Policy and Complex Systems
More subtly , there could also be certain predation strategies that work against continued spread of highly effective genes in the population . For example , many predator species share the results of the hunt , which raises the possibility that the so-called “ free rider ” problem is a limiting factor , by conferring a benefit to a diversity of predator individuals rather than just the most effective hunters . This reduces the gains for effective evolutionary adaptations and therefore increases the cost-to-benefit ratio associated with such gains .
In the present work , the model described in Section 2.1 is expanded to consider two alternative ways to increase ( or decrease ) the efficiency of the predator population . The outcomes of these two changes are then compared and contrasted in terms of the effects on the population as a whole , for both predators and prey . The second set of experiments expands the model even further , to include an explicit “ energy ” requirement for individual predators to move and hunt , as well as a mechanism for evolving efficiency . Of course , there is also the possibility of a concurrent change in the energy expenditure required for increased efficacy , so that an increase in hunting effectiveness requires a greater expenditure in energy .
III - Experimental Design
3.1 - First Experiment
Here we consider two different
methods for increasing ( or decreasing ) the efficiency of the predator population . As noted in Section 1.2 , the number of “ turns per tick ” for the predators can be changed , which has consequences for both predator and prey populations . If this number is reduced , then the predator population as a whole has fewer actions relative to the prey for a given period . Another control for changing the attributes of the predator population has been added to the model called the “ predator success rate .” In the baseline model if a predator finds prey on its current patch it will eat 100 percent of the time . With this new controller there is now a chance that the predator will “ miss ” the prey or that the prey will escape .
Intuitively , these two different methods of controlling the predator population would seem to have very similar effects ; both act to reduce the effectiveness of the predator population . If each predator has , for example , half as many actions per time step , that would seem to be similar in effect as if each predator misses its prey half the time . However , as shown below , this turns out not to be the case .
3.2 - Results — First Experiment
In the first case , where the number of turns per tick is reduced by one third , the new equilibrium result is that the predator population is higher ; also by approximately one third ( Figure 2 ). Interestingly , none of the other monitored outcomes has changed the prey population size , the prey consumption rate or average age , or the predators ’ relative consumption rate and average age . Note that for the predators changing the “ turns per tick ” in reality does change their average age and consumption rate when compared with the prey . However , the average predator still consumes the same number of prey over its own lifetime ; reducing the “ turns per tick ” by one third simply increases the predator lifetime by one third , because a unit of “ lifetime ” is advanced during each turn , not during a simulation time step .
In the second case , we reduce the effectiveness of each predator , so that approximately one third of the time the predator will “ miss ” the local prey . In this
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