Rational satisficing heuristics as determinants of online search behavior

Abstract

We design a set of satisficing heuristic algorithms that mimic the online information retrieval behavior of rational decision makers (DMs) as reflected in their click through rates (CTRs). We illustrate how basic heuristic algorithms formalizing binary decision trees composed by 21 nodes and requiring DMs to observe one satisficing alternative lack the structural capacity to characterize the retrieval process. The algorithm requiring DMs to observe two satisficing alternatives –formalizing binary decision trees composed by 111 nodes – provides a sufficient approximation to their CTRs. Adding a third alternative – accounting for 351 nodes – delivers an almost identical set of CTRs to those displayed by DMs. The mimicking quality of the heuristic algorithms prevails as alternatives are added up to include the ten ranked within the first page of search results, incorporating 2,047 nodes to formalize the corresponding retrieval process. The set of algorithms bridges the gap between purely rational decision theory and heuristic behavior, illustrating how the CTRs observed can be generated by rational DMs performing a sequential search process while aiming to observe two or three satisficing alternatives. The decision-tree algorithmic structures presented are sufficiently malleable to introduce any potential modification to the beliefs and preferences of DMs and study its consequences in terms of CTRs.

Keywords

• Information retrieval
• Online search
• Satisficing
• Click through rates
• Uncertainty
• Heuristics