Complex Systems and
Emergence
Gilberto Câmara
Tiago Carneiro
Pedro Andrade
Where does this image come from?
Where does this image come from?
Map of the web (Barabasi)
(could be brain connections)
Information flows in Nature
Ant colonies live in a chemical world
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)
Information flows in the brain
Neurons transmit electrical information, which generate conscience
and emotions
Information flows generate cooperation
Foto: National Cancer Institute, EUA
http://visualsonline.cancer.gov/
White cells attact a cancer cell (cooperative activity)
Information flows in planet Earth
Mass and energy transfer between points in the planet
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?
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.
What are complex adaptive systems?
Universal Computing
Computing studies information
flows in natural systems...
...and how to represent and
work with information flows in
artificial systems
Computational Modelling with Cell Spaces
Cell Spaces

Components

Cell Spaces

Generalizes Proximity Matriz – GPM

Hybrid Automata model

Nested enviroment
Cell Spaces
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)
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.
Cellular Automata
Neighbourhood
Rules
Space and Time
t
States
t1
Most important neighborhoods
Von Neumann
Neighborhood
Moore Neighborhood
Conway’s Game of Life
1.
2.
3.
4.
5.
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.
Game of Life
Static Life
Oscillating Life
Migrating Life
Conway’s Game of Life

The universe of the Game of Life is an infinite twodimensional grid of cells, each of which is either alive or
dead. Cells interact with their eight neighbors.
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;
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
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
Agent-Based Modelling
Representations
Goal
Communication
Communication
Action
Perception
Environment
Gilbert, 2003
Agents: autonomy, flexibility, interaction
Synchronization of fireflies
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 landcover change.
Four types of agents
Artificial agents, artificial environment
Artificial agents, natural environment
Natural agents, artificial environment
Natural Agents, natural environment
fonte: Helen Couclelis (UCSB)
Four types of agents
e-science
Artificial agents, artificial environment
Behavioral
Experiments
Natural agents, artificial environment
Engineering Applications
Artificial agents, natural environment
Descriptive Model
Natural Agents, natural environment
fonte: Helen Couclelis (UCSB)
Is computer science universal?
Modelling information flows in nature is computer science
http://www.red3d.com/cwr/boids/
Bird Flocking (Reynolds)
Example of a computational model
1. No central autority
2. Each bird reacts to its neighbor
3. Model based on bottom up
interactions
http://www.red3d.com/cwr/boids/
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/
Agents moving
Agents moving
Agents moving
Segregation
Segregation is an outcome of individual choices
But high levels of segregation indicate mean that
people are prejudiced?
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
cell’s 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?
Schelling’s 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
Schelling’s Model of Segregation
Micro-level rules of the game
Stay if at least a
third of neighbors
are “kin”
< 1/3
Move to random location
otherwise
Schelling’s Model of Segregation
Tolerance values above 30%: formation of
ghettos
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?
Zhang: Residential segregation in an allintegrationist world
Some studies show that most people prefer
to live in a non-segregated society.
Why there is so much segregation?
References

J. Zhang. Residential segregation in an allintegrationist world. Journal of Economic
Behaviour & Organization, v. 54 pp. 533-550.
2004

T. C. Shelling. Micromotives and Macrobehavior.
Norton, New York. 1978
Land use change in Amazonia
Some photos from Diógenes Alves (www.dpi.inpe.br/dalves)
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)
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”.
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
Yearly rates of deforestation: 1998-2009
Smallest yearly increase since the 1970s
Doomsday scenario and actual data...
Laurance et al., 2001
Optimistic scenario(2020)
Savannas and deforestation
Moderate degradation
Data from INPE (Prodes, 2008)
Savannas, non-forested areas,
deforested or heavely degrated
Deforestation
Degradação leve
Floresta intocada
Forest
Doomsday scenario and actual data...
Laurance et al., 2001
Optimistic scenario(2020)
About 1 million km2
deforested in 2020
Data from INPE (Prodes, 2008)
About 500.000 km2
deforested in 2010
For Laurance´s optimistic scenario to occur, there should
be 50.000 km2 of deforestation yearly from 2010 to 2020!
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.”
Improving deforestation prediction using agentbased models
Decision
MODEL
Parameters
São Felix do Xingu study: multiscale analysis of the coevolutio
of land use dynamics and beef and milk market chains
São Felix do Xingu
INPE/PRODES 2003/2004:
Deforestation
Forest
Non-forest
Clouds/no data
Change 1997-2006: deforestation and cattle
Beef and milk
market chain model
Land use
Change model
Small
farmers
agents
Medium
and large
farmers
agents
Forest
River
Deforest
Not Forest
Agents example: small farmers in Amazonia
Sustainability path
(alternative uses, technology)
Settlement/
invaded land
Diversify use
money surplus
Subsistence
agriculture
Create pasture/
Deforest
Sustainability
path (technology)
Manage cattle
bad land
management
Move towards
the frontier
Abandon/Sell
the property
Buy new
land
Speculator/
large/small
Agents example: large farmers in Amazonia
Diversify use
money surplus/bank loan
Buy land
from small
farmers
Create pasture/
plantation/
deforest
Manage cattle/
plantation
Buy calves
from small
Buy new
land
Speculator/
large/small
Observed deforestation from 1997 to 2006
Forest
River
Deforest
Not Forest
Regional scale
Frontier
INDIVIDUAL AGENTS
Large and small farmers
Local scale
SCENARIOS
LANDSCAPE DYNAMICS MODEL
- Front
- Medium
- Rear
Local farmers
Region
CATTLE CHAIN MODEL
Flows: goods, information, etc..
Connections: Agents
Landscape model: different rules for two main
types of actors
Landscape
metrics
model
Beef and milk
market chain model
Land use
Change model
Small
farmers
agents
Medium
and large
farmers
agents
Pasture
degradation
model
Several workshops in 2007 to define model rules and
variables
Landscape model: different rules of behavior at different
partitions
SÃO FÉLIX DO XINGU - 1997
FRONT
MIDDLE
BACK
Forest
River
Deforest
Not Forest
Landscape model: different rules of behavior at different
partitions which also change in time
SÃO FÉLIX DO XINGU - 2006
FRONT
FRENTE
MIDDLE
MEIO
BACK
RETAGUARDA
Forest
River
Deforest
Not Forest
Modeling results
97 to 2006
Observed
97 to 2006