Chapter 5 examines expert reasoning, with a focus on detectives solving murder cases. I introduce the data-frame theory of sensemaking, which argues that experts and novices share the same modes of reasoning, both relying heavily on causal models and simulation, but with experts using richer models informed by their experience and training. We also see how detectives use specialist knowledge and organizing structures, including chronologies, crime typologies and legal scripts, all attuned to the investigative context. Finally, I argue that most research on crime investigation focuses on how investigators explain evidence, but it gives us few details about how investigators evaluate complex evidence.

Chapter 1 introduces the main questions to be tackled in the book, tracing a crime case from the discovery of a body to the verdict of a court. How do we generate initial hypotheses from sparse information? How do we develop our hypotheses as we gather new evidence? How do we make sense of a large body of conflicting evidence to reach a final decision? The crime case highlights the challenges we face: to explain what actually happened – the web of cause and effects that led to the death – but also to evaluate the evidence, assessing the many reports, claims and counterclaims. I argue that our minds are better prepared for explaining than evaluating.

For over a century, the field of forensic science has been applying contemporary technology to the investigation of crime. The imperative to identify offenders, particularly in relation to serious offences, has meant that governments are willing to invest in new technologies to achieve this objective. Fingerprinting, first developed in the late 19th century to identify individuals based on the unique patterns on the fingertips, led the way as one of the earliest means of identifying people, and is still used today in a digitised format.