On partial information retrieval: the unconstrained 100 prisoner problem

被引:0
作者
Ivano Lodato
Snehal M. Shekatkar
Tian An Wong
机构
[1] Allos Limited,Department of Scientific Computing, Modeling, and Simulation
[2] Savitribai Phule Pune University,Department of Mathematics and Statistics
[3] University of Michigan-Dearborn,undefined
来源
Acta Informatica | 2023年 / 60卷
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学科分类号
摘要
We consider a generalization of the classical 100 prisoner problem and its variant, involving empty boxes, whereby winning probabilities for a team depend on the number of attempts, as well as on the number of winners. We call this the unconstrained 100 prisoner problem. After introducing the 3 main classes of strategies, we define a variety of ‘hybrid’ strategies and quantify their winning-efficiency. Whenever analytic results are not available, we make use of Monte Carlo simulations to estimate with high accuracy the winning probabilities. Based on the results obtained, we conjecture that all strategies, except for the strategy maximizing the winning probability of the classical (constrained) problem, converge to the random strategy under weak conditions on the number of players or empty boxes. We conclude by commenting on the possible applications of our results in understanding processes of information retrieval, such as “memory” in living organisms.
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页码:179 / 208
页数:29
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