This chapter consists of two parts. In the first, we describe the three types of models. In the second, we cover the uses of models: to reason, explain, design, communicate, act, predict, and explore. These form the acronym REDCAPE, a notso-subtle reminder that many-model thinking endows us with superpowers.

In diesem Kapitel werden zuerst kurz drei unterschiedliche Arten von Modellen unterschieden (embodiment approach, analogy approach, alternative reality approach). Im Hauptteil werden danach sieben Verwendungszwecke von Modellen unterschieden:

• Folgern Um Bedingungen und logische Implikationen ableiten zu können.
• Erklären: Um (testbare) Erklärungen für empirische Phänomene zu bieten
• Entwickeln: Um Eigenschaften von Institutionen, Policies und Regeln zu gestalten.
• Kommunizieren Um Wissen und Verstehen in Beziehung zu bringen.
• Handeln: Um Strategie- und Policy-Entscheide begleiten zu können
• Voraussagen: Um quantitative und kategoriale Vorausssagen zu künftigen und unbekannten Phänomenen machen zu können.
• Erkunden: Um Möglichkeiten und Hypothetisches erkunden zu können.

In this chapter, we define types of models. Models are often described as simplifications of the world. They can be, but models can also take the form of analogies or be fictional worlds mined for ideas and insights. We also describe the uses of models. In school, we apply models to explain data. In practice, we can also use models to predict, design, and take actions. We can use models to explore ideas and possibilities. And we can use models to communicate ideas and understandings.
The value of models also resides in their ability to reveal conditions under which results hold. Most of what we know holds only in some cases: the square of the longest side of a triangle equals the sum of the squares of the other sides only if the longest side is opposite a right angle. Models reveal similar conditions for our intuitions. With models we can parse out when diseases spread, when markets work, when voting leads to good outcomes, and when crowds make accurate predictions. None of those is a sure thing.