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The Turing test for comparing computer performance to that of humans is well known, but, surprisingly, there is no widely used test for comparing how much better human-computer systems perform relative to humans alone, computers alone, or other baselines. Here, we show how to perform such a test using the ratio of means as a measure of effect size. Then we demonstrate the use of this test in three ways. First, in an analysis of 79 recently published experimental results, we find that, surprisingly, over half of the studies find a decrease in performance, the mean and median ratios of performance improvement are both approximately 1 (corresponding to no improvement at all), and the maximum ratio is 1.36 (a 36% improvement). Second, we experimentally investigate whether a higher performance improvement ratio is obtained when 100 human programmers generate software using GPT-3, a massive, state-of-the-art AI system. In this case, we find a speed improvement ratio of 1.27 (a 27% improvement). Finally, we find that 50 human non-programmers using GPT-3 can perform the task about as well as-and less expensively than-the human programmers. In this case, neither the non-programmers nor the computer would have been able to perform the task alone, so this is an example of a very strong form of human-computer synergy. SIGNIFICANCE STATEMENTThe Turing test inspired generations of computer scientists to try to develop software as intelligent as humans. But we also need, not just more intelligent software, but more intelligent human-computer systems. The test proposed here can help scientifically evaluate and, thus, spur progress in developing such systems. For instance, contests like those that helped develop autonomous vehicles might, in this case, stimulate competition among teams of computer and social scientists developing humancomputer systems that perform far better than either people or computers alone. We believe this could benefit our economy and society by fostering the development of software to augment humans rather than just replace them. And in the long run, it might also lead to creating "superintelligent" human-computer systems.

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This article describes changing political visions of the Chinese literati during the two halves of the Song dynasty, as reflected in their discourse on the fengjian (classical enfeoffment) system of antiquity. In the aftermath of the An Lushan rebellion (755-763), a group of political thinkers criticized that system as an ungrounded historical anachronism. This idea gained currency among a majority of the Northern Song statesmen and literati who supported the centralization project of the founding emperors. With the fall of the Northern Song, the ancient fengjian doctrine resurfaced as a sustained constitutional discourse on government. Contesting the imperial vision of centralization and interventionism, Southern Song literati redefined good government for their time.

As moral philosopher Zhu Xi (1130–1200) sought to nurture the autonomous moral self. In his pedagogical scheme, one ought to cultivate the innate goodness of the heart, investigate principles in things, and embody ethical standards in daily life. In Zhu Xi’s view, the ability to exercise moral autonomy is obtained through a long period of moral and ethical training under the close surveillance of one’s immediate surroundings since early childhood. For this reason, Zhu Xi emphasized the practice of social norms as well as the performance of mundane rituals as the preconditions for the development of the autonomous moral self. By combining the Lesser Learning (xiaoxue 小學) with the Great Learning (daxue 大學), Zhu Xi articulated an integrated vision of moral development from the heteronomous performing of ethical duties to the autonomous embodiment of moral principles.