arXiv: cs.AI preprintsInternational6 November 2026
Satisficing as competitive Bayesian weighting: a minimal model of goal-directed choice under an unattained target
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arXiv:2611.07270v1 Announce Type: cross Abstract: Animals and humans often do not maximize; they act relative to goals (aspiration levels) that are frequently not yet met. How can a goal that is not (yet) realized organize choice? Deacon (2012) calls such absence-referenced organization \emph{ententional}, and Garc\'{i}a-Valdecasas and Deacon (2024) argue that ends are best understood as constraints rather than as particular states. We examine this idea in a minimal formal setting: Risk-sensitive Satisficing (RS), a bandit learning rule that values each action by its share of past trials multi
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