The model will never be ready. Rather we build a new version practically every week. New products, research and companies emerge every day, and that is pretty cool. We are happy that already now, in our project’s infant phase, we are able to produce information and understanding that to our knowledge no other system can. However, the road ahead of us keeps us humble and excited.
The model will never be perfect. All models and numerical representations are just proxies, they ARE not the truth. A perfect model accurately depicting all impacts of all companies in real-time is theoretically impossible. However, a net value creation measuring system significantly better than the one at use today CAN be done. Not being able to build something perfect is a lousy excuse for not doing better.
We want to put together all the work of millions of researchers by using scientific articles as our main input data. Also, we want to build a system where everyone who wants to contribute to the building of better understanding of what companies achieve can do so. Our aim is to help people collaborate in making more fact-based decisions.See CONTRIBUTE ⟶
This is our dearest design principle throughout the model. It means we only concentrate on the largest impacts a company has on the surrounding world. It’s the only way to stop losing sight of the Tier 1 priorities (e.g. fighting climate change) in Tier 2-10 data (e.g. whether or not your company serves cool artisanal coffee). Also, it’s a great way to stay sane. Practical example: for an oil company, we don’t care whether or not they use recycled office paper.
Analyzing and optimizing large groups of companies (e.g. investment portfolios).
Making comparisons between products or companies.
Understanding the scale of impacts.
Dividing companies into good and bad ones.
Comparing two brands of the same product with largely similar impact.
The Upright model aims to build a big picture of what kind of value companies create. Our approach can be simplified to being built on three inputs: an understanding of what kind of products and services exists, a list of all the ways a company can impact the world around it, and scientific journal articles as source data for understanding about relationships between the two.
Now comes the tricky part. We need data about the impacts that the products and services have on the environment, health of people, society and creation and distribution of knowledge. This is where we need our engineering skills: to teach a neural network to understand causalities in natural language, i.e., the scientific articles.
Over the last 6 months, we have built a pretty exceptional training data set of over 30 000 scientific articles which we have read and classified manually to train the neural network.
After getting the raw data from the neural network, we form scores for each combination of product and impact using an algorithm that combines information about 1) the prediction distribution, 2) number of articles studying a particular product/impact pair, 3) position and relative relevance of the product in its value chains, and 4) position and relative relevance of the product in its product family hierarchies.
After this, we have the basic building blocks for forming net impact profiles for all products and services. By summing these up, we get companies - which we can further sum up to form portfolios, funds, industries and other entities whose impact we wish to understand.
And that’s it! It’s a pretty crazy exercise, but we really believe this is problem worth fighting for. This is our first effort towards solving it. We invite you to follow our progress, cheer us on, contribute and/or build your own solutions!