In this study, we extend these digital evolution models to explore the conditions under which collective vigilance favors the evolution of gregarious foraging behavior. We assume that vigilance has benefits (e.g., communicating the presence of a predator via alarm signals) but also costs (e.g., reduced foraging rates by watching for the predator). Under the many eyes hypothesis, grouping is beneficial because it reduces the cost of vigilance by sharing the cost of vigilance among the group, but it may have additional costs that must be consid- ered, e.g., increased predation rates on larger groups [37]. Furthermore, this benefit would be diluted if some individuals can freeload on the vigilance of others (as in heterogeneous groups), but magnified if the group members are highly related. The benefits and costs would also be affected by the life history of the prey, in particular whether their reproduc- tion is iteroparous (i.e., repeated) or semelparous (i.e., all at once): Vigilance may be more beneficial in semelparous prey because a predation event can completely prevent them from reproducing, whereas iteroparous prey are more likely to have reproduced at least once prior to experiencing a predation event. To explore these issues, we manipulate the genetic relat- edness and reproductive strategy of groups of prey that are under predation and observe the resulting behavior after thousands of generations of digital evolution have taken place. A preliminary investigation of this work was published in the ALIFE 14 conference [38], and has been significantly extended in this paper. 

## 2 Methods 

Figure 1 depicts our model of predator-prey interactions in a disembodied model, wherein prey must balance the trade-offs between foraging and vigilance [33]. In an embodied model [39], every animat is situated in the world, perceives the world via its sensors, and can act on the world via behavioral or other responses. While embodied models offer more detail, they are also sensitive to implementation-specific details of the sensors and actuators, which can skew results. We therefore focus on a disembodied model 1 for the remainder of 

