Intelligent Systems
Note: This research group has relocated.

Measuring the Costs of Planning

2020

Conference Paper

re


Which information is worth considering depends on how much effort it would take to acquire and process it. From this perspective people’s tendency to neglect considering the long-term consequences of their actions (present bias) might reflect that looking further into the future becomes increasingly more effortful. In this work, we introduce and validate the use of Bayesian Inverse Reinforcement Learning (BIRL) for measuring individual differences in the subjective costs of planning. We extend the resource-rational model of human planning introduced by Callaway, Lieder, et al. (2018) by parameterizing the cost of planning. Using BIRL, we show that increased subjective cost for considering future outcomes may be associated with both the present bias and acting without planning. Our results highlight testing the causal effects of the cost of planning on both present bias and mental effort avoidance as a promising direction for future work.

Author(s): Valkyrie Felso and Yash Raj Jain and Falk Lieder
Book Title: Proceedings of the 42nd Annual Meeting of the Cognitive Science Society
Year: 2020
Month: July
Editors: S. Denison and M. Mack and Y. Zu and B. C. Armstrong
Publisher: Cognitive Science Society

Department(s): Rationality Enhancement
Research Project(s): Measuring the Cost of Planning with Bayesian Inverse Reinforcement Learning
Human Planning and Decision-Making
Bibtex Type: Conference Paper (conference)

Event Name: CogSci 2020
Event Place: Toronto, Canada

State: Accepted

BibTex

@conference{Felso2020CogSci,
  title = {Measuring the Costs of Planning},
  author = {Felso, Valkyrie and Jain, Yash Raj and Lieder, Falk},
  booktitle = {Proceedings of the 42nd Annual Meeting of the Cognitive Science Society},
  editors = {S. Denison and M. Mack and Y. Zu and B. C. Armstrong},
  publisher = {Cognitive Science Society},
  month = jul,
  year = {2020},
  doi = {},
  month_numeric = {7}
}