Evolving scientific thinking in the 20 th century: Any value for social epidemiology?

1. 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)

2. 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.

3. 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.

4. 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.

5. 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.

6. 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

7. 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.

8. Criteria of systems thinking • Integrated wholes, arising from organizing relations of the parts • You can focus attention on different levels of the system. At each level, properties exist that are absent at lower levels (‘emergence’) • Contextual thinking: explanations focus on the environment, not on the component parts. Shift from objects to relationships: ‘objects’ are organizational relationships between parts.

9. 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.

10. Chains vs. Webs; Hierarchies vs. Networks • Self-assertive versus integrative tendencies. Both are parts of all living systems • Balance between these is changing in our thinking.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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 self-reference 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.

16. 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.

17. 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.

18. 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