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Stories and insights from the team behind HASH

Announcing the Block Protocol

Naturally structuring unstructured data One of the biggest blockers to effective and widespread agent-based simulation is the difficulty non-technical users face converting their knowledge and understanding of systems into simulation models. Today, domain experts require both software engineers and data scientists to support them in creating simulations. That process is both slow and time-consuming. For […]

SynPath: NHS Healthcare Simulations

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 […]

Outer Space Datasets in HASH

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 […]

Genetic Algorithms in Simulations

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 […]

Improving the Prisoners Dilemma with Q-Learning

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 […]

Reinforcement Learning in HASH Simulations

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. […]

Physics in HASH

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 […]

Surviving a Chip Shortage

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 […]

Top 10 Datasets: June 2021

HASH’s Index includes a large collection of user-uploaded datasets you can use to generate and power a simulation. Below are some of our favorite datasets:  Master List of LEGO Part Numbers: Master List of Official LEGO™ parts/numbers. Thousands of legos with descriptions of the parts. Provider: Peeron. SARS-CoV-2 Superspreading Events: A dataset with 1,100 SSEs […]

Discrete Event Library

Discrete event modeling is a popular paradigm for building simulations, introducing “events” that cause changes in the simulation state. In between events, no changes happen to the state. This saves computational effort and reduces the time to compute a simulation. You can imagine a simulation of a manufacturing process where a new car rolls off […]
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