Matching, maximizing, and hill-climbing.
Matching and maximizing come from the same moment-by-moment rule—look at local reinforcement rates to understand choice patterns.
01Research in Context
What this study did
The authors built a math model. They asked: can one simple rule explain both matching and maximizing?
The rule is called momentary maximizing. At each choice point, the learner picks the option with the best local payoff.
They showed this hill-climbing rule can make long-run data look like matching even when the learner is really maximizing.
What they found
Matching and maximizing are not two separate laws. One local rule can create both patterns.
The key is the time window you watch. Short views show maximizing. Long views show matching.
How this fits with other research
GRADARDANO et al. (1964) saw pigeons switch between matching and maximizing when they changed small details. The new model explains why: the same bird can do both.
Schenk et al. (2020) later found matching in a basketball video game. Their human data fit the same math the model predicts.
Laposa et al. (2017) showed shoppers maximize per-unit value. The hill-climbing rule works there too, just with different reinforcers.
Why it matters
When a client’s choices look like matching, check the local moment-to-moment payoffs. A small shift in immediate consequences can flip the pattern to clear maximizing. Test one variable at a time and watch short slices of data, not just session totals.
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02At a glance
03Original abstract
In simple situations, animals consistently choose the better of two alternatives. On concurrent variable-interval variable-interval and variable-interval variable-ratio schedules, they approximately match aggregate choice and reinforcement ratios. The matching law attempts to explain the latter result but does not address the former. Hill-climbing rules such as momentary maximizing can account for both. We show that momentary maximizing constrains molar choice to approximate matching; that molar choice covaries with pigeons' momentary-maximizing estimate; and that the "generalized matching law" follows from almost any hill-climbing rule.
Journal of the experimental analysis of behavior, 1983 · doi:10.1901/jeab.1983.40-321