The fine tuning problem

In two previous posts I dealt with the relation between the multiverse theories and the problem of fine tuning, noting that those theories do not solve the problem. This third post describes briefly what is the fine tuning problem.

Brandon Carter

In 1973 Brandon Carter formulated the anthropic principle, a name later deplored by its author, because it may be prone to misunderstandings. This principle is simply the verification that the universe must fulfill all the conditions necessary for our existence, since we are here.

Over a decade later, John Barrow and Frank Tipler published a book entitled The anthropic cosmological principle, which offered a stronger version of the anthropic principle, posing that the values of many of the universal constants are critical and minor variations would make life impossible. This finding raises the fine tuning problem, based on the analysis of the possible effects of changing the values of those constants. In other words, the universe seems designed to make life possible. Let’s look at a few examples:

The probability of existence of extra-terrestrial intelligence

Normal statistical distribution.
The text makes reference to a uniform statistical distribution.

Probability is a well-known mathematical concept that was initially defined to quantify random data in mathematically known environments and has been extended to other situations.

For instance, the probability that the next car passing near me has a license plate with four identical figures is computed by dividing the number of favorable cases between the number of possible cases. The first number is ten: 0000, 1111, 2222, ... , 9999. The second is ten thousand: 0000, 0001, 0002, ... , 9998, 9999, in a uniform distribution. Therefore the indicated probability can be computed as one thousandth. Here we haven’t considered that vehicles can be removed from circulation, an independent random process that would not change significantly the result of the computation.

The problem is, sometimes we are interested in computing data in mathematically unknown environments. This can happen, for instance, when we ignore the number of favorable cases, or the number of possible cases, or both. In such situations, we can estimate the unknown data with more or less uncertainty. We speak then of a priori probability.