Overlooking the Initial Probability
One common trap of human intuition is failing to take into account the plausibility of an event before considering the context-specific evidence of the case at hand. Without knowing how plausible a hypothesis is in general, it is easy to fall into the trap (test yourself!) of overestimating the initial probability for inherently unlikely theories. This is known as the Prosecutor’s fallacy, one of the main flaws of human reasoning.
Farmer or Librarian?
For instance, take the following example (suggested by Kahneman and Tversky):
“Morgan is very shy and withdrawn. He is invariably helpful, but shows little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.”
Most people, when asked if Morgan is more likely to be a farmer or a librarian, respond that he is more likely to be a librarian. However, there are many more farmers in the population as a whole. There are perhaps a million librarians in the world, while there are more than a billion people involved in farming. Therefore, even though the description of Morgan is closer to the stereotypical image that most people have of librarians, there must be many more shy, detail-oriented farmers than librarians overall.
Martians at the Pool
To give another example, imagine that you and your friend frequent a neighborhood swimming pool. One day, your friend John comes back from the pool and tells you, “My friend Alice was at the swimming pool today.” You are likely to believe that Alice was there and the evidence, John’s testimony, is true. The next day, John tells you, “George Clooney was at the swimming pool today.” You are somewhat less likely to believe that the famous actor was there and the evidence is true. The day after that, John tells you, “Aliens from Mars were at the swimming pool today.” You won’t believe that aliens from Mars were there or that the evidence is true. The critical difference between each of these seemingly identical pieces of evidence (John’s testimonies) is simply the initial plausibility of the event.
Assassination or Robbery?
Rootclaim examines the starting plausibility of a hypothesis, before considering any other evidence. By doing so, Rootclaim makes sure that we don’t fall into any of these faulty intuition traps.
In the Why was Seth Rich killed? analysis, Rootclaim examines the circumstances of a young man working for the Democratic National Convention (DNC). Naturally, some have speculated that Rich’s death was related to his job for the DNC. They argued that someone, perhaps even from the DNC itself, killed Rich because of his involvement in politics.
An assassination certainly makes for a compelling story. However, it isn’t particularly likely when one considers the initial probability. Generally speaking, robberies account for 6% of all murders. Political assassinations, on the other hand, especially of relatively low-ranking employees, are extremely rare. So even estimated generously, the initial probability of Rich’s death being connected to his DNC work is very low.
Who Killed Hae Min Lee?
A similar phenomenon occurs in the Rootclaim analysis on Who killed Hae Min Lee?. A Baltimore court found Adnan Syed, Hae’s ex-boyfriend, guilty of Hae’s murder, and sentenced him to life in prison. But in general, ex-boyfriends account for a small minority of murders. Another commonly discussed culprit is Roy Sharonnie Davis III, a local man convicted of another girl’s murder. But Davis didn’t know Lee. Moreover, strangers are relatively uncommon as potential murderers. The most likely initial hypothesis is that someone who not accused or mentioned killed Hae. That could include friends, relatives and acquaintances, a much larger pool of potential murderers.
It can be easy to be tricked by our intuition. Adding too many details can make us see only librarians, and forget just how uncommon they actually are, ignoring more common choices like farmers. Rootclaim deals with this challenge by assigning an initial probability to each hypothesis. This starting point asks how likely the event is before getting into specific details about the case that might bias one’s imagination.