I argue that moral intuitions are guided by social heuristics, which are not distinctive from other heuristics in the adaptive toolbox. One and the same heuristic can solve problems that we call moral and those we do not. That perspective helps explain the processes underlying moral intuition rather than taking it as an unexplained primitive. While moral psychologists debate over whether our moral sense is reflective and rational, as in Lawrence Kohlberg’s theory, or intuitive and nonrational, as in Jonathan Haidt’s theory, I believe that any assumed opposition and ranking is a misleading start. Both intuition and deliberation are involved in moral behavior, as they are in decision-making in general. The result of deliberation may become automized over a lifetime or generations, or intuitive judgments may be justified post hoc. If Darwin is right that the function of morality is to create and maintain the coherence of groups, then social heuristics are the tools towards that goal. This adaptive view explains apparent systematic inconsistencies in moral behavior and takes the phenomenon of moral luck seriously. Virtue is found not only in people, but also in environments.

Intuition is an ultimate experience, beyond words: we know more than we can tell. This phenomenon upsets those who believe in rationality as a purely conscious activity. Its detractors tend to dismiss intuition as crazed superstition, while others have confused it with God’s voice. The Intelligence of Intuition extends the argument for the rationality of intuition made in my book Gut Feelings with a deeper scientific analysis. I locate intuition in its larger societal context and argue that intuition is based on the unconscious use of adaptive heuristics. These simple rules make intuition smart.

Intelligence evolved to cope with situations of uncertainty generated by nature, predators, and the behavior of conspecifics. To this end, humans and other animals acquired special abilities, including heuristics that allow for swift action in face of scarce information. In this chapter, I introduce the concept of embodied heuristics, that is, innate or learned rules of thumb that exploit evolved sensory and motor abilities in order to facilitate superior decisions. I provide a case study of the gaze heuristic, which solves coordination problems from intercepting prey to catching a fly ball. Various species have adapted this heuristic to their specific sensorimotor abilities, such as vision, echolocation, running, and flying. Humans have enlisted it for solving tasks beyond its original purpose, a process akin to exaptation. The gaze heuristic also made its way into rocket technology. I propose a systematic study of embodied heuristics as a research framework for situated cognition and embodied bounded rationality.

Collaboration between researchers has become increasingly common, enabling a level of discovery and innovation that is difficult to achieve by a single person. But how can one establish and maintain an environment that fosters successful collaboration within a research group? In this case study, I use my own experience when directing the ABC Research Group at the Max Planck Institute for Human Development in Berlin. I describe the heuristic principles for setting up a research group, including (i) common topic and multiple disciplines, (ii) open culture, (iii) spatial proximity, and (iv) temporal proximity. I then describe heuristics for maintaining the open culture, such as setting collective goals, including contrarians, distributing responsibility, making bets, the cake rule, and side-by-side writing. These heuristics form an adaptive toolbox that shapes the intellectual and social climate. They create a culture of friendly but rigorous discussion, embedded in a family-like climate of trust where everyone is willing to expose their ignorance and learn from the other members. Feeling accepted and trusted encourages taking the necessary risks to achieve progress in science.

To develop a scientific perspective on intuition, we first need to dispense with the old and misleading dualistic opposition of intuition and reason. Rather, intuition and reason go hand in hand: where a doctor feels that something is wrong with a patient, intuition comes first, followed by a deliberate search for what is wrong. Even abstract disciplines like mathematics, need both intuition and reasoning. As George Pólya emphasized, finding a problem or discovering a proof requires intuition and heuristics; checking whether the proof is correct requires logic and analysis. The theoretical framework to understand the nature of intuition is that of ecological rationality, the study of how mental processes are adapted to their environments, based on Herbert Simon’s notion of bounded rationality, i.e., how people make decisions under uncertainty – where the best action cannot be calculated. Good intuitions rely on adaptive heuristics that are not logically, but ecologically, rational. The fluency heuristic, the recognition heuristic, and satisficing exemplify tools of the adaptive toolbox. Under uncertainty, they can lead to better decisions than complex algorithms.

Libertarian paternalists argue that psychological research has shown that intuition is systematically flawed and we are hardly educable because our cognitive biases resemble stable visual illusions. Thus, they maintain, authorities who know what is best for us need to step in and steer our behavior with the help of nudges. Nudges are nothing new; justifying them on the basis of a latent irrationality is. Technological paternalism is government by algorithms, with tech companies and state governments using digital technology to predict and control citizens’ behavior. This philosophy claims first that AI is, or soon will be, superior to human intuition in all respects; second, people should defer to algorithms’ recommendations. I contend that algorithms and big data can outperform humans in tasks that are well-defined and stable, e.g., playing chess and working on assembly lines, but not in ill-defined and unstable tasks, e.g., finding the best mate and predicting human behavior. Misleadingly, the “dataist” worldview promotes algorithms as if these were omniscient beings and so people should allow them to decide for the good of each what job to accept, whom to marry, and whom to vote for.

Behavioral economics began with the promise to fill the psychological blind spot in neoclassical theory, and ended up portraying intuition as the source of irrationality. The portrait goes like this: people have systematic cognitive biases causing substantial costs, biases are persistent like visual illusions and hardly educable, therefore governments need to step in and steer people with the help of “nudges.” The biases have taken on the status of truism. In contrast, I show that this view of human nature is tainted by a “bias bias,” the tendency to spot biases even if there are none. This involves failing to notice when sample parameters differ from population parameters, mistaking people’s random error for systematic error, and confusing intelligent inferences with logical errors. I use celebrated biases to explain the general problem. Getting rid of the bias bias will be a precondition for a positive role of human intuition and psychology in general.

I distinguish three versions of the idea of a peculiarly female intelligence, each devised by men to explain and justify their superior social position. First, from Aristotle through to the nineteenth century, the difference was understood in terms of polarities, e.g., female intuition version male reason. Abilities such as abstract thought, considered alien to women, were seen as indispensable for grasping moral principles. Influenced by Darwin’s theory of evolution, Francis Galton replaced the polarities with a single continuous general intelligence (“natural ability”), which be believed was inherited by men and women. This second version granted women and men the same kind of intelligence, although women, on average, were believed to have less of it. In the early twentieth century, Louis Terman put an end to this view by eliminating particular items from the Stanford-Binet test so that the means of male and female intelligence were the same – otherwise, female means would, in fact, have been higher. A third version, promoted by the sexologist Havelock Ellis, again attempted to defend male hegemony by asserting that women have lower variability in physical and mental traits.