Evolving scientific thinking in the
20th century:
Any value for social epidemiology?
(Drawing heavily from F. Capra: The Web of Life.
Anchor Books, New York, 1996)
From Parts to Wholes
• Cartesian split of body & mind: traditional focus on
mechanism rather than holism; substance, not form
• 19th century: this changed in the romantic era: Kant and
ideas of ‘things in themselves’; beauty; nature seen as
purposeful; focus shifts to organism
• Kant: in a machine, parts exist for each other; in an
organism, parts exist by means of each other
• Return to antique idea of earth as a living organism
• Late 19th century, pendulum swung back to mechanistic
focus with rise of cell theory and microbiology, etc.
• But some recognized limitations: vitalism held that the
laws of physics and chemistry cannot explain life.
Vitalism and organicism
• Core question: “In what way is the whole greater than
the sum of its parts?”
• Driesch & vitalism (1908): a non-physical force animates
the physical & chemical processes to produce life. Vis,
entelechy, or life force. Later, Rupert Sheldrake (1981)
• Organicism: the key component is that of organization;
there is no additional ‘life force’.
• Ross Harrison & Lawrence Henderson pioneered
systems thinking in 1920s. Focus shifts from function to
organization.
• System = a unified whole whose properties arise from
the relationships between its parts
• CD Broad, 1922: ‘emergent properties’: novel features
that arise at a certain level of complexity.
Gestalt psychology
• 1930s studies of perception recognize that
we perceive things (as in recognizing
someone’s face) not as components but
holistically, as patterns of relations.
Ecology
• Greek oikos = household, the study of the Earth
household. Term coined in 1866 by Ernst Haeckel
• 1920s biologists applied it to food chains and cycles,
plant communities, etc.
• ‘Biosphere’ used around the same time.
• Gradual movement towards seeing communities of
organisms as ecosystems that interrelate with other
organisms and are integrated into the functioning of a
broader whole. Bees and ants must live in colonies that
are interdependent with other species.
• Move from hierarchical models (pyramids) towards
network models with nodes.
Deep ecology
• Sees world as an integrated whole, not a series of parts
• Fundamental interdependence of all phenomena,
embedded in cyclical processes of nature
• Shallow ecology is anthropocentric: humans are above
nature; deep ecology sees humans in networks of
interdependent phenomena.
• ‘Holistic’ implies seeing objects as wholes
• ‘Ecological’ adds the perception of the object in its
environments: the implications of its manufacture, its
impact on systems, etc
Systems thinking
• The essential properties of a living system exist at the
level of the whole, not in its parts.
• Whole always greater than the sum of its parts: the
properties of the parts can be understood only within the
whole.
• This completely rejects Descartes' analytic and
reductionistic thinking. Systems cannot be understood by
analysis, by taking them apart.
• Systems thinking means putting the parts together and
understanding in the context of the whole.
Criteria of systems thinking
1. Integrated wholes, arising from organizing
relations of the parts
2. You can focus attention on different levels of
the system. At each level, properties exist that
are absent at lower levels (‘emergence’)
3. Contextual thinking: explanations focus on the
environment, not on the component parts. Shift
from objects to relationships: ‘objects’ are
organizational relationships between parts.
Current thoughts on the basis of
knowledge
• Shift away from Cartesian certainty in science:
– Heisenberg: we do not observe nature itself, but
nature as represented by our method of studying it.
– Shift from objective knowledge to epistemic science:
our method of studying become integral part of
theories. Epistemology becomes crucial.
– All knowledge is approximate; it can never provide
definitive understanding
– The Aquarian Conspiracy theme
• ‘Knowledge’ as a network of ideas; ideas as a
network of concepts, etc.
Chains vs. Webs; Hierarchies vs. Networks
• Self-assertive versus integrative tendencies.
Both are parts of all living systems
Self assertive
Integrative
Rational
Intuitive
Analysis
Synthesis
Linear
Non-linear
Quantity
Quality
Domination
Partnership
Expansion
Conservation
• Balance between these is changing in our thinking.
Process thinking
• Ludwig von Bertalanffy, general systems theory &
cybernetics (c. 1938-43)
• Builds on ideas of homeostasis & dynamic balance
• General systems theory = general science of wholeness.
Tackled the problem of entropy versus evolution towards
greater complexity.
• ‘Open systems’ cannot be analyzed using classical
thermodynamics. They maintain themselves far from
equilibrium, yet in a steady state characterized by
continual energy flow and change. Applies (e.g.) to
metabolism
• In 1960s, Ilya Prigogine developed mathematical basis
for general systems thinking.
Cybernetics
• Norbert Wiener defined cybernetics as science of control
& communication in animals & machines. General
systems theory was applied to communication and
control.
• Message, feedback and control relate to patterns of
organization. Seen as representing non-material
elements of life.
• At a series of meetings in New York, Wiener, John von
Neumann, Gregory Bateson proposed ways to represent
the human mind.
• They invented digital computers; developed notions of
feedback; information theory; machine learning.
Molecular biology and critique of
systems thinking
• Initially, DNA and genetics returned mechanisms
to centre stage; focus shifted from cells to
molecules.
• But, although the alphabet of the genetic code
was learned, its syntax and how genes
communicate and cooperate, was not
• Still trying to define the essence of life.
• Systems theory was criticized as not usefully
applicable to anything much, and as lacking a
mathematical basis
• Nonlinear mathematics developed in the 1980s,
as computers became more readily available.
Pattern
• Needed a way to study patterns and organization
formally; everyone recognized their importance, but how
to map the configuration of relationships?
• Networks: nonlinear assemblies of nodes that can
include feedback loops, so can regulate themselves. The
feedback is critical to developing patterns that evolve.
• Self-organization (1943): experiments showing that
patterns emerged spontaneously from networks that
follow simple rules of behaviour (cf. flocks of birds).
• Self-organization is the spontaneous emergence of new
forms of behaviour in open systems (those far from
equilibrium), characterized by internal feedback loops,
following nonlinear equations.
Autopoiesis
• Manfred Eigen (1960s) studying complex enzyme
reactions observed catalytic cycles emerge. Do these
represent actual life?
• Maturana (Chile, 1960s): living systems as closed causal
circular processes in which change occurs via selfreference but does not lose the circularity itself.
• Autopoiesis (‘self making’) as the organization central to
all living systems. The function of each component is to
participate in the production or transformation of other
components in the network. The product of its operation
is its own organization.
Gaia
• James Lovelock c.1970: The characteristic of life is that all
living organisms take in matter and energy, and discharge
waste.
• Gaia hypothesis: the whole earth is a self-regulating system
involving organic and non-organic matter. Processes regulate
atmospheric temperature, keeping it at a level conducive to
life. Deviations lead to feed-back corrections.
• E.g., rock weathering forms carbonates that bind CO2. Soil
bacteria catalyze this process, depending on temperature.
Rock carbonates are washed into the ocean where they are
taken up by algae that build minute shells. The algae die and
their shells fall to the ocean floor. The weight eventually
triggers volcanic action that recycles the sediment. The CO2
feedback loop regulates atmospheric temperature: warmer
means more weathering & bacterial action
• Hence, the surface of the earth (the ‘environment’) can be
seen as part of life; the air as a circulatory system. Breaks
down distinction between environment & organisms.
Mathematics of Complexity
• Poincaré (c 1910) tackled the problem of the relative motion of 3
bodies under mutual gravitational pull. Found the answer too
complex to picture.
• He showed that prediction can become practically impossible even
though the equations are deterministic. Forgotten for 50 years.
• 1960s: plots of pendulum movements in phase space (velocity &
angle) led to discovery of attractors: patterns of repetitive movement
around points.
• Attractors demonstrate wide variation in results given small alteration
in initial values; patterns of ‘strange attractors’ that never repeat, yet
form relatively simple patterns.
• It is impossible to make precise predictions from non-linear
equations; but can make predictions of the qualitative features, or
pattern, of results.
• This returns mathematics to geometry of qualitative patterns: the
characteristic responses, but not precise estimates. Cf. Nassim Taleb
& the Black Swan.
Applications to Social Epidemiology?
• Patterns (the SES gradient in health) are
everywhere.
• Traditional epi analyses ignore the context, the
meaning, and do not consider patterns
• Deep ecology & Gaia draw attention to
relevance of all aspects of the environment
• Environment not distinct from the players in it
(again the question of scale of analysis)?
• Many processes are non-linear
• Focus on the time dimension seems relevant