Explaining Human Choice Probabilities with Simple Vector Representations
This provides a simplified model for understanding human decision-making in stochastic tasks, which is incremental as it builds on existing theories of probability matching.
The study tackled the problem of explaining human choice probabilities in stochastic environments by modeling participant choices using simple vector representations derived from choice frequency histograms, finding that only two basis policies—matching/antimatching and maximizing/minimizing—were sufficient to account for human behavior across various conditions.
When people pursue rewards in stochastic environments, they often match their choice frequencies to the observed target frequencies, even when this policy is demonstrably sub-optimal. We used a ``hide and seek'' task to evaluate this behavior under conditions where pursuit (seeking) could be toggled to avoidance (hiding), while leaving the probability distribution fixed, or varying complexity by changing the number of possible choices. We developed a model for participant choice built from choice frequency histograms treated as vectors. We posited the existence of a probability antimatching strategy for avoidance (hiding) rounds, and formalized this as a vector reflection of probability matching. We found that only two basis policies: matching/antimatching and maximizing/minimizing were sufficient to account for participant choices across a range of room numbers and opponent probability distributions. This schema requires only that people have the ability to remember the relative frequency of the different outcomes. With this knowledge simple operations can construct the maximizing and minimizing policies as well as matching and antimatching strategies. A mixture of these two policies captures human choice patterns in a stochastic environment.