Decision-Making in Research Tasks with Sequential Testing
نویسندگان
چکیده
منابع مشابه
Decision-Making in Research Tasks with Sequential Testing
BACKGROUND In a recent controversial essay, published by JPA Ioannidis in PLoS Medicine, it has been argued that in some research fields, most of the published findings are false. Based on theoretical reasoning it can be shown that small effect sizes, error-prone tests, low priors of the tested hypotheses and biases in the evaluation and publication of research findings increase the fraction of...
متن کاملGroup Decision-Making Models for Sequential Tasks
The sequential probability ratio test (SPRT) and related drift-diffusion model (DDM) are optimal for choosing between two hypotheses using the minimal (average) number of samples and relevant for modeling the decision-making process in human observers. This work extends these models to group decision making. Previous works have focused almost exclusively on group accuracy; here, we explicitly a...
متن کاملTesting Optimality of Sequential Decision-Making
This paper provides a statistical method to test whether a system that performs a binary sequential hypothesis test is optimal in the sense of minimizing the average decision times while taking decisions with given reliabilities. The proposed method requires samples of the decision times, the decision outcomes, and the true hypotheses, but does not require knowledge on the statistics of the obs...
متن کاملConvergence in a sequential two stages decision making process
We analyze a sequential decision making process, in which at each stepthe decision is made in two stages. In the rst stage a partially optimalaction is chosen, which allows the decision maker to learn how to improveit under the new environment. We show how inertia (cost of changing)may lead the process to converge to a routine where no further changesare made. We illustrate our scheme with some...
متن کاملLearning Sequential Decision Tasks
This paper presents a new approach called SANE for learning and performing sequential decision tasks. Compared to problem-general heuristics, SANE forms more e ective decision strategies because it learns to utilize domain-speci c information. SANE evolves neural networks through genetic algorithms and can learn in a wide range of domains with minimal reinforcement. SANE's evolution algorithm, ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS ONE
سال: 2009
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0004607