Animals Versus Animats: Or Why Not Model the Real Iguana?  

Oleh:

Webb, Barbara

Jenis: Article from Journal - e-Journal
Dalam koleksi: Adaptive Behavior vol. 17 no. 4 (Aug. 2009), page 269-286.
Topik: models; synthetic; simulation; simple animals; minimal cognition
Fulltext: 269.pdf (209.17KB)

Isi artikel

The overlapping fields of adaptive behavior and artificial life are often described as novel approaches to biology. They focus attention on bottom-up explanations and how lifelike phenomena can result from relatively simple systems interacting dynamically with their environments. They are also characterized by the use of synthetic methodologies, that is, building artificial systems as a means of exploring these ideas. Two differing approaches can be distinguished: building models of specific animal systems and assessing them within complete behavior–environment loops; and exploring the behavior of invented artificial animals, often called animats, under similar conditions. An obvious question about the latter approach is, how can we learn about real biology from simulation of non-existent animals? In this article I will argue, first, that animat research, to the extent that it is relevant to biology, should also be considered as model building. Animat simulations do, implicitly, represent hypotheses about, and should be evaluated by comparison to, animals. Casting this research in terms of invented agents serves only to limit the ability to draw useful conclusions from it by deflecting or deferring any serious comparisons of the model mechanisms and results with real biological systems. Claims that animat models are meant to be existence proofs, idealizations, or represent general problems in biology do not make these models qualitatively different from more conventional models of specific animals, nor undermine the ultimate requirement to justify this work by making concrete comparisons with empirical data. It is thus suggested that we will learn more by choosing real, and not made-up, targets for our models.

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