## Definition **Analogical Thinking** is the cognitive process of mapping relational structure from a source domain onto a target domain — abstracting a pattern from one context and re-instantiating it in another. It is the engine of far transfer: the ability to apply knowledge acquired in one field to solve problems in an unrelated field. Epstein, following work by cognitive scientist Dedre Gentner, presents analogical thinking as the generalist's primary creative tool and the mechanism by which breadth of knowledge converts into novel insight. ## The Mechanism The three-step movement of analogical thought: 1. **Concretise** — identify the structural pattern underlying a specific situation (e.g. Kepler observing that light intensity from a point source decreases with distance from the source). 2. **Abstract** — generalise the pattern into a domain-neutral form (e.g. "a quantity emanating from a point spreads over a growing surface area"). 3. **Re-concretise** — apply the abstracted pattern to an unrelated domain (e.g. Kepler applying the same geometric logic to explain why gravitational force from the Sun weakens with distance — decades before Newton formalised this as an inverse-square law). This sequence is what Epstein's researcher David (the chemist turned Amazon marketer) performs: he maps the abstract structure of experimental hypothesis-testing from physical chemistry onto digital A/B testing. ## Near vs Far Analogies Not all analogies are equally generative: - **Near analogies** draw on cases that are structurally similar and domain-adjacent. They are easy to generate but tend to reproduce existing solutions — the Einstellung effect. If you are designing a bridge and you only consult other bridge designs, you inherit their biases and blind spots. - **Far analogies** draw on cases from distant domains. They are harder to generate (requiring broader knowledge) but avoid local fixation and open genuinely new solution paths. Kepler's use of optics, magnetism, and smell to think about planetary orbits is a paradigm case. Epstein cites a Loran Nordgren (Northwestern) study: for estimating how long a complex project will take, the most accurate method is *not* to analyse the specific project in detail, but to gather an "outside view" — a base rate from a reference class of similar projects. The "inside view" (focusing on project-specific details) inflates optimism. Analogical thinking to far reference classes corrects this bias. ## Kepler as the Archetype Epstein devotes extended attention to Johannes Kepler as an exemplar of analogical cognition under conditions of genuine uncertainty. Working before modern physics, without calculus, and with only imprecise observational data, Kepler's method was not deduction from first principles but cascades of analogy — systematically testing each one, discarding those that failed, and using the failures to generate new analogies. His own description: analogies were his "most faithful teachers." This approach generated the three laws of planetary motion. ## The Einstellung Effect Analogical thinking is inhibited by the Einstellung effect: when a familiar solution is activated by a problem, it suppresses attention to alternative framings. Specialists, whose repertoire is deep but narrow, are most vulnerable. Outsiders and generalists — precisely because they lack the primed "obvious" solution — more readily apply analogies from distant domains. This is why crowdsourced problem-solving platforms (e.g. InnoCentive) find that the most intractable niche scientific problems are disproportionately solved by people with no formal expertise in the target domain. ## Related - [[Generalists vs Specialists]] - [[Kind vs Wicked Learning Environments]] - [[Desirable Difficulties]] ## Sources - [[Range (Epstein 2019)]]