Before you keep reading...
Runestone Academy can only continue if we get support from individuals like you. As a student you are well aware of the high cost of textbooks. Our mission is to provide great books to you for free, but we ask that you consider a $10 donation, more if you can or less if $10 is a burden.
Before you keep reading...
Making great stuff takes time and $$. If you appreciate the book you are reading now and want to keep quality materials free for other students please consider a donation to Runestone Academy. We ask that you consider a $10 donation, but if you can give more thats great, if $10 is too much for your budget we would be happy with whatever you can afford as a show of support.
Agents: They are intended to model people and other entities that gather information about the world, make decisions, and take actions. The agents are usually situated in space or in a network, and interact with each other locally. They usually have imperfect or incomplete information about the world. Often there are differences among agents, unlike previous models where all components are identical. And agent-based models often include randomness, either among the agents or in the world.
Agent-Based Models: An agent-based model is a class of computational models for simulating the actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole.
Computationally Irreducible: There are no shortcuts. The only way to get the outcome is to implement the system.
Emergent Property: It is a characteristic of a system that results from the interaction of its components, not from their properties.
Metabolism: Each agent has some amount of sugar they must consume per time step, chosen uniformly between 1 and 4.
Schelling’s Model: Schelling’s model is a grid where each cell represents a house. The houses are occupied by two kinds of agents, labeled red and blue, in roughly equal numbers. About 10% of the houses are empty. At any point in time, an agent might be happy or unhappy, depending on the other agents in the neighborhood, where the “neighborhood” of each house is the set of eight adjacent cells. In one version of the model, agents are happy if they have at least two neighbors like themselves, and unhappy if they have one or zero.
Sugar: Each agent starts with an endowment of sugar chosen from a uniform distribution between 5 and 25 units.
Sugarscape: It is an agent-based model of an “artificial society” intended to support experiments related to economics and other social sciences.
Vision: Each agent can “see” the amount of sugar in nearby cells and move to the cell with the most, but some agents can see and move farther than others.