292 - Fluid Governance

One of the most basic sources of systematic waste in organizations and governments is the political pendulum, such as when a 51% majority sets policy, while a 2-3% minority decides who wins a given vote, with the minority swapping opportunistically between options. This creates implemented policies that are equally endorsed and vehemently hated in many polarized systems, with the difference just blowing with the winds of opportunity.

Today this often causes policies to be implemented at great financial and time costs, and then promptly abandoned and replaced the moment another party takes the majority, causing heavy long-term uncertainty over all such policies. This instability and uncertainty further balloons costs while wasting a large portion of governance time, and frequently reducing accountability across these transitions on long-term projects.

There are many ways around this problem, such as “Ranked Choice” voting, which partly disarms such abuses, however, they tend to fall far short in matters where the options don’t easily fit onto a multiple-choice list. Unanimous AI became known for a method that effectively treats amateur humans like a swarm of bees, generating a mostly unconscious kind of collective intelligence through a simple real-time group interface. For matters of organizational and governmental policy, the complexity of individual components and arbitrary combinations of them is vastly greater than multiple-choice ranking allows. In these cases, a new method is required.

The most precise solution, which minimizes the swing of that pendulum, is to construct policies based on the weight of votes, collected at whatever level of granularity is required. For example, if 50% favor one type of policy, 40% another, and 10% still another, then a 4th hybrid of those policies may be constructed that is based on a 50/40/10 weighting of the other 3.

This can also be broken out into two steps, with the ranked priorities of each member of those groups, further minimizing the friction of integrating such policies. This integration could even be run through market-like dynamics, with the different groups receiving their portion of the votes to “bid” on the things they really want in a policy, while other groups can prioritize other aspects.

Another more powerful option emerges when you introduce scalable human-like systems to manage organizations and governance processes, as the systems can model and construct novel new policies that better adhere to the stated will of their respective groups, rather than being limited to LEGO-like assembly and disassembly with fixed components. This higher cognitive process and superhuman cognitive bandwidth of scalability also open the door to future-proofing of new policies, reducing long-term overhead costs and potential abuses.

Implementing better governance could easily save companies millions, countries billions, and the world trillions.