Models of the Behavior of People Searching the Internet: A Petri Net Approach

Previous models of searching behavior have taken as their foundation the Markov model of random processes. In this model, the next action that a user takes is determined by a probabilistic rule which is conditioned by the most recent experiences of the user. This model, which has achieved very limited success in describing real data, is at odds with the evidence of introspection in a crucial way. Introspection reveals that when we search we are, more or less, in a state of expectancy, which can be satisfied in a number of ways. In addition, the state can be modified by the accumulated evidence of our searches. The Markov model approach can not readily accommodate such persistence of intention and behavior. The Petri Net model, which has been developed to analyze the interdependencies among events in a communications network, can be adapted to this situation. In this adaptation, the so-called "transitions" of the Petri Net occur only when their necessary pre-conditions have been met. We are able to show that various key abstractions of information finding, such as "document relevance", "a desired number of relevant documents", "discouragement", "exhaustion" and "satisfaction" can all be modeled using the Petri Net framework. Further, we show that this model leads naturally to a new approach to the collection of user data, and to the analysis of transaction logs, by providing a far richer description of the user's present state, without inducing a combinatorial explosion.