Mertonian communism: more is more
The Mertonian norm of communism (obviously, not the political system) refers to the communal ownership of data within groups. Merton argued that, in academics, an individual researcher’s data must eventually be shared with the scientific community at large for knowledge to advance.
“Secrecy is the antithesis of this norm; full and open communication its enactment.” In science, this means that the community has an agreement that research results cannot properly be reviewed without access to the data and a detailed description of the experimental design and methods.
Researchers are entitled to keep data private until published but once they accomplish that, they should throw the doors open to give the community every opportunity to make a proper assessment. Any attempt at accuracy is bound to fall short if the truthseeking group has only limited access to potentially pertinent information. Without all the facts, accuracy suffers.
This ideal of scientific sharing was similarly described by physicist Richard Feynman in a 1974 lecture as “a kind of utter honesty—a kind of leaning over backwards. For example, if you’re doing an experiment, you should report everything that you think might make it invalid—not only what you think is right about it: other causes that could possibly explain your results . . .”
It is unrealistic to think we can perfectly achieve Feynman’s ideal; even scientists struggle with it. Within our own decision pod, we should strive to abide by the rule that “more is more.” Get all the information out there.
Indulge the broadest definition of what could conceivably be relevant.
Reward the process of pulling the skeletons of our own reasoning out of the closet. As a rule of thumb, if we have an urge to leave out a detail because it makes us uncomfortable or requires even more clarification to explain away, those are exactly the details we must share. The mere fact of our hesitation and discomfort is a signal that such information may be critical to providing a complete and balanced account. Likewise, as members of a group evaluating a decision, we should take such hesitation as a signal to explore further.
To the extent we regard self-governance in the United States as a truthseeking experiment, we have established that openness in the sharing
of information is a cornerstone of making and accounting for decisions by the government. The free-press and free-speech guarantees of the Constitution recognize the importance of self-expression, but they also exist because we need mechanisms to assure that information makes it to the public. The government serves the people, so the people own the data and have a right to have the data shared with them. Statutes like the Freedom of Information Act have the same purpose. Without free access to information, it is impossible to make reasoned assessments of our government.
Sharing data and information, like the other elements of a truthseeking charter, is done by agreement. Academics agree to share results. The government shares information by agreement with the people. Without an agreement, we can’t and shouldn’t compel others to share information they don’t want to share. We all have a right of privacy. Companies and other entities have rights to trade secrets and to protect their intellectual property.
But within our group, an agreement to share details pertinent to assessing the quality of a decision is part of a productive truthseeking charter.
If the group is discussing a decision and it doesn’t have all the details, it might be because the person providing them doesn’t realize the relevance of some of the data. Or it could mean the person telling the story has a bias toward encouraging a certain narrative that they likely aren’t even aware of.
After all, as Jonathan Haidt points out, we are all our own best PR agents, spinning a narrative that shines the most flattering light on us.
We have all experienced situations where we get two accounts of the same event, but the versions are dramatically different because they are informed by different facts and perspectives. This is known as the Rashomon Effect, named for the 1950 cinematic classic Rashomon, directed by Akira Kurosawa. The central element of the otherwise simple plot was how incompleteness is a tool for bias. In the film, four people give separate, drastically different accounts of a scene they all observed, the seduction (or rape) of a woman by a bandit, the bandit’s duel with her husband (if there was a duel), and the husband’s death (from losing the duel, murder, or suicide).
Even without conflicting versions, the Rashomon Effect reminds us that we can’t assume one version of a story is accurate or complete. We can’t count on someone else to provide the other side of the story, or any individual’s version to provide a full and objective accounting of all the
relevant information. That’s why, within a decision group, it is helpful to commit to this Mertonian norm on both sides of the discussion. When presenting a decision for discussion, we should be mindful of details we might be omitting and be extra-safe by adding anything that could possibly be relevant. On the evaluation side, we must query each other to extract those details when necessary.
My consultation with the CEO who traced his company’s problems to firing the president demonstrated the value of a commitment to data sharing. After he described what happened, I requested a lot more information. As he got into details of the hiring process for that executive and approaches to dealing with the president’s deficiencies on the job, that led to further questions about those decisions, which, in turn, led to more details being shared. He was identifying what he thought was a bad decision, justified by his initial description of the situation. After we got every detail out of all the dimensions of the decision, we reached a different conclusion: the decision to fire the president had been quite reasonable strategically. It just happened to turn out badly.
Be a data sharer. That’s what experts do. In fact, that’s one of the reasons experts become experts. They understand that sharing data is the best way to move toward accuracy because it extracts insight from your listeners of the highest fidelity.
You should hear the amount of detail a top poker player puts into the description of a hand when they are workshopping that hand with another player. A layperson would think, “That seems like a lot of irrelevant, nitpicky detail. Why are they saying all that stuff?” When two expert poker players get together to trade views and opinions about hands, the detail is extraordinary: the positions of everyone acting in the hand; the size of the bets and the size of the pot after each action; what they know about how their opponent(s) has played when they have encountered them in the past; how they were playing in the particular game they were in; how they were playing in the most recent hands in that game (particularly whether they were winning or losing recently); how many chips each person had throughout the hand; what their opponents know about them, etc., etc. What the experts recognize is that the more detail you provide, the better the assessment of decision quality you get. And because the same types of details are always expected, expert players essentially work from a