A long history of thought holds that stubbornness can be good for science. If individual scientists stick to their theories, even when they are not the most promising, the entire community will consider a wide set of options, engage in debate over alternatives, and, ultimately, develop a better understanding of the world. This talk looks to network modeling to address the question: is intransigence good for group learning? The answer will be nuanced. A diverse set of models show how some intransigence can improve group consensus formation. But another set of results suggests that too much intransigence, or intransigence of a stronger form, can lead to polarization and poor outcomes.