 There are at least three possible responses to the varying answers to the trade-off problem provided by different criteria. One response, favored by Forster and by Sober, is to argue that there is no genuine conflict here because the different criteria have different aims. Thus AIC and BIC might both be optimal criteria, if AIC is aiming to maximize predictive accuracy whereas BIC is aiming to maximize probable truth. Another difference that may influence the choice of criterion is whether the goal of the model is to extrapolate beyond given data or interpolate between known data points. A second response, typically favored by statisticians, is to argue that the conflict is genuine but that it has the potential to be resolved by analyzing (using both mathematical and empirical methods) which criterion performs best over the widest class of possible situations. A third, more pessimistic, response is to argue that the conflict is genuine but is unresolvable. Kuhn (1977) takes this line, claiming that how much weight individual scientists give a particular theoretical virtue, such as simplicity, is solely a matter of taste, and is not open to rational resolution. McAllister (2007) draws ontological morals from a similar conclusion, arguing that sets of data typically exhibit multiple patterns, and that different patterns may be highlighted by different quantitative techniques.