HKUST Business Review

Inspired By Nature: The Gaze Heuristic This framework was inspired by a natural, human phenomenon known as the Gaze Heuristic. The Gaze Heuristic is what allows people to automatically run in the right direction and at the right speed to catch a thrown object: Instinctively, when we see a moving object, we adjust our position such that the angle at which we have to look to keep the object in our sight remains the same. In other words, by continuously tracking the target and making real-time adjustments, we can optimize our decision-making in a complex and dynamic environment. Our framework applies the same principle to adjust any form of complex decision-making in response to shifting real-time data. Of course, as with any theoretical framework, what really matters is how this approach performs in real life. As such, to validate the effectiveness our approach in practice, we ran a series of simulations and experiments using a ride-matching simulator developed by our industry collaborator, based on real-world data in several Chinese cities. The Gaze Heuristic —a natural human phenomenon that inspires the solution framework—encapsulates the principle: Keep your eyes on the ball (target), run, and adjust speed (online action) to maintain a constant gaze angle throughout (attain the target). Leveraging real-time data to dynamically adjust prioritization schemes in the moment. Validated By Real-World Data Through these analyses, we found that our framework consistently and significantly outperformed conventional strategies that focus solely on minimizing pick-up distance. Specifically, real-world data from Beijing City indicated that our ride-matching system was associated with an improvement in average service quality of 0.92% per trip — which reflects more jobs being assigned to higher-quality drivers, which ultimately improves passengers’ experience — as well as a 1.88% increase in revenue per trip, which reflects that the algorithm prioritized longer trips that would bring in more revenue. Importantly, these improvements did come at a cost: In our analysis, average pick-up distance per trip increased by 55.20 meters, and the rate at which ride requests were not fulfilled increased by 1.58%, which reduced revenue. However, taken together, these costs were outweighed by the benefits, as our approach still resulted in a 0.26% increase in net revenue. This suggests that overall, our framework optimized for near-term revenue more effectively than the legacy policy implemented in our industrial simulator, while also boosting both driver satisfaction and service quality (two factors that are critical for fostering sustainable growth and customer loyalty in the long term). Clearly, our framework works well for ride-share matching. But what about other decision-making contexts? To further validate our findings, our framework was tested in two additional industries: food delivery and e-commerce. a a a Insight 10 HKUST Business Review