture
8.1 Architecture as the design of
suitable boundary conditions for
emergence of social systems: metastructures.
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
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PART 6 pp. 24
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
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PART 1
1. The concept of system
1.1 From sets to systems
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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.
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Elements characteristics and characteristics of generated systems.
Football players
Weight, age
Team
Harmony, game strategy
Cells
Function
Students
Numbers
Brain components
Configuration
Single musician
Instrument played
Words
Correct Grammar
Musical notes
Correctness in the score
Couple
Synchronization of interests
Soldiers
Single abilities
Workers
Quantity
Animals
Quantity, single behaviour
Living being
Behaviour
School
Collective ability to learn
Memory, Intelligence
Processing capabilities
Orchestra
Polyphony
A poem, a book, a story
Meaning
Music
Harmony
Family
Emergence of roles
Army
Ability to apply military strategies
Corporation
Value
Herds, swarms, flocks, packs
Collective behaviour
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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., preypredator).
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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
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approach.
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 nonsystemic properties.
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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.
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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.
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Sets
Structured Sets
Systems
Subsystems
Build
components
Buildings as
structures in
engineering
Students in
alphabetical order
or grouped by age
Cells per
dimension, age,
type, etc.,
Words in
alphabetical order
Electronic
components
structured by the
outline of an
electronic circuit
in an electronic
device
Building as
processes
Floors of building
School
Classrooms
Living beings
Organs
A story, Poem
Chapters, Verses
An electronic
device assumes
properties
different from
ones of
components when
interacting, i.e.
when the board is
powered
Swarms, Schools
and flocks
Group of
components classed
by function such as
power supply,
regulators and
decoders.
Students
belonging to a
specific school
Cells available
for experiments
Casual words
Electronic
components
listed by the
designer
Animals available Animals per age
for study
or illness
Groups of puppies,
animals in
reproduction and
parents
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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
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1.2 Introductory history
 System intended as device
Control Theory, Automata, Systems Theory and
Cybernetics
The Watt’s centrifugal regulator:
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The “single loop”
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The “double loop”
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System Dynamics (SD)
Introduced by Jay W. Forrester (1918 -) in 1961, in the
book Industrial Dynamics.
Networks of feedbacks
A
B
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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.
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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.
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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.24
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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))
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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.
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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.
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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!!)
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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.
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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.
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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.
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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2.2 The concept of
emergence
Phase transition
Self-Organisation
Emergence (Cruchtfield, Baas):
computational and phenomenological
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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.
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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.
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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.
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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.
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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 nonperturbed 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).
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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.
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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 nonregular behaviour as in the presence of
any suitable environmental condition, but
displaying the same property to the
observer.
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PART 2
3. SYSTEMICS
Difference between possessing from
acquiring (systemic) properties
The concepts of property, level of
description and the role of the observer
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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 (interdisciplinarity) 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.
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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.
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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.
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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).
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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.
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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.
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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.
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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 interdisciplinarity occurs when using, for instance,
metaphors and analogies instead of models. In
this case conclusions reached have limited
values of robustness and reliability.
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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.
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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, 1970.
There are different international institutions devoted
to research on this subject mostly focusing upon
humanistic interpretations.
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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).
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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?'.
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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
Inter-disciplinarity
Chaoticity is a systemic property, i.e. valid in
different disciplinary fields, with the same modeling
and simulation.
Trans-disciplinarity
When systemic properties are considered per se, in general, i.e. without
considering specific disciplinary fields, and in relation between them.
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
Inter-disciplinarity
Openness is a systemic property, i.e. valid in
different disciplinary fields, having the same
modeling and simulation.
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Generic,
Metaphorical,
Analogue and
General
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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.
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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.
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4.2 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.
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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.
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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 multimodelling 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).
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4.3 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).
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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.
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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
corresponding to n-different models.
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4.4 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.
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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.
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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 contextindependent.
This is the classical approach based on objectivism.
Examples are rules, instructions, and formal language
for programming.
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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.
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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.
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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.
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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.
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Levels of openness
1. Thermodynamic level: crossing
of matter-energy trough
borders of the system
2. Meaning assumed identical
between sender and receiver
3. Interacting systems produce
mutual context-sensitive
models: systems have learning
capabilities
4. Interactive systems produce
dynamic mutual contextsensitive models: systems have
learning capabilities
5. The system may continuously
decide which level to use in
interacting
Related levels of closeness
No crossing of matter/energy
trough borders of the system
Crossing of matter/energy trough
borders of the system, but no
common meaning between sender
and receiver
Meaning assumed identical
between sender and receiver, but
the systems do not produce
mutual context-sensitive models
and have not learning capabilities
Interacting systems produce
mutual, but not dynamic contextsensitive models: systems have
learning capabilities
Interactive systems produce
dynamic mutual context-sensitive
models, systems have learning
capabilities, but they cannot
decide which level to use in
interacting
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4.5 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.
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CLOSED SYSTEMS
LOGICAL OPEN SYTEMS
Passive
Active
Context insensitive
Context sensitive
Non learning
Learning
Object oriented
Process oriented
Non flexible
Flexible
Fixed rules, variable parameters
Changing rules
Contradiction avoiders
Using contradictions at higher level of
description
Mono or non-dynamic strategies
Multi- dynamic strategies (DYSAM,
chapter 5)
Deductive
Inductive and abductive
(constructivism)
Objectivistic
Non objectivistic
Observer as generator of
relativism
Observer as part of the system and
generator of cognitive existence
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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.
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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.
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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.
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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.
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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).
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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DYSAM may be implemented in different ways,
such as
LR
IC (t)
DB
GDM
MS
where
DB data base of models connected by
SIM
a trained Neural Network;
(t)
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.
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6. Logical inferences, language
and process of thinking
6.1 Introduction to Deduction,
Induction and Abduction
• Deduction
• Induction
• Abduction
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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.
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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).
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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.
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In the case of the induction of a rule or of a result (Res)
from a set of configurations (Cn) 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).
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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.
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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).
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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.
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6.2 General Comments
6.2.1 Language and process
of thinking
• Edward Sapir (1884-1936)
• Sapir-Whorf hypothesis
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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.
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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.
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6.2.2 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.
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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.
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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 subsymbolic way.
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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.
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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.
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The model must explain a) in a symbolic or b) subsymbolic 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:
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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.)
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7.1 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.
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7.2 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).
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• Macroscopic (no reference to magnitude)
• Microscopic (no reference to magnitude)
• Mesoscopic (no reference to magnitude)
• Systemic models
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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.
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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.
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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.
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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.
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7.3 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.
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7.4 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.
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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.
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The case of the Basilica di San Gaudenzio, Novara
(1577- 1690). 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,
165-175
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7.5 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.
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7.6 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.
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7.7 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.
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7.8 Social fields
In social sciences and psychology it has been
introduced by the psychologist Kurt Lewin (1890-1947)
the concept of Force Field, see, for instance, Lewin
(1935; 1936; 1951).
Lewin, a social psychologist, introduced this concept in
the framework of the Gestalt Psychology founded by
Max Wertheimer, Wolfgang Köhler and Kurt Koffka
(Koffka, 1935). The Force Field or life space was
assumed to be in any individual or social group,
changing
with
experience
and
intended
as
representation of the environment with personal values,
emotions, goals. We may say the cognitive system
combined with representations and stimula of the
environment.
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7.9 Architecture and social fields
Several studies and approaches introduced in the last years,
see for instance, Batty (2005), Diappi (2004), Marshall (2008),
Hensel et al. (2004) and Weinstock (2010), related to the
social impact and significance of Architecture. In this short
contribution we would like to focus on Architecture as design
of structures able both to represent and induce properties of
the cognitive systems possessed by inhabitants as well their
process of changing relating to social processes in progress
and even in fiery, see, for instance, Minati and Collen (2009).
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In this conceptual framework functionalities and rational
aspects, e.g., economical, environmental, military and
political, are of secondary importance than architectural
properties representing social cognitive properties such as
attention to details; beauty; induction to meet; sense of
hierarchies; multiplicity vs. standardisation; openness due to
opening doors rather lack of boundaries; topologies with
labelled areas corresponding to values; usage of building
material informing about the social status of inhabitants; use
of the territory in a non-optimised way, i.e., dedicated to
green areas, playground and artistic exhibitions; usage of
city lights, semaphores and opening hours of shops to set
social rhythms; systems of garbage collection as information;
colour of the house fronts and their state informing about
maintenance, attention to harmony with neighbours, etc.,
see, for instance, Collen (2009), Di Battista (2009), Fontana
(2010) and Giallocosta (2010).
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The subject is studied by Environmental Psychology,
and dealing with typical cases such as effects induced
by broken windows and study crime prevention through
environmental design in the conceptual framework of
Space Syntax such as to the Space Syntax Laboratory,
see web resource at the references, and, for instance,
Cozens et al. (2005).
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7.10 Coherence
The structure of space made by Architecture both
represent and induce the social field within inhabitants
behave. On the other side inhabitants behave by using such
social field. In this conceptual framework we may
hypothesise a process of self-architecture by social
systems. We mention the coherence in such social field
between different aspects such as architecture, design,
fashion, music and painting.
We may intend that Architecture provides the words by
which social systems formulate sentences about their
identity, how they are changing and what kind of evolution
they can imagine.
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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.
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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.
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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. 391-398).
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Functional definition, proposed by Francesco Milizia
(1725-1798)
“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 (1814-1879)
“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.”
(1863-1872, Entretiens, Paris)
134
Poetic definition, proposed by Le Corbusier (1887-1965)
“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 (18871965)
“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)
135
Meta-systemic definition, proposed by William Morris
(1834-1896)
“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 (19182000)
• “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)
136
8.1 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 subsymbolic) project that relies upon the systemic
approach (Di Battista, 1988; De Matteis, 1995).
137
137
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)
138
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.
139
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;
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140
5. 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)
141
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.
142
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.
143
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. 404-405
144
“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. 404-405
145
“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.
...
146
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.
147
“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.
...
148
“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. 45-46).
149
8.2 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.
150
150
8.3 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.
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151
8.4 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. 152
152
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.
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153
8.5 Meta-elements and Meta-structures
Meta-elements are introduced as sets of timeordered 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.
154
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;
• N1(t) number of elements having the same distance from
their nearest neighbour
• at a given point in time;
• N2(t) number of elements having the same speed at a
given point in time;
• N3(t) number of elements having the same direction at a
given point in time.
155
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;
• N1(t) number of inhabitant agents having the same
distance from their nearest neighbour at a given
point in time;
• N2(t) number of inhabitant agents opening a door
at a given point in time;
• N3(t) number of inhabitant agents using stairs at a
given point in time;
156
• N4(t) number of inhabitant agents opening a
window at a given point in time;
• N5(t) number of inhabitant agents in the same
room at a given point in time;
• N6(t) number of inhabitant agents using the
elevator (s) at a given point in time.
157
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.
158
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
http://www.polimetrica.com/?p=productsMore&iProduct=81&sName=n
ew-approaches-for-modelling-emergence-of-collective-phenomena(gianfranco-minati)
159
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-.
160
PART 6
9. Conclusions
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161
9.1 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 = ex
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. 176-190.
162
162
logistic curve
Introduced by the Belgian mathematician P. Verhulst
(1804-1849) for the study of population growth
It represents changing from an increasing growing to a
163
decreasing growing
163
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;
164
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;
165
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;
166
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;
167
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.
168
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.
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169
• 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.
170
170
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.
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171
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 ?
172
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.
173
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.
174
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.
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Sustainability and openness
Is sustainability 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 SelfArchitecture.
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176
9.2 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.”
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177
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.
178
9.3 Falsification of Systemics
The falsification principle was introduced, in
opposition to the verification principle, by the “Vienna
circle”.
According to K. R. Popper (1902-1994), 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.
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179
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.
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180
9.4 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.
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181
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.
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182
On the other side the systemic view has been
misunderstood as focusing on
1.
2.
3.
4.
5.
popularization,
making no attention to details,
allowing to ignore disciplinary knowledge,
using analogies,
metaphors.
The main success of Systemic is to keep the transdisciplinary level in an age of focus on reduced
interdisciplinary, i.e., usage of models in different
disciplines.
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183
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.
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184