2. Comparison of the Second and Third methods
The second method (1/2) implies a certain assumption that is controversial in terms of maximum invariance. More importantly, it could lead to a contradiction with the third method (1/4), which does not allow them to be considered as mutually alternative. The point of this assumption is that we consider equal distribution of the probability of choosing a random point along the entire radius. This means that there are an equal number of possible random centers of circle chords throughout the length of the radius. Only in this case, the choice of one of the possible radiuses, as a standard of the probability estimation, allows us to assess the overall probability of choosing a random chord. That is, the number of possible random points is the same at any distance from the midpoint of the circle.
At the same time, this assumption means that the number of possible random points on any random smaller circle in the area between the midpoint and the edge of the main circle is equal. Therefore, regardless of the circumference, the number of points is equal. The second method (1/2) can be considered as a solution to the problem only by taking into account this assumption.
On the whole, such an assumption can be justified, since if we consider an infinite number of radiuses that intersect all the given circles, then the number of points on these circles intersected by the radiuses is also equal. With this assumption, the displacement of the radius along the points of the larger of two arbitrary circles will lead to the displacement between the points on the smaller one.
Similarly, two circles inscribed in a cone could be considered (
Figure 2). Let us take a ray from the vertex of a cone that intersects both circles. If we displace the ray along the points of the larger circle, it will cause the displacement between the points of the smaller one. Thus, the number of such points is equal inside both circles. Now let us assume that the radiuses of the circles have a ratio of 1:2 and place both circles on a plane, with their centers aligned. Now let us estimate the probability of a random point falling into the smaller circle and into the remaining area of the larger circle. The probability of falling into the larger circle that is outside of the smaller one is equal to the number of points in the larger circle minus the number of points in the smaller one. Since these numbers are equal, the possibility of falling outside of the smaller circle is zero. Therefore, all randomly chosen points will be in the smaller circle.
Thus, making an analogy between the second and the third method to solve the Bertrand paradox, in the third solution we achieve 100% instead of 1/4. Obviously, this cannot be considered as a correct solution, though we took into account the assumption on which the second classic method for solving the problem is based. In the same way that our exaggerated formulation of the third method ignores all points outside the smaller circle, the second one ignores all additional points outside the smaller circle that appear when moving away from the center of the circle (
Figure 3).
Hence, the second method (1/2) can be considered either as initially incorrect or as not being an alternative to the third solution (1/4), while also being correct only for a more specific problem than the one that was formulated by Bertrand.
As was mentioned above, the main issue of the formulation that limits its application is that the number of points on each arbitrary circle, both around the center of the circle and at its border, is the same, regardless of the circumference. This shortcoming could be eliminated by introducing additional points on the circles that are close to the edge of the circle. In fact, this will provide the third solution to the problem. It is worth noting that the dependence of the number of points on the circle on its circumference in the context of the Bertrand paradox is addressed by D. Rizza [
7], although this concept was not applied to compare the results of two solutions.
A similar misrepresentation of the original assumptions can be found while considering another Bertrand problem described in the same work. The question is to evaluate the probability that a number randomly chosen from 0 to 100 [
8] is greater than 50. Although the intuitively obvious answer is 1/2, Bertrand provides another solution with a different result. Following the statement that each number corresponds to its square, Bertrand chooses a random number out of 10,000 (which is 100 squared) and then calculates its root. Since the numbers greater than 50 correspond to the squared numbers greater than 2500, the probability of favorable cases is 3/4. We can also use cubed numbers or any exponent instead of square numbers.
When considering that solution, it is obvious that in the case of the squared numbers whose roots correspond to the integers in the range from 0 to 100, there are exactly 100 such squared numbers in the range from 0 to 10,000 [
9]. Therefore, the probabilities are actually equal. However, at the same time, as the number increases towards 100, the difference between the squares of consecutive numbers also increases. Thus, if the probabilities of integers from 0 to 100 are estimated by choosing integers between 0 and 10,000 and rounding their roots to integers, then the randomness of the final choice will be distorted. This occurs due to the adding of extra options to the sample while going from 0 to 100. It will be distorted in the same way by any fractional division of any one or both ranges of numbers, that is, from 0 to 100 and from 0 to 10,000.
Thus, a strong analogue can be drawn between large numbers that are close to 100 and large circles that are close to the border of the main circle. The only difference is that these two solutions are completely inverted in terms of distortion. In the case with the chords placed on a plane, the solution is distorted by reducing it to a linear problem, and in the case of number choosing, on the contrary, the linear problem is distorted by exponentiation. We can assume that both problems were described in the same work one after another due to the similarity of their errors.