scholarly journals Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

2022 ◽  
Vol 13 (1) ◽  
pp. 112-122
Author(s):  
Akihiro Oda ◽  
Takatomo Mihana ◽  
Kazutaka Kanno ◽  
Makoto Naruse ◽  
Atsushi Uchida
Keyword(s):  
Decision Making ◽  
Semiconductor Laser ◽  
Time Varying ◽  
Bandit Problem ◽  
Adaptive Decision Making

scholarly journals Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Takatomo Mihana ◽  
Yuta Terashima ◽  
Makoto Naruse ◽  
Song-Ju Kim ◽  
Atsushi Uchida
Keyword(s):  
Decision Making ◽  
Semiconductor Laser ◽  
Environmental Changes ◽  
Optical Feedback ◽  
Slot Machines ◽  
Bandit Problem ◽  
Oscillatory Dynamics ◽  
Degree Of Convergence ◽  
Adaptive Decision Making ◽  
Memory Parameter

We investigate the effect of a memory parameter on the performance of adaptive decision making using a tug-of-war method with the chaotic oscillatory dynamics of a semiconductor laser. We experimentally generate chaotic temporal waveforms of the semiconductor laser with optical feedback and apply them for adaptive decision making in solving a multiarmed bandit problem that aims at maximizing the total reward from slot machines whose hit probabilities are dynamically switched. We examine the dependence of making correct decisions on different values of the memory parameter. The degree of adaptivity is found to be enhanced with a smaller memory parameter, whereas the degree of convergence to the correct decision is higher for a larger memory parameter. The relations among the adaptivity, environmental changes, and the difficulties of the problem are also discussed considering the requirement of past decisions. This examination of ultrafast adaptive decision making highlights the importance of memorizing past events and paves the way for future photonic intelligence.

Training Situation Awareness and Adaptive Decision-Making Skills Using a Desktop Computer Simulation

2009 ◽  
Author(s):  
Robert J. Pleban ◽  
Jennifer S. Tucker ◽  
Vanessa Johnson Katie /Gunther ◽  
Thomas R. Graves
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Situation Awareness ◽  
Desktop Computer ◽  
Adaptive Decision Making

The Emergence of Adaptive Decision Making in Children

1997 ◽  
Vol 24 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jennifer Gregan‐Paxton ◽  
Deborah Roedder John
Keyword(s):  
Decision Making ◽  
Adaptive Decision Making

scholarly journals Age Differences in Adaptive Decision Making: The Role of Numeracy

2014 ◽  
Vol 40 (11) ◽  
pp. 825-833 ◽  
Author(s):  
Yiwei Chen ◽  
Jiaxi Wang ◽  
Robert M. Kirk ◽  
Olivia L. Pethtel ◽  
Allison E. Kiefner
Keyword(s):  
Decision Making ◽  
Age Differences ◽  
Adaptive Decision Making

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

scholarly journals Macaque parvocellular mediodorsal thalamus: dissociable contributions to learning and adaptive decision‐making

2018 ◽  
Vol 49 (8) ◽  
pp. 1041-1054 ◽  
Author(s):  
Subhojit Chakraborty ◽  
Zakaria Ouhaz ◽  
Stuart Mason ◽  
Anna S. Mitchell
Keyword(s):  
Decision Making ◽  
Mediodorsal Thalamus ◽  
Adaptive Decision Making
Sign in / Sign up

Export Citation Format

Share Document