We live in an age of information abundance but know little about how this influences our opinions or attitudes. A common expectation is that people consulting numerous pieces of information, well balancing the different sides of an issue, will adopt a moderate attitude about the issue. We claim that this expectation is deceitful and suggest that people tend to get extreme and dogmatic about an issue when they consult abundant unbiased information. The cause for this extremization is a hardening confirmation bias -- when their attitude gets more extreme, people get more likely to ignore information that differs from their views. Our claim is based on simulations of two fundamentally different computational models: a Bounded Confidence model and an empirically calibrated Persuasive Argument model. For both models, the attitude tends to be extreme when the computational agent consults abundant unbiased information. We analyze the extremization pathways displayed in the models and discuss how our results may affect views on polarization, and on the role of online media.

翻译：我们生活在一个信息充裕的时代，但对其如何影响我们的观点或态度知之甚少。一种普遍的预期是，当人们参考大量信息并充分权衡议题的不同方面时，会就该议题形成温和的态度。我们认为这种预期具有欺骗性，并提出当人们参考大量无偏见信息时，反而容易对议题形成极端且教条化的立场。这种极端化的根源在于一种强化的确认偏误——当态度趋于极端时，人们更可能忽视与自身观点相左的信息。我们的论断基于对两种根本不同的计算模型的模拟：有限信任模型和经实证校准的说服性论证模型。在这两种模型中，当计算主体参考大量无偏见信息时，其态度均倾向于极端化。我们分析了模型中展现的极端化路径，并讨论了研究结果如何影响对极化现象及在线媒体作用的认知。