The chapter traces the process by which German corporations largely, though still partially, abandoned their Jewish colleagues in the first eighteen months of Nazi rule and simultaneously shed their earlier, recurrent demands for a “state-free” economy in favor of accepting the Nazi statist one. The account places more than customary emphasis on the role of intimidation.

Conspiracy theories explain anomalous events as the outcome of secret plots by small groups of people with malevolent aims. Is every conspiracy unique, or do they all share a common thread? That is, might conspiracy explanations stem from a higher-order belief that binds together a wide variety of overtly independent phenomena under a common umbrella? We can call this belief the conspiracy frame. Network science allows us to examine this frame at two different levels: by examining the structural coherence of individual conspiracies and by examining the higher-level interconnectivity of the conspiracy beliefs as a whole.

When nodes share features we can combine those features in many possible ways. One standard way is to base relationships on shared features. But there are other possibilities. Here we will apply a number of approaches to investigate the concept of distinctiveness. Distinctiveness is how easy it is to discriminate one thing from another thing. In an important sense distinctiveness is therefore a hypothesis about how the mind works. We say two things are distinctive because a mind can distinguish them. But what makes something distinctive? In this chapter, I will introduce some of the theory behind distinctiveness and then demonstrate how we can use network science to investigate distinctiveness in children’s abilities to learn words. This takes a multilayer network approach, in which we will examine many different edge types constructed of various combinations of shared and unshared features. By examining these edge types will discover how best to combine features and which feature combinations best predict early word learning.

Structure matters for understanding behavior. This chapters introduces the main theme of the book, provides a number of stories about the importance of structure, and outlines the main structure of the book.

The chapter argues that prior to Hitler’s accession, Germany’s corporate elite was fatefully ambivalent toward Jews: sympathetic to those who were part of it, suspicious of those who were critical of it or newly arrived in the country. This ambivalence meant that corporate executives were generally neither antisemites nor anti-antisemites or that they were simultaneously both.

Degree is the simplest of the node-level measures, but its simplicity often hides its power. Here we will apply degree to the problem of mental structure. Specifically, what is the structure of the relationships between information in the mind? George Kingsley Zipf observed that word frequencies in natural language tend to a follow a scale-free distribution: The most frequent words are few, while the less frequent words are many with a specific linear relationship on a log-log plot. It has also been suggested that this power-law distribution applies to the relationships between words as well as to their meanings. Some words share meanings with many other words while others share few. This is a hypothesis based on the structural distribution of shared meanings, or polysemy (words with multiple meanings). This chapter will explain the theory underlying Zipf’s law of meaning and power laws. It will also show how we can combine these ideas with the most basic node-level network measure: degree.

This chapter demonstrates the routine prevalence of forced labor in Germany prior to World War II, its expansion during the early years of the fighting, the slow introduction of concentration camp slave labor to the German economy, and the reasons for its adoption by nearly every major German enterprise. Contrary to common belief, the chief motivation driving both processes was the shortage of German workers, thanks to conscription and wartime demand for output, not the cost of the forced and slave laborers. But the great growth of German industrial capacity during the war owed much to these labor inputs.

A universal basic income is widely endorsed as a critical feature of effective governance. It is also growing in popularity in an era of substantial collective wealth alongside growing inequality. But how could it work? Current economic policies necessarily influence wealth distributions, but they are often sufficiently complicated that they hide their inefficiencies. Simplifications based on network science can offer plausible solutions and even offer ways to base universal basic income on merit. Here we will examine a case study based on a universal basic income for researchers. This is an important case because numerous funding agencies currently require costly proposal processes with high administrative costs. These are costly for the proposal writers, their evaluators, and the progress of science itself. Moreover, the outcomes are known to be biased and inefficiently managed. Network science can help us redesign funding allocations in a less costly and potentially more equitable way.

The way networks grow and change over time is called network evolution. Numerous off-the-shelf algorithms have been developed to study network evolution. These can give us insight into the way systems grow and change over time. However, what off-the-shelf algorithms often lack are knowledge of the behavioral details surrounding a specific problem. Here we will develop a simple case that we will revisit over the next few chapters: How do children learn words from exposure to a sea of language? One possibility is that the words children learn first influence the words they learn next. Another possibility is that the structure of language itself facilitates the learning of some words over others. Indeed, we know that adults speak differently to children in ways that facilitate language learning, with semantically informative words tending to appear more often around words that children learn earliest. This invites the question: To what extent does the semantic structure of language predict word learning? This chapter will provide a general framework for building and competing models against one another with a specific application to the network evolution of child vocabularies.

A valuable feature of networks is their ability to quantify structural relationships. Much like a chemist who, having purified their sample, can bring to bear a wide range of tools for understanding their sample by transforming data into a network, a network scientist can leverage a range of tools that provide precise measurements of their data’s structure. This includes node-level measures like degree and centrality, meso-scale measures like community formation, and macro-scale measures like density and modularity. This chapter introduces these measures and gives a brief guide to their use.