T16

See the scoped evidence_register and qualification_protocol. Structural plausibility, descriptive surveys and laboratory benchmarks do not alone identify a local commercial policy effect.

See the scoped evidence_register and qualification_protocol. Structural plausibility, descriptive surveys and laboratory benchmarks do not alone identify a local commercial policy effect.

Pre-action shortlist, need, role, constraints and prior exposure can influence assignment and outcomes. Observed funnel rates mix these states. Conditioning on them helps prediction but does not itself identify a causal effect. Compare feasible contact/evaluation/implementation policies, including no action when appropriate, on a stable eligible population. The 95-5 in-market heuristic and pre-contact winner-shortlist prevalence have different denominators and identify no 95% value-creation split or geometric headcount-decay law.

• Martin Bichler — Agentic Markets — 6 June 2026. Full journal article. Conceptual synthesis of agent search, differentiation, market power and platform architecture. Source: https://link.springer.com/article/10.1007/s12525-026-00906-y
• Quanyan Zhu — Agentomics — 9 June 2026. Full preprint. Formal/normative workflow-surplus and cooperative allocation framework. Source: https://arxiv.org/html/2606.14769v1
• G2 — The Answer Economy — 15 April 2026. Primary report. Commercial survey; methods/findings inspected. March online sample of 1,076 B2B decision makers and 39 marketer interviews across regions. Reports AI research alongside continued Google use and frequent inaccuracies. Source: https://learn.g2.com/g2-2026-ai-search-insight-report
• G2 — The Evaluation Maze — 22 July 2026. Primary report. June online survey of 1,038 decision makers plus 55 interviews. Forty percent name evaluation their longest stage; 39% name security the largest post-selection delay; 49% report a CFO veto in the past year. Source: https://sell.g2.com/2026-buyer-behavior-report
• Oded Netzer and Rajan Sambandam — Synthetic Data in Marketing Research How to Evaluate and When to Trust — 12 September, revised 19 September 2026. Record and PDF. Full methods inspected. Real 2025 US sample of 3,063 people, 18 sectors, 108 questions; grounded leave-one-question-out prediction and out-of-bag screening. Rich category histories improve agreement over demographics alone; 46 retained questions perform better than all questions. Source: https://arxiv.org/abs/2609.13995

• Retrospective commercial surveys motivate buyer-state measurement. Effects need randomization or justified exchangeability, positivity, consistency and interference handling; conditional effects are averages, not identifiable individual effects.
• no new calibrated market experiment. Lower technical search costs do not guarantee exhaustive search, equal information, higher welfare or a particular equilibrium. C2/C3/C7/C8.
• the security-operations numerical example is hypothetical. Shapley allocation is a proposed normative benchmark, not an observed pricing equilibrium; coalition values themselves require estimation. C5/C7/C8.
• reported frequency and AI-attributed switching are not observed journey shares or causal switching. Sampling/weighting and question bases constrain universal adoption estimates. C1/C2/C3/C8.

Compare feasible contact/evaluation/implementation policies, including no action when appropriate, on a stable eligible population. The 95-5 in-market heuristic and pre-contact winner-shortlist prevalence have different denominators and identify no 95% value-creation split or geometric headcount-decay law.