Bonnell et al.’s research paper deals with agent based modeling in relation to the feeding and movement patterns of Red Colobus monkeys. They tested for the effects of memory type, memory retention and social order within the group on their movement patterns. From a G.I.Science perspective, this article is interesting due to the fact that the authors used agent based modeling in order to get a better understanding of monkey behavior. By doing research such as this, they help to further expand the field of G.I.Science through the novel use of agent based modeling methods. Further development of A.B.M. (and specifically geospatial agents) could include such things as developing better models that provide more flexibility for individual agent choice, with obvious benefits to the field of G.I.Science.
What I found interesting was the potential that existed for visualizing the geographic data that was produced through the agent-based evaluation of the monkeys. Given that geography is inherently spatial, being able to visualize complex data (such as monkey movement patterns) would enable a better understanding of the processes at work. This visualization could also help with introducing A.B.M. to a wider audience and therefore help expand public participation of G.I.Science.
Tying this article in with the “Geospatial Agents, Agents Everywhere…” article, it is clear that the authors used Artificial Life Geospatial Units as the agents which they based the Red Colobus monkeys off of. It is also evident that the agents used in the study were geospatial in nature, not merely A.I. agents.
-Benny