Herbert Simon coined the term “satisficing” in 1956 to describe a decision-making strategy that aims for adequacy rather than optimality. Instead of exhaustively evaluating every option, a satisficer sets a threshold and picks the first option that clears it. The concept has since become a cornerstone of behavioral economics, but the popular understanding rarely engages with the data on when satisficing actually works — and when it fails.

The Maximiser Trap

Research by Barry Schwartz and colleagues found that self-identified maximisers — people who insist on finding the best possible option — report lower life satisfaction, more regret, and more social comparison than satisficers. This held true even when maximisers achieved objectively better outcomes. The paradox is stark: getting more and enjoying it less.

But this framing oversimplifies the picture. A 2023 meta-analysis pooling data from over 14,000 participants across 34 studies found that the maximiser–satisficer distinction explains only a small fraction of variance in well-being outcomes (roughly 4–7%). Context matters enormously. Maximising pays off in high-stakes, low-frequency decisions — choosing a university, negotiating a salary — where the cost of search is justified by the magnitude of the outcome.

Where Satisficing Wins

The data are clearest in domains with three characteristics: high volume, low stakes, and diminishing marginal returns. Grocery shopping, selecting a restaurant for a weeknight dinner, choosing which article to read next — these are contexts where the search cost quickly exceeds any plausible gain from finding the “optimal” choice.

A natural experiment from a large-scale consumer dataset illustrates the point. When a supermarket chain reduced its jam selection from 24 varieties to 6, conversion rates increased tenfold. The cognitive overhead of maximising across 24 options was not just unpleasant; it was paralysing.

The Threshold Problem

The interesting empirical question is not whether to satisfice, but where to set the threshold. Too low, and you consistently settle for mediocrity. Too high, and you collapse back into maximising under a different name.

Simulated decision models suggest an adaptive approach: start with a moderate threshold and adjust based on the quality of early options. If the first few options are poor, raise your expectations (the environment is rich). If they are surprisingly good, lower the bar (you are already in a favourable region of the choice space). This “aspiration adaptation” strategy outperforms both fixed satisficing and exhaustive search across a wide range of simulated environments.

Why This Matters

The satisficing question is not merely academic. It touches hiring decisions, medical diagnoses, software architecture choices, and the daily triage of information. The data suggest that the optimal strategy is, fittingly, not to optimise your strategy — but to be good enough at deciding when good enough is good enough.