| --- |
| title: "House Oversight: Estate Documents (Nov 12) (HOUSE_OVERSIGHT_016898)" |
| source: "House Oversight: Estate Documents (Nov 12)" |
| sourceUrl: "https://www.justice.gov/epstein" |
| date: "2026-01-01" |
| category: "House Oversight" |
| eftaNumber: "HOUSE_OVERSIGHT_016898" |
| ocrPages: 1 |
| ocrChars: 3782 |
| ocrElapsed: 0.0 |
| parseTier: "external-legacy" |
| engine: "engine undisclosed (ep-nov-12.greg.technology mirror)" |
| externalSource: "greg-ep-nov-12" |
| externalLicense: "not granted" |
| externalCredit: "ep-nov-12.greg.technology" |
| externalUrl: "https://ep-nov-12.greg.technology" |
| --- |
| |
| account the cost of computation. Real agents need to modulate the amount of time they |
| spend thinking by the effect the extra thought has on the results of a decision. If you're |
| trying to choose a toothbrush, you probably don't need to consider all four thousand |
| listings for manual toothbrushes on Amazon.com before making a purchase: You trade |
| off the time you spend looking with the difference it makes in the quality of the outcome. |
| This trade-off can be formalized, resulting in a model of rational behavior that artificial- |
| intelligence researchers call "bounded optimality." The bounded-optimal agent doesn't |
| focus on always choosing exactly the right action to take but rather on finding the right |
| algorithm to follow in order to find the perfect balance between making mistakes and |
| thinking too much. |
| Bounded optimality bridges the gap between rationality and heuristics. |
| describing behavior as the result of a rational choice about how much to think, it provides |
| a generalizable theory-that is, one that can be applied in new situations. Sometimes the |
| simple strategies that have been identified as heuristics that people follow turn out to be |
| bounded-optimal solutions. So, rather than condemning the heuristics that people use as |
| irrational, we can think of them as a rational response to constraints on computation. |
| Developing bounded optimality as a theory of human behavior is an ongoing |
| project that my research group and others are actively pursuing. If these efforts succeed, |
| they will provide us with the most important ingredient we need for making artificial- |
| intelligence systems smarter when they try to interpret people's actions, by enabling a |
| generative model for human behavior. |
| Taking into account the computational constraints that factor into human |
| cognition will be particularly important as we begin to develop automated systems that |
| aren't subject to the same constraints. Imagine a superintelligent Al system trying to |
| figure out what people care about. Curing cancer or confirming the Riemann hypothesis, |
| for instance, won't seem, to such an Al, like things that are all that important to us: If |
| these solutions are obvious to the superintelligent system, it might wonder why we |
| haven't found them ourselves, and conclude that those problems don't mean much to us. |
| If we cared and the problems were so simple, we would have solved them already. A |
| reasonable inference would be that we do science and math purely because we enjoy |
| doing science and math, not because we care about the outcomes. |
| Anybody who has young children can appreciate the problem of trying to interpret |
| the behavior of an agent that is subject to computational constraints different from one's |
| own. Parents of toddlers can spend hours trying to disentangle the true motivations |
| behind seemingly inexplicable behavior. As a father and a cognitive scientist, I found it |
| was easier to understand the sudden rages of my two-year-old when I recognized that she |
| was at an age where she could appreciate that different people have different desires but |
| not that other people might not know what her own desires were. It's easy to understand, |
| then, why she would get annoyed when people didn't do what she (apparently |
| transparently) wanted. Making sense of toddlers requires building a cognitive model of |
| the mind of a toddler. Superintelligent Al systems face the same challenge when trying |
| to make sense of human behavior. |
| Superintelligent Al may still be a long way off. In the short term, devising better |
| models of people can prove extremely valuable to any company that makes money by |
| analyzing human behavior-which at this point is pretty much every company that does |
| business on the Web. Over the last few years, significant new commercial technologies |
| 95 |
| HOUSE_OVERSIGHT_016898 |
|
|
