When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?


  • Smitha Milli University of California, Berkeley
  • Falk Lieder University of California, Berkeley
  • Thomas Griffiths University of California, Berkeley




bounded-optimality, metareasoning, cognitive systems


While optimal metareasoning is notoriously intractable, humans are nonetheless able to adaptively allocate their computational resources. A possible approximation that humans may use to do this is to only metareason over a finite set of cognitive systems that perform variable amounts of computation. The highly influential "dual-process" accounts of human cognition, which postulate the coexistence of a slow accurate system with a fast error-prone system, can be seen as a special case of this approximation. This raises two questions: how many cognitive systems should a bounded optimal agent be equipped with and what characteristics should those systems have? We investigate these questions in two settings: a one-shot decision between two alternatives, and planning under uncertainty in a Markov decision process. We find that the optimal number of systems depends on the variability of the environment and the costliness of metareasoning. Consistent with dual-process theories, we also find that when having two systems is optimal, then the first system is fast but error-prone and the second system is slow but accurate.




How to Cite

Milli, S., Lieder, F., & Griffiths, T. (2017). When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11156