Social Science Statistics Blog

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6 December 2005

The BLOG inference engine

Amy Perfors

There are two ways of thinking about almost anything. Consider family and kinship. One the one hand, we all know certain rules about how people can be related to each other -- that your father's brother is your uncle, that your mother cannot be younger than you. But you can also do probabilistic reasoning about families -- for instance, that grandfathers tend to have white hair, that it is extremely unlikely (but possible) for your mother to also be your aunt, or that people are usually younger than their uncles (but not always). These aren't logical inferences; they are statistical generalizations based on the attributes of families you have experienced in the world.

Though the statistics-rule dichotomy still persists in a diluted form, today many cognitive scientists are not only recognizing that people can do both types of reasoning much of the time but also beginning to develop behavioral methods and statistical and computational models that can clarify exactly how they do it and what that means. The BLOG inference engine, whose prototype was released very recently by Stuart Russell's computer science group at Berkeley, is one of the more promising computational developments for this goal.

BLOG (which stands for Bayesian LOGic, alas, not our kind of blog!) is a logical language for generating objects and structures, then doing probabilistic inference over those structures. So for instance, you could specify objects, such as people, with rules for how those objects could be generated (perhaps a new person (a child) is generated with some probability from two opposite-gender parents), as well as how attributes of these objects vary. For example, you could specify that certain attributes of people depend probabilistically on family structure - if you have a parent with that attribute, you're more likely to have that attribute yourself. Other attributes might also be probabilistically distributed, but not based on family structure: we know that 50% of people are male and 50% are female regardless of the nature of their parents.

The power of BLOG is that it allows you both to specify quite complex generative models and interesting logical rules and to do probabilistic inference given the rules you've set up. Using BLOG, for instance, you could ask things such as the following. If I find a person with Bill's eyes, what is the probability that this person is Bill's child? Is it possible for Bill's son to also be his daughter?

Though a few things are unexpectedly difficult in BLOG - reasoning about symmetric relations like "friend," for instance - I think it promises to be a tremendously valuable tool for anyone interested in how people do probablistic reasoning over structures/rules, or in doing it themselves.

Posted by Amy Perfors at 3:01 AM