Research blueprint — When AI Agents Become Economic Agents (UQO WP 25)
Written 2026-10-27, before any experiment was run. Updated only in the "Status" section.
1. Scientific question
What changes in general equilibrium when economic decisions — not only production tasks — are delegated to autonomous AI agents? Four roles of AI are kept apart throughout: (i) production technology (compute performs tasks), (ii) information-processing service (advisory AI: the human still decides), (iii) delegated decision-maker (the AI agent acts within a mandate; the principal retains residual claims and liabilities), (iv) a hypothetical independent economic principal with its own balance sheet (labelled counterfactual, never a baseline).
2. The mechanism we isolate (the "diversification externality of delegation")
Economic decisions are taken on forecasts of payoff-relevant states. A human decision-maker's forecast error is idiosyncratic: across a continuum of households or firms it washes out by the law of large numbers, so it costs only the person who makes it. A decision delegated to an AI agent built on foundation model m carries model m's error, which is common to every principal that uses model m. Delegation therefore has two faces:
- private face — it raises forecast precision (the model is better informed than the median human) and saves supervision time, at a fee that is priced in the compute market;
- aggregate face — it replaces a noise that averages out with a noise that does not. Aggregate capital
accumulation, asset demand and credit all start to load on the shared model error with weight
D(delegation share) and1/M(number of independent foundation models).
Two results organise the paper.
- Efficiency benchmark (Proposition 3). In a price-taking, complete-markets economy with constant returns,
the private value of delegating equals its social marginal value for every
D,Mand degree of strategic substitutability. Aggregate "model noise" is real — wages, interest rates and investment fluctuate with the foundation model's mistakes — but it is not an inefficiency: delegators dampen their response to the shared signal exactly as a planner would (strategic substitutability in capital accumulation), and the pecuniary effects on others are zero-sum by Euler's theorem. This was derived symbolically (scripts/00_lq_derivations.py) before any numerical work; it is the honest starting point and the first thing a referee should see. - Where delegation becomes a policy problem (Propositions 4–7). The wedge between private and social value
is non-zero exactly when (a) the aggregate action has non-pecuniary effects (
U_xX + U_XX ≠ 0, e.g. knowledge spillovers, market power), (b) price risk created by model noise is uninsurable for non-participants (hand-to-mouth workers, heterogeneous ownership of compute), (c) prices enter collateral constraints (margin spirals; Dávila–Korinek-type pecuniary externality), or (d) execution happens in markets with finite depth (price impact of correlated orders). We derive the sign and size of each wedge, the corresponding Pigouvian delegation/compute tax, and show that model diversityMis a substitute for the tax in cases (b)–(d) but irrelevant in the benchmark.
The compute market ties the decision channel to the production channel: inference demand from delegation and task automation compete for the same compute stock. In the short run (fixed compute) better decision-AI raises the compute rental, crowds out production automation and can raise the labour share; in the long run (elastic compute at user cost) both expand. Compute scarcity rents flow to the owners of algorithmic capital; concentrated ownership raises wealth concentration even when aggregate output rises.
3. Three nested models
| Model | Equilibrium concept | What it answers | Solution |
|---|---|---|---|
| A. Tractable baseline | Linear-quadratic recursive competitive equilibrium with dispersed information (Angeletos–Pavan class), around the deterministic steady state of a task-based stochastic growth economy with a compute market | Props 1–5: delegation threshold, aggregate model noise, efficiency benchmark, inefficiency wedges, compute crowding-out | closed forms (sympy) + numerical cross-checks |
| B. Heterogeneous agents | Stationary recursive competitive equilibrium (Aiyagari class) with idiosyncratic earnings risk, borrowing limit, idiosyncratic return noise (human decision mistakes), delegation fee, task automation, compute ownership (broad vs concentrated) | H1, H3, H6 in the cross-section: who delegates, who owns, Gini / top shares / labour share / r / welfare by percentile; decomposition of productivity, displacement, information, ownership, borrowing effects | EGM/VFI (numba), bisection on r |
| C. Agentic asset market and banking | Bounded-rationality simulated outcome (temporary trading equilibrium per period) with explicit balance sheets, margin rules, defaults and loss allocation; Walrasian call vs continuous double auction vs bilateral bargaining | H2, H4, H5: common-model herding, depth under stress, collateral amplification, leverage limits; LLM agents vs rational/rule controls | discrete-event simulation; LLM via local OpenAI-compatible servers |
4. Hypotheses (pre-registered in experiments/pre_registration.md)
H1 adoption threshold · H2 common-model trade correlation and depth · H3 ownership concentration and wealth concentration · H4 credit/collateral feedback · H5 diversity/leverage/ownership policies · H6 compute scarcity rents. Each hypothesis has a mechanism, a prediction, a falsification condition, an isolation experiment and a limitation.
5. Calibration and validation
docs/parameter_ledger.csv (one row per parameter, source, moment, range). Macro moments from FRED/BEA/BLS,
wealth shares from the Fed Distributional Financial Accounts, AI parameters from Epoch AI / Census BTOS /
provider price lists, all fetched on 2026-10-27 and logged in data/raw/MANIFEST.json. The AI-specific
behavioural parameters (signal precisions, supervision time, delegation disutility) have no accepted empirical
counterpart: they are illustrative and swept over wide ranges. Validation is limited to the zero-AI limit
(standard RBC/Aiyagari moments) and to qualitative facts about algorithmic trading correlation.
6. LLM laboratory
Local open-weight models (Qwen2.5-7B, Mistral-7B-v0.3, Llama-3.2-3B, 4-bit, mlx_lm.server) on MacLustr
nodes; optional BYOK providers through environment variables only. Roles: household, investor, firm manager,
lender, market maker, AI portfolio manager, procurement agent, regulator. JSON-schema validated actions, hard
constraints enforced in deterministic code, full audit log (model, prompt hash, context hash, temperature, seed,
response, latency, tokens), content-addressed cache. Controls: Bayesian mean-variance agent, rules, single LLM,
heterogeneous LLMs, shared LLM with varied prompts. Simulated LLM behaviour is never interpreted as human
preference evidence.
7. Gates and deliverables
G1 novelty → docs/literature_matrix.md; G2 theory → docs/model_specification.md + Props 1–3 with proofs;
G3 numerics → tests/ (accounting identities, zero-AI limit, LQ closed forms vs Monte Carlo, Aiyagari vs known
case); G4 agents → schema + constraint tests + audit logs; G5 science → factorial experiments with CRN and CIs;
G6 writing → paper/main.pdf, docs/referee_report.md, README, spbgit wp25_uqo.git.
8. Status
- 2026-10-27 — blueprint written; LQ derivations done (wedge = 0 in the benchmark, confirmed by direct expected-utility computation); local inference servers up; literature and calibration audits running.