An Agent-Based Model of Saving under Quasi-Hyperbolic Discounting on a Social Network. (1.0.0)
An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.
Release Notes
Initial public release (v1.0.0). Includes the core agent-based model (Model.py), the analysis and visualisation pipeline (analyze_results.py), the ODD model description, pinned dependencies, and run/replication instructions.
Requires mesa < 3 (the model uses RandomActivation and MultiGrid, removed in Mesa 3.x).
Associated Publications
An Agent-Based Model of Saving under Quasi-Hyperbolic Discounting on a Social Network. 1.0.0
An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.
Release Notes
Initial public release (v1.0.0). Includes the core agent-based model (Model.py), the analysis and visualisation pipeline (analyze_results.py), the ODD model description, pinned dependencies, and run/replication instructions.
Requires mesa < 3 (the model uses RandomActivation and MultiGrid, removed in Mesa 3.x).