Universally better decision-making
We believe that most bad things in the world are the product of some form of information failure; from economic collapse and the outbreaks of war and disease, to choosing the right life partner or university degree.
We’re on a mission to help everybody make the right decisions and overcome information failure once and for all.
Brilliant innovators have sought to organize the world’s information, but the next step on this journey is to make information understandable, usable, and accessible to everybody.
While high-tech, well-funded organizations like hedge-funds are able to process vast swathes of the world’s information efficiently for minute gains and millisecond edges in economic trades, the vast majority of businesses and individuals have no systematic way of parsing the wealth of signals contained in the world around them.
Simulation has the power to unlock a better world: advancing our understanding and appreciation of the environments around us. It can alert us to possibilities never considered, highlight hidden relationships between the agents within them, and force us to make explicit our assumptions about how the world works.
Not only are simulations useful cognitive tools for humans, but they have the potential to be rich, machine-readable representations of real world problems as well.
At HASH, we see simulation as the universal interface for both humans and AI — and in our view the best bet we have for growing connective tissue between human and machine learning.
We hope to enable better human and automated decision-making: bringing about the rational resolution of conflicts of all kinds, reducing and eliminating market failures, and supporting people to achieve happier, healthier lives. We don’t want to wait for this.
Good simulation models are today expensive to build, and costly to run as well as maintain. Software for agent-based modeling has been stuck in the 90s, and the resulting models are grossly over-complicated or questionably opaque.
We believe the solution to better modeling lies in allowing individual high-quality, high-fidelity models to be built, which can be mapped to common data structures that describe the agents and events within simulations. This allows for individual models to be combined into more trustworthy, high-fidelity models of domains or problems.
You can think of HASH’s Index as a computational Wikipedia: an open community of simulation models where individual simulations are built and maintained by domain-experts, and anybody can utilize and combine these.
Minimally intelligent agents can be created within simulations from ground-up first principles, allowing for fast counterfactual “what if” hypothesis testing.
More complex and intelligent agents can be evolved through reinforcement and other machine learning to automate the safe exploration and optimization of simulations: from virtual social networks, to digital twins of real-world physical systems. For example, in epidemiology simulation might be used to forecast the spread of disease throughout society, and in public health how information spreads in response. The results can then be used to implement better policies, and engage in more effective advertising, reducing the ultimate burden of disease.
We’re building a whole platform around better data management and modeling. You can learn more over on our platform page >
To eliminate information failure, we need to build tools that have never been created before to solve problems that can’t be solved today. We need to give people superpowers, and that’s what we’re on a mission to do.
If you want to join us, you can help by publishing simulations, behaviors and data to the hIndex, or apply for any one of our open roles at hash.ai/careers
And finally, if you’re a decision-maker interested in learning how simulation can be applied to help you, get in touch at hash.ai/contact