Receding Horizon Planning for Economic Coordination

Python-implementation of receding horizon planning technique for macroeconomic coordination of production. Code is readily used together with real economic data in the form of supply and use tables.

Background

In the era of climate change, viable societies will have to rapidly adapt to extreme weather events as well as coordinate economic activities in an efficient manner towards long-term goals. This will require a careful design and experimentation of coordination protocols and optimization procedures for large-scale economic systems.

The code in this repository implements a type of optimization procedure which we call receding horizon planning (RHP). It builds on ideas from optimization theory, economic analysis and control theory (cf. the works of Kantorovich, von Neumann, Bellman and Bertsekas). An introductory technical description is provided here.

A first example with Swedish national accounts data is given here.

Python implementation

The goal is to coordinate $m$ units of production, so that their production levels utilize the minimal amount of resources required to meet material constraints over a future time horizon. This coordination process is revised at every time step.

Variables

Note: full implies that exports are included, they are otherwise omitted from the target output in question.

| TeX paper | Code | Description | Type & length | | --------- | -------------------------------- | ----------------------------------------------------- | ---------------------------------------------- | | $T$ | time_steps | simulated time steps | non-negative integer & 1 | | $N$ | planning_horizon | planning horizon | non-negative integer & 1 | | $c$ | primary_resource_list | it might be worked hours or co2 for example | list, each item in the list numpy.matrix & n1 | | $J$ | supply_use_list | supply minus use list in price unit | list, each item in the list numpy.matrix & nm | | $\bar{T}$ | use_imported_list | use imported list in product price unit | list, each item in the list numpy.matrix & nm | | $D$ | depreciation_matrix_list | depreciation matrix list | list, each item in the list numpy.matrix & nn | | $r$ | full_domestic_target_output_list | domestic production target in product price units | list, each item in the list numpy.matrix & n1 | | | export_constraint_boolean | a boolean being true if the export constraint is used | boolean | | $r_{exp}$ | augmented_export_vector_list | export vector in product price unit | list, each item in the list numpy.matrix & m1 | | $p_{exp}$ | export_prices_list | export prices list | list, each item in the list numpy.matrix & m1 | | $p_{imp}$ | import_prices_list | import prices list | list, each item in the list numpy.matrix & m1 | | | upper_bound_on_activity | sets an upper bound on activity | None or int | | | max_iterations | sets an upper bound on iteration number | int | | | tolerance | sets a tolerance for what constitutes a solution | int |