The integration of artificial intelligence (AI) into judicial decision-making presents both opportunities and challenges, particularly in balancing legal certainty and judicial discretion. While AI-driven tools are designed to enhance consistency and efficiency, their growing influence may subtly reshape judicial reasoning, potentially narrowing judicial discretion. This chapter examines how algorithmic recommendations – rather than merely assisting adjudication – can become dominant reference points, steering judicial outcomes toward standardisation over case-specific interpretation. Drawing on empirical psychological research, behavioural law, and economics, and the works of Richard Posner, Aharon Barak, and other legal theorists, the chapter explores the psychological mechanisms underlying this shift, particularly phenomena known as ‘automation bias’ and the ‘anchoring effect’, which may unconsciously influence judicial decision-making. The analysis highlights these psychological and structural challenges, inviting reflection on how AI-driven legal certainty impacts judicial discretion and the space for individualised legal reasoning in modern adjudication.

Wise discourse in deliberative spaces requires rational thinking. This chapter presents a revised dual-process theory that integrates heuristic reasoning and Bayesian updating. The model envisions multiple intuitive systems and a metasystem that resolves conflicts among intuitions, selects strategies, and directs the search of evidence and hypothesis spaces. Reflecting bounded resource rationality, these searches are often inadequate (and overly passive) due to uncertainty about the benefits of further inquiry. Environmental feedback can prompt quasi-Bayesian updating of intuitions and strategies, but such feedback is often missing, leading to cognitive biases. Additional biases stem from memory sampling, misinformation, poor adaptation to environmental change, nonepistemic goals like identity protection, and ad hoc auxiliary assumptions. The relationship to argumentation and deliberative spaces is also analyzed. The chapter concludes that most people are basically rational but must learn which strategies are adaptive in particular environments – a task made harder in today’s rapidly changing digital landscape.

Chapter 9 synthesizes the book’s core themes on the waning of wisdom, human rationality, and wise deliberative spaces. Since the 1960s, wise spaces have declined due to eroding social capital, weakening trust in social institutions, shrinking civics education, diminished investigative journalism, media fragmentation, social media’s addictive design, and a revival of relativistic notions of truth. Rationality is compromised by cognitive biases, poorly adapted heuristics, digital misinformation, and insufficient search of evidence and hypothesis spaces. Groups can overcome these limits when deliberations follow wise discourse practices. The chapter offers cautious optimism. Human reasoning is guided by a reflective metasystem – pursuing accuracy or directional goals – and by intuitive subsystems, some quasi-Bayesian. This core capacity for rational thought is often obscured by biased views of outgroups, which wise deliberative spaces can counter by fostering understanding and finding common ground. Moreover, new face-to-face and online forums are being designed to strengthen civic discourse and bridge divides. A growing public appetite for such solutions signals potential for renewal.

Chapter 3 picks up on the topic of cheating and the benefits this adaptation confers to those who are able to pillage others’ resources undetected. It looks at the evolutionary implications of this capability in the human species, which can be traced back to the development of Theory of Mind. It then proceeds to consider cognitive adaptations that characterise mental life in the human species, marked by biases and heuristics that confer evolutionary advantages in terms of efficiency in the cognitive processing of salient information. These include stereotyping and xenophobia, which enabled our ancestors to distinguish friend from foe and to limit collaboration with similarly interested others for mutual benefit.

This chapter of the handbook examines the complex relation between empathy and prosociality by drawing on evolutionary theory, neuroscience, psychology, and behavioral economics. The author begins by distinguishing three components of the broader phenomenon of empathy: emotional contagion, empathic concern, and perspective taking. He reviews evidence suggesting that emotional contagion of a conspecific’s pain often leads to helping behavior, but such contagion is modulated by group membership, levels of intimacy, and attitudes toward the other. Empathic concern, too, is a powerful motivator of prosocial behaviors but is also socially modulated – extended to some people more than others and to individuals more than groups. Effortful perspective taking, finally, can provide a better understanding of other people’s minds but does not always generate prosocial behavior, even when it facilitates empathic concern. In sum, various forms of empathy can motivate prosocial behaviors, but empathy is fragile and often stops short of its potential when people engage with large groups, people outside of their tribe, or anonymous strangers.

This chapter of the handbook posits utilitarianism as a standard of rational moral judgment. The author does not directly defend utilitarianism as a theory but investigates cases of apparent contradiction between people’s moral decisions (sometimes grounded in nonutilitarian principles) and the consequences of those decisions that they themselves would consider worse for themselves and everybody else. For example, when some people use a moral principle (e.g., bodily autonomy) to assertively make a decision (e.g., to not get vaccinated), it has negative moral consequences for others (e.g., infecting people) and for themselves (risking infection). The author asks whether such contradictions in moral reasoning can provide insights into some of the determinants of such reasoning. These insights, importantly, are valuable even for those who do not adopt utilitarianism as a normative model. From over a dozen candidate moral contradictions, the author concludes that many deviations from utilitarian considerations in moral contexts are reflections of familiar nonmoral cognitive biases, but some arise from adherence to strong moral rules or principles (e.g., protected or sacred values).

Human minds are particularly biased when processing information in digital environments. Behavioral economics has highlighted many cognitive biases that afflict our economic decision making. We may choose people like ourselves for important jobs or we may focus on irrelevant characteristics. We may also focus on recent, available information because our brains interpret that as more relevant for the current situation, whereas, optimally, we might benefit from a deeper dive into collecting more representative or comprehensive data and analyzing it appropriately. Even the way information is presented influences whether we believe it. Designers of digital content and experiences need to be aware of and account for such biases when engaging users.

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.

The Conclusion revisits the general considerations introduced at the beginning of the book: what values are reflected in decisions to enforce some family law agreements, to refuse to enforce some others, and to regulate in various ways the rest. The summary urges both a general presumption of enforceability and a presciption of regulatory restrictions fitted to the different transaction types.

People must often make inferences about, and decisions concerning, a highly complex and unpredictable world, on the basis of sparse evidence. An “ideal” normative approach to such challenges is often modeled in terms of Bayesian probabilistic inference. But for real-world problems of perception, motor control, categorization, language comprehension, or common-sense reasoning, exact probabilistic calculations are computationally intractable. Instead, we suggest that the brain solves these hard probability problems approximately, by considering one, or a few, samples from the relevant distributions. By virtue of being an approximation, the sampling approach inevitably leads to systematic biases. Thus, if we assume that the brain carries over the same sampling approach to easy probability problems, where the “ideal” solution can readily be calculated, then a brain designed for probabilistic inference should be expected to display characteristic errors. We argue that many of the “heuristics and biases” found in human judgment and decision-making research can be reinterpreted as side effects of the sampling approach to probabilistic reasoning.

Describes insights from behavioural economics that challenge the standard assumptions about consumer and firm behaviour. Considers the implications of these insights for economic regulation

This chapter provides an introduction to behavioural health economics. Far from attempting to replace what we know about health economics as a discipline, behavioural health economics aims at complementing its foundations by relaxing some of its core assumptions. This implies taking a more ‘realistic depiction’ of individual motivation even though it makes it more complex work beyond simple mathematical formulation. By incorporating what are otherwise anomalies of rational decision-making (defined as purposeful decision-making), health economics can go the extra mile with this extended toolkit which we define as behavioural health economics. Our agent is constrained by the social norms of its place and suffers from status quo bias and endowment effects that introduce bias into making decision and evaluations. ‘Real individuals’ care about others and have social preferences with regard to other people’s well-being, and often suffer from self-control problems, where impulsivity and emotion translate into suffering from a specific form of short sightedness otherwise known as ‘present bias’). These problems are arguably more prominent in the health domain. Market price is not the only relevant variable guiding behaviour in health and health care, where insurance is the most common form of payment, and tangible monetary incentives are often not made salient to influence behaviour.

This chapter examines several behavioural regularities explaining health behaviours that provide alternative behavioural explanations of actual preventative choices (e.g., smoking, weight loss, exercise, safe sex). The chapter discusses the roles of taxes and information and how social incentives and designs that incorporate social and monetary incentives keeping in mind biases such as loss aversion can help change behaviour. The chapter describes biases related to prevention failures such as optimism, present and status quo biases and includes examples of prevention failures in health-related behaviours.