Satisficing vs Maximizing: Which Decision Style Actually Wins, According to Research

TL;DR. Neither style wins outright, and the research says why. In Schwartz and colleagues’ 2002 studies (1,747 participants across seven samples), scores on the Maximization Scale correlated .52 with regret, -.25 with happiness and .34 with depression. In Iyengar, Wells and Schwartz’s 2006 job-search study, students high in maximizing secured jobs with 20% higher starting salaries and were less satisfied with them. Then the measurement debate began. When Diab, Gillespie and Highhouse (2008) measured maximizing as high standards only, its correlation with life satisfaction was .04 instead of -.24. Sorting 36 published findings by component, CEOtudent’s analysis finds that high standards were linked to a worse result in 2 of 10 findings, while decision difficulty was linked to a worse result in 4 of 5. The CEO move: write the stopping rule for each class of decision before looking at options. The student move: keep standards high where learning compounds, and let “good enough” handle the rest.

This piece pairs with the personal decision stack, with the regret minimization framework for the few choices that deserve a full search, and with kill criteria, which apply the same “decide the rule first” logic to quitting.

What do satisficing and maximizing actually mean?

The words come from Herbert Simon. In “A Behavioral Model of Rational Choice” (1955) he replaced the all-knowing “economic man” with a chooser who sorts outcomes into satisfactory and unsatisfactory and takes the first alternative that clears an “aspiration level”. His example is a house seller who regards $15,000 as an acceptable price, anything above it as satisfactory and anything below it as unsatisfactory. A year later, in “Rational Choice and the Structure of the Environment” (1956), he put the idea in one sentence: organisms adapt well enough to “satisfice”; they do not, in general, “optimize.”

Two details matter for what follows. First, satisficing was never laziness. It is a search rule with a threshold: stop at the first option that meets the bar, so the outcome depends on where the bar is set. Second, Simon expected the bar to move. He wrote that when a person “finds it easy to discover satisfactory alternatives, his aspiration level rises”, and when it is hard, the level falls. That drifting threshold returns in the section on AI tools below.

Nearly half a century later, Barry Schwartz and five co-authors turned Simon’s distinction into a personality difference. Their 2002 paper proposed that some people (maximizers) habitually try to find the best option, while others (satisficers) habitually stop at good enough, and it introduced a 13-item Maximization Scale to tell them apart. That scale is why the question has a research literature, and also why the literature disagrees with itself.

What did the original maximizer studies find?

The 2002 paper administered the scale to seven samples, four of university students and three of community adults. Across all samples the scores ranged from 1.15 to 6.62 on a 7-point scale, with a mean of 3.88 and a median of 3.85. The headline was the pattern of correlations with wellbeing measures.

Table 1. Maximizing and wellbeing as printed in the original studies

Source and sample	Measure correlated with the Maximization Scale	Result as printed
Schwartz et al. 2002, all samples	Regret Scale	r = .52
Schwartz et al. 2002, samples where assessed	Subjective Happiness Scale	r = -.25
Schwartz et al. 2002, samples where assessed	Beck Depression Inventory (short form)	r = .34
Schwartz et al. 2002, Sample 3 (n = 100)	Satisfaction With Life Scale	r = -.27
Schwartz et al. 2002, Sample 1 (n = 82)	Regret / happiness / depression	.61 / -.21 / .24
Schwartz et al. 2002, Sample 2 (n = 72)	Regret / happiness / depression	.45 / -.34 / .44
Schwartz et al. 2002, Sample 4 (n = 401)	Regret / happiness	.39 / -.10
Schwartz et al. 2002, Sample 5 (n = 752, health care providers)	Regret / happiness / depression	.46 / -.28 / .31
Schwartz et al. 2002, Sample 6 (n = 220, urban train station)	Regret / happiness	.50 / -.17
Schwartz et al. 2002, Sample 6	Perfectionism	r = .25
Iyengar, Wells and Schwartz 2006 (abstract)	Starting salary of the job secured	20% higher for students with high maximizing tendencies
Iyengar, Wells and Schwartz 2006 (abstract)	Satisfaction with the job obtained	Maximizers less satisfied; more negative affect throughout the search

Sources: Schwartz et al. (2002), Table 2 and results text; Iyengar, Wells and Schwartz (2006), abstract. Correlations are copied as printed. The sign for Sample 4 happiness follows Table 2.

Three observations from recomputing the table (calc.py).

The six samples with printed correlations account for 1,627 of the 1,747 participants. In all six the happiness correlation is negative, from -.34 to -.10, and the regret correlation runs from .36 to .61. The direction is consistent; the size is not.

The effects are modest in variance terms. A correlation of -.25 means maximizing and happiness share about 6% of their variance. For depression (.34) the figure is about 12%, and for regret (.52) about 27%. Regret is the strong link.

The job-search study supplied the twist. Students high in maximizing got objectively better offers, 20% higher in starting salary, and evaluated the result more negatively. The authors attribute the gap to maximizers’ greater reliance on external sources of information and their fixation on realized and unrealized options. Only the percentage printed in the abstract is used here, because the full paper was not accessible.

If the story ended in 2006, the verdict would be simple: maximize for money, satisfice for peace of mind. It did not end there.

Why did later studies disagree?

Because the 13-item scale measures three things at once. Nenkov and colleagues (2008) confirmed a three-part structure: alternative search (keep looking at more options), decision difficulty (finding choices hard), and high standards (refusing to settle for second best). The full scale’s reliability ranged from .54 to .75 with a mean alpha of .70, and the three subscales averaged .60, .62 and .61, below the usual .70 convention. The facets also behaved differently: decision difficulty had a negative and significant correlation with life satisfaction and happiness, while the high standards facet did not, and did not correlate positively with depression.

That observation split the field:

• Diab, Gillespie and Highhouse (2008) built the Maximizing Tendency Scale around high standards alone and gave both scales to 210 students. The original scale correlated -.24 with life satisfaction and .45 with regret. The new scale correlated .04 with life satisfaction and .27 with regret. They concluded that the unhappy-maximizer picture may be due to poor measurement.
• Lai (2010) measured high standards and alternative search without decision difficulty. Maximizing then correlated positively with optimism and self-efficacy, and its association with regret was inconsistent.
• Turner and colleagues (2012) built three separate scales (decision difficulty, alternative search, satisficing) with 828 undergraduates. Satisficing correlated with subjective happiness at r = .44, alternative search was uncorrelated with the wellbeing indices, and decision difficulty correlated -.41 with optimism.
• Roets, Schwartz and Guan (2012) compared adults in the U.S. (N = 307), Western Europe (N = 263) and China (N = 218). After demographic controls, maximizing predicted lower wellbeing in the U.S. (beta = -.30) and Western Europe (beta = -.48) but not in China (beta = -.11, not significant).
• Giacopelli and colleagues (2013) took three maximizing measures to work, with 237 employed respondents. The original scale correlated -.16 with job satisfaction and .04 with annual income. The high-standards scale correlated .20 with job satisfaction and .15 with income. Among the facets, decision difficulty correlated -.21 with job satisfaction and high standards .28. Their caution: however measured, maximizing is not a particularly strong predictor next to conscientiousness and core self-evaluations.
• Oishi and colleagues (2014, abstract) found that American maximizers were more depressed and less happy on the Schwartz scale but actually happier than satisficers on the Diab scale. Among Japanese respondents, maximizers reported lower wellbeing regardless of the scale used.
• Dalal and colleagues (2015, abstract) went back to Simon’s text and concluded the Maximizing Tendency Scale is more theoretically and psychometrically valid than the Maximization Scale.
• Cheek and Schwartz (2016) reviewed 11 published measures and proposed a two-component model: a maximization goal (choose the best) pursued through a maximization strategy (alternative search). Decision difficulty and regret are, in their view, outcomes or causes, not components.
• Kim and Miller (2017) found that maximizers’ larger drop in liking for a chosen option after negative feedback was driven mainly by the decision difficulty factor.
• Misuraca and Fasolo (2018) called for a stop to the development of more maximization scales.
• Cheek and Goebel (2020) showed across two studies (total N = 639) that decision difficulty scales and indecisiveness scales correlate at .85 or higher. On this evidence, that part of the old scale is indecisiveness under another name.
• Vargová and colleagues (2020) tested the two-component model on 514 people from the general population in Slovakia. The search strategy correlated -.10 with happiness and .13 with depression. The goal (high standards) correlated .03 with happiness and .05 with depression.

One thread runs against the salary result. Parker, Bruine de Bruin and Fischhoff (2007), studying 360 people recruited in the Pittsburgh area, report that higher scores on the original scale went with lower decision-making competence (r = -.19 in their earlier study) and worse scores on an inventory of 41 negative life events (standardized beta = -.25 alone, -.12 with other variables included). On the original scale, maximizers did not reliably get better outcomes either.

So which style wins on outcomes, and which on wellbeing?

To answer, the findings above were coded one by one: which component was measured, whether the outcome was subjective (regret, happiness, depression, satisfaction) or objective (salary, income, competence tasks, reported life events), and whether scoring high went with a worse, null or better result. Unstarred correlations were coded null below the two-tailed .05 critical value for the sample size (.127 at N = 237).

Table 2. CEOtudent analysis of 36 published findings by component and outcome type

What was measured	Subjective outcomes: worse / null / better	Objective outcomes: worse / null / better	All findings: share worse
Composite Maximization Scale (search + difficulty + standards)	11 / 1 / 0	2 / 1 / 1	13 of 16 (81%)
High standards (maximizing as a goal)	2 / 3 / 3	0 / 0 / 2	2 of 10 (20%)
Alternative search (maximizing as a strategy)	2 / 2 / 0	0 / 1 / 0	2 of 5 (40%)
Decision difficulty	4 / 0 / 0	0 / 1 / 0	4 of 5 (80%)
All components	19 / 6 / 3 (28 findings)	2 / 3 / 3 (8 findings)	21 of 36 (58%)

CEOtudent analysis of Schwartz et al. (2002), Iyengar et al. (2006), Parker et al. (2007), Diab et al. (2008), Nenkov et al. (2008), Turner et al. (2012), Roets et al. (2012), Giacopelli et al. (2013), Oishi et al. (2014), Kim and Miller (2017) and Vargová et al. (2020). The full coding list is in calc.py. This is a tally of direction, not a meta-analysis: findings are not weighted by sample size, several come from the same sample, and the selection is limited to the studies reviewed here.

The composite scale looks bad because of what is inside it. Thirteen of 16 findings point the wrong way. The single “better” cell is the 20% salary result.

High standards look fine. Only 2 of 10 findings are worse: a link to regret in Diab’s data (r = .27, against .45 for the composite) and the Japanese sample in Oishi’s study. Five of 10 are better, including both objective findings (income).

Search is where the cost starts. No finding in this set links more searching to higher wellbeing. The effects are small (-.10 with happiness is about 1% of variance), but they never point up.

Decision difficulty is the real problem. Four of 5 findings are worse, and the fifth (income) is null. Since this component is close to indistinguishable from indecisiveness, the practical reading is that agonizing, not ambition, is what hurts.

The verdict

1. On subjective wellbeing, satisficing wins, but only against the kind of maximizing that means endless comparison and agonizing. Against high standards alone it does not win. Turner’s satisficing scale correlated with happiness at .44, but Vargová’s separate satisficing measure was related to nothing, so even this point is less settled than popular summaries suggest.
2. On objective outcomes, the evidence is thin and mixed. Eight findings: three better, three null, two worse. High standards went with slightly higher income and one study found a 20% salary premium, while two findings link the composite scale to weaker decision competence and more negative life events.
3. The style question is the wrong unit. The research supports separating three things the first scale fused: the standard, the amount of search, and the emotional difficulty. Keep the first high. Cap the second in advance. Treat the third as a signal that the cap is missing.

Does having more options change the answer?

The situational literature asks the same question about the environment instead of the person. Iyengar and Lepper (2000) ran the well-known field experiment with a limited array of 6 jams against an extensive array of 24. Nearly 30% of consumers who saw the limited display later purchased (31 people), against 3% for the extensive display (4 people), among the 249 shoppers who stopped at the booth: a tenfold difference in purchase rate. Then came the replications.

Table 3. Choice overload in two meta-analyses, as printed

Meta-analysis	Data	Result as printed
Scheibehenne, Greifeneder and Todd (2010)	63 conditions from 50 published and unpublished experiments, N = 5,036	Mean effect size D = 0.02 (95% CI -0.09 to 0.12); “virtually zero” with considerable variance between studies
Chernev, Böckenholt and Goodman (2015)	99 observations from 53 studies in 21 articles, N = 7,202	Four moderators significant: choice set complexity, decision task difficulty, preference uncertainty, decision goal
Chernev, Böckenholt and Goodman (2015)	Mean effect of assortment size, split by study type	b = .41 for studies without moderators; b = .04 (not significant) for studies with moderators

Sources: Scheibehenne et al. (2010), abstract and Results; Chernev et al. (2015), abstract and results text. In both papers a positive effect indicates choice overload.

The two meta-analyses are closer than they look. The first says the average effect of more options is about zero and varies a lot. The second says the variation is systematic: more options hurt when the options are complex, the task is hard, the chooser does not know what they want, and the aim is to pick rather than browse. Preference uncertainty is the moderator a person controls. Writing down what “good enough” means removes the condition under which a large option set does damage.

The maximizing literature reached the same conclusion from the other direction. Option count is not the problem, and neither are standards. An open-ended search with no pre-set threshold is.

Why do AI tools make the stopping rule more important?

This section is reasoning from the sources above, not new data.

Simon’s satisficer stops because search is costly. Generative AI tools remove much of that cost for a large class of decisions. More headline variants, more itineraries, another rewrite of the cover letter: each costs one prompt.

Three consequences follow from the research reviewed here.

The natural brake is gone. If cost was what ended search, and cost approaches zero, then search ends only when a rule ends it. Without one, anyone can behave like the high-search maximizer, the component that never showed a wellbeing benefit in the findings coded above.

The aspiration level drifts upward. Simon wrote in 1955 that the aspiration level rises when satisfactory alternatives are easy to discover. When an acceptable option appears in seconds, every option looks improvable and the bar moves each time it is met. A threshold written before the first prompt cannot drift.

Preference uncertainty gets exposed, not solved. Chernev’s moderators say large sets hurt most when the chooser does not know what they want. Asking a model for more options is often a way to postpone that question. The remedy is to state the criteria first and ask the tool to evaluate against them, not to generate until something feels right.

There is an opposite failure, covered in the piece on first-conclusion bias: taking the first AI answer without any standard at all. That is not satisficing in Simon’s sense, because no threshold is applied. The two errors have the same fix.

How should the stopping rule be set for each kind of decision?

CEOtudent editorial framework: the Stopping Rule Matrix. This is an editorial tool built on the pattern in Tables 1-3, not a research finding. The numeric defaults (three must-haves, a shortlist of five) are starting points to adjust.

Decision class	Reversibility	Stakes	Options	Rule	Threshold to write down first
A. Daily operating choices (what to eat, which template, which meeting slot)	Easy to reverse	Low	Any number	Satisfice hard: take the first option that passes	One or two must-haves and a time cap in minutes
B. Trials and tools (a subscription, a workflow, a course module)	Reversible at a known cost	Medium	Many	Satisfice, then review	Three must-haves, a spending cap, and a review date
C. Small but permanent (a gift, a one-off purchase)	Hard to reverse	Low	Many	Satisfice with a fixed sample	Number of options to look at, set before looking
D. Major commitments with few real options (a job offer among two or three, a co-founder)	Hard to reverse	High	Few	Maximize on standards: compare every option against all criteria	Ranked criteria and a minimum acceptable level for each
E. Major commitments with many options (a city, a degree, a career direction)	Hard to reverse	High	Many	Two stages: satisfice to a shortlist, maximize within it	Screening threshold, shortlist size of five, and a decision date
F. Skill and craft output (the quality of work that builds reputation)	Cumulative	High over time	Not a choice set	Keep the standard high, cap the search for tools and methods	The quality bar for the output, plus a limit on tool-switching

How the matrix maps to the evidence:

• Classes A-C are where the composite maximizer pays the regret cost for nothing: the gap between the first acceptable option and the best one is small, and the search is the expense. The piece on decision fatigue and day structure covers how to remove many of these choices entirely.
• Class D is where the salary result lives. With stakes high and options few, comparing everything is cheap and can pay. The risk is subjective, so the comparison should end on a date and the unchosen options should be closed.
• Class E is the case the choice-overload moderators describe: complex options, hard task, uncertain preferences. Stating criteria before searching is the step that matters most.
• Class F is the student lens. High standards carried little or no wellbeing cost in the data and went with slightly better objective results. Standards belong on the work; the search cap belongs on everything around it.

The CEO lens is that the rule is set in advance, by class, the way a company sets approval limits by amount. The decision journal protocol is the place to record the threshold and check later whether it was too high or too low.

Which rule fits the decision in front of you? A seven-question test

Answer before looking at any options.

1. Can this be undone within a month at a cost that would not matter in a year? If yes, it is class A or B. Satisfice.
2. Will the difference between a good option and the best option still be visible in a year? If not, satisfice regardless of how the choice feels.
3. Is the full set of realistic options small enough to compare completely in one sitting? If yes and the stakes are high, compare all of them once (class D).
4. Can the must-haves be written in one sentence right now? If not, the problem is preference uncertainty. Stop searching and define them first.
5. Is there a number or a date at which the search ends? If not, write one. An open-ended search is the pattern linked to lower wellbeing in the studies above.
6. Is the standard about the quality of the work, or about having picked the single best item? Keep the first. Drop the second for anything outside classes D and E.
7. After choosing, is there a plan to stop comparing? Unsubscribe from the alerts, close the tabs, archive the shortlist. Fixation on unrealized options is the mediator named in the job-search study.

Scoring: a “no” on question 4 or 5 means do not start searching yet. A “yes” on question 1 or a “no” on question 2 means the first option that meets the must-haves is the answer. Only high-stakes decisions that pass questions 3-5 earn a full comparison.

What this does not mean

It does not mean satisficers are happier people in general. The original correlations with happiness explain about 6% of variance, and almost every study here is cross-sectional. Regret-prone or low-mood people may be the ones who search more. Roets and colleagues note that their “effects” are relationships, not experimentally established causality.

It does not mean the scales measure the same thing. Eleven measures existed by 2016. In Diab’s data the two best-known scales correlated .48 with each other, which is about 23% shared variance. A headline about “maximizers” may describe high standards, endless search or indecisiveness, depending on the instrument.

It does not mean the pattern is universal. Maximizing was unrelated to wellbeing in the Chinese sample in Roets’s study and related to lower wellbeing on both scales in Oishi’s Japanese sample.

It does not mean maximizing pays. The 20% salary premium is one study of graduating students. Two other findings link the same scale to weaker decision competence and more negative life events, and in Giacopelli’s working sample the composite scale’s correlation with income was .04.

It does not mean lowering standards. Simon’s satisficer has a threshold, and the threshold can be demanding. The evidence is against unlimited search and against agonizing. It is not against wanting excellent results.

It does not mean Table 2 is a meta-analysis. It is an unweighted direction tally over a hand-selected set of studies with overlapping samples, and should not be quoted as an effect size.

Frequently asked questions

Is it better to be a satisficer or a maximizer?
For regret and day-to-day wellbeing, the research favors satisficing over the search-heavy, agonizing form of maximizing. For high standards alone, the evidence shows little or no wellbeing cost. The better question is which rule fits which decision.

Do maximizers really earn more?
One 2006 study found students high in maximizing secured jobs with 20% higher starting salaries. A 2013 study of 237 employed respondents found a correlation of .04 between the original scale and income, and .15 for a high-standards scale. The effect exists in places and is small.

Are maximizers more depressed?
On the original 13-item scale, maximizing correlated .34 with depression scores in the 2002 samples. Scales that measure high standards without decision difficulty mostly do not show that link, and a 2020 Slovak study found a correlation of .05 between the maximizing goal and depression.

What is a stopping rule?
A threshold or limit set before search begins that says when to stop: a list of must-haves, a number of options to examine, a deadline, or a budget. It is Simon’s aspiration level written down so that it cannot drift.

Is choice overload real?
Sometimes. One meta-analysis found a mean effect near zero (D = 0.02). A later one found reliable overload under four conditions: complex choice sets, difficult tasks, uncertain preferences, and a goal of choosing rather than browsing.

How does this apply to using AI tools?
When a tool can produce unlimited alternatives at almost no cost, search no longer ends on its own. Set the criteria and the number of rounds before prompting, and evaluate outputs against the criteria instead of generating until one feels perfect.

Sources

1. Simon, H. A. (1955). A Behavioral Model of Rational Choice. Quarterly Journal of Economics, 69(1), 99-118.
2. Simon, H. A. (1956). Rational Choice and the Structure of the Environment. Psychological Review, 63(2), 129-138.
3. Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., and Lehman, D. R. (2002). Maximizing Versus Satisficing: Happiness Is a Matter of Choice. Journal of Personality and Social Psychology, 83(5), 1178-1197.
4. Iyengar, S. S., Wells, R. E., and Schwartz, B. (2006). Doing Better but Feeling Worse: Looking for the “Best” Job Undermines Satisfaction. Psychological Science, 17(2), 143-150. (abstract)
5. Iyengar, S. S., and Lepper, M. R. (2000). When Choice Is Demotivating: Can One Desire Too Much of a Good Thing? Journal of Personality and Social Psychology, 79(6), 995-1006.
6. Parker, A. M., Bruine de Bruin, W., and Fischhoff, B. (2007). Maximizers versus Satisficers: Decision-Making Styles, Competence, and Outcomes. Judgment and Decision Making, 2(6), 342-350.
7. Diab, D. L., Gillespie, M. A., and Highhouse, S. (2008). Are Maximizers Really Unhappy? The Measurement of Maximizing Tendency. Judgment and Decision Making, 3(5), 364-370.
8. Nenkov, G. Y., Morrin, M., Ward, A., Schwartz, B., and Hulland, J. (2008). A Short Form of the Maximization Scale: Factor Structure, Reliability and Validity Studies. Judgment and Decision Making, 3(5), 371-388.
9. Lai, L. (2010). Maximizing Without Difficulty: A Modified Maximizing Scale and Its Correlates. Judgment and Decision Making, 5(3), 164-175.
10. Scheibehenne, B., Greifeneder, R., and Todd, P. M. (2010). Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload. Journal of Consumer Research, 37(3), 409-425.
11. Turner, B. M., Rim, H. B., Betz, N. E., and Nygren, T. E. (2012). The Maximization Inventory. Judgment and Decision Making, 7(1), 48-60.
12. Roets, A., Schwartz, B., and Guan, Y. (2012). The Tyranny of Choice: A Cross-Cultural Investigation of Maximizing-Satisficing Effects on Well-Being. Judgment and Decision Making, 7(6), 689-704.
13. Giacopelli, N. M., Simpson, K. M., Dalal, R. S., Randolph, K. L., and Holland, S. J. (2013). Maximizing as a Predictor of Job Satisfaction and Performance: A Tale of Three Scales. Judgment and Decision Making, 8(4), 448-469.
14. Oishi, S., Tsutsui, Y., Eggleston, C., and Galinha, I. C. (2014). Are Maximizers Unhappier Than Satisficers? A Comparison Between Japan and the USA. Journal of Research in Personality, 49, 14-20. (abstract)
15. Dalal, D. K., Diab, D. L., Zhu, X., and Hwang, T. (2015). Understanding the Construct of Maximizing Tendency: A Theoretical and Empirical Evaluation. Journal of Behavioral Decision Making, 28(5), 437-450. (abstract)
16. Chernev, A., Böckenholt, U., and Goodman, J. (2015). Choice Overload: A Conceptual Review and Meta-Analysis. Journal of Consumer Psychology, 25(2), 333-358.
17. Cheek, N. N., and Schwartz, B. (2016). On the Meaning and Measurement of Maximization. Judgment and Decision Making, 11(2), 126-146.
18. Kim, K., and Miller, E. G. (2017). Vulnerable Maximizers: The Role of Decision Difficulty. Judgment and Decision Making, 12(5), 516-526.
19. Misuraca, R., and Fasolo, B. (2018). Maximizing Versus Satisficing in the Digital Age: Disjoint Scales and the Case for “Construct Consensus”. Personality and Individual Differences, 121, 152-160.
20. Cheek, N. N., and Goebel, J. (2020). What Does It Mean to Maximize? “Decision Difficulty,” Indecisiveness, and the Jingle-Jangle Fallacies in the Measurement of Maximizing. Judgment and Decision Making, 15(1), 7-24.
21. Vargová, L., Zibrínová, Ľ., and Baník, G. (2020). The Way of Making Choices: Maximizing and Satisficing and Its Relationship to Well-Being, Personality, and Self-Rumination. Judgment and Decision Making, 15(5), 798-806.

Table 2 and the Stopping Rule Matrix are CEOtudent analyses and editorial frameworks: Table 2 is a direction tally recomputed from the cited papers, and the matrix and seven-question test are editorial tools, not research findings. Tables 1 and 3 reproduce figures as printed in the sources. Deliberately not used: the dollar salary figures often quoted for the 2006 job-search study (only the abstract was available, so only the printed 20% is used); any claim that a specific share of the population are maximizers (the sources report a scale mean and median, not a population split); and popular counts of how many decisions a person makes per day.

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This content was compiled with the support of AI following in-depth research, then written and prepared for publication by the CEOtudent editorial team.

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