1. 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

2. Complex adaptive systems

3. Characteristics of complex adaptive systems
The economy, ecosystems, ant colonies, the brain, our immune systems, traffic flow, crowd or swarm behavior
Have path dependence
Exhibit emergence
Are dynamic and adaptive
Control is distributed rather than centralized

5. The Newtonian world….
Mechanistic and deterministic, predictable

6. The Newtonian World

7. Laplace’s demon
If a demon (or some all-knowing 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

8. However, Newtonian concepts are not capable of explaining ecological change, which is also strongly coupled to human systems

9. We live in a world in which Newtonian mechanics has limits
The shear number of species and their interactions limits prediction of change using Newtonian concepts alone
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
This is only a static representation of the many players and interactions found in the food web. In actual fact, the food web is a ‘living' dynamic system that responds to variation in time and space. The incredibly flexible food-web architecture actually expands and contracts, like an accordion.

10. Complex adaptive systems dynamics
Can be simulated in
Cellular automata
Agent-based modeling

11. Cellular automata
Originated in 1940’s

12. Cellular automata Global patterns can emerge from simple local rules
Cellular automata can be used to model real-world socio-ecological phenomena
Examples:
Game of life
Schelling’s segration model
Wildfire modeling

13. In the Game of Life, simple rules are assigned to cells, and then the cellular automata is allowed to play out over time. Each individual cell is sensitive to the neighborhood around it and responds to it according to the rules that define it. Often, how the game plays out is sensitive to initial conditions. The original configuration of black and white grid cells will determine how the system evolves. Game of Life because the basis for studying
artificial life.

14. In the cellular automata devised to study segregation by Schelling, each cell can be assigned a tolerance value. For example, a tolerance value of 25% implies that a person will move if one quarter of their neighbors is not of the same race/ethnicity. What is unique about Schelling’s segregation model is that even when someone is tolerant (i.e., a tolerance value of 50%), neighborhoods will still become segregated if people are allowed to move to a new home. Despite a willingness to have half of their neighbors of a different race or ethnic identity, the environment can become segregated.

15.
An example of cellular automata modeling to predict and understand the dynamics of wildfire. There are several software packages to model fire, this is just one of them. The fire burns according to pixel rules
that define its flammability, wind direction, temperature, and slope of the terrain. The cellular automata can be
adjusted to allow fires to spot – for small embers to float out from the main fire and initiate new fires.

16. Agent-based modeling
Agents are programmed to interact with other agents
Agents have local instead of global knowledge
Agents can exhibit adaptation
Agent-based modeling can be used to simulate real-world ecological phenomena

17. In an agent-based model, individual agents have local rules and a much more mobile capacity than with cellular automata. They can ‘see’ their local environment and respond accordingly. With many agents, their individual behaviors can scale up to create emergent patterns. In this agent-based model, the agents are kids in different neighborhoods. They interact with each other and their neighbors in how they buy and talk about candy sold by three companies. Agent-based models are ways to simulate the dynamics of the world that are not readily done with Newtonian equations.

18. Many video games, like Minecraft, are designed around an agent-based framework.

19. NetLogo is a very popular modeling platform for creating agent-based models

20. How to describe a complex adaptive system
They reflect an interaction of determinism and contingency, of order and disorder

21. How to describe a CAS
As a system having path dependency – the events that have unfolded in the past constrain the immediate future
Microsoft staff, Albuquerque, Dec 7, 1978
Querty

22. How to describe a CAS
They have global properties emerge from interactions among many individual components, which then may feed back to influence the subsequent development of these interactions.

23. How to describe a CAS They exhibit self-organization
Process where some form of order arises from local interactions and feedbacks between parts of an initially disordered system.
The process is spontaneous, and is not controlled by an external agent.
Promotes stability of the system so that it and the feedbacks within it persist

24. How to describe a CAS
As an open-ended system exhibiting self organization and adaptation
Daisyworld

25. Gaia theory (Lovelock and Margulis, 1974)
The Earth is a complex adaptive system
Biotic and abiotic components of the Earth have co-evolved through feedbacks
Cumulative global effects can arise through essentially local phenomena.
Feedback loops can become established that support life
Adaptation is open-ended and path dependent
Daisyworld illustrates the complex adaptive systems dynamics of Gaia theory
Lovelock and Margulis, 1974

26. How complexity is used in ecology
Convergence (Clementsian)
Whatever the starting points or variations in initial conditions, system self-organizes toward same particular end state
Divergence (Gleasonian)
System becomes more diverse over time. Initial differences and disturbances tend to persist and grow.

27. How complexity is used in ecology
Provides a more nuanced way to understand how nature changes and how it exihibits stability

28. How complexity is used in ecology
Stability in complex adaptive systems is different from how we typically think of it
Ecological systems can exhibit more than one stable state (you on the other hand, as shown at right, have one)

29. The working vocabulary of complex adaptive systems
Vocabulary varies depending upon intellectual tradition, but in general these phenomena are recognized
Stability domains
Critical transitions
Tipping points
Hysteresis
Bistability

30. Stability domains Fire-reinforcing longleaf pine stability domain
Fire-resisting longleaf pine stability domain 30

31. Stability domains

32. Examples (drawn in class)
Fire-dependent forests
Grasslands
Semi-arid rangelands
Shallow lakes
Rocky nearshore marine communities

33. Critical transitions
A system may change, or transition, from one stable state or stability domain to another
Systems can transition gradually, or sharply when a threshold is crossed
Tipping implies a sudden and sometimes irreversible change

34. Gradual and developmental transition
Threshold transition without hysteresis
Threshold transition with pronounced tipping and hysteresis

35. Hysteresis and bistability
Multiple states may persist under equal environmental conditions with hysteresis.