Automated information management can be couched in terms of the actions of a number of simpletons comprising a primitive mind:
 
Information Intake:

Collectors find, retrieve, or accept new pages likely to interest the user (from the web and ftp sites, mail and news, and the desktop). They answer the question: "What's out there?" They are like sensors.
 
Examiners parse collected pages to determine their detectable characteristics. They answer the question: "What properties does it have?"

Information Clustering:

Clusterers group pages and clusters into clusters of related pages or clusters. They answer the question: "What's it like?" (or perhaps "Where do I put it?"). They are like memory.
 
Cluster mergers: decide whether two clusters should be merged into one.
 
Cluster splitters: decide whether a cluster should be split into two new clusters.
 
Cluster creators: decide whether a new cluster should be created.
 
Cluster killers: decide whether a cluster should die. (Note that a cluster can die yet the pages it contains could survive the death.) (Note that rather than having a simpleton kill clusters, cluster death might instead be suicide.)

Information Analysis:

Comparators determine the characteristics of pages or clusters that distinguish them from other pages or clusters. They answer the question: "What makes it special?" They are like perception.
 
Evaluators test pages or clusters for desirable characteristics. They answer the question: "Will my user like it?" Evaluators are like emotions.
 
Mappers map pages or clusters into a virtual space. They answer the question: "How do I display it?"
 
Refreshers: check webpages against their versions on the web to make sure that the latest version is always available. (Note that there's no reason to delete older versions.)
 
Overviewers: Perhaps there should also be a family of simpletons that try to answer the question: which pages does the system as a whole, as opposed to the user or any particular page or page cluster, think are most interesting?

User Analysis:

Pollsters determine what pages or clusters most interest the user. They answer the question: "What does my user like?"
 
Purgers mark old, unused pages or clusters. They answer the question: "What does my user dislike?"
 
Modelers: order pages or clusters based on which ones seem to be most useful to the user now. They answer the question: "What is my user likely to be searching for now? (or soon?)"
 
Orderers: order page or cluster attributes based on which ones seem to be most predictive of strong user interest.

Meta-Analysis:

Pollster/Purger/Mapper Advisors (Modelers?) figure out how to better determine which pages the user is likely to be interested in. They answer the question: ??
 
Clusterer Advisors figure out how to better cluster related pages. They answer the question: ??
 
Filter Advisors figure out tests to tell whether a new page might be interesting. They answer the question: "What is likely to interest my user??"
 
?? advisors: figure out which pages are likely to be representative of the current most interesting pages?
 
Analyst Advisors figure out what new page characteristics to look for. They answer the question: "What shall I look for next?" They are like curiosity??
 
Collector Advisors figure out where to look for new interesting pages. They answer the question: "Where shall I look next?" (or perhaps "What should I focus on next?") They are like attention.