Milli, Smitha (University of California, Berkeley) | Lieder, Falk (University of California, Berkeley) | Griffiths, Thomas L. (University of California, Berkeley)

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.

As businesses integrate Artificial Intelligence into their systems, technology professionals are looking at a new frontier of AI innovation. This is in the area of Meta-Learning. Meta-Learning is simply learning to learn. We humans have the unique ability to learn from any situation or surrounding. We can figure out how we can learn.

As businesses integrate Artificial Intelligence into their systems, technology professionals are looking at a new frontier of AI innovation. This is in the area of Meta-Learning. Meta-Learning is simply learning to learn. We humans have the unique ability to learn from any situation or surroundings. We adapt to our learning.

Lieder, Falk, Plunkett, Dillon, Hamrick, Jessica B., Russell, Stuart J., Hay, Nicholas, Griffiths, Tom

Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.