Chapter 12 is the conclusion. It presents a discussion of how the components of performance evaluation for learning algorithms discussed throughout the book unify into an overall framework for in-laboratory evaluation. This is followed by a discussion of how to move from a laboratory setting to a deployment setting based on the material covered in the last part of the book. We then discuss the potential social consequences of machine learning technology deployment together with their causes, and advocate for the consideration of these consequences as part of the evaluation framework. We follow this discussion with a few concluding remarks.

Experimental practices developed in different scientific disciplines following different historical trajectories. Thus, standard experimental procedures differ starkly between disciplines. One of the most controversial issues is the use of deception as a methodological device. Psychologists do not conduct a study involving deception unless they have determined that the use of deceptive techniques is justified by the study’s significant prospective scientific, educational, or applied value and that effective nondeceptive alternative procedures are not feasible. In experimental economics it is strictly forbidden and a ban on experiments involving deception is enforced by all major economic journals. In the sociological scientific community, there is no clear consensus on the matter. Importantly, the disagreement is sometimes based on ethical considerations, but more often it is based on pragmatic grounds: the anti-deception camp argues that deceiving participants leads to invalid results, while the other side argues that deception has little negative impact and, under certain conditions, can even enhance validity. In this chapter, we first discuss the historical reasons leading to the emergence of such different norms in different fields and then analyze and separate ethical and pragmatic concerns. Finally, we propose some guidelines to regulate the use of deception in sociological experiments.

This chapter is devoted to the study of infinitely divisible laws. It begins in §3.1 with a few refinements (especially the Lévy Continuity Theorem) of the Fourier techniques introduced in §2.3. These play a role in §3.2, where the Lévy–Khinchine formula is first derived and then applied to the analysis of stable laws.

This chapter provides the tools to compute catastrophe (CAT) risk, which represents a compound measure of the likelihood and magnitude of adverse consequences affecting structures, individuals, and valuable assets. The process consists of first establishing an inventory of assets (here real or simulated) exposed to potential hazards (exposure module). Estimating the expected damage resulting from a given hazard load (according to Chapter 2) is the second crucial step in the assessment process (vulnerability module). The application of damage functions to exposure data forms the basis for calculating loss estimates (loss module). To ensure consistency across perils, the mean damage ratio is used as the main measure for damage footprints D(x,y), with the final loss footprints simply expressed as L(x,y) = D(x,y) × ν(x,y), where ν(x,y) represents the exposure footprint. Damage functions are provided for various hazard loads: blasts (explosions and asteroid impacts), earthquakes, floods, hail, landslides, volcanic eruptions, and wind.

This chapter goes beyond the description of individual events by covering extremes caused by a combination of multiple events. Two main types of interactions are covered: domino effects and compound events. Domino effects, which represent one-way chains of events, are quantified using Markov theory and graph theory. Compound events, which include complex feedback loops in the complex Earth system, are modelled with system dynamics (as in Chapter 4). Two such systems are provided, the ESCIMO climate model and the World2 model of world dynamics. The impact of global warming, pollution, and resource depletion on catastrophes is investigated, as far as ecosystem and societal collapse. The types of catastrophes considered in this chapter are as follows: storm clustering, earthquake clustering (with accelerated fatigue of structures), domino effects at refineries (explosions, fires, toxic spills), cascading failures in physical networks (more precisely blackouts in a power grid), rainforest dieback, lake eutrophication, and hypothetical human population collapse.

Sociology is a science concerning itself with the interpretive understanding of social action and thereby with a causal explanation of its course and consequences. Empirically, a key goal is to find relations between variables. This is often done using naturally occurring data, survey data, or in-depth interviews. With such data, the challenge is to establish whether a relation between variables is causal or merely a correlation. One approach is to address the causality issue by applying proper statistical or econometric techniques, which is possible under certain conditions for some research questions. Alternatively, one can generate new data with experimental control in a laboratory or the field. It is precisely through this control via randomization and the manipulation of the causal factors of interest that the experimental method ensures – with a high degree of confidence – tests of causal explanations. In this chapter, the canonical approach to causality in randomized experiments (the Neyman–Rubin causal model) is first introduced. This model formalizes the idea of causality using the "potential outcomes" or "counterfactual" approach. The chapter then discusses the limits of the counterfactual approach and the key role of theory in establishing causal explanations in experimental sociology.

This final chapter demonstrates how the catastrophe (CAT) models described in previous chapters can be used as inputs for CAT risk management. CAT model outputs, which can translate into actionable strategies, are risk metrics such as the average annual loss, exceedance probability curves, and values at risk (as defined in Chapter 3). Practical applications include risk transfer via insurance and CAT bonds, as well as risk reduction, consisting of reducing exposure, hazard, or vulnerability. The forecasting of perils (such as tropical cyclones and earthquakes) is explored, as well as strategies of decision-making under uncertainty. The overarching concept of risk governance, which includes risk assessment, management, and communication between various stakeholders, is illustrated with the case study of seismic risk at geothermal plants. This scenario exemplifies how CAT modelling is central in the trade-off between energy security and public safety and how large uncertainties impact risk perceptions and decisions.

Field experiments have a long tradition in some areas of the social and behavioral sciences and have become increasingly popular in sociology. Field experiments are staged in "natural" research settings where individuals usually interact in everyday life and regularly complete the task under investigation. The implementation in the field is the core feature distinguishing the approach from laboratory experiments. It is also one of the major reasons why researchers use field experiments; they allow incorporating social context, investigating subjects under "natural" conditions, and collecting unobtrusive measures of behavior. However, these advantages of field experiments come at the price of reduced control. In contrast to the controlled setting of the laboratory, many factors can influence the outcome but are not under the experimenter’s control and are often hard to measure in the field. Using field experiments on the broken windows theory, the strengths and potential pitfalls of experimenting in the field are illustrated. The chapter also covers the nascent area of digital field experiments, which share key features with other types of experiments but offer exciting new ways to study social behavior by enabling the collection large-scale data with fine-grained and unobtrusive behavioral measures at relatively low variable costs.

This chapter focuses in more detail on the role of incentives in experimental sociology. Providing the right incentives in an experiment is an important precondition for drawing valid inferences. This is a predominant view in experimental economics based on the induced-value theory assuming that monetary incentives override any other human motivation in laboratory economic experiments. A slightly less demanding assumption is that subjects can be incentivized by monetary payoffs but are also motivated by other-regarding preferences or reciprocity. On the other hand, psychologists focus on motivations that subjects bring into the laboratory as a predisposition to behavior and on the framing of the situation. Sociological research takes elements from both perspectives and emphasizes institutional, cultural, and social determinants of human behavior. An important theoretical framework for experimental work is sociological work on framing. According to sociological framing theories, subjects interpret the situation in terms of the given cues and select an action that is appropriate to the situation. The chapter discusses the implications of these three views on the design of experiments in sociology.