Accidents happen in nature, from simple incidents like bumping into obstacles, to erroneously arriving at the wrong location, to mating with an unintended partner. Whether accidents are problematic for an animal depends on their context, frequency, and severity. In this article, we investigate the question of how accidents affect the task performance of agents in an agent-based simulation model for a wide class of tasks called “multi-agent territory exploration” tasks (MATE). In MATE tasks, agents have to visit particular locations of varying quality in partially observable environments within a fixed time window. As such, agents have to balance the quality of the location with how much energy they are willing to expend reaching it. Arriving at the wrong location by accident typically reduces task performance. We model agents based on two location selection strategies that are hypothesized to be widely used in nature: best-of-n and min-threshold. Our results show that the two strategies lead to different accident rates and thus overall different levels of performance based on the degree of competition among agents, as well as the quality, density, visibility, and distribution of target locations in the environment. We also show that in some cases, individual accidents can be advantageous for both the individual and the whole group.
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