A Framework for Long-Term Thinking About Invisible Things
One of the most widespread images from my work, as reproduced in both Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, and in The Decision Intelligence Handbook, is the “Lobster Claw” pattern below. It shows how single-link thinking (what George Lakoff calls “Direct Causation”) can lead us to actions that may seem attractive in the short term, but are ultimately harmful.
As I was walking my dog this morning, I witnessed a troubling scene: a man with a young puppy yanking on its leash and pushing on its backside to sit, all the time repeatedly yelling, “sit, sit, sit”. And last night, I watched a disturbing documentary about an authoritarian movement and its impact on victims of abuse.
Both of these situations – plus a number of other domains like climate, wealth inequality, and more – are illustrated by thinking that’s limited to the upper-left quadrant of the diagram at the top of this article: we make a choice to achieve an action that appears to be effective in the short run, but in the long run — and often in a way that’s less immediately visible — creates bigger problems.
This is true for dog training –where the Reinforcement Plus movement has recently taken hold – as well as for humans. Out of fear, the dog sits. But the negative consequences for your relationship with him – and his anxiety as he grows – are massive.
Many unsolved problems require us to understand a wider dimension of both time and space. In my work with governments, nonprofits, and companies worldwide we draw diagrams like the ones above, which show actions on the left and outcomes towards the right. Built originally to help to integrate technology to people, we’ve found that these Causal Decision Diagrams (CDDs) are of value on their own, even without any tech.
Do you face any problems that live outside of Quadrant 1? What new tools and approaches do you use for 2, 3, and 4?
Computer scientist Dr. Lorien Pratt is one of the pioneers of artificial intelligence and is credited with inventing transfer learning. Following a 36-year career delivering applied AI systems, along with leadership and program committee responsibilities at the industry-leading NeurIPS conference, Pratt is recognized alongside Marie Curie and others by the Women Innovators and Inventors Project. Today, Pratt is chief scientist at www.quantellia.com, where she continues to push the boundaries of technology as a creator of and evangelist for Decision Intelligence (DI), a cofounder of www.opendi.org, and through building Large Language Models (LLMs) integrated with DI and ML. Pratt’s The Decision Intelligence Handbook (O’Reilly, 2023) is receiving worldwide attention. With recent publications in Foresight, Futures, and Frontiers, Pratt's DI innovations connect AI to human decision making in a way that is transparent, auditable, and accessible, bringing this important technology to the masses, and reducing information inequality.
