I would like to start a discussion on posssible causes for the saeculum. I'd like to start with what Strauss and Howe suggested as a cause of the cycle in Generations. They used the concept of generational constellation in their discussion of causes. The generational constellation is easiest to understand in terms of an idealized set of generations, each of which is as long as a 22-year-long phase of life. Assume at time zero generation type D starts being born; at year 22 generation type C starts being born; at year 44 generational type B starts being born and at year 66, generation type A starts being born. Now imagine New Year's day in year 88. Assume everyone before year zero is dead. How old will members of each generation be?
Members of generation type A will be age 0-21 years, and will exactly fill the youth phase of life. Generation type B will be age 22-43 and will exactly fill the rising adult phase of life. Everyone who is in the mature adult phase of life (age 44-65) will belong to the type C generation and everyone over age 65 will belong to a type D generation and occupy the elder phase of life.
If time is moved up 22 years, each generation moves up one notch: A to rising adult (age 22-43); B to mature adult (age 44-65);and C to elderhood (age 65-87). Generation D will have passed from the scene, and a new generation of the same type will have been born to replace them. This generation type D will fully occupy the youth phase of life. Strauss and Howe call the specific times, when generations match up with phases of life, aligned generational constellations. When an aligned constellation occurs, a new generation starts being born. Two to five years after this, a new turning starts. It is the periodic alignment of generations with phases of life that acts as the pacemaker for the saeculum.
The question is how well does this work?
Of course, real generations are not typically exactly 22 years long. One can still identify aligned constellations by calculating the percentage of each phase of life that is filled by the proper type of generation. For example, consider 1767, the first year of the Compromise generation. This generation is of the adaptive type, making generation D in the earlier example adaptive. The table lists the other generations.
Generational constellation on New Year's Day 1767
Youth = 1745-1766--> Civic Gen = 1742-1766 . Youth 100% Filled by Civics
Adult = 1723-1744--> Reactive .= 1724-1741 . Adult 82% Filled by Reactives
Mature=1701-1722--> Idealist . = 1701-1723 . Maturity 100% Filled by Idealists
Elder = 1679-1700--> Adaptive, = 1674-1700 . Elderhood 100% Filled by Adaptives
Each phase of life is completely filled by the correct generation, except Rising Adult. The reactive Liberty generation was only 18 years long and cannot fill more than 82% of a 22-year phase of life. Thus, each phase of life in Table 8 is maximally filled, making 1767 a perfectly aligned constellation.
Most generational constellations are not so well aligned. For example, the 1618 constellation (1618 is the first year of the reactive Cavalier generation) shows 100% youth, 41% rising adult, 64% maturity and 50% elderhood occupation by the appropriate generations--not a very good alignment. Yet the alignment of the very same generations with phase of life is much better in the years just before 1618. In 1609, the alignment was best: 95%-95%-100%-91%. The year 1609 can be considered as the optimally-aligned generational constellation for the start of the Cavalier generation. It can be considered as the predicted date for the start of the Cavalier generation based on the generational constellation. In this particular case, the actual start of the Cavalier generation (determined from biographical information) was nine years after the predicted date. The start of the Compromise generation in 1767 was exactly on schedule according to the constellation model, while the 1943 start of the Boomer generation was four years before the constellation prediction. These differences between the actual values and the expected values based on the model are called model residuals.
The residuals represent that portion of real-world behavior not explained by the model. A good model will show residuals that are randomly distributed around zero. Any pattern to residuals implies that some important factor has not been captured by the model. The Figure shows a plot of the residuals for the generational constellation model.
The residuals are not randomly scattered. Rather, they show a highly significant downward trend with regression coefficient R of 0.82, indicating 99.999% statistical significance. Also they average 2.6 years rather which is more than 10% of a generational length away from zero. When Strauss and Howe developed this model, they did not have the first six generations in Table 3. If the residuals obtained from these generations are left out the average value of the residual falls to just 0.7. It is clear that the constellation model was developed because it fit the observable data on average quite well. But when the structure of the residuals are examined it is clear that the model does not fit the data well.