I'll preface this response as I entirely agree with Gavin, and if you're interested in fitting any type of model it should be reflective of the phenomenon under study. What the problem is with the logic of identifying any and all effects (and what Gavin refers to when he says data dredging) is that you could fit an infinite number of interactions, or quadratic terms for variables, or transformations to your data, and you would inevitably find "significant" effects for some variation of your data.
As chl states, these higher order interaction effects don't really have any interpretation, and frequently even the lower order interactions don't make any sense. If your interested in developing a causal model you should only include terms you believe could be pertinent to your dependent variable A priori to fitting your model.
If you believe they can increase predictive power of your model, you should look up resources on model selection techniques to prevent over-fitting your model.