3. Rapid change on Earth
⢠The world is changing as issues become
more pressing â need for systems thinking
â Interactions between energy, carbon,
climate, water, water, soils, biodiversity,
food security, population, animal disease
⢠John Beddington, UK: âThe perfect stormâ
â Tipping point in 25-50 years?
â Poor assessments of risk: Dan Gardner
⢠Urgent need for new regional approaches
4. Multiple capitals
⢠World is overlapping set of stocks and flows
with non-linear, adaptive interactions
â Biodiversity: genes, populations, species
â Biogeochemistry: water, energy, nutrients
â Capitals: natural, physical, human, financial
⢠Complexity, emergence, thresholds, tipping
points, surprises (inc. financial crashes)
⢠So the natural world is not just complicated it
is formally complex: uncertain, unpredictable
5.
6. What is sustainability?
⢠âdevelopment that meets the needs of the
present without compromising the ability of
future generations to meet their needsâ
â Strong sustainability â more than just
economic welfare and âchoiceâ - there are
absolutes, so âthe capacity to endureâ
⢠Act here and now so that the environment
and quality of life later and elsewhere will
not be eroded
7. The flip side of sustainability
⢠The (inverse) flip side is risk...
â Seeking sustainability means minimising risk
amidst complexity and uncertainty
â Risk is about reality, beliefs and culture
⢠So we require analytical tools to understand
the behaviour of interacting systems and...
⢠Participatory tools to deal with beliefs and
values, debate options, communicate risk
and act
10. Cause and effect
⢠Need to understand relationships between
parts and wholes, wholes and parts
â Local <-> regional <-> global
â Scaling, fractals, emergence
⢠BMPs to catchment outcomes â EU WFD
â Risk, load apportionment: DEFRA, EA
â Local actions to regional outcomes
⢠Cause and effect across scales is a problem
â Global CO2 reductions: national jurisdictions
11. The science âframing issueâ
⢠Usual scientific debate framed around
balance and equilibrium â has very old roots
â Theory, data collection and analysis issues
⢠Philosophical basis is idealised (Wimsatt)
â Not appropriate for complex systems
⢠Analysis tools â monitoring and assessment
generally about stocks not flows
⢠NRM institutions, bureaucracy, policy only
focussing on the participation tools
12. The Complexity âturnâ (sociologists!)
⢠Adaptive interactions between capitals
â agents, institutions, systems evolve
⢠Resilience and tipping points
â Precariousness and thresholds
⢠Uncertainty: knowledge and models partial
â Emergence, surprises will occur
⢠Multiple stressors â âcausal thicketsâ
â Predict-act frameworks unreliable
⢠Many players, institutions, governance
13. More is different â things donât scale well
Make no mistake: âcomplexityâ is a
major shift in world view which
requires changes in culture and
practice
Business as usual is not an option!
14. The uniqueness of place
⢠The concept of place arises from complexity
â Nested spatial and temporal heterogeneity,
contingent history, stocks and flows
⢠Requires complexity of governance: decision
theory, robustness and resilience
â No universal Best Management Practices
⢠Perhaps there never will be a simple theory
of place â so just how much is predictable?
â We are âwaiting for Carnotâ......
15. We cannot ignore the flows between human and natural systems 2
STOCKS description
Not Gaia; Medea things
No homeostasis
contingency
Complex systems
PAST then Ecosystems now PRESENT
Human systems
Small scale process
Spatially discrete interactions
stuff
Patterned
Temporally evolving FLOWS
16. Incentives and restoration
⢠Targets, reference sites, valuation
techniques and MBIs at risk from
contingency, uncertainty and emergence
⢠Complexity makes restoration difficult
â Change leads to new ânon-homologousâ
novel ecosystems (Hobbs et al.) Base lines??
⢠Focus on inputs rather than outcomes
reflects complexity of situation and
difficulties with âprograms of measuresâ
17. Inability to detect effects of management interventions:
also there are multiple stressors
and surprises!!
Billions invested: no apparent result?
18. New models for self organising systems
⢠Urgent search for new models for complex
(fractal, SO) landscape systems
â Agent Based, CA, emulation (Young) or high
level analytical (Kirchner, Rodriguez-Iturbe)
⢠Search for techniques to predict thresholds
â critical slowing down (Scheffer, Carpenter)
⢠But will the warnings be timely or sufficient?
⢠GRID models of everything everywhere â
including uncertainty (Beven)
19. Clearly a tipping point has been reached!
Death of Red Gum and Black Box forests
20. The evolution of modelling
⢠From âmean fieldâ simulations, to Neural
Networks, to Genetic Algorithms, to Agent
Based, to Adaptive Cellular Automata
â populations â> individuals -> information
⢠Discrete, spatial, adaptive, self-organised
properties (no âequilibriumâ solutions)
⢠Landscapes as spatially heterogeneous,
information processing, self-organising,
uncertain, temporally evolving entities
â New approaches to industrial ecology
21. Hierarchical (nested) dynamics
⢠The small and fast are really important
â Emergence and non-linearity
⢠Both bottom up and top down causation
â Philosophers have real problems with this!
⢠Modelling from the middle-out: emulation
â Systems biology idea attributed to Sydney
Brenner but actually a very old concept
⢠Capturing the essence whilst recognising
uncertainty (Unknown Unknowns again)
22. The non-equilibrium hierarchical patch dynamics view
3
Big, slow drivers
Biophysical constraints
Climate change
Macro-scale Extreme events
models management
Meso-scale world
Resilience
Multiple states
Local Hysteresis
drivers Âľ scale
Small scale âhot spotsâ
Spatially discrete Diverse emergent
Behaviour, Physiology components
Evolution Interactions
Stocks and flows
23. New data â spatial and temporal
⢠New data from web enabled sensors and
systems: âeverything, everywhereâ
â High resolution DEMs, GIS, time series
â Stocks and flows, history, development
⢠Insights into small scale pattern and process
â The âhigh frequencyâ wave of the future
â âBeethoven symphoniesâ with orchestration
⢠Use of personal devices: GPS, mobile phones
with on-board cameras and other sensors
24. New theories of risk management
⢠Need new risk management tools: Scenarios
for future likely paths
â Decision frameworks with âminimum regretâ
to manage unpredictable events
â Lempert et al â Robust Decision Making
⢠âpredict-actâ oversold: need adaptive mgmt
â Therefore more likely âobserve-reflect-actâ
â Data, models, uncertainty, robust options
⢠The past is no guide to the future
25. Approaching the undefinable
⢠If âsustainabilityâ is a complex goal and the
uncertainty is great
â Then how to proceed?
⢠One option is to reduce unsustainable
practices and apply biophysical limits
â Moving in the right direction
⢠The other is Robust (âminimum regretsâ)
Decision Making â data and models
â Risk management under uncertainty