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Wald意义下的局部π-Bayes最优决策
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作者 胡必锦 《应用数学》 CSCD 北大核心 1993年第1期96-101,共6页
本文在某些必要的适当条件下,采用一种独特的方式证明Wald意义下的最优决策的存在性.并表明:Wald意义下的最优决策作为一种π-Bayes最优决策实际上是局部的.
关键词 最优决策规则 Wald猜想 假设检验
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Evolution of a flexible rule for foraging that copes with environmental variation 被引量:2
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作者 Andrew D. HIGGINSON Tim W. FAWCETT Alasdair I. HOUSTON 《Current Zoology》 SCIE CAS CSCD 2015年第2期303-312,共10页
Models of adaptive behaviour typically assume that animals behave as though they have highly complex, detailed strategies for making decisions. In reality, selection favours the optimal balance between the costs and b... Models of adaptive behaviour typically assume that animals behave as though they have highly complex, detailed strategies for making decisions. In reality, selection favours the optimal balance between the costs and benefits of complexity. Here we investigate this trade-off for an animal that has to decide whether or not to forage for food - and so how much energy reserves to store - depending on the food availability in its environment. We evolve a decision rule that controls the target reserve level for different ranges of food availability, but where increasing complexity is costly in that metabolic rate increases with the sensitivity of the rule. The evolved rule tends to be much less complex than the optimal strategy but performs almost as well, while being less costly to implement. It achieves this by being highly sensitive to changing food availability at low food abun- dance - where it provides a close fit to the optimal strategy - but insensitive when food is plentiful. When food availability is high, the target reserve level that evolves is much higher than under the optimal strategy, which has implications for our under- standing of obesity. Our work highlights the important principle of generalisability of simple decision-making mechanisms, which enables animals to respond reasonably well to conditions not directly experienced by themselves or their ancestors. 展开更多
关键词 Behavioural mechanisms Fat storage Fluctuating environments GENERALISATION OBESITY Rule of thumb
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