Inheritance simulation

An agent-based model simulating the UK population, focusing on the effects of inherited wealth.

Requirements

Python 3.6
Packages listed in requirements.txt

Running the simulation

Before your first run, initialise the data:

make data

python data.py

To run the simulation:

make run

python adapter.py model.yaml

Outputs

Running the simulation produces an output.h5 HDF5 file, containing the following tables:

data: top-level data at each tick (year) of the simulation
by_age: data subdivided by the age of the agents, at each tick
history: full history of each agent through the simulation
skill_dist: number of agents at each skill level at each tick
tree: full list of family links between pairs of agents
trusts: data on each inheritance trust at each tick

A function is provided for plotting the life history of an individual agent:

import pandas as pd
import sinks

data = pd.HDFStore('output.h5')

sinks.plot_history(data, 123)

Input parameters

Parameters of the model are set in model.yaml. Under control_parameters you can set the number of ticks and number of agents. The available model_parameters are:

inheritance: type of inheritance, "direct" or "trust"
trust_first_generation: first generation to benefit from trusts (1 = children)
trust_last_generation: last generation to benefit from trusts (1 = children)
assortative_mating: strength of assortative mating (0 = none, 1 = very strong)
savings_rate: fraction of income that gets saved
interest_rate: interest rate for wealth and trusts

Files

README.md: this file
requirements.txt: python packages required
raw_data/*/*.xls: raw data from the ONS and census
data.py: script for processing raw data into numpy arrays
model.yaml: model specification
inheritance.py: core model logic
adapter.py: wrapper for inheritance.py that loads in the data and parameters
scenarios.py: initialises agent population
sinks.py: processes output data

Resources

PyData London 2017