>But the behavior you observed is still pathological, dependent on fine tuning of the model parameters, and has nothing to do with the relationship between the

>But the behavior you observed is still pathological, dependent on fine tuning of the model parameters, and has nothing to do with the relationship between the model and reality. Basically, a mirror of modern DNN approaches: data driven correlations and hyperparemter tuning. Main difference are that with MAS, humans are usually tuning (this is changing though) vs DNN approaches, the tuning is algorithmically driven. The one benefit I would claim is that with these MAS, you at least know which base assumptions you're really tweaking to converge on states of interest. The question is (as always) if you've accurately: reduced the problem and represented principle interactions in the model. Given the complexity of real world systems MAS try to model, I think this is optimistically incredibly challenging and more realistically, nearly impossible to achieve currently. I think it's still a useful modeling approach that should be explored though I feel it's still in it's infancy compared to numeric, analytic, statistical, etc. models. I think if its taken the surge of computing infrastructure to drive the current successes we're seeing in DNN, it may take an equally larger surge to see improvements in MAS.