Blog

A few months ago we revealed we were working on something new. The Block Protocol is now in public draft, and an early preview of our new product, HASH, is available for download on GitHub under an open-source license.

We’re developing a new open standard for building blocks of the sort you might find included in document editing applications like Notion or Almanac, as well as content management systems like WordPress. The Block Protocol will make sure blocks developed in one application are interoperable with others, and information captured within them is accessible and suitable for use in agent-based models, as well as other analytics applications.

SynPath is an NHSX project that seeks to use simulation modeling to simulate patient pathways in England’s National Health Service. For long-term conditions involving complex medical treatments and procedures, patients often need to navigate a series of general practitioner and specialist services as part of their care. Patient pathways are the specific route that a […]

Few things capture the imagination quite like outer space. As more and more countries and companies venture into the final frontier, high quality models and data can inform their exploration. Below are some of our favorite datasets about space hosted on HASH: Space Flights: A record of historical space flights from 1957 to today. A […]

Explore a Genetic Algorithm Based Employee Scheduling Simulation Genetic algorithms follow the logic of evolution – from a pool of solutions, evolve the best solution for a given problem. This is an effective approach to finding optimal solutions to tricky, complex multi-dimensional optimization problems. The basic process is an evolutionary loop: A number of potential […]

We recently published HASH’s Q-Learning Library, to make it easier to start building simulations and agents that use reinforcement learning. Let’s take a look at how we can use the library to update an older simulation. We’ll take the classic Prisoner’s Dilemma simulation and add an agent which uses q-learning to determine its strategy. The […]

Q-Learning Map Explorer Reinforcement Learning (RL) is a way to teach an agent how to behave in an environment by rewarding it when it does well and penalizing it when it does poorly. Using RL in HASH, you can create complex agents that figure out ‘on their own’ optimal strategies to follow in a simulation. […]

We recently released the HASH Physics Library, which provides behaviors that can help you start creating physics simulations in HASH. The library is written in Rust, which means that it’s optimized to run in our engine. The HASH docs contain more information about each of the behaviors. You can add the physics library to your […]

The COVID-19 pandemic has been a source of exogenous shocks to supply and demand in many industries across the market. One specific market currently experiencing shortages is the semiconductor industry. This has far-reaching consequences, because “chips” have become ubiquitous as raw components for many different industries The auto and consumer electronics industry are both experiencing […]