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Mailly, J.

Publications and source records attributed to Mailly, J..

2 recordsLinked to original sources

Human performance in the Traveling Salesman Problem is influenced by spatial scale

Like many other animals, we humans frequently face complex route optimization problems when planning journeys between multiple locations. Several strategies can be employed to approach such Traveling Salesman Problems. However, these may be strongly constrained by spatial scales, for instance if the goal is to navigate in a supermarket, in a city, or across a continent. Here, we monitored the route optimization performances of human subjects collecting objects in a video game simulating 3D environments at various spatial scales. Unexpectedly, route optimization performances peaked at intermediate spatial scales, where participants could use global optimization strategies. At very large and very small spatial scales, however, participants predominantly employed less efficient local optimization strategies. At the very large scale, the considerable distances between objects prevented them from being seen all at once, making global planning difficult. At the very small scale, participants reported having chosen the shortest path, although they did not. This mismatch between perceived and actual performances suggests suboptimal alternatives were sufficiently short to be considered equivalent to the optimal route. Spatial scale thus strongly influences route planning in human navigators and may determine the spatial behaviors of a wide range of animals facing similar routing problems in their everyday lives.

animal behavior and cognition↗

The influence of bee movements on patterns of pollen transfer between plants: an exploratory model

Most -if not all- pollinators make foraging decisions based on learning and memory. In interaction with environmental conditions and competitive pressure, pollinators cognition shapes their movement patterns, which in turn determine pollen transfers. However, models of animal-mediated pollination often make simplifying assumptions about pollinator movements, notably by not incorporating learning and memory. Better considering cognition as a driver of pollinators movements may thus provide a powerful mechanistic understanding of pollen dispersal. In this exploratory study, we connect pollinator behaviour and plant reproduction by using an agent-based model of bee movements implementing reinforcement learning. Simulations of two bees foraging together in environments containing twenty flowers shows how learning can improve foraging efficiency as well as plant pollination quality through larger mating distances and smaller self-pollination rates, while creating spatially heterogeneous pollen flows. This suggests that pollinators informed foraging decisions contribute to genetic differentiation between plant subpopulations. We believe this theoretical exploration will pave the way for a more systematic analysis of animal-mediated plant mating patterns, as model predictions can be tested experimentally in real bee-plant systems.

animal behavior and cognition↗