While I'm not sure thousands of if/then/else rules are actually the best knowledge representation for his domain (it sounds like he really wants to learn some model from data, in which case throwing some off-the-shelf machine learning may be a better fit), the area of "expert systems" may be relevant if they really are rules extracted directly from some source that need to be managed, such as a human domain expert.
Expert systems were particularly big in AI in the '80s, and considerably less hot now, but still widely used in industry. There are a lot of issues that come up, most of which aren't really purely technical, such as: 1) how you extract knowledge from people who might know it; and 2) how you validate that the knowledge you've extracted is actually what they know, for example by validating that it produces the same decisions that they would make; and 3) how you allow updates/revisions to the knowledge base over time. Once you have the rules and some reason to believe that they're any good, actually managing and applying them can be done through one of several rule engines, such as Drools or Jess.
The keyword "knowledge engineering" may also turn up relevant info.
Expert systems were particularly big in AI in the '80s, and considerably less hot now, but still widely used in industry. There are a lot of issues that come up, most of which aren't really purely technical, such as: 1) how you extract knowledge from people who might know it; and 2) how you validate that the knowledge you've extracted is actually what they know, for example by validating that it produces the same decisions that they would make; and 3) how you allow updates/revisions to the knowledge base over time. Once you have the rules and some reason to believe that they're any good, actually managing and applying them can be done through one of several rule engines, such as Drools or Jess.
The keyword "knowledge engineering" may also turn up relevant info.