As someone who used to be fully sequence thinking-oriented and gradually came round to the cluster thinking view, I think it’s useful to quote from that post of Holden’s on when it’s best to use which type of thinking:
I see sequence thinking as being highly useful for idea generation, brainstorming, reflection, and discussion, due to the way in which it makes assumptions explicit, allows extreme factors to carry extreme weight and generate surprising conclusions, and resists “regression to normality.”
However, I see cluster thinking as superior in its tendency to reach good conclusions about which action (from a given set of options) should be taken. …
Note that this distinction is not the same as the distinction between explicit expected value and holistic-intuition-based decision-making. Both of the thought processes above involve expected-value calculations; the two thought processes consider all the same factors; but they take different approaches to weighing them against each other. Specifically:
Sequence thinking considers each parameter independently and doesn’t do any form of “sandboxing.” So it is much easier for one very large number to dominate the entire calculation even after one makes adjustments for e.g. expert opinion and other “outside views”...
The two have very different approaches to what some call Knightian uncertainty (also sometimes called “model uncertainty” or “unknown unknowns”): the possibility that one’s model of the world is making fundamental mistakes and missing key parameters entirely…
Also this:
Cluster thinking is more similar to empirically effective prediction methods
Sequence thinking presumes a particular framework for thinking about the consequences of one’s actions. It may incorporate many considerations, but all are translated into a single language, a single mental model, and in some sense a single “formula.” I believe this is at odds with how successful prediction systems operate, whether in finance, software, or domains such as political forecasting; such systems generally combine the predictions of multiple models in ways that purposefully avoid letting any one model (especially a low-certainty one) carry too much weight when it contradicts the others.
As someone who used to be fully sequence thinking-oriented and gradually came round to the cluster thinking view, I think it’s useful to quote from that post of Holden’s on when it’s best to use which type of thinking:
Also this: