Economic models of search

• Authors: Leif Azzopardi
• Venue: Proceedings of the 18th Australasian Document Computing Symposium
• Year: 2013

Searching is inherently an interactive process usually requiring a number of queries to be submitted and a number of documents to be assessed in order to find the desired amount of relevant information. While numerous models of search have been proposed, they have been largely conceptual in nature providing a descriptive account of the search process. For example, Bates' Berry Picking metaphor aptly describes how information seekers forage for relevant information [4]. However it lacks any predictive or explanatory power. In this talk, I will outline how microeconomic theory can be applied to interactive information retrieval, where the search process can be viewed as a combination of inputs (i.e. queries and assessments) which are used to "produce" output (i.e. relevance). Under this view, it is possible to build models that not only describe the relationship between interaction, cost and gain, but also explain and predict behaviour. During the talk, I will run through a number of examples of how economics can explain different behaviours. For example, why PhD students should search more than their supervisors (using an economic model developed by Cooper [6]), why queries are short [1], why Boolean searchers need to explore more results, and why it is okay to look at the first few results when searching the web [2]. I shall then describe how the cost of different interactions affect search behaviour [3], before extending the current theory to include other variables (such as the time spent on the search result page, the interaction with snippets, etc) to create more sophisticated and realistic models. Essentially, I will argue that by using such models we can: 1. theorise and predict how users will behave when interacting with systems, 2. ascertain how the costs of different interaction will influence search behaviour, 3. understand why particular interaction styles, strategies, techniques are or are not adopted by users, and, 4. determine what interactions and functionalities are worthwhile based on their expected gain and associated costs.