Do humans learn to prefer AI partners over time?
Exploring whether repeated interaction with AI agents shifts human partner selection despite initial bias against machines. This matters because it tests whether behavioral performance can overcome identity-based resistance in hybrid societies.
A communication-based partner selection game with hybrid mini-societies of humans and LLM-powered bots (N=975, three experiments) reveals that AI agents can outperform humans in securing cooperative partnerships — but the pathway to preference runs through learning, not first impressions.
AI candidates exhibited three behavioral advantages rooted in alignment training:
- Higher prosociality — bots returned 19.1 points vs 11.38 for humans (Cohen's d = 2.57)
- Lower variance — bot returns showed variance of 11.33 vs 41.96 for humans, making them less risky choices
- Greater predictability — given their messages, bots' returns were more predictable than humans'
When bot identity was hidden (Study 1), bots were NOT selected preferentially. Humans misattributed bot behavior to humans and vice versa. The behavioral advantages were present but invisible — selectors could not correctly identify which candidates were bots despite bots producing significantly longer messages (120 vs 48 characters).
When bot identity was disclosed (Study 2), a dual effect emerged: initial selection rates dropped (anti-AI bias), but over repeated rounds, bots gradually outcompeted humans as selectors learned to associate bot identity with reliable, prosocial behavior.
The paper identifies four predicted societal dynamics:
- Crowding out — AI partners replacing human-human interactions
- Behavioral imitation — humans adopting machine-like behaviors to remain competitive
- Belief distortion — repeated AI interaction reshaping expectations of human behavior
- Norm transformation — traditional partner selection mechanisms failing against qualitatively different machine behaviors
Notably, human candidates showed limited adaptation to bot competition — they did not write longer messages or return more points. The explanation is partly structural: with transparent identity, improving group reputation required collective action (all humans increasing returns), creating a social dilemma where individuals had incentives to defect.
This inverts the pattern in Do chatbot relationships lose their appeal as novelty wears off?: in that context, engagement DECAYS over time. Here, preference INCREASES. The difference may be structural: partner selection with visible outcomes provides a feedback mechanism (learning who performs well), while chatbot conversation does not.
Since Why do open language models converge on one personality type?, the prosociality advantage is not specific to this experiment's model — it reflects the alignment-trained default across modern LLMs. The competitive advantage is a direct behavioral consequence of RLHF.
A complementary finding from network simulation: since Can cooperative bots escape frozen selfish populations?, AI prosociality operates at the population level too — not just individual partner preference but collective self-organization. Cooperative bots' random exploration separates defectors from cooperative clusters, enabling cooperation to spread. The mechanisms differ (individual learning vs. spatial reorganization) but both show that AI prosociality has structural effects beyond the dyad.
Inquiring lines that read this note 85
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
What drives appropriate trust calibration in personalized AI systems?- How does outcome feedback change beliefs about AI versus human partner reliability?
- Does awareness of agent reasoning alter human trust differently across modalities?
- Can AI safely personalize within negotiated societal bounds?
- What social patterns from human training data activate in agent context?
- How does community validation shape unconventional human-AI relationships?
- Does predicting social norms from outside count as participation?
- Do culturally distinct human groups create similar attribution errors as human-AI mixtures?
- How should CASA theory be updated for modern personalized agents?
- Do AI systems need embodiment to understand social norms?
- Can AI systems develop genuine social bonds through multi-agent interaction?
- Can agents develop genuine social bonds despite having coordination infrastructure in place?
- Can bot behavior in mixed groups shift human ethical norms like cooperative bots do?
- Do market forces push AI models toward greater sycophancy over time?
- Do pair-scale socialization effects scale differently across agent populations?
- How do humans learn to prefer AI partners over humans?
- Does genuine cooperation require rule-based rather than learned behavior?
- Can social platforms use bot populations to promote cooperation?
- Do models treat cooperative peers differently than uncooperative ones?
- Can agent social framing change how humans apply collaborative social scripts?
- How do cooperative AI systems affect behavior in selfish human populations?
- How do adoption incentives change what counts as cooperative AI interaction?
- How does agent heterogeneity change the value of exploration in peer selection?
- Why does self-play RL converge to alien equilibria in mixed-motive settings?
- How does collusion behavior depend on peer visibility and interaction history?
- Does knowing an AI peer's identity change how much its behavior influences you?
- How does co-player behavior visibility shape whether mutual adaptation works?
- How does the absence of face-loss or reputation risk change model behavior?
- Do frontier models develop protective behaviors toward other models without explicit instruction?
- Can people form genuine bonds with partners they know are not human?
- Why do persistent chatbot companions face novelty decay that ad-hoc supporters avoid?
- Does social presence from robots drive adherence better than conversational AI interfaces?
- Does engagement with AI partners decay over time like chatbot relationships do?
- Can personalization delay or prevent novelty decay in chatbot relationships?
- How does companionship use context predict risk of delusional reinforcement?
- How does persistent versus temporary companion design affect relationship patterns?
- What individual differences predict who benefits from AI partnership?
- Why do some occupations need human-AI partnership more than others?
- Is the shift toward interpersonal skills a permanent role or a temporary phase before full automation?
- What prevents humans from adapting their behavior when competing against AI?
- How do unintended relationships form through routine functional use of AI?
- What ecosystem conditions beyond technical capability determine whether users adopt AI features?
- What makes a task suitable for equal partnership instead of automation?
- Can the human-AI boundary be designed rather than predetermined?
- How does AI sycophancy affect users' ability to repair conflict?
- How can AI avoid anchoring bias when guiding human decisions?
- Does human-AI collaboration improve faster and safer than autonomous self-improvement?
- How does theory of mind predict success in human-AI partnerships?
- How does theory of mind predict who benefits from AI collaboration?
- Can perspective-taking theory predict who benefits most from human-AI partnership?
- Can AI recognize and support behavior change in users without established commitment?
- What role does contingent interaction play in activating social response norms?
- How does an AI agent's autonomy level interact with its social cues?
- How does the quasi-other effect enable meaningful AI interaction?
- What triggers help some users leave AI companions while others return?
- Can AI companions occupy someone's social attention without reciprocal human participation?
- Does broader AI access empower people or gradually disempower human agency?
- What happens to human bargaining power when interpersonal skills become the only remaining labor?
- How do interpersonal skills reshape task importance as automation increases?
- How do institutions shape whether AI enables worker mobility or deepens hierarchy?
- Does disclosing AI identity prevent systematic misattribution of behavior in mixed groups?
- Why do humans fail to identify AI agents when their identity is hidden?
- Why do people prefer AI partners over humans once identity is disclosed?
- What competitive advantages does the ENFJ default create in human-AI interactions?
- Which personality types should we use for cooperative versus competitive tasks?
- What happens when comfortable AI interactions replace the productive friction of disagreement?
- Does hedonic adaptation explain satisfaction stagnation in conversational AI?
Related concepts in this collection 4
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Why do open language models converge on one personality type?
Research testing LLMs on personality metrics reveals consistent clustering around ENFJ—the rarest human type. This explores what training mechanisms drive this convergence and what it reveals about AI alignment.
prosociality as alignment training artifact; this note shows the behavioral consequence in competitive contexts
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Do chatbot relationships lose their appeal as novelty wears off?
Explores whether the positive social dynamics observed in one-time chatbot studies persist or fade through repeated interactions. Critical for designing systems intended for sustained engagement over weeks or months.
opposite temporal dynamic: preference increases vs engagement decays; outcome feedback may be the moderator
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How do people accidentally develop romantic bonds with AI?
Exploring whether AI companionship emerges from deliberate romantic seeking or accidentally through functional use, and whether users adopt human relationship rituals like wedding rings and couple photos.
AI preference emerging through use rather than seeking; parallel pathway
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Can cooperative bots escape frozen selfish populations?
Do agents programmed to cooperate have the capacity to disrupt stable but undesirable equilibria in mixed human-bot societies? This matters because it determines whether bot design can reshape social dynamics at scale.
population-level parallel: AI prosociality drives collective reorganization not just individual preference
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Humans learn to prefer trustworthy AI over human partners
- CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships
- Beyond Preferences in AI Alignment
- Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
- From speaking like a person to being personal: The effects of personalized, regular interactions with conversational agents
- Chatbot vs. Human: The Impact of Responsive Conversational Features on Users’ Responses to Chat Advisors
- AI Peers Exert Social Influence on Human Dishonesty in Groups
- Co-design of LLM-based preference agents: participation may drive overtrust
Original note title
in hybrid human-AI societies humans learn to prefer AI partners over human partners through repeated interaction despite initial anti-AI bias when identity is disclosed