(Replying to PARENT post)
The potential for ABMs are huge now that we have access to cheap and massively parallel compute. Imagine arbitrarily complex models of each individual in an economy interacting with each other over a distributed network of thousands of machines -- that's possible now. Instead of trying to predict the future, we can compute it.
[1] https://github.com/iodide-project/pyodide
[2] https://github.com/gpuweb/gpuweb/wiki/Implementation-Status
(Replying to PARENT post)
I'm excited by what you're building here!
(Replying to PARENT post)
Firstly, fantastic work.
I'd love to see more details on 'H-Cloud' regarding infrastructure, pricing, and feasibility/licensing options for self-deployment should I need to recreate/run a similar environment on premise vs. at a remotely hosted service.
I'm also quite interested in what's going to be FOSS'd in terms of the 'H-Engine' (written in Rust) and if some friendly interface (similar to H-Core) will still be provided.
(Replying to PARENT post)
We have some users representing networks in the 3D viewer at present, and have seen three ways implementing networks to date:
1. Edges are represented as agents. They are used to store properties such as edge length, and to provide nodes with a way of accessing other nodes.
2. Edges are represented AND USED as agents. Edges not only store properties but themselves exhibit behaviors.
3. Nodes are given a network object which contains information about their network neighbors and all relevant properties (such as directed/undirected edge, edge length, etc...)
Re: your question around experiments... yes to all three (parameter sweeping, Monte Carlo, and sensitivity analysis), and a bunch more. We'll be shipping this alongside H-Cloud. More on that in the full explainer at https://hash.ai/about/mission
Thanks for giving the beta a spin! Happy to chat in more depth over on our public Slack.