ArticleBehavior research methods2021
ADOpy: a python package for adaptive design optimization.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Rapid and reliable computational markers of decision-making for predicting daily smoking behavior and smoking cessation treatment outcomes.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization.Behavior research methods · 2024Article
- A systematic investigation into the reliability of inter-temporal choice model parameters.Psychonomic bulletin & review · 2023Review
- Rumination Derails Reinforcement Learning with Possible Implications for Ineffective Behavior.Clinical psychological science : a journal of the Association for Psychological Science · 2022Article
- Adaptive Design Optimization for a Mnemonic Similarity Task.Journal of mathematical psychology · 2022Article
- Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.Scientific reports · 2020Article
- Digital Learning Games for Mathematics and Computer Science Education: The Need for Preregistered RCTs, Standardized Methodology, and Advanced Technology.Frontiers in psychology · 2020Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Experimental design is fundamental to research, but formal methods to identify good designs are lacking. Advances in Bayesian statistics and machine learning offer algorithm-based ways to identify good experimental designs. Adaptive design optimization (ADO; Cavagnaro, Myung, Pitt, & Kujala, 2010; Myung, Cavagnaro, & Pitt, 2013) is one such method. It works by maximizing the informativeness and efficiency of data collection, thereby improving inference. ADO is a general-purpose method for conducting adaptive experiments on the fly and can lead to rapid accumulation of information about the phenomenon of interest with the fewest number of trials. The nontrivial technical skills required to use ADO have been a barrier to its wider adoption. To increase its accessibility to experimentalists at large, we introduce an open-source Python package, ADOpy, that implements ADO for optimizing experimental design. The package, available on GitHub, is written using high-level modular-based commands such that users do not have to understand the computational details of the ADO algorithm. In this paper, we first provide a tutorial introduction to ADOpy and ADO itself, and then illustrate its use in three walk-through examples: psychometric function estimation, delay discounting, and risky choice. Simulation data are also provided to demonstrate how ADO designs compare with other designs (random, staircase).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.