How will superhuman artificial intelligence (AI) affect human decision-making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 y (1950 to 2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players’ strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.
How will superhuman artificial intelligence (AI) affect human decision making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 years (1950-2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players’ strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.
This article presents Exploratory Only: an intuitive tool for conducting large-scale exploratory analyses easily and quickly. Available in three forms (as a web application, standalone program, and R Package) and launched as a point-and-click interface, Exploratory Only allows researchers to conduct all possible correlation, moderation, and mediation analyses among selected variables in their data set with minimal effort and time. Compared to a popular alternative, SPSS, Exploratory Only is shown to be orders of magnitude easier and faster at conducting exploratory analyses. The article demonstrates how to use Exploratory Only and discusses the caveat to using it. As long as researchers use Exploratory Only as intended—to discover novel hypotheses to investigate in follow-up studies, rather than to confirm nonexistent a priori hypotheses (i.e., p-hacking)—Exploratory Only can promote progress in behavioral science by encouraging more exploratory analyses and therefore more discoveries.
Previous research has demonstrated that consumers experiencing a self-threat can cope with the threat by engaging in compensatory consumption; that is, they consume products symbolic of the threatened attribute (e.g., sophisticated books symbolic of intelligence) to signal to themselves and others the possession of the threatened attribute. Little research, however, has examined how levels (i.e., severity or magnitude) of self-threat relate to compensatory consumption. In the present research, we manipulate levels of threat in the domain of intelligence and argue that—unlike a low-level threat—a high-level threat can discourage compensatory consumption. While compensatory consumption may sufficiently reduce self-discrepancy from a low-level threat, it may not sufficiently reduce self-discrepancy from a high-level threat. We therefore hypothesize that consumers will be less likely to engage in compensatory consumption after experiencing a high-level (vs. low-level) threat. Across four studies and an internal meta-analysis, we find directional (but not significant) evidence in support of our hypothesis.
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