Agentbased models and social simulation Gilberto Cmara Tiago

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Agent-based models and social simulation Gilberto Câmara Tiago Carneiro Pedro Andrade Licence: Creative Commons

Agent-based models and social simulation Gilberto Câmara Tiago Carneiro Pedro Andrade Licence: Creative Commons By Attribution Non Commercial Share Alike http: //creativecommons. org/licenses/by-nc-sa/2. 5/

Uncertainty on basic equations What about the unknowns? Social and Economic Systems Quantum Gravity

Uncertainty on basic equations What about the unknowns? Social and Economic Systems Quantum Gravity Particle Physics Living Systems Chemical Reactions Hydrological Models Global Change Solar System Dynamics Meteorology Complexity of the phenomenon source: John Barrow (after David Ruelle)

Complex adaptive systems Systems composed of many interacting parts that evolve and adapt over

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.

Is computing also a natural science? http: //www. red 3 d. com/cwr/boids/ “Information processes

Is computing also a natural science? http: //www. red 3 d. com/cwr/boids/ “Information processes and computation continue to be found abundantly in the deep structures of many fields. Computing is not—in fact, never was—a science only of the artificial. ” (Peter Denning, CACM, 2007).

Where does this image come from?

Where does this image come from?

Where does this image come from? Map of the web (Barabasi) (could be brain

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

Information flows in Nature Ant colonies live in a chemical world

Conections and flows are universal Yeast proteins (Barabasi and Boneabau, Sci. Am, 2003) Scientists

Conections and flows are universal Yeast proteins (Barabasi and Boneabau, Sci. Am, 2003) Scientists in Silicon Valley (Fleming and Marx, Calif Mngt Rew, 2006)

Information flows in the brain Neurons transmit electrical information, which generate conscience and emotions

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

Information flows generate cooperation Foto: National Cancer Institute, EUA http: //visualsonline. cancer. gov/ White cells attact a cancer cell (cooperative activity)

A plague of locusts

A plague of locusts

Collective spatial action: volunteered GI Are Brazilians less cooperative? Less tech-savvy? Does google solve

Collective spatial action: volunteered GI Are Brazilians less cooperative? Less tech-savvy? Does google solve their problems? Are they happy with their public data?

Computing is also a natural science Computing studies information flows in natural systems. .

Computing is also a natural science Computing studies information flows in natural systems. . . and how to represent and work with information flows in artificial systems

Computing is also a natural science Computing studies information flows in natural systems. .

Computing is also a natural science Computing studies information flows in natural systems. . . and how to represent and work with information flows in artificial systems

Collective spatial action: pedestrian modelling Notting Hill Carnival (London) Batty, “Agent-Based Pedestrian Modelling”, in:

Collective spatial action: pedestrian modelling Notting Hill Carnival (London) Batty, “Agent-Based Pedestrian Modelling”, in: Advanced Spatial Analysis, ESRI Press, 2003.

Information flows in planet Earth Mass and energy transfer between points in the planet

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

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

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.

Clouds: statistical distributions Clocks, clouds or ants? Clocks: deterministic methods Ants: emerging behaviour

Clouds: statistical distributions Clocks, clouds or ants? Clocks: deterministic methods Ants: emerging behaviour

1973 1987 2000 Slides from LANDSAT images: USGS Modelling Human-Environment Interactions How do we

1973 1987 2000 Slides from LANDSAT images: USGS Modelling Human-Environment Interactions How do we decide on the use of natural resources? What are the conditions favoring success in resource mgnt? Can we anticipate changes resulting from human decisions? What GIScience techniques and tools are needed to model human-environment decision making?

What are complex adaptive systems?

What are complex adaptive systems?

Universal Computing studies information flows in natural systems. . . and how to represent

Universal Computing studies information flows in natural systems. . . and how to represent and work with information flows in artificial systems

Agents as basis for complex systems An agent is any actor within an environment,

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 Action Perception Space Gilbert, 2003

Agent-Based Modelling Representations Goal Communication Action Perception Space Gilbert, 2003

Agents: autonomy, flexibility, interaction Synchronization of fireflies

Agents: autonomy, flexibility, interaction Synchronization of fireflies

Why is it interesting? Structure structure is emergent from agent interaction this can be

Why is it interesting? Structure structure is emergent from agent interaction this can be directly modeled Agency agents have goals, beliefs and act this can be directly modeled Dynamics things change, develop, evolve agents move (in space and social location) and learn these can be directly modeled Source: (Gilbert, 2006)

Is it qualitative or quantitative? Agent-based models can handle all types of data quantitative

Is it qualitative or quantitative? Agent-based models can handle all types of data quantitative attributes age size of organization qualitative ordinal or categorical (e. g. ethnicity), relational (e. g. I am linked to him and her) vague A sends B a message about one time in three Source: (Gilbert, 2006)

It has been used in different areas of science economy sociology archaeology ecology linguistics

It has been used in different areas of science economy sociology archaeology ecology linguistics political sciences . . .

Source: http: //www. leggmason. com/thoughtleaderforum/2004/conference/transcripts/arthur_trans. asp

Source: http: //www. leggmason. com/thoughtleaderforum/2004/conference/transcripts/arthur_trans. asp

Agents changing the landscape An individual, household, or institution that takes specific actions according

Agents changing the landscape An individual, household, or institution that takes specific actions according to its own decision rules which drive land-cover change.

Four types of agents Artificial agents, artificial environment Artificial agents, natural environment Natural agents,

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

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.

Is computer science universal? Modelling information flows in nature is computer science http: //www. red 3 d. com/cwr/boids/

Bird Flocking (Reynolds) Example of a computational model 1. No central autority 2. Each

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. red 3 d. com/cwr/boids/

Bird Flocking: Reynolds Model (1987) Cohesion: steer to move toward the average position of

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. red 3 d. com/cwr/boids/

Four types of spatial agents Artificial agents, artificial environment Artificial agents, natural environment Natural

Four types of spatial agents Artificial agents, artificial environment Artificial agents, natural environment Natural agents, artificial environment Natural Agents, natural environment source: Couclelis (2001)

Some caution necessary. . . “Agent-based modeling meets an intuitive desire to explicitly represent

Some caution necessary. . . “Agent-based modeling meets an intuitive desire to explicitly represent human decision making. (…) However, by doing so, the well-known problems of modeling a highly complex, dynamic spatial environment are compounded by the problems of modeling highly complex, dynamic decision-making. (…) The question is whether the benefits of that approach to spatial modeling exceed the considerable costs of the added dimensions of complexity introduced into the modeling effort. The answer is far from clear and in, my mind, it is in the negative. But then I am open to being persuaded otherwise ”. (from “Why I no longer work with agents”, 2001 LUCC ABM Workshop) Helen Couclelis

Some caution necessary. . . “Complexity is more and more acknowledged to be a

Some caution necessary. . . “Complexity is more and more acknowledged to be a key characteristic of the world we live in and of the systems that cohabit our world. It is not new for science to attempt to understand complex systems: astronomers have been at it for millennia, and biologists, economists, psychologists, and others joined them some generations ago. (…) If, as appears to be the case, complexity (like systems science) is too general a subject to have much content, then particular classes of complex systems possessing strong properties that provide a fulcrum for theorizing and generalizing can serve as the foci of attention. ” (from “The Sciences of the Artificial”, 1996) Herbert Simon (1958)

ABM in Terra. ME: Types and Functions

ABM in Terra. ME: Types and Functions

Terra. ME: nature-society modelling Agent Space Nature represented in cellular spaces, society represented as

Terra. ME: nature-society modelling Agent Space Nature represented in cellular spaces, society represented as agents T. Carneiro, P. Andrade, et al. , “An extensible toolbox for modeling nature-society interactions”. Enviromental Modelling and Software, 2013 (Two Ph. Ds).

Types in Terra. Lib ecosystem: new tools, new types 2014 Time Series Trajectory Cellular

Types in Terra. Lib ecosystem: new tools, new types 2014 Time Series Trajectory Cellular Space 2010 Agent Social Network Object Geometry 2002 Event Coverage

for. Each. Connection for. Each. Cell Agent for. Each. Neighbor Cell for. Each. Agent

for. Each. Connection for. Each. Cell Agent for. Each. Neighbor Cell for. Each. Agent for. Each. Cell Society Cellular. Space Group Trajectory DBMS

Agents within cells agents = cell: get. Agents() if #(agents) == 0 then --

Agents within cells agents = cell: get. Agents() if #(agents) == 0 then -- empty agent: leave(oldcell) agent: enter(cell) end

Society CBB CAC CBA CCB ACA CCC AAC BBC AAA 上海宋 BBA BAB

Society CBB CAC CBA CCB ACA CCC AAC BBC AAA 上海宋 BBA BAB

Society create. Agent = function(capital) return Agent { capital = capital, --. . .

Society create. Agent = function(capital) return Agent { capital = capital, --. . . } end data = {} data[1] = 100; data[2] = 50; data[3] = 25 mag = Society(create. Agent, data) capital = 100 capital = 50 capital = 25 mag = Society(create. Agent, 50)

Society function create. Agent (capital) person = Agent { init = function (self), --.

Society function create. Agent (capital) person = Agent { init = function (self), --. . . } end data = {} data[1] = 100; data[2] = 50; data[3] = 25 mag = Society(create. Agent, data) capital = 100 capital = 50 capital = 25

Group CBB CAC CBA CCB ACA CCC AAC BBC AAA BBA ABC BAB

Group CBB CAC CBA CCB ACA CCC AAC BBC AAA BBA ABC BAB

Group g = Group{mag, function(agent) return agent. capital > 40 end, function(a 1, a

Group g = Group{mag, function(agent) return agent. capital > 40 end, function(a 1, a 2) return a 1. capital > a 2. capital end } capital = 100 capital = 50 capital = 25

Traversing the Society capital = 100 capital = 50 capital = 25 for. Each.

Traversing the Society capital = 100 capital = 50 capital = 25 for. Each. Agent(mag, function(agent) agent. capital = agent. capital + 100 end) capital = 200 capital = 150 capital = 125

Emergence “Can you grow it? ” (Epstein; Axtell; 1996) source: (Bonabeau, 2002)

Emergence “Can you grow it? ” (Epstein; Axtell; 1996) source: (Bonabeau, 2002)

Epstein (Generative Social Science) • If you didn´t grow it, you didn´t explain its

Epstein (Generative Social Science) • If you didn´t grow it, you didn´t explain its generation • Agent-based model Generate a macrostructure • Agents = properties of each agent + rules of interaction • Target = macrostruture M that represents a plausible pattern in the real-world

Scientific method Science proceeds by conjectures and refutations (Popper)

Scientific method Science proceeds by conjectures and refutations (Popper)

Explanation and Generative Sufficiency Conjectures Agent model A 1 Macrostructure ? Agent model A

Explanation and Generative Sufficiency Conjectures Agent model A 1 Macrostructure ? Agent model A 2 Agent model A 3 Spatial segregation Bird flocking ? Refutation

Explanation and Generative Sufficiency Agent model A 1 Macrostructure ? Agent model A 2

Explanation and Generative Sufficiency Agent model A 1 Macrostructure ? Agent model A 2 Occam´s razor: "entia non sunt multiplicanda praeter necessitatem", or "entities should not be multiplied beyond necessity".

Explanation and Generative Sufficiency Agent model A 1 Macrostructure ? Agent model A 2

Explanation and Generative Sufficiency Agent model A 1 Macrostructure ? Agent model A 2 Popper´s view "We prefer simpler theories to more complex ones because their empirical content is greater and because they are better testable"

Explanation and Generative Sufficiency Agent model A 1 Macrostructure ? Agent model A 2

Explanation and Generative Sufficiency Agent model A 1 Macrostructure ? Agent model A 2 Einstein´s rule: The supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience" "Theories should be as simple as possible, but no simpler.