Weathering the Storm: Discordant Learning about Reputations for Reliability

Abstract It is well established that state reputations impact international politics, but less is known about how these reputations change. We investigate one form of change by examining how individuals process new information. Using a logic of discordant learning, we expect good reputations to survive new and incongruent information that counters expectations. Good reputations can help states “weather the storm” in times of crisis. Such buffers have their limits, however, as strong incongruent signals can trigger large corrections in a state’s reputation. To analyze these expectations, we focus on alliance reliability. Using a pair of survey experiments, we find that individuals alter their perceptions of a state’s reputation when observing signals that deviate from the state’s prior reputation, and that good reputations are able to “weather the storm”. We also find that strongly incongruent signals affect good reputations more than others, suggesting “the bigger they are, the harder they fall” may also apply. Even in these large corrections, however, a reputation for reliability has lasting benefits. The analysis helps us understand when to expect changes in reputations for alliance reliability, which in turn may inform when reputation loss can influence alliance politics.