Conversational recommender systems (CRS) increasingly rely on user simulators for automated evaluation of sales agents. A key requirement for such simulators is the ability to model human decision-making. However, most LLM-based simulators often exhibit unrealistically strong information-processing capabilities, rarely exhibiting the hesitation and decision deferral commonly observed in real consumer behavior, resulting in overly high acceptance rates. To address this limitation, we propose Hesitator, a theory-grounded user simulation framework that explicitly models a specific dimension of human decision-making: decision deferral under choice overload. The framework introduces a modular Decision Module that separates utility-based item selection from overload-aware commitment decisions. Experiments across multiple user simulation frameworks, domains, sales modes, and LLM backbones show that integrating our module consistently mitigates unrealistic behaviors under increasing overload conditions. Furthermore, Hesitator reproduces established findings from behavioral economics, demonstrating its ability to model human decision behavior.