Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating
Researchers investigated how users react to autonomous LLM agents acting as intermediaries in online dating environments. The study examines the asymmetry between delegating one's own conversations to an agent versus receiving messages from another agent.
- Agentic communication requires mutual consent from both the delegating user and the receiving user.
- User receptivity is asymmetrical, meaning people may prefer using agents themselves but dislike receiving agent-led messages.
- Large-scale survey data (N > 5,000) provides empirical grounding for agentic social design.
As LLM agents become capable of managing personal interactions, a new challenge arises in social platforms: the two-sided nature of agentic communication. It is not enough for a user to trust an AI to speak for them; they must also be willing to interact with an AI representing someone else.
Using large-scale surveys from a major dating platform, researchers analyzed thousands of users across different languages. The study focuses on the psychological and social barriers that prevent users from fully embracing autonomous conversational agents in high-stakes social matching scenarios.
By applying a latent-variable measurement model, the study quantifies how receptivity varies depending on whether the user is the delegator or the recipient. This research provides a framework for understanding the social friction inherent in agentic recommender systems.
Understanding user receptivity is crucial for designing social AI that does not alienate human users.
Platforms must account for the social friction of agentic communication to ensure successful feature adoption.
Provides a methodological framework for measuring human-agent interaction in social contexts.
- Agentic Recommender Systems
- Recommendation systems that use autonomous agents to interact with users on their behalf.
- Latent-variable measurement model
- A statistical technique used to identify unobservable variables through observed indicators.
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