Psychological research has identified different patterns individuals have while making decisions, such as vigilance (making decisions after thorough information gathering), hypervigilance (rushed and anxious decision-making), and buckpassing (deferring decisions to others). We examine whether these decision-making patterns affect peoples’ engagement with AI-generated information in decision-making. In an online experiment with 810 participants tasked with distinguishing food facts from myths, we found that a higher buckpassing tendency was positively correlated with the likelihood of seeking AI information and reported reliance on AI, while being negatively correlated with the time spent reading AI explanations. In contrast, the higher a participant tended towards vigilance, the more carefully they scrutinized the AI’s information, as indicated by an increased time spent looking through the AI’s explanations. These findings suggest that a person’s decision-making pattern plays a significant role in their interactions with AI suggestions, which provides a new understanding of individual differences in AI-assisted decision-making.

In the board game Ticket to Ride, players race to claim routes and connect cities on a map of the U.S. In this work, we identify winning strategies for and potential improvements to Ticket to Ride by applying probabilistic and graph-theoretic concepts. We find that longer routes are overvalued, presenting a simple winning strategy for opportunistic players. The scoring scheme we propose—based on indicator random variables—prevents exploitation from this strategy and improves the competitive nature of the game. Using a variety of game data visualizations, we also investigate why players who connect particular pairs of cities perform better than others. In addition, we build a statistical model from the effective resistance of the game’s underlying graph structure to suggest how to choose the best pairs of cities.