HASH logoGlossary

Key concepts in simulation and modeling explained

Actor Model

There are two main approaches to building agent-based simulations: object-oriented programming and the actor-based model.

Agent-Based Modeling

ABMs simulate entities in virtual environments, or digital twins, in order to help better understand both entities and their environments.

Artificial Neural Networks

Artificial Neural Networks are computer models inspired by animal brains. They consist of collections of nodes, arranged in layers, which transfer signals.


Autocorrelation is a measure of the degree of similarity between any time series and a lagged or offset version of itself over successive time intervals.

Business Process Modeling

Business Process Modeling (BPM) helps organizations catalog, understand and improve their processes.

Data Drift

Data Drift is the phenomenon where changes to data degrade model performance.

Data Mining

Data Mining is a process applied to find unknown patterns, correlations, and anomalies in data. Through mining, meaningful insights can be extracted from data.

Data Pipelines

Data pipelines are processes that result in the production of data products, including datasets and models.

Deep Reinforcement Learning

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.


Diffs are used to track changes between different versions or forks of a project, providing an overview regarding files changed, and the nature of those changes.

Directed Acyclic Graphs

If you don’t know your DAGs from your dogs, you can finally get some clarity and sleep easily tonight. Learn what makes a Directed Acyclic Graph a DAG.

Discrete Event Simulation

DES is a modeling approach that focuses on the occurrence of events in a simulation, separately and instantaneously, rather than on any chronological-scale.

Discrete vs Continuous Time

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.


Forking something means to create a copy of it, allowing individual developers or teams to work on their own versions of it, in safe isolation.

Graph Databases

Graph Databases are a type of database that emphasizes the relationships between data.

Knowledge Graphs

Knowledge Graphs contextualize data and power insight generation.

Machine Learning

Machine Learning is a subfield of Artificial Intelligence where parameters of an algorithm are updated from data inputs or by interacting with an environment.


Merging is the process of reconciling two projects together. In HASH merging projects is handled by submitting, reviewing and approving “merge requests”.


Metadata is data about data. It’s quite simple, really. Learn more about how it’s used within.

Model Drift

Models tend to become less accurate over time.

Model Licensing

There are lots of ways to license simulation models. Here we outline some key considerations and things to be aware of.

Model Sharing

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.

Optimization Methods

The key to finding the best solution to any problem.

Process Mining

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.


Schemas are descriptions of things: agents in simulations, and the actions they take. They help make simulations interoperable, and data more easily understood.

Simulation Modeling

Simulation Models seek to demonstrate what happens to environments and agents within them, over time, under varying conditions.


Stochasticity is a measure of randomness. The state of a stochastic system can be modeled but not precisely predicted.

System Dynamics

System Dynamics models represent a system as a set of stocks and the rates of flows between them.

Time Series Data

Time series data is data that has been indexed, listed, or graphed in time order. For example, the daily closing value of the NASDAQ, or a single step in a simulation run.