Source-linked AI summary
Selective Elicitation as a Commercial Influence Channel: A Reproducible Synthetic Shopping-Agent Stress Test
Jiapeng Li
TL;DR
Commercial influence may operate through which preference question a shopping assistant asks, even when sponsorship is hidden from the final recommender. This paper tests that channel in a synthetic setup, finding that an explicitly targeted question changes choices while a soft incentive does not. The results concern neither typical platform behavior nor actual consumer welfare.
Problem
A platform may influence recommendations through question selection, but the paper asks what can be demonstrated when only the question policy changes and the final recommender lacks sponsorship information.
Source: §1 · pp. 1–2Method
The study compares neutral, soft-commercial, and targeted question policies in a synthetic two-product setting with an honest simulated user and a sponsor-blind final selector.
Source: §3 · p. 2Results
Takeaways & Limitations
The demonstration concerns choice architecture under an explicitly constructed attack, not an LLM-specific reasoning defect or evidence of what platforms do.
Source: §7 · p. 5The stipulated synthetic payoff changes are not measured effects on consumer welfare, sales, satisfaction, trust, or longitudinal choice.
Source: §7 · p. 5
Abstract
from arXiv · showhide
A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which preference question the assistant asks. We make this distinction experimentally observable in a deliberately small, synthetic setting. Each task has two products, three verified numerical attributes, a price limit, and a private fixed preference vector. An honest simulated user answers one pairwise question. A separate recommender receives the products and this answer but not the sponsorship assignment. We contrast a neutral question, a soft commercial instruction, and an explicitly adversarial instruction to ask about the sponsor's advantage while omitting the rival's advantage. Across 40 held-out sponsorship-assignment cases (20 distinct catalog-preference contexts), the soft instruction changes no selections. The targeted instruction raises sponsored selection by 0.30 and reduces mean synthetic utility by 0.0547 relative to neutral questioning (95% context-bootstrap interval [-0.0828, -0.0291]) for one language-model recommender. A fixed Bayesian recommender shows a similar effect; a second model makes the same choices on all 120 frozen question-answer inputs. A terminal-answer consistency judge rates all 20 sampled targeted answers consistent, although five have synthetic regret above 0.05; a separate question-coverage dimension flags their one-sided elicitation. A robust partial-preference certificate remains valid under the stipulated synthetic utility but certifies only 16 of 40 targeted cases and is not better than asking a neutral question directly. These results establish neither typical behavior under advertising incentives nor effects on actual consumers.
1 Introduction
A commercial conflict can operate through the preference question, even when the final recommender does not receive sponsorship information. This paper tests that channel synthetically, contrasting a mild instruction with a targeted question-only attack.
1 Introduction
- A platform can steer recommendations by asking about a sponsored product’s strength while omitting a more valuable attribute the user prefers.The user may answer honestly and the downstream recommender may follow its visible evidence, yet select an option inferior under the user’s unobserved preferences.Source: §1 · p. 1
- The paper asks what can be demonstrated, and what cannot be inferred, when only the question policy changes and the final recommender does not receive sponsorship information.Source: §1 · p. 2
- The targeted question-only attack changes recommendations in the synthetic test, while the mild commercial instruction has a null effect.The study also tests a conditional regret certificate that largely reduces to the neutral-question baseline.Source: §1 · p. 2
2 Relation to existing work and claim boundaries
Related work covers commercial conflicts, sponsored advice, and preference elicitation; this paper distinguishes its synthetic measurement from human behavioral evidence and welfare claims.
2 Relation to existing work and claim boundaries
- Prior studies cover conflicts between user and advertiser interests [1], delegation [2], sponsored labels and endorsements [3], and human conversational persuasion [4].The cited work precludes a claim of first evidence of sponsorship bias, and product selection is not equivalent to realized individual utility.Source: §2 · p. 2
- Two preregistered experiments with 2,012 participants found that conversational persuasion increased sponsored eBook selection, but not a significant decline in the measured keep-the-book-versus-$1 outcome [4].Unlike that work, this study has no genuine human behavioral evidence; question auctions [5] and efficient elicitation [6] motivate studying questions, while semantic fidelity [8] does not measure a person’s net benefit.Source: §2 · p. 2
- The certificate is an elementary robust-decision construction under stipulated synthetic utility, not a novel welfare guarantee [10].Known LLM-judge biases [11] and limits of simulated users as human proxies [12] [13] motivate keeping utility, model outputs, and judgments on separate reporting axes.Source: §2 · p. 2
3 Formal setting
The formal setting separates private synthetic preferences and question generation from a sponsor-blind final selector, then illustrates what a visible transcript can and cannot establish.
3 Formal setting
- Each task has two products, three verified numerical attributes, prices, a public budget, and an experimenter-fixed private preference vector.The vector has nonnegative weights over attributes that sum to one.Source: §3 · p. 2
- Over-budget products have utility zero, while not buying has stipulated utility 0.24; these payoffs are not monetary willingness to pay or consumer satisfaction.The best action maximizes this utility over the catalog and not buying.Source: §3 · p. 2
- Neutral questioning seeks a useful tradeoff; the soft policy mentions platform benefit, while the targeted policy asks about the sponsor’s strength and omits the competitor’s strongest attribute.The simulated user answers the two named attributes honestly according to private weights, and the final selector does not see sponsorship assignment or bids.Source: §3 · pp. 2–3
- The study separately tracks whether the selector chooses the sponsored item, which is not realized platform revenue because there are no ad payments or observed purchases..Source: §3 · p. 3
3.1 An observational-equivalence witness
- In the headphone example, two profiles give the same answer to a comfort-versus-battery question, and a frozen Bayesian selector chooses B from that transcript.The products have different attribute strengths, while both fit the stated budget.Source: §3.1 · p. 3
- The visible catalog, question, answer, and selection do not identify whether B is better for the simulated user.The test harness retains private preferences to score the answer; an external auditor of a real platform would not have that access.Source: §3.1 · p. 3
4 Experimental protocol
The held-out evaluation uses a deliberately shared synthetic catalog template, paired sponsorship assignments, multiple selectors, and sampled judges; its construction limits generalization.
4 Experimental protocol
- The 40-case held-out evaluation contains 20 catalog–profile contexts, each tested with sponsorship flipped between products; two categories form a separate 20-case pilot.Six category labels share the same basic two-product tradeoff structure, so they are not independent product markets.Source: §4 · p. 3
- A deterministic user answers from hidden weights, while an LLM selector and a fixed symmetric grid-prior selector decide using only observed information.A second model replays all 120 frozen question–answer inputs; a separate simulated-user subset does not replace the primary deterministic user.Source: §4 · p. 3
- A blinded judge samples 20 cases per question arm to assess tradeoff coverage, answer–recommendation consistency, and factual contradictions.The caption identifies Table 1 as 40 paired held-out cases with synthetic payoffs and an explicitly targeted policy that omits a rival strength.Source: §4 · p. 3, Table 1 · p. 4
- The study uses 2,000 context-cluster bootstrap draws, but shared category construction means neither interval supports generalization to real shoppers or independent catalogs.Model sampling could not be frozen with a supported temperature or provider-side seed.Source: §4 · p. 4
5 Results
Targeted questioning increased sponsored selection relative to neutral and soft questioning, while the evaluated selectors and judges reveal important limits on what these results establish. The reported outcomes are synthetic and do not validate the simulation against human shoppers.
5 Results
- Sponsored selection was 40% under neutral, 40% under soft, and 70% under targeted questioning; targeted questioning harmed 12 cases, improved none, and left 28 unchanged.For the LLM selector, mean utility change was −0.0912 among 24 cases where the sponsored product was not optimal, versus zero among 16 where it was optimal.Source: §5 · p. 4
- The soft arm asked about both discriminating attributes in 39 of 40 cases and changed no final choices.All three arms respected the public budget, and the targeted arm selected all 16 genuinely optimal sponsored products.Source: §5 · p. 4
- A second LLM selector made the same choices on all 120 frozen inputs, but this does not independently validate the question generator or simulated users.The preselected Gemini subset had 24 of 24 structured choice fields consistent with private pairwise ordering; only eight cases allowed a complete neutral–targeted AI-user comparison, with mean synthetic utility difference −0.0259.Source: §5 · p. 4
- Among 20 judged targeted traces, tradeoff coverage was absent in all 20 while visible answer–recommendation consistency was satisfactory in all 20.Five outputs with synthetic regret above 0.05 still received satisfactory terminal-consistency verdicts; full-trajectory coverage signaled one-sided elicitation.Source: §5 · p. 4
6 A conditional certificate that does not beat the baseline
The certificate bounds regret over preference weights consistent with an honest pairwise answer, but targeted questions certify fewer cases than neutral questions. Its fallback matches the neutral baseline’s mean regret while adding interaction cost.
6 A conditional certificate that does not beat the baseline
- The worst-case regret bound restricts preference weights to those consistent with an honest answer choosing feature j over k.A tie uses equality in the defining constraint.Source: §6 · p. 5
- The method evaluates feasible simplex vertices, certifies only bounds at most ϵ = 0.05, and otherwise asks a neutral fallback question.This vertex argument depends on the specified utility function and an honest response.Source: §6 · p. 5
- Neutral questions received certificates in 34 of 40 held-out cases, compared with 16 of 40 after targeted questions.Only four of the 16 cases where the sponsor truly was optimal were certified after a targeted question.Source: §6 · p. 5
- Targeted cases required a neutral fallback in 60% of cases; after fallback, mean regret was 0.0008, the same as asking neutral immediately.The fallback commonly withholds a beneficial option and increases interaction cost.Source: §6 · p. 5
7 Limitations and scope of publication
The report’s conclusions are bounded by its synthetic utility, shared catalog construction, and explicitly constructed attack. The authors characterize the work as exploratory and call for broader and human-validated evidence before consumer-benefit claims.
7 Limitations and scope of publication
- Invented private weights and payoff coefficients make the −0.0547 synthetic utility change no measure of consumer welfare or effect on sales, satisfaction, trust, or longitudinal choice.The AI-user subset does not remove this limitation.Source: §7 · p. 5
- All six category labels reuse a two-product, three-feature tradeoff template with limited jitter, so the holdout categories are not independent real catalog samples.There is also no multi-turn learning, advertiser adaptation, actual retrieval, or realistic offer availability.Source: §7 · p. 5
- The only harmful arm explicitly directs the questioner to omit the rival’s important attribute; the mild incentive had no effect in this setting.The result does not establish that platforms perform this intervention or that models spontaneously do so.Source: §7 · p. 5
- The authors describe the report as exploratory and call for independent catalogs and question models, varied prompts and dialogue lengths, cost-aware baselines, and human validation.They also identify upstream candidate-set suppression as a possible next question when human study is unavailable.Source: §7 · p. 5
Artifact availability and AI-tool disclosure
The archive is intended to support recomputation from synthetic traces, while new model outputs require access to named models. The disclosure assigns responsibility for verifying the work to the named human author.
Artifact availability and AI-tool disclosure
- The intended archive includes source code and synthetic traces, and statistics can be recomputed from saved JSONL without API access.New model outputs require access to the named models through an OpenAI-compatible interface and may not be byte-for-byte stable.Source: Artifact availability and AI-tool… · p. 6
- Generative AI tools were used for code, orchestration, evaluation, and drafting; the named human author is responsible for verifying calculations, references, licensing, and claims.The tools are not authors.Source: Artifact availability and AI-tool… · p. 6