Satisficing vs Maximising: Why "Good Enough" Often Beats "The Best"

The economist Herbert Simon coined a useful pair of terms for two different ways of ending a search for the best option. A maximizer keeps comparing until they have found, or are reasonably confident they have found, the single best option available. A satisficer sets a threshold for what counts as good enough in advance, and takes the first option that clears it, stopping the search right there. The two are not simply “careful” and “lazy” versions of the same process — they are genuinely different stopping rules, and research comparing the two styles has consistently found that maximizers tend to report lower satisfaction with the choices they end up making, even in cases where those choices are, by external measures, objectively just as good or better. That last clause is the counterintuitive part worth actually working through, rather than just repeating as a slogan, and a worked example computed with ReDecide’s own weighted decision matrix shows exactly where the gap between “better outcome” and “more satisfied” opens up.

A worked example: touring four apartments in order

Suppose you are apartment hunting, using four criteria — rent affordability, commute, space, and building condition — weighted 30, 25, 25, and 20 points respectively. You tour four apartments in the order they became available and score each one from 1 to 10 on every criterion as you see it. Feeding all four into the matrix at once, for reference, gives: Apartment 1 scores 54.5% of the maximum, Apartment 2 scores 77.5%, Apartment 3 scores 55.5%, and Apartment 4 — the last one toured — scores 84.5%, the actual best of the four.

Now replay that as a live search rather than a finished table. A satisficer who set a threshold of 75% before starting would tour Apartment 1 (54.5%, below the bar, keep looking), then Apartment 2 (77.5%, clears the bar) — and stop right there, signing a lease without ever touring Apartments 3 or 4. A maximizer touring the same four in the same order keeps going through all four, notices Apartment 3 (55.5%) does not beat Apartment 2, but Apartment 4 (84.5%) does, and rents Apartment 4 instead. On the matrix’s own terms, the maximizer’s search paid off: Apartment 4 is a genuinely better apartment than Apartment 2, by a full 7 percentage points. This is not a case where maximizing wastes effort chasing an illusion — the extra searching found a real, measurable improvement. And yet the documented pattern holds anyway: across many such searches, maximizers as a group tend to report being less satisfied with what they end up with, despite outcomes that are, on average, at least as good and often better than what satisficers land on.

Why a better outcome doesn't buy more satisfaction

Several ordinary, well-understood mechanisms combine to produce this gap, and none of them require assuming maximizers are doing anything irrational.

• More visible alternatives mean more available regret. Having toured all four apartments, the maximizer has a vivid, specific memory of the three not chosen. Every minor way Apartment 4 falls short of perfect — a slightly awkward kitchen layout, a longer walk to the nearest store — has a concrete alternative sitting right next to it in memory, ready to be compared against. The satisficer, having stopped at the second apartment they saw, simply has fewer specific counterfactuals on hand to generate that comparison in the first place.
• The search does not end when the decision does. A maximizer’s decision rule has no natural stopping point built into it beyond “the best I can find,” which is a moving target that new information can always challenge. That habit of continued comparison does not switch off the moment the lease is signed — noticing a fifth apartment listed the week after move-in is a very different experience for someone whose rule is “keep comparing” than for someone whose rule already delivered a clean, closed answer.
• Diminishing returns on search have no natural brake for a maximizer. The gain from touring a third and fourth apartment can be large, as it was here, or it can be marginal — there is no way to know in advance which. A satisficer’s threshold caps the search cost regardless of which case turns out to be true. A maximizer’s rule keeps paying the cost of continued search even during long stretches where it is not turning up meaningful improvements, and that sunk search effort itself becomes one more thing to feel was either well spent or wasted, depending on how the eventual result compares.
• Awareness of the road not taken. Simply having seen more options increases how concretely you can picture what you gave up, independent of how good your actual choice turned out to be. This is closely related to the regret-minimization idea covered elsewhere on this site: more visible alternatives create more surface area for anticipated or after-the-fact regret to attach to, regardless of the objective quality of what you chose.

It is a habit per decision, not a fixed personality

It is tempting to read all of this as sorting people into two permanent types, but the more useful way to think about it is as a habit that gets applied, or not, decision by decision, and often varies quite a lot for the same person across different areas of life. Someone can be a committed maximizer about which restaurant to try, reading a dozen reviews before booking a table, while being a comfortable satisficer about which shirt to buy, grabbing the first one that fits and looks fine. Recognizing that the tendency is domain-specific rather than a fixed trait is useful precisely because it means the fix is available on a case-by-case basis: you do not need to overhaul your entire decision-making personality, you just need to notice, for the specific decision in front of you right now, whether the search you are running actually matches the stakes involved.

A quick diagnostic helps catch a maximizing habit running on a decision that does not deserve it. Ask whether you are still comparing options mainly because new information keeps changing your view, or mainly out of a background worry that something better might exist somewhere you have not looked yet. The first is a legitimate reason to keep searching. The second is the maximizing instinct running on autopilot, largely independent of whether more searching is actually likely to turn up anything that matters. Ask, too, whether you have a specific number or standard in mind for “good enough,” or whether the bar is implicitly “whatever the best thing I eventually find turns out to be” — a standard defined only in hindsight, which by construction can never be cleared until the search simply stops from exhaustion rather than from genuine satisfaction.

Setting a satisficing threshold well

A satisficing threshold only works if it is set honestly and set in advance, for the same reason a decision matrix’s weights need to be set before you see the scores. Two practical anchors help. First, borrow directly from the fix described in the piece on overcoming analysis paralysis: write down the specific, concrete bar an option must clear — not a vague sense of “good enough,” but the actual rent ceiling, the actual maximum commute, the features you refuse to live without — before you start touring or comparing anything. Second, if you are running a weighted decision matrix rather than a simple pros-and-cons list, the tool’s percentage-of-maximum output, introduced in the guide on using a weighted decision matrix, gives you a natural, comparable number to set a threshold against — “anything at or above 75% of the maximum clears my bar” is a concrete, checkable rule in a way that “a pretty good apartment” never quite manages to be.

When maximizing is worth the extra cost anyway

None of this is an argument that satisficing is always the right call. The apartment example shows a real case where continued search found a genuinely better outcome, and for some decisions that extra margin is worth far more than the search cost or the satisfaction hit. The decisions worth maximizing on tend to share a few traits: they are infrequent rather than routine, so the search cost is not something you are paying over and over; they are hard to reverse once made, which is covered in more depth in the piece on reversible versus irreversible decisions, so a mediocre outcome carries a real, lasting cost rather than one you can easily correct later; and the gap between a good option and the best option is plausibly large, rather than a case of diminishing returns where the tenth option compared is barely distinguishable from the third. A once-in-a-decade decision that is hard to undo and where options genuinely vary a great deal is a reasonable candidate for maximizing despite the satisfaction cost. A choice you make often, can easily change later, and where the options cluster closely together in quality is exactly the kind of decision where satisficing wins on both time and how you end up feeling about the result — which, for the great majority of everyday choices, describes the situation far more often than it does not.