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Titlebook: Decision Making and Imperfection; Tatiana V. Guy,Miroslav Karny,David Wolpert Book 2013 Springer-Verlag Berlin Heidelberg 2013 Computation

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樓主: 候選人名單
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發(fā)表于 2025-3-23 12:41:09 | 只看該作者
Effect of Emotion and Personality on Deviation from Purely Rational Decision-Making,making their decision in association with positive emotions. Honest, conscientious, and introverted participants were more likely to accept offers. Our results suggest that factors others than a rational strategy may predict economic decision-making in the UG.
12#
發(fā)表于 2025-3-23 15:27:12 | 只看該作者
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發(fā)表于 2025-3-23 18:07:33 | 只看該作者
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發(fā)表于 2025-3-23 22:11:41 | 只看該作者
Data Mining: Statistical Analysiss is investigated so as to achieve the minimum mean Bayes risk; optimal quantizers are designed by a novel extension to the Lloyd-Max algorithm. It is proven that agents using identical quantizers are not optimal. Thus diversity in the individual agents’ quantizers leads to optimal performance. In a
15#
發(fā)表于 2025-3-24 03:13:58 | 只看該作者
16#
發(fā)表于 2025-3-24 08:53:09 | 只看該作者
Book 2013sions;.?.·???????? ?how to decrease decision makers‘ imperfection by reducing knowledge available;.?.·???????? ?how to decrease imperfection via automated elicitation of DM preferences;.?.·???????? ?a human‘s limited willingness to master the available decision-support tools as an additional source
17#
發(fā)表于 2025-3-24 13:18:41 | 只看該作者
18#
發(fā)表于 2025-3-24 18:09:03 | 只看該作者
Distributed Decision Making by Categorically-Thinking Agents,al thinking by Bayesian decision makers is modeled as classifying objects into a small number of categories with respect to their prior probabilities. The classification follows a quantization rule for the prior probabilities. The categorical thinking enables decision makers to handle infinitely man
19#
發(fā)表于 2025-3-24 22:33:22 | 只看該作者
20#
發(fā)表于 2025-3-25 00:37:34 | 只看該作者
Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate the Future,the following desirable characteristics: (1) Bounded rational players, (2) strategic players (i.e., players account for one another’s reward functions when predicting one another’s behavior), and (3) computational tractability even on real-world systems. We achieve these benefits by combining concep
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