ChrisRackauckas OP t1_issyai3 wrote on October 18, 2022 at 2:02 PM

Reply to comment by EmmyNoetherRing in [P] Stochastic Differentiable Programming: Unbiased Automatic Differentiation for Discrete Stochastic Programs (such as particle filters, agent-based models, and more!) by ChrisRackauckas

We mean the standard "agent based model" https://www.pnas.org/doi/10.1073/pnas.082080899, https://en.wikipedia.org/wiki/Agent-based_model . The kind of thing you'd use Agents.jl for. For example, look at agent-based infection models. In these kind of models you create many individuals (agents) with rules. Each agent moves around, but if one is standing near an agent that is infected, there's a probability of infecting the nearby agent. What is the average percentage of infected people at time t?

EmmyNoetherRing t1_ist2jei wrote on October 18, 2022 at 2:32 PM

Thanks! Sounds very related, but from a different angle than the one I've worked with before (which is closer to multi-agent systems, by the wikipedia article's nomenclature). Still discrete underneath though, if I'm reading it correctly? Or I guess looking at real valued parameters for the agents programs/states/locations?

ChrisRackauckas OP t1_ist3pfj wrote on October 18, 2022 at 2:40 PM

Yes, our case would be real-valued parameters for discretely valued simulations.