Why future Legislators will need to be Data Scientists, not Lawyers
A system of governence is a form of a model that helps predict the best decisions that will optimize the results for all its stakeholders. However, the data on which our current Democratic model is trained, the training data, is quite out of date. The world has changed too much, both economically, technologically and demographically. Disenfranchisement of large population sets means that the training data is also no longer a representative sample.
The pace of legislative change, the model updates, can’t keep up with the rate of change in the environment. Governance today also doesn’t completely leverage the scalability available to our society. If we continue to be restricted by the tools of governance we picked in the past, we will continue to descend into a post-factual democracy, where short sighted decisions made with a limited interpretation of chaotic data will keep resulting in suboptimal outcomes for all the stakeholders.
Bitcoin demonstrated how distributed consensus can be achieved via technology. The technology behind Ethereum alters how we think of legal contracts that are defined in computer code rather than the ambiguous language of the law. DAO (Decentralized Autonomous Organizations) show how future organizations can completely transcend today’s legal and financial norms and the world is still figuring out what they even mean. Backfeed is building a social operating system for decentralized organizations that puts the stakeholders directly in control. These developments can enable us to achieve large things, collectively, at unprecedented scale.
These technologies can and will revolutionize governance. But there are some other problems to be solved for this to truly work. There is a data problem — we can only improve what we can measure. And it’s really really hard to measure and quantify what we care to achieve. There is an optimization problem — the system only works when outcomes are optimal for all stakeholders and that means defining the stakeholders, and the mutual importance of their often conflicting goals. There’s a feedback loop problem — the biases of the decisions have to be changed to reflect the real world performance of the outcomes they achieve which means iterations, measurements, adjustments to a model using new data. These are problems that Data Scientists are best trained to solve.
“Data Scientist” is an ambiguous term. Here’s an example of what it means: