complex systems and emergence gilberto câmara tiago carneiro pedro andrade
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Complex Systems and Emergence
Gilberto CâmaraTiago CarneiroPedro 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
White cells attact a cancer cell (cooperative activity)
Foto: National Cancer Institute, EUA http://visualsonline.cancer.gov/
Information flows in planet Earth
Mass and energy transfer between points in the planet
Complex adaptative systems
How come that a city with many inhabitants functions and exhibits patterns of regularity?
How come that an ecosystem with all its diverse species 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 SpacesCell 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
RulesNeighbourhood
States
Space and Time
t
t1
Von Neumann Neighborhood
Moore Neighborhood
Most important neighborhoods
Conway’s Game of Life
1. At each step in time, the following effects occur:2. Any live cell with fewer than two neighbors dies, as
if by loneliness. 3. Any live cell with more than three neighbors dies,
as if by overcrowding. 4. Any live cell with two or three neighbors lives,
unchanged, to the next generation. 5. 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 two-dimensional 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 modelsthe basic CA principles too constrained to be useful
Extending the basic CA paradigm From binary (active/inactive) values to a set of
inhomogeneous local statesFrom 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 neighbourhoodFrom system closure to external events to external output
during transitions
Agents as basis for complex systems
Agent: flexible, interacting and autonomous
An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.
Agent-Based Modelling
Goal
Environment
Representations
Communication
ActionPerception
Communication
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 land-cover change.
Four types of agents
Natural agents, artificial environment
Artificial agents, artificial environment Artificial agents, natural environment
Natural Agents, natural environment
fonte: Helen Couclelis (UCSB)
Four types of agents
Natural agents, artificial environment
Artificial agents, artificial environment Artificial agents, natural environment
Natural Agents, natural environment
fonte: Helen Couclelis (UCSB)
e-science Engineering Applications
BehavioralExperiments
Descriptive Model
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 model1. No central autority2. Each bird reacts to its neighbor3. Model based on bottom up
interactionshttp://www.red3d.com/cwr/boids/
Bird Flocking: Reynolds Model (1987)
www.red3d.com/cwr/boids/
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
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 neighboursWhite cells change to black if there are X or more black
neighboursBlack 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
< 1/3
Micro-level rules of the game
Stay if at least a third of neighbors are “kin”
Move to random location otherwise
Tolerance values above 30%: formation of ghettos
http://ccl.northwestern.edu/netlogo/models/Segregation
Schelling’s Model of Segregation
The Modified Majority Model for Segregation
Include random individual variationSome individuals are more susceptible to their neighbours
than othersIn 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”
neighboursVariation of individual difference
What happens in this case after 50 iterations and 500 iterations?
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?
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
Some photos from Diógenes Alves (www.dpi.inpe.br/dalves)
Land use change in Amazonia
~230 scenes Landsat/year
Yearly detailed estimates of clear-cut areas LANDSAT-class data (wall-to-wall)
INPE: Clear-cut deforestation mapping of Amazonia since 1988
Is this sound science?
Scenarios for Amazônia in 2020Otimistic 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”.
W. Laurance et al, “The Future of the Brazilian Amazon?”, Science, 2001
The Future of Brazilian Amazonia?
Optimistic scenario: 28% of deforestation (1 million km2) by 2020Complete degradation up to 20 km from roads (existing and
projected)Moderate degradation up to 50 km from roadsReduced degradation up to 100 km from roads
Smallest yearly increase since the 1970s
Yearly rates of deforestation: 1998-2009
Laurance et al., 2001Optimistic scenario(2020)
Savannas and deforestation
Moderate degradation
Degradação leve
Floresta intocada
Doomsday scenario and actual data...
Data from INPE (Prodes, 2008)
Savannas, non-forested areas, deforested or heavely degrated
Deforestation
Forest
Laurance et al., 2001Optimistic scenario(2020)
Doomsday scenario and actual data...
Data from INPE (Prodes, 2008)
About 1 million km2 deforested in 2020
For Laurance´s optimistic scenario to occur, there should be 50.000 km2 of deforestation yearly from 2010 to 2020!
About 500.000 km2 deforested in 2010
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 agent-based models
Decision
MODEL
Parameters
São Felix do Xingu study: multiscale analysis of the coevolution of land use dynamics and beef and milk market chains
São Felix do Xingu
Deforestation
Forest
Non-forest
Clouds/no data
INPE/PRODES 2003/2004:
Forest
Not ForestDeforest
River
Change 1997-2006: deforestation and cattle
Land use Change model
Beef and milk market chain model
Small farmersagents
Medium and largefarmersagents
Land use Change model
Beef and milk market chain model
Small farmersagents
Medium and largefarmersagents
Create pasture/Deforest
Speculator/large/small
bad land management
money surplus
Subsistenceagriculture
Diversify use
Manage cattle
Move towardsthe frontier
Abandon/Sellthe property
Buy newland
Settlement/invaded land
Sustainability path(alternative uses, technology)
Sustainability path (technology)
Agents example: small farmers in Amazonia
Create pasture/plantation/deforest
Speculator/large/small
money surplus/bank loan
Diversify use
Buy newland
Manage cattle/plantation
Buy calvesfrom small
Buy landfrom smallfarmers
Agents example: large farmers in Amazonia
Forest
Not ForestDeforest
River
Observed deforestation from 1997 to 2006
Local scale
Regional scale
CATTLE CHAIN MODEL Flows: goods, information, etc.. Connections: Agents
LANDSCAPE DYNAMICS MODEL - Front- Medium- Rear
INDIVIDUAL AGENTSLarge and small farmers
Loca
l far
mer
sFr
ontie
r Re
gion
SCENARIO
S
Land use Change model
Beef and milk market chain model
Small farmersagents
Medium and largefarmersagents
Land use Change model
Small farmersagents
Medium and largefarmersagents
Landscapemetrics model
Pasture degradation
model
Several workshops in 2007 to define model rules and variables
Landscape model: different rules for two main types of actors
Landscape model: different rules of behavior at different partitions
Forest
Not ForestDeforest
River
FRONT
MIDDLE
BACK
SÃO FÉLIX DO XINGU - 1997
Landscape model: different rules of behavior at different partitions which also change in time
FRENTE
MEIO
RETAGUARDA
Forest
Not ForestDeforest
River
FRONT
MIDDLE
BACK
SÃO FÉLIX DO XINGU - 2006
Modeling results 97 to 2006
Observed 97 to 2006