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agent_based_model:start [2013/02/15 13:43]
juliana
agent_based_model:start [2013/02/18 18:08]
juliana
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 ====== ​ Spatially explicit agent based model of rabbit population ====== ​ ====== ​ Spatially explicit agent based model of rabbit population ====== ​
 +**Alessandro Ribeiro Campos, Juliana Leroy Davis, William Leles Souza Costa and Britaldo Silveira Soares Filho**
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 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. 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; Hegselmann e Terna 1997; Epstein ​Axtell, 1996; Janssen ​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).+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. 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.
  
-Here we describe a spatially explicit ABM developed using DINAMICA-EGO ​that model the dynamics of a population of rabbits.\\+Here we describe a spatially explicit ABM developed using DINAMICA-EGO ​to represent ​the dynamics of a population of rabbits ​This model was developed as part of the modeling worshop class of the graduate course on Environmental Modeling at UFMG.\\
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-The model is composed of agents distributed on a grid cell 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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   * //Vision//: (number of cells in the neighborhood) range of vision of the agent to search for resources on the landscape.   * //Vision//: (number of cells in the neighborhood) range of vision of the agent to search for resources on the landscape.
    
-  * //Energy spent to survive//: amount of energy ​necessary to survive at each model step.+  * //Energy spent to survive//: amount of calories ​necessary to survive at each model step.
    
   * //Maximum absorption of energy//: maximum capacity for energy absorption of agent per model step.   * //Maximum absorption of energy//: maximum capacity for energy absorption of agent per model step.
  
-  * //Energy spent to move//: amount of energy ​an agent spends to move across a cell per  model step.+  * //Energy spent to move//: amount of calories ​an agent spends to move across a cell per  model step.
  
   * //Average age//: average of a normal distribution that determines the likelyhood of agent death at a model time step.   * //Average age//: average of a normal distribution that determines the likelyhood of agent death at a model time step.
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 The model iterates as follows: The model iterates as follows:
-{{ :​agent_based_model:​pop_landscape3.jpg |}}+{{ :​agent_based_model:​pop_landscape4.jpg |}}
  
 **Births** **Births**
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 Recovery occurs every time step according to the current amount of resources in the cell and the maximum capacity of resources using a logistic function.{{ :​agent_based_model:​mils.jpg |}}{{ :​agent_based_model:​movement_intake_and_recovery.jpg |}} Recovery occurs every time step according to the current amount of resources in the cell and the maximum capacity of resources using a logistic function.{{ :​agent_based_model:​mils.jpg |}}{{ :​agent_based_model:​movement_intake_and_recovery.jpg |}}
  
-Death+**Death:**
 There are two causes of death:\\ There are two causes of death:\\
  
--Starvation: lack of enough energy to stay alive. It occurs when the agent'​s stock reaches zero calories.\\+**Starvation:** lack of enough energy to stay alive. It occurs when the agent'​s stock reaches zero calories.\\
  
--Age: the number of deaths in a time step is defined by the average of a normal distribution of the agent lifespan. The model uses a stochastic draw to determine whether the agent lives or dies.\\+**Age:** the number of deaths in a time step is defined by the average of a normal distribution of the agent lifespan. The model uses a stochastic draw to determine whether the agent lives or dies.\\
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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 ​of agents, patterns resulting from agent movementresources ​depletion, and population ​energy ​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 movementsresource ​depletion, and population ​calorie ​inequality.
  
 ==== Homogeneous 100 ==== ==== Homogeneous 100 ====
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 {{ :​agent_based_model:​gini_coefficient_new2.jpg |}} {{ :​agent_based_model:​gini_coefficient_new2.jpg |}}
 Gini Coefficient in three different landscapes: Hom 100- with resources distributed homogeneously,​ Het 100-with the same amount of resources distributed heterogeneously and Het 50-with half of resources amount distributed heterogeneously ​ Gini Coefficient in three different landscapes: Hom 100- with resources distributed homogeneously,​ Het 100-with the same amount of resources distributed heterogeneously and Het 50-with half of resources amount distributed heterogeneously ​
 +
 +[[:​agent_based_model:​ gini_calculation|Here]] you can see how Gini Coefficient is calculated\\
  
 ==== Population ==== ==== Population ====
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-On heterogeneous landscapes, the agents move to locations with larger concentration of resources. As a result, this areas are depleted first.+On heterogeneous landscapes, the agents move to locations with larger concentration of resources. As a result, this areas are depleted ​in first place.
  
 ==== Conclusions ==== ==== Conclusions ====
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-[[http://www.csr.ufmg.br/​wiki/Model.ego|Download model]]+[[http://​csr.ufmg.br/​~bruno/Rabbit_population_ABM.zip|Download model]] ​( with Gini Coefficient Submodel included)
  
 ==== Download model inputs ==== ==== Download model inputs ====
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-It is a measure of inequality developed by the Italian statistician Corrado Gini and published in 1912. It varies from 0 to 1; 0 corresponds to complete equality and 1 corresponds to complete inequality. We use the GINI index to measure the inequality in the rabbit calorie distribution..+It is a measure of inequality developed by the Italian statistician Corrado Gini and published in 1912. It varies from 0 to 1; 0 corresponds to complete equality and 1 corresponds to complete inequality. We use the GINI index to measure the inequality in the rabbit calorie distribution. 
 + 
 +[[:​agent_based_model:​ gini_calculation|Gini Coefficient Calculation]] 
 + 
 +Here you can download Gini Coefficient Submodel separately.\\ 
 +To open it with the ABM you can follow the  [[:​submodels#​sharing |instructions here]].
  
-[[http://www.csr.ufmg.br/​wiki/CalculateGiniCoefficientMap.egoml|Download Gini Coefficient submodel]]\\+[[http://​csr.ufmg.br/​~bruno/Gini_Coefficient.ego|Download Gini Coefficient submodel]]\\
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 Chen, L. Agent-based modeling in urban and architectural research: A brief literature review. //Frontiers of Architectural Research// v.1, p.166-177. 2012 Chen, L. Agent-based modeling in urban and architectural research: A brief literature review. //Frontiers of Architectural Research// v.1, p.166-177. 2012
  
-Conte, R., Hegelmann, R., and Terna, P., (eds.), 1997, Simulating Social Phenomena. Springer, Berlin+Conte, R., Hegelmann, R., and Terna, P., (eds.), 1997, //Simulating Social Phenomena//. Springer, Berlin
  
 DeAngelis, D.L., Mooij, W.M. Individual-based modeling of ecological and evolutionary processes. //Annual Review of Ecology, Evolution, and Systematics//​. v.36, p.147–168. 2005 DeAngelis, D.L., Mooij, W.M. Individual-based modeling of ecological and evolutionary processes. //Annual Review of Ecology, Evolution, and Systematics//​. v.36, p.147–168. 2005
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-Soares-Filho,​ B. S.; Cerqueira, G. C.Araújo, W. L. Modelagem de dinâmica de paisagem: concepção e potencial de aplicação de modelos de simulação baseados em autômato celular. In: Albernaz, A. L.;Silva, J. M. C. D.Valeriano,​ D. (org.). //​Ferramentas para modelagem da distribuição de espécies em ambientes tropicais//​. Belém: ​Editora ​Museu Paraense Emílio Goeldi 1 ed., v.1, 2003+Soares-Filho,​ B. S.; Cerqueira, G. C.Araújo, W. L. Modelagem de dinâmica de paisagem: concepção e potencial de aplicação de modelos de simulação baseados em autômato celular. In: Albernaz, A. L.;Silva, J. M. C. D.Valeriano,​ D. (org.). //​Ferramentas para modelagem da distribuição de espécies em ambientes tropicais//​. Belém: ​Ed Museu Paraense Emílio Goeldi 1 ed., v.1, 2003
  
 Soares-Filho,​ B.S.; Rodrigues, H.O.; Costa, W.L.S. DINAMICA-EGO,​ uma plataforma para modelagem de sistemas ambientais. In: Simpósio Brasileiro de Sensoriamento Remoto (SBSR), 13, 2007, Florianópolis. //Anais XIII Simpósio Brasileiro de Sensoriamento Remoto//. São José dos Campos: INPE, 2007. Artigos, p. 3089-3096. ​ Disponível em: < http://​marte.dpi.inpe.br/​col/​dpi.inpe.br/​sbsr@80/​2006/​11.06.17.59/​doc/​3089-3096.pdf >. Acesso em: 29 jul. 2012. Soares-Filho,​ B.S.; Rodrigues, H.O.; Costa, W.L.S. DINAMICA-EGO,​ uma plataforma para modelagem de sistemas ambientais. In: Simpósio Brasileiro de Sensoriamento Remoto (SBSR), 13, 2007, Florianópolis. //Anais XIII Simpósio Brasileiro de Sensoriamento Remoto//. São José dos Campos: INPE, 2007. Artigos, p. 3089-3096. ​ Disponível em: < http://​marte.dpi.inpe.br/​col/​dpi.inpe.br/​sbsr@80/​2006/​11.06.17.59/​doc/​3089-3096.pdf >. Acesso em: 29 jul. 2012.