Many-Model Paradigm

In this book we advocate using not just one model in a given situation but not many models. The logic behind the many-model approach builds on the age-old idea that we achieve wisdom through a multiplicity of lenses. The idea traces back to Aristotle, who wrote of the value of combining the excellences of many. A diversity of perspectives was also a motivation for the great-books movement, which collected 102 important transferable ideas in The Great Ideas: A Syntopicon Of Great Books Of the Western World. The approach finds a modern voice in the work of Maxine Hong Kingston, who wrote in The Woman Warrior, “I learned to make my mind large, as the universe is large, so that there is room for paradoxes.”  It is also the basis for pragmatic actions in the world of business and policy. Recent books argue that if we want to understand of international relations, we should not model the world exclusively as a group of self-interested nations with well-defined objectives, or only as an evolving nexus of multinational  corporations and intergovernmental organizations. We should do both.

As commonsensical as the many-model approach may seem, keep in mind that it runs counter to how we teach models and the practice of modeling. The traditional approach — the one taught in the high school — relies on a one-to-one logic: one problem requires one model. For example: now we apply Newton’s first law; now we apply the second; now the third. Or: here we use the replicator equation to show the size of the rabbit population in the next period. In this traditional approach, the objective is to (a) identify the one proper model and (b) apply it correctly. Many-model thinking challenges that approach. It advocates trying many models. Had you used many-model thinking in ninth grade, you might have been held back. Use it now, and you will move forward.

A model might represent beliefs as probability distributions over states of the world or preferences as rankings of alternatives. By simplifying and making precise, they create tractable spaces within which we can work through logic, generate hypotheses, design solutions, and fit data. Models create structures within which we can think logically. As Wittgenstein wrote in his Tractatus Logico-Philosophicus, “Logic takes care of itself; all we have to do is to look and see how it does it.” The logic will help to explain, predict, communicate, and design. But the logic comes at cost, which leads to their third characteristic: all models are wrong, as George Box noted. That is true of all models; even the sublime creations of Newton that we refer to as laws hold only certain scales. Models are wrong because they simplify. They omit details. By considering many models, we can overcome the narrowing of rigor by crisscrossing the landscape of the possible.

This is an extract from Scott Page's The Model Thinker published by Basic Books