If some is good, more must be better.

Mustn’t it?

Of course not, although figuring out where to draw the line isn’t always obvious.

Take, for example, the laudable principle that inclusion and collaboration usually yield better results than isolation and authoritarian decision-making.

Just don’t take it off a cliff.

Which appears to be exactly what’s happening in your average corporation — at least it is if the evidence and conclusions presented in “The Hard Evidence: Business is Slowing Down,” (Tom Monahan, Fortune, 1/28/2016) are to be believed. Among Monahan’s findings: The number of collaborators people have to work with in a typical corporate effort is at least 10, and often 20 or more.

Before we go on: I doubt this is really the number of collaborators people have to work with. I’d bet as much as a quarter Monahan counted the number of colleagues people include and involve in an effort in any capacity.

Not that I’m criticizing. Inclusion and involvement provide numerous benefits. No matter what problem you’re trying to solve you get more eyes on it, more different perspectives, more creativity, and when you’re finished, greater acceptance of whatever solution you and your collaborators have developed.

But (is anyone surprised this was the next word to appear?).

But, inclusion and collaboration don’t scale all that well, which is why there’s such a huge gulf separating consensus from voting as decision-making techniques.

All voting requires is an opinion and a willingness to invest the time needed to cast a ballot. This willingness increases in direct proportion to the level of distrust each voter has in their fellow voters.

Consensus, in contrast, depends on trust among those involved in a decision. Trust doesn’t require everyone involved in a decision to have the same goals, perspectives, assumptions  (conscious or otherwise), experiential background, or time zone of residence.

It does require the assumption on the part of everyone involved that they’re dealing with social peers who have no hidden agendas, who are open to new evidence and logic, and who honestly want to participate in creating a positive outcome.

Which leads us to a pair of mathematical curves.

The first curve is a polynomial, the output of the formula that computes the number of relationships between pairs of individuals on a team. The formula is K = P(P-1)/2.

K, that is, is the number of relationships that need maintaining in a team. So a ten-person team consists of 45 interpersonal relationships, while a 20-person team consists of 190. Maintaining trust among 45 pairs of individuals is difficult but not impossible. Maintaining it among 190 pairs is, at best, unlikely, even if one person has handpicked each team member with team compatibility their first selection criterion.

This is why inclusion and involvement don’t scale.

Which brings us to curve #2. It’s a variant of the logistics equation. In this case, dI/dP= rP*(1-P/K), where dI/dP represents how the number of good ideas (I) increases with team size (P).

In this formula, r is a constant — the raw idea generation power of an average team member. The logistics equation says that as team size increases, the outcome when it comes to more and better ideas will be an s-shaped curve, with the limiting factor (K) being the number of trust-based relationships required due to increasing team size.

This is nifty, don’t you think? It gives us a mathematical theory of team size optimization.

Sure, it isn’t all that surprising that the result is a point of diminishing returns as teams get bigger.

But reducing it to an equation is still pretty cool.