Glossary

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.

Applicant tracking systems help employers manage recruitment and hiring.

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.

The open Block Protocol standardizes the means by which blocks and the applications that embed them communicate.

Blocks are single units of content that are assigned ‘types’ which determine how they display data.

Business Intelligence allows companies to make data-driven decisions.

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

Content management systems allow you to build and manage websites.

Customer relationship management systems track and coordinate interactions between a company and its customers.

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

Data meshes are decentralized database solutions.

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

Data types describe a space of possible values through the specification of constraints

Datasets are collections of numbers or words, generally centered around a single topic or subject.

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.

Digital twins are a detailed simulated analogue to a real-world system

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.

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.

Document management systems allow you to store, manage and track documents (both physical and digital).

Ego networks are a framework for local analysis of larger graphs.

Enterprise resource planning uses an integrated software system to manage a business' daily tasks.

Entities are individual ‘things’ with a distinct, independent existence.

Entity types represent commonly recurring classes of entities, and describe their properties.

An electronic healthcare standard for data interoperability.

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

Graph representation learning is a more tailored way of applying machine learning algorithms to graphs and networks.

A graph is a collection of entities which may be connected to other entities by links.

Integrations allow information from different systems to be brought together, and actions coordinated across them.

Knowledge graphs are information-dense inputs to machine learning algorithms, and can capture more human-readable outputs of algorithms.

Knowledge Graphs contextualize data and power insight generation.

There are lots of ways to license intellectual property, including your datasets, simulation models, and other code. Here we outline some key considerations and things to be aware of.

Links between different entities represent the relationships and connections between them.

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.

Models tend to become less accurate over time.

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.

The key to finding the best solution to any problem.

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.

Project management software is used to manage teams completing complex projects.

Properties store individual pieces of information about entities. All property fields on an entity are inferred from its entity type(s).

Robotic process automation uses software to perform repeatable business tasks.

Robustness is a measure of a model's accuracy when presented with novel data.

Schemas are descriptions of things, and in HASH they are used to describe 'types'. These make simulations interoperable, and data more understandable.

Web scraping is the process of automatically extracting data from websites efficiently and reliably.

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.

Generating data that mimics real data for use in machine learning.

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

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

Types describe the shape that information is expected to take, through rules and constraints associated with it.

Values are the information contained within a single property on a specific instance of an entity.