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lesson_23 [2019/08/23 13:53]
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-====LESSON 20:  ​Agent Based Model  ==== +=====Agent Based Model  ​===== 
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 ==== What will you learn? ==== ==== What will you learn? ====
-* A spatially explicit ABM developed using DINAMICA-EGO.+  * What is an agent-based model \\ 
 +  ​* A spatially explicit ABM developed using DINAMICA-EGO.
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-A spatially-explicit ​agent-based model was designed using Dinamica EGO to represent interactions between ​rabbit population ​and the environment developed by Alessandro Ribeiro CamposJuliana Leroy DavisWilliam Leles Souza Costa and Britaldo Silveira Soares FilhoThe model simulates agents'​movements, internal ​statesspatial distribution, and demographic featuresSeveral scenarios were run to analyze ​the responses ​of agents to changes in their environment.\\ +===What is an agent-based model=== 
-\\+Agent based models (ABM) attempts to reproduce individual processes of movement, behavior, birth, growth and death according ​to a set of information,​ such as genotype, history ​and location of agents. In these models ​the agents are the crucial components of the analysis. ABMs can be designed primarily as deliberative or reactive architecture systems (Carneiro2003). As deliberative systemsthe agents have an internal model of the environment ​and the decisions are made through some type of logical reasoningOn the other handagent reactive architectures avoid these internal ​representations and action choices are made based on the occurrence of a set of conditions of the environment that are pre-programmed in the model. A great variety of entities are represented through ABM approach: atomscellsanimals, people ​and organizations (Batty, 2005; Carneiro, 2003; Conte et al1997; Epstein and Axtell, 1996; Janssen and Jager, 2000; Scanlan et al., 2006; Weiss, 1999; McLane et al., 2011). ABMs have been applied ​to several studies involving natural resource management, human decisions (Bousquet and Le Page, 2004, An, 2012; Barbati et al., 2012; Sun and Muller, 2012), urban processes (Batty, 2005; Chen , 2012), and land-use change (Carneiro, 2003; Matthews et al., 2007). In ecology, ABMs focus on how species respond to a set of circumstances,​ modeling population dynamics, animal movements and behavior (DeAngelis and Mooij, 2005, Nathan et al., 2008; McLane, 2011). Some of these applications take advantage of the spatial dynamic representation ​of CA models. This possibility was explored particularly by Epstein and Axtell (1996) who developed an ABM of an artificial society, known as Sugarscape, for the study of social phenomena and human behavior
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-Simulation models are designed to help understanding causal mechanisms and related processes of environmental system dynamics under different scenarios of socioeconomic,​ policies, and environmental contexts (Soares-Filho et al. 2007). Spatially explicit models (Parker et al. 2001), also known as landscape dynamics models (Soares-Filho et al., 2003), mimic environmental phenomena across space and time. Dynamic spatial models employ cell grids to represent set of input maps. Cellular automata (CA) computational approach is widely used to implement such models. Whereas CA models focus on landscapes patterns, agent-based models focus on individuals'​ behavior that produces the spatial patterns. Agent based models (ABM) attempts to reproduce individual processes of movement, behavior, birth, growth and death according to a set of information,​ such as genotype, history and location of agents. In these models the agents are the crucial components of the analysis. ABMs can be designed primarily as deliberative or reactive architecture systems (Carneiro, 2003). As deliberative systems, the agents have an internal model of the environment and the decisions are made through some type of logical reasoning. On the other hand, agent reactive architectures avoid these internal representations and action choices are made based on the occurrence of a set of conditions of the environment that are pre-programmed in the model. A great variety of entities are represented through ABM approach: atoms, cells, animals, people and organizations (Batty, 2005; Carneiro, 2003; Conte et al. 1997; Epstein and Axtell, 1996; Janssen and Jager, 2000; Scanlan et al., 2006; Weiss, 1999; McLane et al., 2011). ABMs have been applied to several studies involving natural resource management, human decisions (Bousquet and Le Page, 2004, An, 2012; Barbati et al., 2012; Sun and Muller, 2012), urban processes (Batty, 2005; Chen , 2012), and land-use change (Carneiro, 2003; Matthews et al., 2007). In ecology, ABMs focus on how species respond to a set of circumstances,​ modeling population dynamics, animal movements and behavior (DeAngelis and Mooij, 2005, Nathan et al., 2008; McLane, 2011). Some of these applications take advantage of the spatial dynamic representation of CA models. This possibility was explored particularly by Epstein and Axtell (1996) who developed an ABM of an artificial society, known as Sugarscape, for the study of social phenomena and human behavior. ​ 
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 +**A spatially-explicit agent-based model was designed using Dinamica EGO to represent interactions between a rabbit population and the environment**. This model is developed by Alessandro Ribeiro Campos, Juliana Leroy Davis, William Leles Souza Costa and Britaldo Silveira Soares Filho. The model simulates agents'​movements,​ internal states, spatial distribution,​ and demographic features. Several scenarios were run to analyze the responses of agents to changes in their environment.\\
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 The model is composed of agents distributed on a cell grid that interact with different amount of resources distributed heterogeneously on the landscape. Agents and environment coexist through a set of behavioral rules. {{ :​agent_based_model:​rules.jpg?​200 |}} The model is composed of agents distributed on a cell grid that interact with different amount of resources distributed heterogeneously on the landscape. Agents and environment coexist through a set of behavioral rules. {{ :​agent_based_model:​rules.jpg?​200 |}}
  
-==== Overview ​====+=== Overview ===
  
  
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 Below you can find more details on the agents and landscape attributes and how the model works: Below you can find more details on the agents and landscape attributes and how the model works:
  
-==== Agents ​====+=== Agents ===
  
  
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 | Maximum Birth rate   | 0.0323 |  | Maximum Birth rate   | 0.0323 | 
  
-==== Landscape ​====+=== Landscape ===
  
 The landscape is represented as a cell grid in which the cell values contain a certain amount of resource.\\ The landscape is represented as a cell grid in which the cell values contain a certain amount of resource.\\
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 | Recovery rate of the landscape ​ | 0.01  | | Recovery rate of the landscape ​ | 0.01  |
  
-==== Processes ​====+=== Processes ===
  
  
 The model iterates as follows: The model iterates as follows:
-{{ :​agent_based_model:​pop_landscape4.jpg |}}+{{ :​agent_based_model:​pop_landscape4.jpg?600 |}}
  
 **Births** **Births**
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-===== Scenarios ​=====+=== Scenarios ===
  
 We ran the model under different scenarios represented as different landscape maps (100×100 raster). The amount and distribution of the resources were modified to analyze population dynamics, patterns resulting from agent movements, resource depletion, and population calorie inequality. We ran the model under different scenarios represented as different landscape maps (100×100 raster). The amount and distribution of the resources were modified to analyze population dynamics, patterns resulting from agent movements, resource depletion, and population calorie inequality.
  
-==== Homogeneous 100 ====+=== Homogeneous 100 ===
  
 - This scenario represents a landscape where the resources are distributed equally across the space. In this way, every cell contains the same value: 100 - Totaling 1000000 units of resources, average cell value: 100 -\\ - This scenario represents a landscape where the resources are distributed equally across the space. In this way, every cell contains the same value: 100 - Totaling 1000000 units of resources, average cell value: 100 -\\
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 {{ :​agent_based_model:​homogeneos_landscape2.jpg |}}\\ {{ :​agent_based_model:​homogeneos_landscape2.jpg |}}\\
  
-==== Heterogeneous 100 ====+=== Heterogeneous 100 ===
  
 - This scenario represents a landscape where the resources are distributed heterogeneously across space. There are three points with the highest concentrations of resources and the resource decreases as a function of the distance to these peaks. In this way the cells contain different values: 0 to 147 - Totaling 1000000 units of resources, average cell value: 100 \\ - This scenario represents a landscape where the resources are distributed heterogeneously across space. There are three points with the highest concentrations of resources and the resource decreases as a function of the distance to these peaks. In this way the cells contain different values: 0 to 147 - Totaling 1000000 units of resources, average cell value: 100 \\
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 {{ :​agent_based_model:​heterogeneos_landscape2.jpg |}}\\ {{ :​agent_based_model:​heterogeneos_landscape2.jpg |}}\\
  
-==== Heterogeneous 50 ====+=== Heterogeneous 50 ===
  
 - This scenario represents a landscape where the resources are distributed heterogeneously across space. There are three points with the highest concentrations of resources and the resource decreases as a function of the distance to these peaks. In this way the cells contain different values: 0 to 73 - Totaling 500000 units of resources, average cell cell value: 50. \\ - This scenario represents a landscape where the resources are distributed heterogeneously across space. There are three points with the highest concentrations of resources and the resource decreases as a function of the distance to these peaks. In this way the cells contain different values: 0 to 73 - Totaling 500000 units of resources, average cell cell value: 50. \\
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-===== Results and Conclusions ​=====+=== Results and Conclusions ===
  
 ==== GINI coefficient ==== ==== GINI coefficient ====
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 [[:​agent_based_model:​ gini_calculation|Here]] you can see how Gini Coefficient is calculated\\ [[:​agent_based_model:​ gini_calculation|Here]] you can see how Gini Coefficient is calculated\\
  
-==== Population ​====+=== Population ===
  
  
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 In the three scenarios, there are many deaths in the first steps of the model. This is due to the deaths of Type 1 rabbits, which eat much less than they need to survive. After the step, there are no deaths from starvation in homogeneous scenario and most of the deaths occur on the heterogeneous landscape scenario with half amount of resources. This is due many to rabbits that are born in areas with few resources, hence they cannot accumulate energy and waste their stock and thus die from starvation. In the three scenarios, there are many deaths in the first steps of the model. This is due to the deaths of Type 1 rabbits, which eat much less than they need to survive. After the step, there are no deaths from starvation in homogeneous scenario and most of the deaths occur on the heterogeneous landscape scenario with half amount of resources. This is due many to rabbits that are born in areas with few resources, hence they cannot accumulate energy and waste their stock and thus die from starvation.
  
-==== Landscape ​====+=== Landscape ===
  
 -The amount of resources available stabilizes as the population also stabilizes leading the amount of resources consumed to a dynamic equilibrium. -The amount of resources available stabilizes as the population also stabilizes leading the amount of resources consumed to a dynamic equilibrium.
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 {{ :​agent_based_model:​resources2.jpg |}} {{ :​agent_based_model:​resources2.jpg |}}
  
-==== Agents movement and landscape dynamics ​====+=== Agents movement and landscape dynamics ===
  
 Videos show rabbits position map and landscape maps under the three different scenarios Videos show rabbits position map and landscape maps under the three different scenarios
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 **Homogeneous scenario: every cell has the same value in this map: 100 -  average cell value: 100 -** **Homogeneous scenario: every cell has the same value in this map: 100 -  average cell value: 100 -**
  
-<html><iframe width="​420"​ height="​315"​ src="​http://​www.youtube.com/​embed/​_bGHgmhAFqM"​ frameborder="​0" allowfullscreen></​iframe></​html>​ +{{ youtube>_bGHgmhAFqM?​size=420x315&​rel=0 }}
  
 **Heterogeneous scenario: cells have values from 0 to 147 - average cell value: 100 -** **Heterogeneous scenario: cells have values from 0 to 147 - average cell value: 100 -**
  
-<html><iframe width="​420"​ height="​315"​ src="​http://​www.youtube.com/​embed/​SK80UU8QwHw?​rel=0" frameborder="​0"​ allowfullscreen></​iframe></​html>​ +{{ youtube>​SK80UU8QwHw?​size=420x315&​rel=0 }}
  
 **Heterogeneous scenario: cells have values from 0 to 73 - average cell value: 50 -** **Heterogeneous scenario: cells have values from 0 to 73 - average cell value: 50 -**
  
-<html><iframe width="​420"​ height="​315"​ src="​http://​www.youtube.com/​embed/​ayTFM5yKmaU?​rel=0" frameborder="​0"​ allowfullscreen></​iframe></​html>​ +{{ youtube>​ayTFM5yKmaU?​size=420x315&​rel=0 }}
  
 On heterogeneous landscapes, the agents move to locations with larger concentration of resources. As a result, this areas are depleted in first place. On heterogeneous landscapes, the agents move to locations with larger concentration of resources. As a result, this areas are depleted in first place.
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-===== Download - Model and Inputs ​=====+=== Download - Model and Inputs ===
  
  
-==== Download our AMB model and run it yourself. ​====+=== Download our AMB model and run it yourself. ===
  
  
 [[http://​csr.ufmg.br/​~bruno/​Rabbit_population_ABM.zip|Download model]] ( with Gini Coefficient Submodel and Heterogeneous lanscape 100 map input included) [[http://​csr.ufmg.br/​~bruno/​Rabbit_population_ABM.zip|Download model]] ( with Gini Coefficient Submodel and Heterogeneous lanscape 100 map input included)
  
-==== Download model inputs ​====+=== Download model inputs ===
  
 [[http://​www.csr.ufmg.br/​wiki/​recurso_coelhos_homogeneo.tif| Homogeneous Landscape 100 ( Hom 100)]]\\ [[http://​www.csr.ufmg.br/​wiki/​recurso_coelhos_homogeneo.tif| Homogeneous Landscape 100 ( Hom 100)]]\\
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-==== Download GINI coefficient ​====+=== Download GINI coefficient ===
  
  
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-===== References ​=====+==== References ====
  
  
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 Weiss, G. //​Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence//​. MIT Press, Cambridge. 1999 Weiss, G. //​Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence//​. MIT Press, Cambridge. 1999
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 +===Congratulations,​ you have successfully completed this lesson!===
 +\\
 +☞[[multiple_criteria|Next:​ Multiple Criteria Evaluation]]
 +\\
 +☞[[:​guidebook_start| Back to Guidebook Start]]
 +