Take the classic GBM (geometric Brownian motion) model for equities as an example:
ds = mu * S * dt + sigma * S * dW.
It is the basis for the classic Black-Scholes formula.
The model says volatility is constant, which is apparently not true considering the volatility smile. However, many practitioners use the formula, although they apply some interpolation scheme. For example, if the stock price is \$100, to price an option with strike price \$130, people may
- Ask big banks what Black-Scholes volatility they are using for strike prices of \$100, \$120, and \$140.
- Interpolate for a stock price of \$130.
- Plug that vol into Black-Scholes and calculate the option price.
Since everyone is applying the same formula, there's no risk or bad consequences to using an inaccurate formula, as long as it's "smartly" used, as in the example, with some interpolation to handle the volatility smile.
What's more, if there's any mispricing, it seems it's also hard to say what's the cause -- if a new model projected a different option price and the options on the market gradually converged to this value, it can be any reason, maybe the Black-Scholes model is not wrong but the users' interpolation is not accurate, maybe the whole environment changed so convergence is just by chance?
In this case, if there's another model, for example, a modification to the GBM model leading to a formula slightly different from Black-Scholes, how could one argue it's better?