Reputation systems are a fundamental component of modern internet marketplaces and social networks. Sharing economy apps like Uber and AirBnb rely heavily on Member generated ratings before, during and after Member events, whereas social networks like Twitter and Instagram use centralized processes for Member verification (such as the infamous 'blue check'). In most cases, algorithms track various Member interactions and outcomes to form specific opinions, and dynamically adjust a Members online experience.
More often than not, algorithm's and reputation systems utilized by major internet platforms are opaque, making it difficult (if not impossible) for Members to understand the mechanisms being used to create their online experience. In recent years fake accounts and fake news have become a fundamental problem for the Internet at large, which advances in machine learning is only likely to increase, unless a new type of architecture and business model is introduced. Greater experimentation, transparency, and innovation in reputation systems will be a critical aspect to building better online systems.
For example, a DAO that highly values timeliness might algorithmically assign Omega to Members who score highly on timeliness, while another DAO might algorithmically assign Omega to Members who score highly on quality of work. This way, instead of needing to pass Choices related to Omega on a case-by-case basis, the DAO can pass algorithms that work repeatedly over time. It’s also possible that a DAO’s Omega distribution algorithms might factor in data other than a reputation, such as how long the individual has been a Member, how many tasks the Member has completed with the corresponding Network (with maybe something like bonus levels involved), or, to some extent, how much token the Member has staked in support of the Network.