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Deliberate ignorance is the willful choice not to know the answer to a question of personal relevance. The question of whether a man is the biological father of his child is a sensitive issue in many cultures and can lead to litigation, divorce, and disinheritance. Thanks to DNA tests, men are easily able to resolve the uncertainty. Psychological theories that picture humans as informavores who are averse to ambiguity suggest men would do a DNA test, as does evolutionary theory, which considers investing in raising a rival's offspring a mistake. We conducted two representative studies using computer-based face-to-face interviews in Germany (n = 969) and Spain (n = 1,002) to investigate whether men actually want to know and how women would react to this desire. As a base line, Germans (Spanish) estimated that 10% (20%) of fathers mistakenly believe that they are the biological father of their child. Nevertheless, in both countries, only 4% of fathers reported that they had performed a DNA paternity test, while 96% said they had not. In contrast, among men without children, 38% (33%) of Germans (Spanish) stated they would do a DNA test if they had children, mostly without telling their partners. Spanish women with children would more often disapprove of a paternity test or threaten their husbands with divorce (25%) than would German women (13%). We find that a simple test of risk aversion, measured also by the purchase of non-mandatory insurances, is correlated with not wanting to know. [ABSTRACT FROM AUTHOR]

Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency—the human tendency to rely on the most recent information and ignore base rates—can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends's big-data algorithms. The second uses a result from memory research—the paradoxical effect that making numbers less precise increases recall—in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms. [ABSTRACT FROM AUTHOR]

I argue that psychology can learn from the natural sciences and focus on the weight that physics attributes to precise theories. Much of psychology can be increasingly characterized by theory aversion—yet not the kind motivated by positivism. Theory aversion in psychology arises from a conflict between two desires: to come up with a theory, and to avoid the necessary mental effort and time as well as the risk of refutation. The results are ersatz theories, or surrogates. I outline three common, but independent, research practices that avoid building precise theories of psychological processes: the null ritual, which allows researchers to get away with not specifying their research hypothesis; as-if theories, which refrain from modeling psychological processes; and lists of binary oppositions, as in dual-system theories, which consist of vague dichotomies. Psychologists could learn from physics to walk forward on two feet—theory and experiment—rather than hobble on one. [ABSTRACT FROM AUTHOR]