hawkdovemulti

Hawk-Dove with multiple risk attitudes

This is a variation of the Hawk/Dove game with risk attitudes. This version adds multiple risk attitudes, with options for updating risk attitudes periodically based on comparing success of neighboring agents.

The basic mechanics of the game are the same. This model adds options for agent risk adjustment (none, adopt, average) and period of risk adjustment (by default, every ten rounds). The payoff used to compare agents when adjusting risk attitudes can either be recent (since the last adjustment round) or total points for the whole game. The adjustment neighborhood, or which neighboring agents are considered when adjusting risk attitudes, can be configured to 4, 8, or 24.

Initial risk attitudes are set by the model. Risk distribution can be configured to use a normal distribution, uniform (random), bimodal, skewed left, or skewed right.

Like the base hawk/dove risk attitude game, there is also a configuration to add some chance of agents playing hawk/dove randomly instead of choosing based on the rules of the game.

Convergence

The model is configured to stop automatically when it has stabilized. Convergence is reached when an adjustment round occurs and zero agents adjust their risk attitude.

If adjustment is not enabled, convergence logic falls back to the implementation of the hawk/dove single-risk attitude simulation, which is based on a stable rolling % average of agents playing hawk.

Model and agent data collection also includes reports on whether agents updated their risk level in the last adjustment round, and model data collection includes a status of "running" or "converged".

Batch running

This module includes a custom batch run script to run the simulation and collect data across a large combination of parameters and generate data files with collected model and agent data.

To run the script locally from the root project directory:

simulatingrisk/hawkdovemulti/batch_run.py

Use -h or --help to see options.

If this project has been installed with pip or similar, the script is available as simrisk-hawkdovemulti-batchrun.

To run the batch run script on an HPC cluster:

• Create a conda environment and install dependencies and this project. (Major mesa dependencies available with conda are installed first as conda packages)

module load anaconda3/2023.9
conda create --name simrisk pandas networkx matplotlib numpy tqdm click
conda activate simrisk
pip install git+https://github.com/Princeton-CDH/simulating-risk.git@hawkdove-batchrun

For convenience, an example slurm batch script is included for running the batch run script (some portions are specific to Princeton's Research Computing HPC environment.)

• Customize the slurm batch script as desired, copy it to the cluster, and submit the job: sbatch simrisk_batch.slurm

By default, the batch run script will use all available processors, and will create model and agent data files under a data/hawkdovemulti/ directory relative to the working directory where the script is called.