A causal model approach to dynamic control

Zach Davis, Neil Bramley, Bob Rehder, Todd Gureckis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Acting effectively in the world requires learning and controlling dynamic systems, that is, systems involving feedback relations among continuous variables that vary in real time. We introduce a novel class of dynamic control environments using Ornstein-Uhlenbeck processes connected in causal Markov graphs that allow us to systematically test people's ability to learn and control various dynamic systems. We find that performance varied across a range of test environments, roughly matching with complexity defined by a set of models trained on the task (an optimal model, a deep Reinforcement Learning agent, and a PID controller). The testbed of dynamic environments and class of models introduced in this paper lay the groundwork for the systematic study of people's ability to control complex dynamic systems.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Conference of the Cognitive Science Society
Place of PublicationAustin, TX
PublisherCognitive Science Society
ISBN (Print)9780991196784
Publication statusPublished - 31 Dec 2018
Event40th Annual Meeting of the Cognitive Science Society - Madison, United States
Duration: 25 Jul 201828 Jul 2018


Conference40th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2018
Country/TerritoryUnited States
Internet address


  • dynamic control
  • causal learning
  • dynamic deci-sion making
  • reinforcement learning
  • control theory


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