Consumer#

class bamengine.roles.consumer.Consumer(income, savings, income_to_spend, propensity, largest_prod_prev, shop_visits_head, shop_visits_targets)[source]#

Bases: Role

Consumer role for households.

Represents the consumption and savings state for households. Each array index corresponds to a household ID (0 to n_households-1).

Parameters:

income (Float1D) – Total income received this period from wages.
savings (Float1D) – Accumulated savings from previous periods.
income_to_spend (Float1D) – Portion of income allocated for consumption this period.
propensity (Float1D) – Propensity to consume (calculated from income and savings).
largest_prod_prev (Idx1D) – ID of firm with highest production in previous period (search hint).
shop_visits_head (Idx1D) – Queue head pointer for shop visits.
shop_visits_targets (Idx2D) – Queue of firm IDs to visit, shape (n_households, max_Z).

Examples

Access from simulation:

>>> import bamengine as bam
>>> sim = bam.Simulation.init(n_households=500, seed=42)
>>> con = sim.con
>>> con.income.shape
(500,)
>>> con.savings.shape
(500,)

Calculate aggregate consumption spending:

>>> import numpy as np
>>> total_spending = con.income_to_spend.sum()
>>> total_spending
525.5

Find households with high savings:

>>> high_savers = con.savings > 100
>>> high_savers.sum()
45

Check propensity to consume distribution:

>>> mean_propensity = con.propensity.mean()
>>> mean_propensity
0.75

Find households targeting specific firm:

>>> target_firm = 5
>>> targeting = con.largest_prod_prev == target_firm
>>> targeting.sum()
12

Notes

The Consumer role is one of two roles assigned to households:

Worker: employment and labor supply (see Worker)
Consumer: consumption and savings

The propensity to consume is calculated using the formula: propensity = income^beta / (income^beta + savings^beta) where beta is a configuration parameter (typically beta=2.5).

Consumer#

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