Papers for

wildlife conservation planners

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Fish school behavior explained by position and exploration combined

Positional choice and robust collective behavior in fish schools: biohybrid experiments and modeling

Abstract: Collective behavior of fish schools is usually modeled on the assumption that each individual follows specific rules of motion that depend on its position and velocity relative to its neighbors. Although these models reproduce many schooling patterns observed in nature, it remains unclear whether the assumed rules are realistic at the individual level. To address this question, we first analyzed a set of experiments in which a live fish interacted with four moving robotic fish in a tank, and measured the time it spent in each position relative to the robots. We then simulated this experiment, replacing the fish with an agent, and assessed the extent to which classical models agree with the experimental observations. An extensive exploration of the parameter space showed that these models closely matched the experimental observations, with a very specific choice of parameters. However, they were highly sensitive to the parameter values: a minor perturbation caused them to fail completely. To resolve this, we propose a new model that incorporates an additional exploratory term characterizing the agent's positional preference at every moment. This model proved robust to small errors in the parameters while successfully replicating the experiments. Furthermore, when generalised to multiple fish, the model reproduced schooling behaviour and common schooling patterns, while also exhibiting exploratory behaviour at both the individual and the school level. This shows that social cohesion coexists with individual exploratory behaviour, and that realistic models of collective behaviour should account for both social interactions and this exploratory component.

Mon 28 SeptMultiagent Systems
The gist
Fish swim together in groups by following simple rules about where their neighbors are and how they move. The authors studied how a real fish behaved when swimming near robotic fish and then tested whether computer models could match this behavior. They found that classic models needed very precise settings to work, but by adding a factor for the fish’s own exploration, their new model was more realistic and stable. This improved model not only matched individual behavior better but also showed how groups of fish can stay together while still allowing for individual freedom to explore.
Open → 2609.35554v1