Complex adaptive systems
• How are ecosystems and economic markets the
same? They are both complex adaptive
systems.
• Characteristics of CAS
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Interaction of determinism and contingency
Are supported by simple rules
Have path dependence
Exhibit emergence
Are dynamic and adaptive
Control is distributed rather than centralized
Forests as complex adaptive
system
Complexity
• A more recent
conceptualization of
how to look at nature
and our interaction with
it
• Originated in general
systems theory, a way
of looking at the world in
which phenomena
interact as sets of
systems
1968
The Newtonian world….
• Mechanistic and deterministic, predictable
The Newtonian World
Laplace’s demon
If a demon (or some allknowing entity) had
knowledge of the positions
and direction of all particles
in the universe at a single
instant, Newtonian
equations could be invoked
to explain all future events
and hindcast all of history
However, Newtonian concepts are not capable of
explaining ecological change, which is also strongly
coupled to human systems
The shear number of species and
their interactions limits prediction
of change
Contingencies, historical events,
ongoing evolution limit prediction
into the future.
Interactions can develop which
we often cannot anticipate, and
these in turn guide the expression
of other interactions and
outcomes.
We live in a world in which
Newtonian mechanics has limits
Newton versus complexity
By observing emergent properties at higher scales that sum across the interactions of
smaller components, one can make probabilistic assumptions of the behavior of
systems. Do not (and cannot) rely solely on an individualistic Newtonian approach,
although it is useful to have knowledge of both individualist and emergent properties of
a system
Complex adaptive systems
Examples of complex adaptive
systems dynamics
• Dynamics can be simulated in
– Cellular automata
– Agent-based modeling
Cellular automata
Cellular automata
• Global patterns can
emerge from simple local
rules
• Emergence of these
large-scale patterns is
non-linear: small changes
in initial variables can lead
to disproportionate
changes in outcomes
• Cellular automata can be
used to model real-world
socio-ecological
phenomena
Agent-based modeling
• Agents are programmed to interact with other
agents
• Agents have local instead of global knowledge
• Agents can exhibit adaptation
• Simulations allow different futures to play out,
which can then be assessed in terms of
probability
• Cellular automata can be used to simulate realworld socioecological phenomena