Traditional study of Roman military communities has ignored or erased women and their families from daily military life. Archaeological and documentary evidence reveal the inescapable fact that residents of extended military communities interacted inside and outside Roman forts through habitation, commercial endeavors, and social obligations. As a result of having been segregated by historians into external communities women have been acknowledged as existing, but otherwise ignored. Not only have their social and economic contributions been disregarded, but even their identities have been overlooked. This chapter reviews the basic reasons historians have removed women from our conception of life in military contexts and then discusses the evidence for the presence and contributions of military women. The chapter closes with discussion of how the volume is organized. As becomes clear, the presence of women, children, and families within the forts and in the extramural settlements of the Roman army is beyond doubt, thanks to the diligent and sometimes contentious work of scholars over the last thirty years.

Predictions often falter because of human error. Most misses have much more to do with our own human shortcomings than with the technical sophistication of the method at hand. In our experience, forecasting errors occur when we discard or misinterpret evidence right in front of us. The clues are there, but we are blinded by our own filters. This is why it is essential to tackle such biases and discuss corresponding solutions. In this chapter, we’ll look at studies on the forecasting prowess of experts. Then, we’ll focus on cognitive biases that skew predictions. Finally, we’ll present an applied approach to minimize such biases.

The Conclusion argues that the reasonable person possesses an essence that can be traced across time and across the different jurisdictions we encountered. This essence concerns the concept’s fundamental acceptance that ours is always just one perspective among many and that the best way to understand and assess what others think, do and feel is to empathise with those others. Since the standard is not always understood or applied in this manner, the Conclusion offers a restatement of the function and rationale of the common law’s most illustrious character; the aim is to contribute to the realisation of the concept’s potential and to make it easier to identify instances of misuse. The section unfolds in three parts, which correspond to the three steps of judgement making through empathetic perspective taking: the intention to take the reasonable person’s perspective; the assumption of the reasonable person’s perspective; and the making of a judgement by reference to the reasonable person’s perspective. Ultimately the Conclusion argues that the concept of the reasonable person has significant potential to facilitate the making of tolerant and humane judgements in a diverse, globalised and dynamic society, provided that one remembers that the reasonable person is always someone else.

Insight experiences are powerful: They feel true, they are remembered, and they can shift our decisions and our beliefs. Feelings of insight are also accurate most of the time. However, recent work shows that it is possible to systematically induce false insights and even misattribute our Aha! moments to make false facts seem true. Insights, therefore, seem to be adaptive on average but error prone. This chapter suggests that these results can be integrated by thinking of insights as a metacognitive heuristic for selecting ideas from the stream of consciousness (dubbed the “Eureka heuristic”), reviews key findings about the accuracy of insights and where and why insights go wrong, and discusses implications for our understanding of the development of delusions, false beliefs, and misinformation. Ultimately, understanding the role that feelings of insight play in human cognition may make us better decision-makers in an uncertain world.

Much effort has been spent trying to determine who ‘Q’ is, yet little is known about the characteristics of who follows ‘Q’ and QAnon. This chapter discusses the cognitive processes, cognitive biases, and traits (e.g., beliefs and individual characteristics) possibly associated with QAnon followers. Cognitive processes such as delusional ideation, teleological thinking, cognitive closure, and Pierre’s (2020) socio-epistemic model are examined in the context of QAnon followers, along with a variety of cognitive biases (e.g., groupthink, confirmation bias, jumping to conclusions bias). Additionally, traits such as being racist (e.g., holding anti-Black and anti-Semitic attitudes), narcissism, Machiavellianism, and political affiliations are possible common traits among QAnon followers. A brief discussion of how QAnon is similar or different from other groups (e.g., conspiracy or religious groups) is offered, along with some research questions for future study about QAnon specifically. This understanding is crucial, especially as QAnon followers are gaining political power and agency.

Chapter 1 begins with the distinction between reasoning from associations and reasoning from rules – a distinction that will resurface in subsequent chapters on creativity and innovation. The associative system is reproductive, automatic, and emphasizes similarity. The rule-based system is productive, deliberative, and emphasizes verification. Daniel Kahneman’s (2011) best-selling book Thinking Fast and Slow introduced readers to how associative and rule-based reasoning influence the speed of responses. The third section on biases in reasoning describes Kahneman’s classic research with Amos Tversky on how the use of heuristics such as availability and representativeness influence frequency estimates. The final section discusses monitoring reasoning in which people use knowledge to improve their thinking skills. Monitoring reasoning is a metacognitive skill that controls the selection, evaluation, revision, and abandonment of cognitive tasks, goals, and strategies.

People’s impressions, attitudes, and judgments necessarily rely on samples of information. We introduce a sampling principle according to which people seek distinct information that is rare and diverse, and that allows to differentiate between contexts, objects, people, or groups. Among distinct information samples, however, negative information is overrepresented. This follows because in most information ecologies, negative compared to positive information is less frequent, but more diverse. Consequently, when perceivers sample distinct information, resulting impressions, attitudes, and judgments will be negatively biased.

Cross-sectional studies are a type of observational studies in which the researcher commonly assesses the exposure, outcome, and other variables (such as confounding variables) at the same time. They are also referred to as “prevalence studies.” These studies are useful in a range of disciplines across the social and behavioral sciences. The common statistical estimates from these studies are correlation values, prevalence estimates, prevalence odds ratios, and prevalence ratios. These studies can be completed relatively quickly, are relatively inexpensive to conduct, and may be used to generate new hypotheses. However, the major limitation of these studies are biases due to sampling, length-time bias, same source bias, and the inability to have a clear temporal association between exposure and outcome in many scenarios. The researcher should be careful while interpreting the measure of association from these studies, as it may not be appropriate to make causal inferences from these associations.

This chapter examines the role of behavioural incentives as influencing healthcare delivery. It describes decision-making in clinical contexts and the roles of biases that naturally occur in these settings. Sometimes these have negative impacts (e.g., medical errors) and other times positive impacts (e.g., role of social norms for positive change). Pay for performance (P4P) programmes are discussed in this context. This chapter also includes examples of interventions such as defaults in the electronic health record and how antibiotic prescribing programmes that use social norms can be important to change behaviours. However, they need to be applied carefully and in concert with clinical collaboration.

This chapter sets out the elements of multiple regression analysis. If properly designed this enables us to estimate the effect of each separate factor upon wellbeing. To find the explanatory power of the different factors, we run the equation using standardised variables, that is, the original variables minus their mean and divided by their standard deviation. The resulting coefficients – or partial correlation coefficients – reflect the explanatory power of the independent variation of each variable.

The surest way to determine a causal effect is by experiment. The best form of experiment is by random assignment. We then measure the wellbeing of the treatment and the control group before and after the experiment. The difference-in-difference measures the average treatment effect on the treated. Where random assignment is impossible, naturalistic data can be used and the outcome for the treatment group compared with a similar untreated group chosen by Propensity Score Matching.

I begin with the origins of reciprocity, since this motivational force takes a central position in my political economy of behavioural public policy. The behavioural influences that tend to be labelled as errors by most behavioural economists, and as such have served as the justification for a paternalistic direction in behavioural public policy, in an ecological sense may not be errors at all. We thus cannot conclude that attempts to modify people’s choices in accordance with these so-called errors will improve the lives of those targeted for behaviour change. Where people are imposing no substantive harms on others, policy makers should restrict themselves to protecting and fostering reciprocity, which benefits the group and most of the people who comprise it, irrespective of their own personal desires in life. However, when one party to an exchange uses the behavioural affects to benefit themselves but imposes harms on the other party, the concept of a free and fair reciprocal exchange has been violated. I thus argue that there is an intellectual justification to introduce behavioural-informed regulations against activities that impose unacceptable harms on others.