I particularly liked how O’Sullivan’s introduced the various types of ABMs by separating them into three categories depending on how realistic they are because it reminds me that, although we can create incredibly complex ABMs that resemble reality very closely, the value in “simple abstract models as thoughts experiments” (542) should not be underestimated. Bearing in mind that “complicated models may remain just as baffling as the world they purport to represent” (546), perhaps for many research questions, extremely realistic stimulations are not necessary. Simple models that explore the interactions between only a few theories can no doubt shed new light on problems even if the stimulated scenarios are not observed in reality. Thus, building ABMs to stimulate thought experiments could prove to be a useful tool at the exploratory stages of research and theory building.
The concept of equifinality and model verification also got me thinking. In complex and flexible systems, isn’t it more common to be able to reach a certain outcome through different means than through only one means? I think learning to accept the fact that many models may be “valid” and evaluating model outcomes in terms of “… the trajectory by which those outcomes are reached” (546-547) must go hand-in-hand. For instance, in a game of chess, the same final outcome (e.g. checkmate) is reached through thousands of different sets of moves. Thus, when comparing two highly skilled chess players, it is much more convincing to evaluate how each player executes his moves than to see whether or not he/she can deliver a checkmate.
-Ally_Nash