In this post, I will share different choices the engineering team has made throughout the early phase of the product development to navigate uncertainty, to the extent of building uncertainty within the product. For those curious about how life is like building a product for the government, you get a rare peek into how one team does it.
Recently, my tribe held our promotion nomination exercise using my quadratic voting app. The exercise allow all members of the tribe to vote one another for the upcoming promotion. One of the concerns of using the quadratic voting application was that I could potentially read and change the votes since I've database access. How would I convince my colleagues to trust me?
In a series of unfortunate events, you and your friends are left stranded in the middle of the Atlantic ocean. Together, you have managed to salvage some items from the wreck. As a group, your task is to agree on the importance of these items. Your decisions will determine if you get to walk away alive. The problem now is how do you decide on the importance? Quadratic voting can help!
Given a basket of assets, how would you allocation your capital across the different assets to maximize returns and minimize risk? This problem can be seen as a classic optimisation problem in data science. In this experiment, I will attempt to compare the performance of three different techniques, Monte Carlo, SLSQP and Bayesian Optimisation on a simple 3-Fund Portfolio for investors in Singapore