Transcript Slide 1

lecture 4: complexity
• simple systems, complex systems
– parallel developments that are joining
together:
• systems literature
• complexity literature
– most systems of interest to IE/OR are
complex
– to understand the causes that gave rise to
these developments we need to understand
how science and the scientific method work
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science and the scientific method
• ways to knowledge:
– authoritarian and mystical mode versus the rational mode
– the Enlightenment, Galileo and Newton
– Newtonian mechanics
• aims of science – seeking reality and truth
– explanation
– prediction
– understanding
• the scientific method - positivism
– objectivity
– the research cycle: theory-hypothesis-observation and
experimentation-generalisation-theory
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• calculus and analytic functions
– analysis
– reduction
• the Newtonian paradigm
– objective knowledge is possible
– cause and effect act linearly
– nature is deterministic or predictable
– reduction works
• Newtonian science has been a great success;
it has created today’s technological
society
• the Newtonian view of the world dominates
our thinking even today
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complexity
• quantum mechanics and relativity
• insufficiency of analytical thinking in the study of living
organisms that must import energy
• failure of calculus in studying complex shapes
• realisation that nature is more complex than previously
thought led to the development of the new
field of
complexity studies.
• complexity theory is as yet not fully developed
• its aim is to discover unified laws governing complex
systems through interdisciplinary inquiry
• it is early to say whether this aim will be achieved
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concepts of complexity
• nonlinear dynamics
– the two-body and the three-body problems
– chaotic behaviour
• the butterfly effect
• glasses, mountains, earthquakes etc.
– thermodynamic equilibrium – maximum entropy
• environment
– boundary setting and closure
– separation of scales – e.g. the molecular motion of air
– the impossibility of the separation of scales
– environmental fallacy
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• emergence
– emergence is the fundamental property of systems
– examples of emergence
– emergent behaviour at a higher level of scale arises
from lower levels of scale although the mechanisms
involved are difficult to comprehend
– emergent complexity – emergent simplicity
– emergent properties will be lost to reduction
• adaptation and evolution
– two way interaction between the system and the
environment
– a decentralised process acting on local information
– goal seeking adaptation, self referencing feedback
and learning
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• complexity measures
– the degree of complexity can be expressed in terms
of structural aspects, the complexity of structure
– it can also be measured in terms of function
– a mathematical measure of complexity is given by
the amount of information needed to describe it
– the length of the binary string that can contain 2
messages is 1; for 4 or 22 messages, it is 2; for 8
or 23 messages it is 3 where log2(8)=3 etc.
– for a complex system with k possible states we need
N bits of information where N = log2(k)
– these ideas originated in communication theory and
are relevant in combinatorial mathematics also as
the problem of computational complexity
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systems thinking and complexity
• in ST, we can define a system as “interacting components
with emergent properties” whether the system is
complex or simple
• ST and complexity share a lot of concepts
• it is not clear yet if the mathematical constructs and results
from complexity research can be directly applied to
the study of socio-economic or socio-technical
human systems
• however an understanding of complexity will help us
understand human systems and the “soft” methods of
inquiry that are needed to study them
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readings on complexity
The literature on complexity is vast and not all of
it very accessible because of the mathematics
involved. You must read Michel Baranger’s
article: “Chaos, complexity and entropy: a
physics talk for non physicists” and Richard
Seel’s “Complexity and OD” under “Lecture 4”
on the website. Another article written from the
management point of view is Jonathan
Rosenhead’s “Complexity theory and
management practice”. This article is optional
and is available in the “Resources” area of the
course site.
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