Safely, cheaply test in silico
Optimize complex systems
Discover black swan risks

HASH was started as a research lab in NYC, studying multi-agent systems

Since the start of 2019, we’ve been developing software that helps domain experts build multi-agent simulations of complex systems. From supply chain disruption to vaccine rollouts, we’ve worked on it all.

Our simulation tools are free, permissively licensed, and available on GitHub

We’ve not yet integrated agent-based modeling capabilities into HASH. However, our legacy hCore IDE and hEngine simulation runner are freely and publicly available on GitHub. Learn more over on our developer website

How can simulation be used?
Case StudyOptimizing the rollout of COVID-19 vaccines in the US state of Virginia with HASHAgent-based simulation by the Virginia Modeling, Analysis & Simulation Center
The Problem

How do you roll out vaccines to as many people as possible, without leaving anybody out?


In 2021, with COVID-19 sweeping through the United States, Virginia’s Department of Health approached the Virginia Modeling and Simulation Center (VMASC) for strategic and policy support in modeling and optimizing the rollout of vaccines across the state. Facing urgent time pressure and a recommendation from colleagues at Boston University, VMASC looked to HASH’s first browser-based simulation IDE, hCore, to quickly spin up a custom simulation model.


VMASC needed to develop a simulation they could trust, and fast. Existing tools were non-starters.

With HASH, the team’s researchers were able to confidently:

    test that real-world dynamics were accurately modelled and reliably simulated;
    securely combine sensitive internal data with generally-available public data and statistics;
    update parameter estimates relating to the virus’ characteristics continuously, whenever new information became available.

The team were also able to derive insights from these simulations:

    automatically recomputing scenarios when parameter estimates or other underlying data updated;
    running large numbers of simulations in parallel as part of experiments;
    producing informative visualizations to communicate impacts over time and according to various vaccine distribution policies;
    easily sharing the model with end users on non-specialized mobile and desktop devices;
    allowing non-technical users to experiment with the model, vary assumptions, and create large scale experiments themselves, to run on high-performance computing infrastructure.

Speed was of the essence in VMASC’s choice to use HASH. Being users of other simulation tools, the team knew that they would need something faster, more modern, and more capable if the simulation they produced was to be of any use in the real-world. It would need to be built and calibrated within hours or days, as opposed to weeks and months, and prove itself capable of turning around valuable metrics and insights in that timeframe as well. At the same time, it would need to remain fast and accurate throughout the model’s lifetime, and the team would need to be able to clearly communicate its results to a wide variety of stakeholders, with different levels of technical sophistication.

The Results

Time to insight measured in hours, not weeks

“The model informed key scenarios around vaccine distribution, such as the optimal number of vaccines for a distributor to keep on hand”

An extensible, maintainable model that survived the lifetime of the crisis

Multi-parameter optimization

Alex Nielsen & John Shull, Lead Project Scientists at VMASC, reached out to HASH and began work with researchers, analysts, and subject matter experts on a simulation model which addressed allocation, supply management, spoilage, and herd immunity impacts based on vaccine manufacturers. The “optimal” vaccine rollout had to take into account a wide variety of factors, minimizing potential negative externalities, while equitably reaching as many people as possible within the state on a need-weighted basis.

“When our customers came to us with new emerging questions, HASH made it incredibly simple to get down to work and create a model that could serve as a foundation for sharing knowledge and hooking in our data. What would normally take weeks instead only took hours.”

Alex Nielsen, Team Project Scientist, VMASC

Informing the policy response to COVID

The resulting simulation model included realistic micro-models of “distributor” agents – including pharmacies, hospitals, and clinics – administering vaccines to patients. The model informed key scenarios around vaccine distribution, such as the optimal number of vaccines for a distributor to keep on hand or the order of patients to receive vaccines.

What stood out about HASH?

A particular draw of HASH was the ease with which it facilitated collaboration with others. The browser-based nature of hCore meant that there were no special environments to configure, executables to run, or pieces of software to install required to share the model with a wider audience - things that had introduced friction into the process of sharing simulation models with policymakers before. Being able to direct-link to the simulation on the web, and embed the simulation on other webpages (through iFrames) provided a flexible way for VMASC to showcase their simulation to a wider audience.

The result was a portable, easy to use simulation that demonstrated the state of vaccine distribution in Virginia, and allowed easy experimentation with different parameters. The simulation helped to inform key decisions in vaccine distribution when it was needed most.

“What would normally take weeks instead only took hours. The simulation helped to inform key decisions in vaccine distribution when it was needed the most.

Alex Nielsen, Team Project Scientist, VMASC

Modern agent-based simulation

Be the first to hear about simulations in HASH

Start creating entities ready to use in agent-based simulations

Create a free HASH account to start preparing for the new era of simulation

By signing up you agree to our terms and conditions and privacy policy