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Comment by vikramkr | Hacker News Reader

Why is too few models the issue? Wouldn't the problem be that the existing models arent good enough? Its not clear to me how an increased diversity in models addresses the problem - at the end if the day a form has to pick one to make any particular decision. If each firm is using a different model in the name of diversity, then while the few firms with more accurate models potentially do well the subpar models will underperform in general and probably increase the overall risk level of the industry. Is there something I'm overlooking from my skin through the article here?

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No model is going to be perfect and given that, you want errors that models make to be as uncorrelated as possible.

In the extreme case, if all firms use a single model that underestimates the risk of an event then that event occurring poses a threat to the entire industry.

With more models, each making different errors, only parts of the industry are exposed to each error. The more models, the smaller the number of affected firms and with reinsurance between firms the risk to the whole industry is much smaller.

The basic question is whether the reinsurance companies are properly capitalized given the homogeneity of risk taken on by the insurance companies they cover.

There can be a lot of modeling homogeneity without lots of unanticipated risk (e.g., natural disasters in disparate geographic regions).

It's a question of fragility. If everyone thinks the same about risk, they will be holding similar bags when something happens.

Think of it like an ecology. If there's some firms being on A and some on B, the catastrophe selects for one set. If everyone is the same they all die.

Why would you assume that models can be ranked on a single one-dimensional scale from good to bad? I mean, they can, but only retroactively and by throwing away a lot of other useful information.

Maybe few permutations tend towards local Optima? Like in genetic algorithm models.

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