Complex Systems and Emergence

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Complex Systems and Emergence

• Gilberto Câmara
• Tiago Carneiro
• Pedro Andrade

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Where does this image come from?
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Where does this image come from?
Map of the web (Barabasi) (could be brain
connections)
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Information flows in Nature
Ant colonies live in a chemical world
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Conections and flows are universal
Interactions yeast proteins (Barabasi e Boneabau,
SciAm, 2003)
Interaction btw scientits in Silicon
Valley (Fleming e Marx, Calif Mngt Rew, 2006)
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Information flows in the brain
Neurons transmit electrical information, which
generate conscience and emotions
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Information flows generate cooperation
Foto National Cancer Institute, EUA
http//visualsonline.cancer.gov/
White cells attact a cancer cell (cooperative
activity)
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Information flows in planet Earth
Mass and energy transfer between points in the
planet
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Complex adaptative systems
How come that an ecosystem with all its diverse
species functions and exhibits patterns of
regularity?

• How come that a city with many inhabitants
  functions and exhibits patterns of regularity?

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What are complex adaptive systems?

• Systems composed of many interacting parts that
  evolve and adapt over time.
• Organized behavior emerges from the simultaneous
  interactions of parts without any global plan.

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What are complex adaptive systems?
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Universal Computing
Computing studies information flows in natural
systems...
...and how to represent and work with information
flows in artificial systems
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Computational Modelling with Cell Spaces
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Cell Spaces
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Cellular Automata Humans as Ants

• Cellular Automata
• Matrix,
• Neighbourhood,
• Set of discrete states,
• Set of transition rules,
• Discrete time.

CAs contain enough complexity to simulate
surprising and novel change as reflected in
emergent phenomena (Mike Batty)
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2-Dimensional Automata

• 2-dimensional cellular automaton consists of
  an infinite (or finite) grid of cells, each in
  one of a finite number of states. Time is
  discrete and the state of a cell at time t is a
  function of the states of its neighbors at time
  t-1.

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Cellular Automata
Rules
Neighbourhood
Space and Time
t
States
t1
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Most important neighborhoods
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Conways Game of Life

• At each step in time, the following effects
  occur
• Any live cell with fewer than two neighbors dies,
  as if by loneliness.
• Any live cell with more than three neighbors
  dies, as if by overcrowding.
• Any live cell with two or three neighbors lives,
  unchanged, to the next generation.
• Any dead cell with exactly three neighbors comes
  to life.

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Game of Life
Static Life
Oscillating Life
Migrating Life
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Conways Game of Life

• The universe of the Game of Life is an infinite
  two-dimensional grid of cells, each of which is
  either alive or dead. Cells interact with their
  eight neighbors.

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Characteristics of CA models

• Self-organising systems with emergent
  properties locally defined rules resulting in
  macroscopic ordered structures. Massive amounts
  of individual actions result in the spatial
  structures that we know and recognise

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Which Cellular Automata?

• For realistic geographical models
• the basic CA principles too constrained to be
  useful
• Extending the basic CA paradigm
• From binary (active/inactive) values to a set of
  inhomogeneous local states
• From discrete to continuous values (30
  cultivated land, 40 grassland and 30 forest)
• Transition rules diverse combinations
• Neighborhood definitions from a stationary 8-cell
  to generalized neighbourhood
• From system closure to external events to
  external output during transitions

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Agents as basis for complex systems
An agent is any actor within an environment, any
entity that can affect itself, the environment
and other agents.

• Agent flexible, interacting and autonomous

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Agent-Based Modelling
Goal
Gilbert, 2003
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Agents autonomy, flexibility, interaction
Synchronization of fireflies
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Agents changing the landscape
It is the agent (an individual, household, or
institution) that takes specific actions
according to its own decision rules which drive
land-cover change.
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Four types of agents
Artificial agents, natural environment
Artificial agents, artificial environment
Natural agents, artificial environment
Natural Agents, natural environment
fonte Helen Couclelis (UCSB)
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Four types of agents
Engineering Applications
e-science
Artificial agents, natural environment
Artificial agents, artificial environment
Behavioral Experiments
Descriptive Model
Natural agents, artificial environment
Natural Agents, natural environment
fonte Helen Couclelis (UCSB)
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Is computer science universal?
Modelling information flows in nature is computer
science
http//www.red3d.com/cwr/boids/
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Bird Flocking (Reynolds)

• Example of a computational model
• No central autority
• Each bird reacts to its neighbor
• Model based on bottom up interactions

http//www.red3d.com/cwr/boids/
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Bird Flocking Reynolds Model (1987)
Cohesion steer to move toward the average
position of local flockmates Separation steer
to avoid crowding local flockmates Alignment
steer towards the average heading of local
flockmates
www.red3d.com/cwr/boids/
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Agents moving
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Agents moving
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Agents moving
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Segregation

• Segregation is an outcome of individual choices
• But high levels of segregation indicate mean that
  people are prejudiced?

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Schelling Model for Segregation

• Start with a CA with white and black cells
  (random)
• The new cell state is the state of the majority
  of the cells Moore neighbours
• White cells change to black if there are X or
  more black neighbours
• Black cells change to white if there are X or
  more white neighbours
• How long will it take for a stable state to
  occur?

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Schellings Model of Segregation

• Schelling (1971) demonstrates a theory to explain
  the persistence of racial segregation in an
  environment of growing tolerance
• If individuals will tolerate racial diversity,
  but will not tolerate being in a minority in
  their locality, segregation will still be the
  equilibrium situation

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Schellings Model of Segregation
Micro-level rules of the game
Stay if at least a third of neighbors are kin
lt 1/3
Move to random location otherwise
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Schellings Model of Segregation

• Tolerance values above 30 formation of ghettos

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The Modified Majority Model for Segregation

• Include random individual variation
• Some individuals are more susceptible to their
  neighbours than others
• In general, white cells with five neighbours
  change to black, but
• Some white cells change to black if there are
  only four black neighbours
• Some white cells change to black only if there
  are six black neighbours
• Variation of individual difference
• What happens in this case after 50 iterations and
  500 iterations?

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Zhang Residential segregation in an
all-integrationist world
Some studies show that most people prefer to
live in a non-segregated society. Why there is
so much segregation?
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References

• J. Zhang. Residential segregation in an
  all-integrationist world. Journal of Economic
  Behaviour Organization, v. 54 pp. 533-550. 2004
• T. C. Shelling. Micromotives and Macrobehavior.
  Norton, New York. 1978

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Land use change in Amazonia
Some photos from Diógenes Alves
(www.dpi.inpe.br/dalves)
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INPE Clear-cut deforestation mapping of Amazonia
since 1988
230 scenes Landsat/year
Yearly detailed estimates of clear-cut areas
LANDSAT-class data (wall-to-wall)
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(No Transcript)
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Is this sound science?
W. Laurance et al, The Future of the Brazilian
Amazon?, Science, 2001

• Scenarios for Amazônia in 2020
• Otimistic scenario 28 of deforestation
• Pessimistic scenario 42 of deforestation

We generated two models with realistic but
differing assumptions--termed the "optimistic"
and "nonoptimistic" scenarios--for the future of
the Brazilian Amazon. The models predict the
spatial distribution of deforested or heavily
degraded land, as well as moderately degraded,
lightly degraded, and pristine forests.
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The Future of Brazilian Amazonia?

• Optimistic scenario 28 of deforestation (1
  million km2) by 2020
• Complete degradation up to 20 km from roads
  (existing and projected)
• Moderate degradation up to 50 km from roads
• Reduced degradation up to 100 km from roads

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Yearly rates of deforestation 1998-2009
Smallest yearly increase since the 1970s
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Doomsday scenario and actual data...
Laurance et al., 2001 Optimistic scenario(2020)
Data from INPE (Prodes, 2008)
Savannas, non-forested areas, deforested or
heavely degrated
Savannas and deforestation
Moderate degradation
Deforestation
Degradação leve
Forest
Floresta intocada
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Doomsday scenario and actual data...
Laurance et al., 2001 Optimistic scenario(2020)
Data from INPE (Prodes, 2008)
About 500.000 km2 deforested in 2010
About 1 million km2 deforested in 2020
For Laurances optimistic scenario to occur,
there should be 50.000 km2 of deforestation
yearly from 2010 to 2020!
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Brazilian scientists write to Science

• Amazon Deforestation Models Challenging the
  Only-Roads Approach
• Deforestation predictions presented by Laurance
  et al. are based on the assumption that the
  governmental road infrastructure is the prime
  factor driving deforestation. Simplistic models
  such as Laurance et al. may deviate attention
  from real deforestation causes, being potentially
  misleading in terms of deforestation control.

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Improving deforestation prediction using
agent-based models
Decision
MODEL
Parameters
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São Felix do Xingu study multiscale analysis of
the coevolution of land use dynamics and beef and
milk market chains

• São Felix do Xingu

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Change 1997-2006 deforestation and cattle
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Agents example small farmers in Amazonia
Sustainability path (alternative uses, technology)
Settlement/invaded land
Diversify use
money surplus
Manage cattle
Sustainability path (technology)
Buy newland
Create pasture/ Deforest
Subsistenceagriculture
bad land management
Abandon/Sellthe property
Speculator/large/small
Move towardsthe frontier
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Agents example large farmers in Amazonia
Diversify use
money surplus/bank loan
Buy newland
Manage cattle/plantation
Create pasture/plantation/ deforest
Buy landfrom smallfarmers
Buy calvesfrom small
Speculator/large/small
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Observed deforestation from 1997 to 2006
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Regional scale
SCENARIOS
CATTLE CHAIN MODEL  Flows goods, information,
etc.. Connections Agents
Region
LANDSCAPE DYNAMICS MODEL - Front- Medium- Rear
Frontier

INDIVIDUAL AGENTSLarge and small farmers
Local farmers
Local scale
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Landscape model different rules for two main
types of actors
Landscape metrics model
Land use
Beef and milk
Land use
Change model
market chain model
Change model
Pasture degradation model
Medium
Medium
Small
Small
and large
and large
farmers
farmers
farmers
farmers
agents
agents
agents
agents
Several workshops in 2007 to define model rules
and variables
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Landscape model different rules of behavior at
different partitions
SÃO FÉLIX DO XINGU - 1997
FRONT
MIDDLE
BACK
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Landscape model different rules of behavior at
different partitions which also change in time
SÃO FÉLIX DO XINGU - 2006
FRONT
MIDDLE
BACK
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Modeling results 97 to 2006
Observed 97 to 2006