Daniel Kahneman

The basis of standard finance is classical decision theory, where people are unbiased and maximize their self-interests. This approach assumes that individuals are rational. Being rational requires applying unlimited processing power to all available information and making optimal choices with internally consistent preferences to maximize an action's benefit. Classical decision theory explains how people should make decisions involving uncertainty. However, decades of research show that classical decision theory does not explain how people make decisions. Individuals suffer from many constraints on their cognitive capacity and time, making the necessary decision functions difficult and impractical. Thus, they make decisions without all available information and the necessary cognitive power.To make judgments under these constraints, people employ heuristics. Heuristics are mental shortcuts that can facilitate problem-solving and probability judgments. These mental strategies are rules-of-thumb that reduce the cognitive load otherwise needed. Heuristics are important and effective for making direct judgments. Nevertheless, they often result in irrational and predictably biased conclusions. This chapter begins by reviewing Tversky and Kahneman's (1974) original heuristics, then describes other behavioral biases, and ends with cultural biases.

Do larger incomes make people happier? Two authors of the present paper have published contradictory answers. Using dichotomous questions about the preceding day, [Kahneman and Deaton, Proc. Natl. Acad. Sci. U.S.A. 107, 16489–16493 (2010)] reported a flattening pattern: happiness increased steadily with log(income) up to a threshold and then plateaued. Using experience sampling with a continuous scale, [Killingsworth, Proc. Natl. Acad. Sci. U.S.A. 118, e2016976118 (2021)] reported a linear-log pattern in which average happiness rose consistently with log(income). We engaged in an adversarial collaboration to search for a coherent interpretation of both studies. A reanalysis of Killingsworth’s experienced sampling data confirmed the flattening pattern only for the least happy people. Happiness increases steadily with log(income) among happier people, and even accelerates in the happiest group …

In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose urgent priorities for AI R&D and governance.

During the pandemic, people have had to make elaborate risk assessments to decide whether to visit loved ones, or send their kids to school, or sometimes just leave the house. How did you see the phenomena that you've explored in your work–our intuitive and effortful ways of thinking and our mental shortcuts and biases–operating in the context of the pandemic?Kahneman: Well, the first thing that was very salient at the beginning of the pandemic was that people really find it difficult to deal with exponential growth. I recognized this in myself. I was about to take a flight to France when there were just a hundred cases in France; that didn't look like much, except it was doubling every couple of days. And that was really quite powerful.

Human behavior is a diverse, complex, and highly interesting phenomenon. Despite the many di erences that exist between any two people, we can find patterns that characterize typical behaviors in various situations, such as in a classroom or at a family dinner. The research field called behavioral economics studies human behavior in financial situations. In this article, I will present the main findings of the prospect theory, which I developed together with the late Amos Tversky. Prospect theory explains human choices in situations that involve gambling, and it answers questions such as whether people consider gains and losses equally and how a person’s initial financial situation influences the value they give to gains and losses. At the end of the article, I will share important insights from my scientific career and explain why happiness has two faces.

In this chapter, the authors concentrate on the “P300” wave and its possible psychological meaning. They begins by presenting a “classical data base”. A convenient approach to the analysis of the reactions to stimuli that is to the overt output of the system, derives from signal detection theory. This theory asserts that the overt output (i.e., the subject’s descriptions of his perceptions) is determined by the combined effects of the internal representation of the data and some action rules. The initial evaluation of a stimulus activates the controlled processing necessary to select an appropriate response. P300 latency can then occur before reaction time.

A key but neglected issue in the search for collective intelligence principles is the role of noise. Does noise inhibit collective intelligence or can it amplify the discovery of intelligent solutions? In this exchange of letters, the authors explore the pros and cons of noise.

A phenomenon labeled “group processing” is observed when arrays of letters or digits organized in perceptual groups are presented for immediate recall. Recall probabilities are similar within each group and vary sharply between groups. Grouped items tend to be recalled in all-or-none fashion, but there is evidence of competition between groups in recall. Adding an item to the last group in the array depresses performance in that group. The interference is equally severe when the added item is a relevant digit or an irrelevant suffix. These effects are predicted by a model of allocation rules for attention or processing resources. Alternative interpretations, in terms of visual interference or hierarchical coding, are tested and rejected. Grouping structure has very strong effects in a task of selective attention by color: total performance depends on the complexity of grouping, and the groups that occur late in the processing …