Managing Humans with Math
I was in a funky corner of the internet, reading about reinforcement learning when I chanced upon this article that compares a number of reinforcement learning algorithms. Since we as humans are glorified neural networks amenable to reinforcement learning and companies are nothing but hierarchical relationships between humans, it was very interesting to go through that article with a business point of view.
Long story short, the article figures out various conditions in which the algorithms are able to break down a task recursively, the optimality convergence conditions and the "knowledge" required of the lowest level agents to learn successfully. From my reading of the article, I'm most drawn to the "Options" style of management: work with high quality people, give them maximum freedom to act and step in only at decision points.
This is the style of management that I'd grown used to at Google (and that I've seen work really well there) and is quite in contrast to other managements that I have experienced. Not to say that the other managements were ineffective, they just took different routes to get to the same goals.
In summary: prefer the "Options" style of management, work with smart people, give them all the freedom they want and deserve, provide a strong learning function and clear feedback on how they're performing. Step in only at "Choice" points and then too defer to people with context of the problem. Most decisions are reversible, so just take one and let's keep going!