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61 lines
2.3 KiB
ReStructuredText
boltzmann1d
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===========
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This example provides a one-dimensional Boltzmann wealth model (BWM)
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as an example of a simple application of the one-dimensional gridded
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system.
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The BWM is a type of agent-based model used to study the distribution
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of wealth among individuals or agents within a population. The model
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is named after the physicist Ludwig Boltzmann, who first proposed
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a similar model to study the distribution of energy among particles
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in a gas.
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In a BWM, agents are assigned a certain amount of wealth, and the
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model simulates their interactions over time. These interactions
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can include buying and selling goods and services, lending and
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borrowing money, and inheriting wealth from other agents.
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The key feature of the BWM is that it incorporates a "wealth-exchange
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mechanism" which determines the probability of agents making a
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wealth exchange with each other. This mechanism is often based on
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the difference in wealth between agents, with wealthier agents more
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likely to make exchanges with other wealthy agents.
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The model can be run for a specified number of time steps, and the
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resulting wealth distribution can be analyzed to study the emergence
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of wealth inequality and the factors that contribute to it. The
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model can also be used to study the effects of different policies
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or interventions on the wealth distribution.
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The BWM has been used to study a variety of different economic
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systems, including capitalist, socialist, and feudal systems.
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However, it is important to note that like other agent-based models,
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the BWM is a simplified representation of the real world and may
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not capture all the nuances of the economic system being studied.
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Overall, the BWM is a useful tool for studying the distribution of
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wealth and the emergence of wealth inequality in a population. It
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can provide insight into the factors that contribute to wealth
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inequality and the effects of different policies on the distribution
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of wealth.
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.. list-table::
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:widths: 30 70
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:header-rows: 1
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* - Option
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- Description
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* - -c *agent_count*
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- Set the number of agents
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* - -l *log_level_option*
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- Set the logging level
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* - -n *max_steps*
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- Set the number of steps to run the model
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* - -s *initial_seed*
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- Set the initial seed
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* - -x *x_size*
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- Set the number of columns
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.. toctree::
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