There are two main approaches to building agent-based simulations: object-oriented programming and the actor-based model.
ABMs simulate entities in virtual environments, or digital twins, in order to help better understand both entities and their environments.
Business Process Modeling (BPM) helps organizations catalog, understand and improve their processes.
DRL is a subset of Machine Learning in which agents are allowed to solve tasks on their own, and thus discover new solutions independent of human intuition.
Digital twins are a detailed simulated analogue to a real-world system
DES is a modeling approach that focuses on the occurrence of events in a simulation, separately and instantaneously, rather than on any chronological-scale.
In continuous time, variables may have specific values for only infinitesimally short amounts of time. In discrete time, values are measured once per time interval.
Ego networks are a framework for local analysis of larger graphs.
There are lots of ways to license simulation models. Here we outline some key considerations and things to be aware of.
There are lots of ways to share simulation models: blackbox, greybox, closed, open, transparent, and output-only. Here we explain what these terms all mean.
Multi-Agent Systems represent real-world systems as collections of intelligent agents.
Parameters control specific parts of a system's behavior.
Process mining is an application of data mining with the purpose of mapping an organization’s processes. It is used to optimize operations, and identify weaknesses.
Robustness is a measure of a model's accuracy when presented with novel data.
Schemas are descriptions of things: agents in simulations, and the actions they take. They help make simulations interoperable, and data more easily understood.
Simulation Models seek to demonstrate what happens to environments and agents within them, over time, under varying conditions.
Single synthetic environments allow you to build, run, and analyze data-driven models and simulations.
Stochasticity is a measure of randomness. The state of a stochastic system can be modeled but not precisely predicted.
System Dynamics models represent a system as a set of stocks and the rates of flows between them.