Modeling Trial-and-Error Navigation With a Sequential Decision Model of Information Scent

2026-03-12

AI Summary

This study addresses a key limitation of traditional information scent models, which assume users make decisions only after thoroughly examining a page—an assumption that fails to account for the rapid trial-and-error behaviors observed in real-world navigation. The authors propose modeling information foraging as a sequential decision-making process constrained by memory and time, wherein users perform local sampling of page content and integrate both local and global information scent to guide “just enough” exploration and backtracking. By embedding information scent theory within a sequential decision framework and incorporating heuristic search strategies under cognitive constraints, the model successfully reproduces hallmark user behaviors such as premature clicking, erroneous selections, and revisitation. These results demonstrate the framework’s explanatory power and validity in capturing authentic navigation dynamics.

Technology Category

• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

Users often struggle to locate an item within an information architecture, particularly when links are ambiguous or deeply nested in hierarchies. Information scent has been used to explain why users select incorrect links, but this concept assumes that users see all available links before deciding. In practice, users frequently select a link too quickly, overlook relevant cues, and then rely on backtracking when errors occur. We extend the concept of information scent by framing navigation as a sequential decision-making problem under memory constraints. Specifically, we assume that users do not scan entire pages but instead inspect strategically, looking "just enough" to find the target given their time budget. To choose which item to inspect next, they consider both local (this page) and global (site) scent; however, both are constrained by memory. Trying to avoid wasting time, they occasionally choose the wrong links without inspecting everything on a page. Comparisons with empirical data show that our model replicates key navigation behaviors: premature selections, wrong turns, and recovery from backtracking. We conclude that trial-and-error behavior is well explained by information scent when accounting for the sequential and bounded characteristics of the navigation problem.

Problem

• information scent
• trial-and-error navigation
• sequential decision-making
• memory constraints
• web navigation

Innovation

• information scent
• sequential decision-making
• memory constraints
• trial-and-error navigation
• bounded rationality