Attention Competitive Bayes：用未达目标建模选择行为的最小模型
Satisficing as competitive Bayesian weighting: a minimal model of goal-directed choice under an unattained target
arXiv 上一篇理论论文，把满足化学习规则改写成贝叶斯估计器 ACB，还证明不可达目标下探索不会收敛，喜欢决策理论建模的可以看看推导。
论文提出风险敏感满足规则（RS），一种多臂老虎机学习规则，按各动作历史试验份额乘以相对抱负水平的盈余或缺口来估值。作者将 RS 改写为贝叶斯式估计器 Attention Competitive Bayes（ACB），其中抱负水平充当每个动作的先验均值，有限试验预算决定证据对先验的覆盖强度。模型推出两个结论：未被采样的动作会向抱负水平松弛，形成无需丢弃数据的伪遗忘；在 Bernoulli bandit 仿真中，依据证据调整抱负水平可降低 regret。ACB 为未实现目标如何组织选择提供了可分析框架，并对有机体如何重估已放弃选项给出可检验预测。
Satisficing as competitive Bayesian weighting: a minimal model of goal-directed choice under an unattained target
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 García-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 multiplied by its surplus or shortfall relative to an aspiration level. We show that RS can be rewritten as a Bayesian-like estimator, Attention Competitive Bayes (ACB), in which the aspiration level is the prior mean of every action and the finite budget of trials sets how strongly evidence can override it. Two consequences follow. First, actions that are no longer sampled are partially overridden by the goal rather than by past observations: their estimates relax toward the aspiration level without any data being discarded. This ``pseudo-forgetting'' distinguishes ACB from models with an explicit forgetting rate. Second, when the aspiration is unattainable, exploration does not settle; in Bernoulli bandit simulations, adjusting the aspiration level from evidence reduced regret (expected loss). ACB offers an analyzable model in which an unattained goal and a limited attentional resource jointly constrain behavior, and it yields testable predictions about how organisms revalue options they have abandoned.