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