Persuasion through conversation has been the focus of much research. Nudging is a popular strategy to influence decision-making in physical and digital settings. However, conversational agents employing "nudging" have not received significant attention. We explore the manifestation of cognitive biases-the underlying psychological mechanisms of nudging-and investigate how the complexity of prior dialogue tasks impacts decision-making facilitated by conversational agents. Our research used a between-group experimental design, involving 756 participants randomly assigned to either a simple or complex task before encountering a decision-making scenario. Three scenarios were adapted from Samuelson's classic experiments on status-quo bias, the underlying mechanism of default nudges. Our results aligned with previous studies in two out of three simple-task scenarios. Increasing task complexity consistently shifted effect-sizes toward our hypothesis, though bias was significant in only one case. These findings inform conversational nudging strategies and highlight inherent biases relevant to behavioural economics.

翻译：通过对话进行说服一直是众多研究的焦点。助推作为一种在物理和数字环境中影响决策的流行策略，已得到广泛应用。然而，采用"助推"策略的会话智能体尚未受到充分关注。本研究探讨了认知偏差——即助推的潜在心理机制——的表现形式，并考察了先前对话任务的复杂性如何影响会话智能体促成的决策。我们采用组间实验设计，将756名参与者随机分配至简单任务组或复杂任务组，随后让其面对决策场景。实验中的三个场景改编自萨缪尔森关于现状偏差（默认助推的底层机制）的经典实验。在三个简单任务场景中，有两个场景的结果与先前研究一致。增加任务复杂性持续使效应量向我们的假设方向偏移，尽管仅在一个案例中偏差达到显著水平。这些发现为会话助推策略提供了参考，并揭示了与行为经济学相关的固有偏差。