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Multi-Agent AI as a Nested Principal-Agent Problem in Private Wealth Management: Mandate Representation and Evidence Control in Switzerland, Germany and Austria

Walter Kurz, Reinhard Magg, Florian Kollberg, Wojtek Stricker, Stefan Marx, Frank Reinhardt, Velimir Dedić

arXiv:2610.02863v1q-fin.GNcs.MA

TL;DR

In private wealth management, incomplete information passes through a manager who represents the client and specifies the AI’s task. The paper formulates that task as constrained joint optimisation of separate client and manager outcomes, then illustrates its implications with analytical constructions and simulation. In the constructed cases, declared weights often selected a higher service tier than the client-best alternative, and subsequent outcomes could differ from decision-time benchmarks.

  • Problem

    In nested client–manager–AI delegation, information omissions and specification choices can affect admissibility, selection, and the outcomes of both parties.

    Source: §1 · p. 1, §5 · p. 6

  • Method

    The paper formulates AI delegation as constrained joint maximisation over portfolio–workflow pairs, with separate client and manager outcomes and evidence-conditioned admissibility.

    Source: §5 · p. 7, §8 · p. 25

  • Results

    In six of eight states, the declared weight selected a higher service tier than the client-best pair; the 2022 simulation also showed lower subsequent client outcomes despite expected benchmark preservation.

    Source: §7 · p. 24

  • Takeaways & Limitations

    The constructions and simulation suggest that making mandate choices explicit could support more structured mandate design and review.

    Source: §8 · p. 25

    Strategic disclosure and hidden effort require behavioural models and observations of how parties respond to the specification.

    Source: §9 · p. 26

Abstract

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In private wealth management, a manager delegating to artificial intelligence (AI) acts as the client's agent and the system's principal. We introduce a model-independent formulation that combines nested principal--agent delegation with constrained joint maximisation as the task assigned to the AI system. The objective represents client and manager outcomes separately over portfolio--workflow pairs. Legal duties, mandate requirements and evidence sufficiency determine admissibility, with Switzerland, Germany and Austria supplying the legal context. Weights and reference-service floors make the trade-off explicit; concession accounting separates their effects on the client. Analytical constructions and a simulation using public-market observations illustrate the approach. Across eight decision states from four constructed mandates, omitted client liabilities caused two liquidity violations, omitted manager terms caused two capacity violations, and mistranslated weights changed four otherwise admissible choices under faithful optimisation. At the declared weights, six states selected a higher service tier than the client-best alternative, with client concessions of EUR 1,178 to EUR 2,264 and manager gains of EUR 3,062 to EUR 10,381. Three instruction forms each reached all 32 specified decisions under shared numerical, evidence and simulated approval controls; professional instructions matched explicit nested delegation on accuracy and clarification count. Subsequent 2022 exchange-rate and yield paths, combined with constructed growth scenarios, produced lower client outcomes than the reference service although the selected services met the decision-time forecast benchmarks. These examples suggest that the approach could help make mandate choices and their consequences easier to examine. Professional and field studies could assess whether this improves oversight and client outcomes.

1 Introduction

The paper frames wealth-management AI as a second delegation in which the manager represents the client while specifying the AI’s task. It proposes making both parties’ outcomes, applicable constraints, and unresolved information explicit in that specification.

1 Introduction

  • The manager’s dual role creates a specification risk: information gaps may be silently filled with learned defaults or unsupported assumptions that flow into downstream decisions.Gaps can concern client circumstances and preferences, the manager’s account of objectives, or service conditions.Source: §1 · p. 1
  • The proposed client–manager–AI structure treats the client as the manager’s principal and the manager as both the client’s agent and the AI system’s principal.The structure locates responsibility for representing interests and transmitting information at each interface.Source: §1 · p. 2
  • The AI’s task is constrained joint maximisation over portfolio–workflow pairs, using separate client and manager outcomes and admissibility requirements.Client outcomes include return, liquidity, and financial security; manager outcomes include service quality, resource use, and commercial value.Source: §1 · p. 2
  • Weights and reference-service floors make the trade-off between parties explicit and require justification and authorization.The proposed engineering use translates the specification into agent instructions and workflow rules, with clarification or human review when information or objective choices remain unresolved.Source: §1 · p. 2
  • In the DACH legal context, the response to missing client information depends on jurisdiction and service type, so the specification must state what an unresolved gap permits.The passage distinguishes the Swiss notification duty from the EU rule barring recommendations when necessary information is missing.Source: §1 · p. 2

2 Related Work

The related work spans agency theory, automated advice, specification risk, wealth-management benchmarks, and multi-objective control. The paper positions its client–manager–AI setting as successive delegation and distinguishes decision selection from permission to act.

2 Related Work

  • Hierarchical agency informs successive delegation: here, the manager is the client’s agent and then the AI system’s principal, unlike common agency’s agent serving several principals at once.Related work also covers effort, incentives, monitoring, and automated advice.Source: §2 · p. 2
  • Research on incomplete objective specification examines proxy objectives and interactive revision, while other work locates specification cost in delegation losses or contractual incentives.The passage also notes work treating incomplete specification as a financial-agent governance problem.Source: §2 · p. 2
  • Wealth-management benchmarks and evidence frameworks address paired tasks, investor context, suitability, source evidence, and human approval, while related mandate models connect authority and evidence to permission.The paper identifies a need to distinguish client-authorized preferences from factual claims with service-dependent evidence requirements.Source: §2 · p. 3
  • Multi-objective optimization and runtime shield synthesis inform the distinction between selecting under declared evaluations and enforcing permission to act.The related work also references rules and guidance on suitability, data processing, and institutional AI governance.Source: §2 · p. 3

3 Contribution

The contribution is an inspectable formulation of nested client–manager–AI delegation as constrained joint optimisation. It also decomposes departures from the intended mandate and illustrates instruction and approval controls.

3 Contribution

  • The AI maximises a joint client–manager objective subject to binding constraints, with the manager acting as the client’s agent and the AI’s principal.Separate outcome functions cover portfolio–workflow pairs; legal, mandate, and evidence requirements determine admissibility before optimisation.Source: §3 · p. 3
  • Concession accounting separates benchmark restrictions from weighting and supports policy review of the total concession.Disclosure, translation, and execution decompose departures under stated counterfactual and tie-selection conventions.Source: §3 · p. 3
  • The assessment asks how to represent delegation with binding constraints, how omissions and representation choices affect decisions and outcomes, and how to instantiate the specification in instructions and controls.The questions distinguish client-protection, mandate, and evidence constraints from effects on each party’s outcomes.Source: §3 · p. 3
  • Formal specification, analytical constructions, simulation, and instruction comparisons address the questions while retaining the original mandate for evaluating transmitted specifications.The implementations include deterministic, single-agent, and multi-agent workflows; the constructions also examine required-input recovery, numerical selection, and version-bound approval.Source: §3 · p. 3

4 Methods

The methods combine analytical constructions, an economic simulation, and worked implementations to examine specification, selection, and approval. The evaluation separates what the specification transmits from the original mandate used to assess it.

4 Methods

  • The study combines analytical constructions, economic simulation, and worked implementations for artificial evaluation; the formal results depend on stated objectives, constraints, and information structure.Any executor preserving these rules can instantiate the specification.Source: §4 · p. 4
  • The simulation varied client-liability omissions, manager-term omissions, and transmitted weights across four constructed mandate families, three portfolios, and three service workflows.Faithful optimisation of each altered specification was evaluated against the original mandate, while simulated approval checked evidence, calculations, constraints, reference floors, and evidence version.Source: §4 · p. 4
  • The economic model combined expected wealth, downside penalty, and declared service value for clients with net fee income and service quality for managers.It used 144 monthly ECB exchange-rate and government-yield observations from 2014–2025 alongside authored growth returns, preferences, service conditions, and scenario probabilities.Source: §4 · p. 4
  • Professional prose and two explicit instruction forms received the same objectives, evidence, questions, calculators, resource ceilings, and simulated approval controls.The evaluation comprised 96 assignments across four mandate families and eight information conditions, with one run per treatment and condition.Source: §4 · p. 4
  • The retained implementation studies tested supplied client-selection policies using deterministic, single-agent, and multi-agent workflows.The revised narrative interface combined typed extraction, clearer instructions, and reusable arithmetic receipts, then was re-evaluated on the same cases.Source: §4 · p. 4
  • Scoring separated mandate fidelity, proposal defects, issuance, clarification burden, the joint optimum, and each party’s economic outcomes.Independent cash-flow identities checked arithmetic, saved actions were replayed, and conditions sharing a mandate were treated as dependent observations.Source: §4 · p. 4
  • The legal mapping concerns investment advice and portfolio management for private or retail clients, with other services used as applicability contrasts.The analysis maps service-specific information requirements in Switzerland, Germany, and Austria; its legal cutoff is 30 September 2026.Source: §4 · pp. 4–5
  • The artifact’s uniform hold policy is a conservative design choice where the mapped Swiss provision specifies an information duty.The legal contrasts concern domestic retail services; cross-border application depends on provider authorization, client category, and service-request origin.Source: §4 · p. 5

5 From Classical Agency to Nested Delegation

This section models wealth management as nested delegation, making information transmission, admissibility, reference-service benchmarks, and client–manager trade-offs explicit. It also describes a restricted client selector that evaluates evidence-compatible states against client utilities and constraints, routes consequential gaps through clarification or hold, and binds issuance to current gates and human approval.

5 From Classical Agency to Nested Delegation

  • Wealth management is modeled as nested delegation: the manager is the client’s agent and the AI’s principal, so admissibility precedes the manager–AI objective.The client relationship ranks above the second delegation; the manager’s commercial interest competes only among alternatives permitted by the mandate and applicable duties.Source: §5 · pp. 5, 6, 8
  • The AI is modeled as an executor without separate utility or strategic effort, steered by a specification defining its objective, alternatives and permitted information.The design variable shifts from a payment schedule to the specification; information omitted earlier remains unavailable downstream unless clarification or supplementary evidence supplies it.Source: §5 · p. 6
  • When two client states produce the same complete AI input but have disjoint client-optimal portfolios, no rule using only that input can guarantee the optimum in both.A permitted question may distinguish the states, while a hold preserves the unresolved decision; the limit is informational, not computational.Source: §5 · p. 7
  • Admissibility requires necessary information and satisfaction of legal, risk, processing, mandate and service-viability constraints across every evidence-compatible state.Missing required information or inconsistent evidence holds the recommendation; an empty feasible set calls for revised alternatives or a held decision.Source: §5 · pp. 7–8
  • Within the admissible set, the proposed weighted objective combines declared client and manager evaluations; its solutions are Pareto-efficient and approach each party’s programme at extreme weights.The weight is left to the mandate and must be declared, justified and authorized before the system acts.Source: §5 · p. 8
  • The client concession is accepted only when the manager gain is at least λ/(1 − λ) times as large on the declared scales.The decision record separates weighting concessions from benchmark-floor concessions, since a zero weighting concession can coexist with a positive benchmark concession.Source: §5 · pp. 8–9
  • A counterfactual decomposition assigns departures from the intended choice to disclosure, manager translation or AI execution, while faithful execution under the same tie rule makes the execution term zero.Different tied choices can still produce different regret under the authorized objective, and specification responsibility remains with the manager.Source: §5 · p. 9
  • The selector filters candidates dominated across state/facet utilities, then applies a finite-client Nash-product rule to the remaining candidates.Utilities use the lowest value across evidence-compatible states; empty feasibility holds for human resolution, and remaining ties receive human adjudication.Source: §5 · pp. 10–11
  • Clarification thresholds require manager and domain-review authorization, separate materiality rationales, and prospective calibration beyond the constructed checks.The scheduler routes consequential unresolved inputs to permitted questioning or holds when mandatory refusal, exhausted resources or unavailable questions prevent resolution.Source: §5 · p. 11
  • Human review receives eligible candidates, gaps, consequence diagnostics and evidence/policy versions; approval binds one candidate to that version.Evidence acceptance, expiry, revocation or policy revision advances the version and clears approval.Source: §5 · pp. 12–13
  • Under the transition contract and trusted role boundary, every issuance satisfies the required gates at its event time.Evidence or policy changes clear approval and advance the version; issuance rechecks gates, eligibility and approval/version equality.Source: §5 · p. 13
  • For fixed primitives, restricting a non-empty joint-state set weakly expands robust feasibility, but Pareto membership, maximisers and review eligibility may change.The inclusion applies to the feasible set only; new information or invalidated sources require renewed assessment.Source: §5 · pp. 13–14
  • Every issued candidate is undominated among current robust-feasible candidates across the declared state/facet utilities.The guarantee depends on the supplied utilities and states being adequate for the mandate.Source: §5 · p. 14

6 Results

This section examines how mandate information, weights, floors, representation choices, and evidence controls affect admissibility, selection, clarification, and decisions across constructed examples and workflow comparisons. It also considers client outcomes under subsequent 2022 market paths.

6 Results

  • With an established EUR 0.2 million obligation, B maximised client value, but unresolved support for EUR 0.2 million or EUR 1.8 million required a hold; C alone was robust across both.An obligation above EUR 2 million required revised alternatives or a continued hold; an accepted answer was needed to distinguish the client-optimal choices B and C.Source: §6 · p. 14
  • The efficient pairs meeting both floors were (B, ω1) and (C, ω2); at λ = 2/3 they tied, and crossing that weight changed the selected pair.At λ = 1/2, (C, ω2) scored 7/8 versus 3/4 for (B, ω1); at λ = 3/4, (B, ω1) scored 7/8 versus 13/16. The λ = 1/2 concession pair was ∆C = 1/4 and ∆W = 1/2.Source: §6 · pp. 14–15, Figure 4 · p. 15
  • In the two-pair capacity construction, the reference service left a client concession of zero but a benchmark concession of 1/4, so unsupported benefit evidence returned the policy for revision.Changing the agreed reference to (B, ω2) reduced both concessions to zero; a separate delivery restriction made the client comparator and constrained choice coincide, leaving a client concession of 8 that required its own supported benefit assessment.Source: §6 · p. 16
  • Six of eight mandate states selected an advisory or specialist workflow over the client-best standard workflow, while the two business-sale states selected the client-best pair.The client and manager scales were fixed at 10% and 1% of initial wealth, respectively; all hard-feasible pairs met both reference floors in these mandates.Source: Table 3 · p. 17, §6 · p. 17
  • Across eight states, omitting liabilities caused two liquidity violations, omitting manager terms caused two capacity violations, and a mistranslated client weight changed four admissible choices.Faithful optimisation preserved all eight original decisions; the interventions changed specification content, while deterministic execution under the shared tie rule introduced no execution error.Source: §6 · pp. 17–18, Figure 6 · p. 18
  • All three instruction forms reached 32 prescribed decisions, including 28 issuances and four justified holds, with zero regret across 84 issuances.Professional prose and nested-delegation instructions each used 20 questions; explicit joint-objective instructions used 26, adding EUR 450 in nominal client charges and EUR 672 in manager labour cost.Source: Table 4 · p. 18, §6 · p. 18
  • On 2022 paths, client outcomes averaged EUR 77,967 below the reference service while manager net income was EUR 8,816 above it in every treatment.The calculation retained the constructed growth scenarios and their probabilities; the client measure reflects the 2022 paths, whereas manager net income does not depend on the market path.Source: Figure 7 · p. 19, §6 · p. 19
  • Across 18 constructed aggregation cases, 15 had conflicting rankings among robust-feasible alternatives, and the separately weighted sensitivity produced disjoint winners.Duplicating the liquidity facet changed the complete-context products from (18, 36, 27) to (36, 216, 243) and selected C, whereas the original two-facet representation selected B.Source: Table 5 · p. 19, §6 · pp. 19–20

Analytical constructions

  • Nash products selected B at 0.36, whereas equal-weight sums selected A at 0.65, despite common feasibility and zero reference values.The shared constraints admitted both selections, so their relative desirability depended on the client criterion used to assess the scalarisation; exact witnesses and reference checks reproduced all 18 Nash calculations.Source: §6 · p. 20

Uncertainty and clarification

  • The product of componentwise minima selected Y at 4, while the worst joint-state product selected X at 9, exposing different uncertainty policies.With X incurring zero monetary loss and Y incurring EUR 100 in each state, the unresolved choice had EUR 100 regret; the executed case asked at a EUR 10 threshold. In a partial-answer cell, switching A to B raised the client criterion by 3 and reduced monetary loss by EUR 100, while a further answer reversed the preference to A with a client-criterion gain of 5. With equal zero losses, resolution could yield a client-criterion gain of 96 and trigger a question at a threshold of at most 96.Source: §6 · p. 20

Diagnostics and evidence resolution

  • Pareto filtering and answer-cell assessment changed eligibility or detected a portfolio change even when unresolved and singleton choices agreed.Constructed comparisons also found zero individual diagnostics despite dominance changes or a jointly material winner change from B with product 4 to A with product 100. When an optional answer could restore an empty robust-feasible set, the procedure held for human evidence resolution; externally accepted evidence restored review.Source: §6 · p. 20

Policy and liability examples

  • Under the same financial state, the Swiss portfolio-based policy asked and held after refusal, while the transaction-specific policy permitted review of candidate C.Both policies retained C with zero unmet-liquidity loss; the difference followed from the declared applicability mapping and conservative institutional hold. Table 6 also contrasts the mandatory amount with an optional communication-format preference that leaves utilities and constraints unchanged. The constructed CHF 1.4 million obligation against EUR 1.2 million in liquid assets shows that carrying forward the 14 January valuation on a later date could leave an apparent surplus despite a current-rate shortfall.Source: §6 · pp. 20–21, Table 6 · p. 21

Pilot and conformance checks

  • All workflows reached the 36 structured-pilot reference decisions, while the multi-agent workflow incurred 3.18 times the single agent’s reported model cost.The finite procedure passed 18 checks comprising 26 constructed traces and 14 issuance events; evidence withdrawal, expiry, role checks, and replacement evidence were tested. Independent reconstruction checked 252 narrative records and found zero violations across 117 issued outputs under the supplied requirements; domain validity remained with case-policy review.Source: §6 · pp. 20, 21, 23, Table 8 · p. 21

Reserve and investment constructions

  • The retirement construction required a EUR 168,000 liquid reserve, and only the EUR 180,000 alternative covered it.A competing questionnaire implied a EUR 96,000 requirement, so signed reconciliation determined the governing expenditure figure. In the investment construction, available assets were EUR 98,000; R90, the EUR 90,000 alternative, met both bounds, while the EUR 80,000 reserve was insufficient and EUR 100,000 exceeded available assets. The investment result was a EUR 2,000 loss, but feasibility used available assets.Source: §6 · pp. 21–22

Initial narrative comparison

  • The initial narrative comparison recorded 42, 30, and 22 correct decisions for deterministic, single-agent, and multi-agent workflows, respectively.Model-workflow rates were 71.4% and 52.4%; correct recommendations numbered 14 and seven out of 24 issuance cases. Each workflow asked 30 relevant questions and zero redundant questions. Under fixed responses, all three interfaces reached the required outcomes in all 14 cases, but the initial run included calculator-budget exhaustion and response-format failures; the enforced issuance invariant recorded zero violations.Source: §6 · p. 22, Table 10 · p. 22

Revised narrative interface

  • 42/42 reference decisions were correct for each revised workflow; model expenditure was USD 0, USD 2.13, and USD 5.63 for deterministic, single-agent, and multi-agent execution, respectively.Failures, validation errors, and enforced gate violations were zero; all 126 assignments were completed. On conformance and model expenditure, the deterministic workflow strictly dominated both model arrangements, and the single agent strictly dominated the multi-agent arrangement. The comparison uses the supplied policy and experimental grammar, and counts correct recommendations and required holds.Source: Figure 8 · p. 23, §6 · p. 23, Table 11 · p. 23

7 Discussion

The discussion argues that mandate representation and execution both require review because omissions or mistranslations can change admissibility and service choices. Its constructed examples connect those choices to client concessions, evidence controls, and limits on what the simulations establish.

7 Discussion

  • In constructed cases, omitted client liabilities, omitted manager terms, and mistranslated weights produced liquidity, capacity, and service-selection departures, respectively.The information limit is that selecting a client-optimal outcome across states with disjoint optima requires complete decision inputs that distinguish those states.Source: §7 · pp. 23–24
  • Nested delegation organizes review around the manager’s dual role, separating mandate review of objectives and evidence criteria from execution review of application and current authorization.Positive weights select a Pareto-efficient pair among alternatives that satisfy legal, mandate, and evidence constraints and both reference-service floors; utility scales and benchmarks still require assessment.Source: §7 · p. 24
  • In six of eight states, the declared weight selected a higher service tier than the client-best pair, with client concessions equal to total concessions because floors were nonbinding.In the 2022 simulation, expected benchmark preservation coexisted with lower subsequent client outcomes and higher manager net income; that comparison measures a different quantity from the risk-adjusted decision criterion.Source: §7 · p. 24
  • Service classification changes how information gaps affect permission to proceed, while the specification records both required information and permitted acquisition routes.The Swiss hold policy adds an institutional restriction to the mapped FinSA duty; the EU necessary-information gate follows Article 54(8).Source: §7 · p. 24
  • Clarification rules depend on declared states, answer partitions, and thresholds, and the appropriate treatment of uncertainty remains a client-dependent design choice.Question sequencing would require accounting for costs and possible answers, including probabilities for expected-value treatment, and calibration on representative mandates before held-out evaluation.Source: §7 · p. 24
  • After recommendations, evidence changes can warrant reassessment or renewed approval when support is lost or a changed valuation leaves an obligation underfunded.The withdrawal and replacement checks distinguish lost support from surviving independent evidence.Source: §7 · p. 25
  • Revised narrative interfaces reached all reference outcomes on the same development cases, but their combined changes leave individual contributions unresolved.The changes combined typed extraction, clearer instructions, and reusable arithmetic receipts; the correction mechanism remained unused.Source: §7 · p. 25
  • Professional prose, explicit joint-objective, and nested-delegation instructions reached the same prescribed decisions, while revised deterministic and single-agent workflows matched multi-agent conformance at lower measured model cost.The observed agreement concerns extraction and evidence-aware sequencing; comparing service workflow with agent architecture in applied systems requires measurements of professional effort, infrastructure cost, and delivered service quality.Source: §7 · p. 25

8 Conclusion

The paper formulates delegated AI decisions as constrained joint maximisation within a nested client–manager–AI relationship, making separate interests and their trade-offs explicit. Its constructions and simulation illustrate how the framework may support mandate design and review, while professional and field studies remain needed to assess its effects.

8 Conclusion

  • The framework represents client and manager interests separately and selects portfolio–workflow pairs through constrained joint maximisation within nested delegation.Legal duties, mandate requirements and evidence sufficiency determine admissibility; weights and reference floors make trade-offs explicit.Source: §8 · p. 25
  • Concession accounting makes the client consequences of reference floors and trade-off weights available for review.Source: §8 · p. 25
  • Analytical constructions and simulation illustrate how the framework can guide calculations, clarification questions and approval controls.The proposal is model-independent; professional specification studies and field evaluations could assess whether it improves decision quality, oversight and client outcomes.Source: §8 · p. 25

9 Limitations and Future Research

The paper identifies boundaries on its simulation, procedures and legal application, and outlines studies to test the framework’s broader relevance. Several assumptions and evaluation settings constrain how far the reported demonstrations can be generalized.

9 Limitations and Future Research

  • The simulation uses constructed mandates, preferences, service terms and growth scenarios, with public exchange-rate and yield observations anchoring selected market inputs.Client evaluation scales, reference service, quality values and managerial commercial interests require domain validation.Source: §9 · p. 25
  • The simulation does not model strategic disclosure, hidden effort or incentive-compatible implementation, which require behavioral models and observations of party responses.The legal mapping also requires institution-specific review.Source: §9 · p. 26
  • The procedure is bounded by finite state enumeration, evidence sufficiency and declared cardinal utilities; its indistinguishability result assumes identical complete inputs and fixed policy primitives.Joint inputs can conceal selection gains when individual consequences are zero, and question probabilities, costs and compound partitions remain open.Source: §9 · p. 26
  • Applying the legal mapping to an actual mandate requires current provider authorization or exemption, client category, territorial facts, processing arrangements and production identity and storage controls.Source: §9 · p. 26
  • The structured pilot uses six families sharing three motivating source clusters, while narrative cases span two source-linked families with controlled grammar.The revised narrative interface was developed from failures on the same cases used for evaluation, and the deterministic parser is tailored to the document grammar.Source: §9 · p. 26
  • The joint-objective comparison covers four base mandates, eight dependent conditions per mandate and one repetition per instruction treatment.Evaluation mandates share templates and weights with development mandates; broader language variation, independently authored mandates and budget-matched controls would assess transfer.Source: §9 · p. 26
  • A prospective reviewer comparison would test the framework against a competent governance checklist, measuring accuracy, responsibility assignment, unnecessary holds and review effort separately.Practitioner responses and independent assessment of case labels could test professional specification and review; field evaluation would examine client outcomes after oversight and implementation costs.Source: §9 · p. 26
  • A prospective screening extension would model disclosure menus, truth-telling and participation, while behavioral studies would examine generative moral hazard and its dependence on effort and generation incentives.Source: §9 · p. 26

Declarations

The declarations identify the article’s publication details, author contributions, funding and conflicts, as well as research-record access and AI-tool use.

Declarations

  • All authors contributed equally, and the research received no external funding; the authors declare no conflicts of interest.Source: Declarations · p. 30
  • The authors retain the source code, constructed mandates, market inputs, recorded responses, experimental configurations and verification instructions, but access is restricted.Market inputs include source URLs and content hashes.Source: Declarations · p. 30
  • GPT-6.1 Astra and Claude Opus 5.5 were used for the simulation.Source: Declarations · p. 30
  • The article is a 2026 research article published in Swissi AI Journal under a CC BY 4.0 licence.Source: Declarations · p. 30
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