Agent-based Computational Economics using NetLogo

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In this case, developers of commercial units prefer to be near the main road of the city, and still value low-priced land.

Agent-Based Modeling

In addition, the parameters for developers are modified. As is depicted in Table 2 , for the third scenario, the developers of low-income neighborhoods look for land units nearest to working centers, and assign low weight to density and attractiveness. Developers of high-income neighborhoods, by contrast, assign a high value to density and attractiveness.

Table 2. Parameters of Scenario 3 strategic infrastructure. Figure 7 shows the final urban configuration with the assumptions of Table 2. Work centers are located near the main road, and low-income neighborhoods grow around them. High-income neighborhoods move to the suburbs. Figure 7. Simulation results for the strategic infrastructure dcenario Scenario 3. The segregation of low and high-income neighborhoods observed in Fig. In this article we illustrate the capability of agent-based simulation for studying the behavior of complex social systems.

The land-use model we present is a simplified model of the decisions made by urban developers. The behavior of agents is based on economic theory, such as utility maximization, and on dynamic rules that establish how state and auxiliary variables are updated.


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Agents' preferences over land attributes are represented by a Cobb-Douglas utility function and uncertainty in decision-making is modeled by examining random samples of available land. In addition to the dynamic rules for updating the state of the system along time, the model has a spatial dimension and changes in the attributes of a land cell also affect the state of neighboring cells. As results show, the agent-based model allows for a wide range of settings that lead to different spatial and social configurations of the city from the same initial conditions.

Future work includes testing the model with GIS data from a real location, and estimating the parameters of utility functions using real and experimental data. A review and assessment of land-use change models dynamics of space, time, and human choice. Center for the Study of Institutions. Uber den Standort der industrien Tubingen.

English translation The theory of the location of industries Chicago. The law of retail gravitation New York. Die zentralen orte in sudden deutschland.


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Central places in southern Germany New Jersey. The economics of location New Haven.

Agent based modelling of economic crises

The structure and growth of residential neighborhoods in American cities", Washington, U. Government Printing Office. JUE, 22, pp. Location and Land Use.

Agent-Based Economic Models NetLogo

Dynamics of Urban Residential Growth. JUE, January, pp. General equilibrium models of polycentric urban land use with endogenous congestion and Job agglomeration. Journal of Urban Economics. The dynamics of urban spatial structure: the progress of a research program. Urban dynamics. Cities and Complexity. Massachusetts: MIT, Simulating spatial urban expansion based on a physical process. Landscape and Urban Planning, pp: , In Distributed Artificial Intelligence, Volume 2. San Francisco, Calif.

References

Coherent Cooperation among Communicating Problem Solvers. Coordination of Distributed Problem Solvers. Boston: Kluwer Academic. Social Conceptions of Knowledge and Action. Artificial Intelligence 47 , pp: , Designing a Family of Coordination Algorithms. Experiences with an Architecture for Intelligent, Reactive Agents. In Intelligent Agents II, eds. Wooldridge, J. Muller, and M. Tambe, pp. Lecture Notes in Artificial Intelligence New York: Springer-Verlag, Artificial Intelligence 75 2. Philosophical Transactions: Biological Sciences, Vol.

European Journal of Operational Research 46 2. Handbook of Computational Economics, Capt. UCL working paperspaper. Available under License. Perrone, F. Wieland, J. Agent-Based models and human subject experiments. Handbook of Computational Economics, V. Pittsburgh, Agent learning representation: advice modelling economic learning. Jena, R y Ketchpel, S. Communications of the ACM 37 7 , pp: , Analysis of land use change: theoretical and modelling approaches. Web Book of Regional Science. NetLogo urban suite - economic disparity model. Services on Demand Article. English pdf Article in xml format Article references How to cite this article Automatic translation Send this article by e-mail.

Patch setting option in Netlogo From Wilensky Agents. From Wilensky Agent variables. The model components are summarized below: Algorithm 1. Algorithm for agents' random movement 3. At each time step, , the following sequence of events is followed: Housing demand increases as a result of population growth. New land cells are developed according to the developers' preferences and available savings. Savings are updated. Attributes of developed land cells are updated.

Developed land units are assigned to demand, and land prices are updated. Neighborhood The model runs for time steps with a time step of 1. Variables are labels that have a number of values during the simulation. Procedures are a finite list of well-defined operations that are working with the passive part of an-agent based computational model agents, parameters and variables and with the active part other procedures.

In the frame of NetLogo there are two types of procedures: commands and reporters. A command is an action for an agent to carry out. A reporter computes a result and reports it. Initializations I are a set of identities that have in the left side the variable or parameter name and in the right side the associated value.

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In the frame of NetLogo platform, these initializations are implemented inside Procedure Tab the workspace where the code for the model is stored. The simulation specifications R are a set of identities that have in the left side the control parameter name and in the right side the associated value. This type of specification is defined in Interface Tab where you watch your model run using interface items such as buttons, sliders, switchers, monitors, plotters etc. Under these circumstances an agent-based computational model ACM can be defined as a list of three arguments: the set of agents A , the initializations I and simulation specifications R.

In order to validate ACM, we followed the following steps: 1 The analysis of real economic systems; 2 Defining the objective of research and the precise task of the model; 3 Building the conceptual model; 4 Validation of conceptual model; 5 Transformation of conceptual model in a computerized model using NetLogo software platform; 6 The operational validation of computerized model; 7 The analysis of computer experiments results and interpretation from economic point of view.

Based on these, we will formulate a number of conclusions. Steps 1 - 4 are done in Section 3, steps 5 - 6 are done in Section 4, and step 7 is in the final section of this paper. Figure 4. The interface tab of NetLogo software platform.

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Conceptual Validation Despite the fact conceptual validation is very important there is little literature that addresses this issue Robinson According to Heath , the conceptual model forms the foundation of an ABM model; an invalid conceptual model indicates the model may not be an appropriate representation of reality. The Analysis of Real Economic Systems In this subsection we analyze the main features of economic systems implemented by the following types of society: i Agrarian society is based on natural economy defined as a system where the majority of goods are produced for direct consumption subsistence.

Defining the Objective of Research and the Precise Task of the Model Taking into account these three types of economic systems we defined the purpose of research as being the relation between technological progress and the distribution of wealth. The technological progress is implemented like a set of major changes in agricultural and industrial sectors. As we know both types of sectors use resources like water, food, coal, oil, gas, uranium, wind, sun, tides, and geothermal heat.

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To model the phenomenon of technological progress we conceived an agent-based artificial society described in Damaceanu as an evolutionary system that starts from the simplest type of economy natural economy based only on water and food resources , continue with the industrial economy that uses with maximum efficiency the resources of natural economy and discover nonrenewable resources such as coal, oil, gas, and uranium , and ends with the new economy still using with maximum efficiency the resources of natural and industrial economies if they are available based on renewable resources like wind, sun, tides, and geothermal heat.

The technological progress is introduced in this model in linear way starting from agricultural technologies used in the case of natural economy, continuing with industrial technologies used for extracting nonrenewable resources in the following order coal, oil, gas, and uranium and ending with nonrenewable resources used in the following order wind, sun, tides, and geothermal heat.

The distribution of wealth is analyzed using Gini coefficient - see Gini This coefficient is a measure of the inequality of a distribution, a value of 0 expressing total equality and a value of 1 maximal inequality. This index is defined as a ratio of the areas on the Lorenz curve diagram - see Figure 5. Figure 5. Graphical description of Lorenz Curve. Building the Conceptual Model In section 2, we defined agent-based computational model ACM like a list of three arguments: the set of agents A , the initializations I and the simulation specifications R.

The process of building the conceptual model can be defined like constructing the initial set of agents A 0 , setting the initial values of initializations I 0 and specifying the initial values for simulation specifications R 0 - see Figure 6, for a graphical description of the process.

This figure shows us how the initial form of the ACM 0 enters in the phase of conceptual validation. If this validation fails the conceptual model is modified in a new form ACM 1 and the process is reiterated until conceptual validation is a success and the model become subject to operational validation. Figure 6. The process of building the conceptual model. Validation of Conceptual Model The validation of conceptual model is a process that checks the integrity of all component elements of agent based model ACM k divided in three major sets: the set of agents A k , the set of initializations I k and the set of simulation specifications R k.

Figure 7. The elements of Observer O. Figure 8. The elements of turtles Ti. Figure 9. The elements of patches Pxy. Agent-based modeling is an alternative approach of complex systems not opposed to equation-based mod- eling. These two approaches can be combined for modeling economic complex systems. Researchers can now model a large variety of complex phenomena associated with market economies.

A part of these tools are given by agent-based modeling that uses computational methods to study economies in the frame of some controlled experimental conditions [13]. In addition, individual models can be run as Java applets inside web pages. Netlogo uses three types of agents: turtles, patches and observer [14]. Turtles are agents that are moving inside the world. The world is a bi-dimensional lattice composed by patches. The observer can create new turtles. In addition, the patches can do the same thing. Turtles and patches have coordinates determined by the variables xcor and ycor for turtles and, respectively, pxcor and pycor for patches.

The patch with the coordinate 0, 0 is called origin. The total number of patches is determined by the parameters min-pxcor, max-pxcor, min-pycor and max-pycor. In NetLogo, commands and reporters tell agents what to do. A command is an action that an agent must exe- cute. A reporter calculates a result and reports it. NetLogo uses three types of variables: global variables, turtles variables, patches variables and system variables. The next two types can be accessed only by the agent inside whom the variables were created. Examples of system variable are the next: color sets the color of tur- tle , pcolor sets the color of patch , xcor, ycor, heading sets the orientation in space of turtles , pxcor, pycor, etc.

In NetLogo, you have the choice of viewing models found in the Models Library, adding other models to existing ones, or creating your own models. The NetLogo interface was designed to meet all these needs. The interface is divided into two main parts: NetLogo menus and the main NetLogo window. Only one tab at a time can be visible, but you can switch between them by clicking on the tabs at the top of the window.

Right below the row of tabs is a toolbar containing a row of buttons. The available buttons vary from tab to tab. The rest of the paper is organized as follows: Section 2 describes the model of wealth distribution, Section 3 presents the computational experiments done with this model and Section 4 presents the conclusions.

Each patch has an amount of resources and a certain resources capacity the amount of resources it can grow. The turtles collect resources from the patches and process the resources in order to survive. How much resources each turtle accumulates is his or her wealth.

This last parameter has values between 1 and In Fig. The seed may be any integer in the range supported by NetLogo to


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