论文标题
部分可观测时空混沌系统的无模型预测
Are you aware of what you are watching? Role of machine heuristic in online content recommendations
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Since recommender systems have been created and developed to automate the recommendation process, users can easily consume their desired video content on online platforms. In this line, several content recommendation algorithms are introduced and employed to allow users to encounter content of their interests, effectively. However, the recommendation systems sometimes regrettably recommend inappropriate content, including misinformation or fake news. To make it worse, people would unreservedly accept such content due to their cognitive heuristic, machine heuristic, which is the rule of thumb that machines are more accurate and trustworthy than humans. In this study, we designed and conducted a web-based experiment where the participants are invoked machine heuristic by experiencing the whole process of machine or human recommendation system. The results demonstrated that participants (N = 89) showed a more positive attitude toward a machine recommender than a human recommender, even the recommended videos contain inappropriate content. While participants who have a high level of trust in machines exhibited a negative attitude toward recommendations. Based on these results, we suggest that a phenomenon known as algorithm aversion might be simultaneously considered with machine heuristic in investigating human interaction with a machine.
