The science behind
eLLMo simulation.
Peer-reviewed research in AI agent behavior, preference heterogeneity, and chain-of-thought reasoning shapes how eLLMo is built and what its outputs mean. This is that foundation — without the footnotes.
Research topics
Position effects are structural, not visual
In controlled e-commerce experiments, AI agents exhibit strong position bias: items placed in certain structural positions within a product …
Framing and anchoring effects are measurable and exploitable
Replication studies of classic behavioral economics experiments confirm that AI agents exhibit the same framing effects documented in human …
Credibility signals vs. self-assertion: a persistent divide
Controlled experiments consistently find that AI buyers heavily weight third-party credibility signals — editorial curation, press mentions,…
Choice homogeneity: AI buyers collapse to consensus
Human consumers distribute purchase intent across a range of options. AI buyers, evaluated at scale, show significantly higher consensus: de…
Model version shifts are distributional, not incremental
When a frontier model releases a new version, the behavioral profile of AI buyers changes in ways that cannot be predicted by extrapolation …
Persona conditioning produces demographically coherent responses
When AI models are conditioned on detailed socio-demographic backstories — age, income, lifestyle, purchase history, personality traits — th…
Conjoint-style preference elicitation yields economically meaningful outputs
Research testing LLM preferences through conjoint-style prompts — varying price, product features, and consumer attributes systematically — …
Why persona conditioning produces better signals than direct elicitation
Research comparing persona-conditioned simulation against direct preference elicitation — simply asking a model 'would you buy this?' — demo…
Persona-differentiated variation is robust
The variation between differently configured personas is robust and directionally consistent with human consumer research. A persona constru…
Chain-of-thought prompting partially corrects absolute calibration
Research systematically demonstrates that asking AI models to reason through their decision before rendering a verdict produces outputs clos…
Language structure affects AI preference signals
AI model outputs are shaped by the linguistic register of the prompts they receive. Models produce meaningfully different preference signals…
Openness and novelty processing
High-Openness buyers are reliably more receptive to novel mechanisms, unconventional aesthetics, and innovation language. They engage more d…
Conscientiousness and information completeness
High-Conscientiousness buyers are detail-oriented and process-focused. They exhibit strong sensitivity to missing information: if a landing …
Neuroticism and risk signal detection
High-Neuroticism buyers — emotionally sensitive, risk-averse — are disproportionately affected by ambiguity in pricing, unclear refund polic…
Empirical purchase behavior mappings by OCEAN dimension
Regression studies with large consumer panels establish specific, measurable links between Big Five trait scores and purchase behavior types…
Cross-persona pattern analysis as the primary signal
The research-derived insight that preference heterogeneity is more reliable than absolute preference levels translates directly into report …
Ranked friction patterns, traceable to buyer segments
eLLMo simulation identifies which aspects of a landing page create friction across different buyer types, and which copy and design elements…
Hypothesis generation, then validation
Simulation is most powerful as a pre-spend instrument: it produces ranked findings about what to fix, which the brand can then validate thro…
Credibility-vs-self-promotion as a simulation dimension
Research consistently demonstrates that AI buyers heavily reward third-party credibility markers and discount self-promotion. eLLMo builds t…
The proprietary calibration gap
The most significant open research question is the calibration gap between AI buyer behavior and human buyer behavior — and specifically, ho…
AI agents execute real economic transactions through natural language reasoning
Project Deal agents identified potential trading matches, proposed prices, negotiated counteroffers, and closed deals across a diverse trans…
Simulation instruments fail silently — and users don't notice
Project Deal's most consequential finding is not the performance gap between Opus and Haiku agents — it is that participants using the weake…
The reasoning trace, not the verdict, is where buyer psychology lives
Project Deal agents negotiated in natural language — proposing, countering, reasoning about value. Prompting agents to negotiate aggressivel…
Methodology note
Built on the research. Designed for decisions.
eLLMo simulation surfaces ranked friction patterns across calibrated buyer personas — specific findings, traceable to buyer segments, actionable on the same day. The methodology is grounded in peer-reviewed research on AI agent behavior and OCEAN psychometrics. The output is a prioritized list of what to fix before your campaign launches — and why it matters for each buyer type.