Issuance, Pricing, and Budget Targeting in a Dual-Cost Incentive Currency
SF Express pays couriers in Fengdou, a closed-loop token earned through work and redeemed for either zero-cost virtual goods or budget-consuming real goods. The firm sets how many tokens to issue and what they cost — but not how couriers spend them — and must land real-goods spending close to an annual budget it loses if it underspends.
Second layer these levers are set by a forecasting-and-inventory algorithm and adjusted by human operators — a human–AI collaboration we also study, using the recorded recommendation-vs-override trace.
A closed corporate token economy with a defining feature: two redemption channels with heterogeneous marginal cost to the firm.
Tokens accrue per delivery job. Issuance volume is the "money supply" — a firm-controlled lever.
Skins, badges, status items — real utility to couriers, zero marginal cost to SF.
Paper, food, daily necessities — every redemption draws on the annual budget.
The bite: the budget must be spent close to target — overspend is infeasible, and underspend triggers a budget cut next year (a use-it-or-lose-it ratchet). Levers are set before redemption is observed, and the firm cannot dictate the virtual-vs-real split. It can only steer.
Three levers, three goals — and a near-clean mapping between them. The virtual-goods price is a free budget-steering valve.
| Lever | Primarily steers | Mechanism |
|---|---|---|
| 1 Token issuance | Incentive power + float | More tokens ⇒ stronger effort signal, larger outstanding liability. |
| 2 Real-goods prices | Budget burn rate | Sets how fast real redemptions draw down the annual budget. |
| 3 Virtual-goods prices | Budget steering — the valve | Diverts token demand toward the zero-cost channel at will. |
Maximize effort / retention value per yuan of budget.
Land real-goods spend near target — avoid overspend and the underspend ratchet.
Keep outstanding Fengdou in a healthy band — avoid hoarding or collapse.
Illustrative. As virtual-good attractiveness λ rises, real-goods spend falls; the two-sided budget penalty is U-shaped with an interior optimum λ*.
Virtual goods absorb token demand at zero cost, so their relative price — not issuance — is the sharpest budget-steering instrument. Issuance is then freed to do its real job: motivation.
Levers commit before stochastic redemption realizes, with a two-sided over/under penalty — a newsvendor-type critical-fractile policy, with the virtual good thinning the tails.
Multi-period: token accumulation as a Miller–Orr float band, and the use-it-or-lose-it ratchet making the firm's problem an MDP.
A forecasting-and-inventory algorithm recommends the levers; human operators adjust them with private information the model lacks — the ratchet, HQ pressure, fairness. We observe every step.
Predicts redemption demand and the float; recommends issuance & prices.
Operators override using ratchet risk, HQ pressure, campaigns, fairness.
The implemented vector — which may differ from the recommendation.
Budget adherence · incentive value · float — the override's score.
The data asset. Both the algorithmic recommendation and the human-set decision are recorded, so we can isolate where operators deviate and whether each deviation moved the firm toward its goals — separating information deviations (the human knows more) from complexity / discretion deviations (the human won't or can't follow exactly).
Two on the token economy itself; one on the human–AI layer.
How to set issuance and dual-cost pricing to hit the budget target while maximizing incentive value — the pressure-valve / separation result.
How to manage the token supply over time — the float band and the multi-year use-it-or-lose-it ratchet that distorts the static optimum.
Using the recommendation-vs-override trace: which operator deviations from the algorithmic levers add value, and how to make the recommendation adherence-aware.
Primarily a token-economy / incentive-currency paper; secondarily a human–AI collaboration paper.
Special Issue: The Human–Algorithm Connection — Manag. Sci. 72(1), 2026.
Kesavan, Kushwaha & Steele — judgmental adjustments lift profit (+4.92%), Manag. Sci. 2025. link
Kesavan & Kushwaha — merchant overrides cut profit 5.77% on average, Manag. Sci. 2020 — value is conditional. link
Balakrishnan, Ferreira & Tong — collaboration with private information, Manag. Sci. 2026. link
Adherence-Aware Recommendations — "The Best Decisions Are Not the Best Advice," Manag. Sci. 2024. The template for Q3. link
Angelova, Dobbie & Yang — "Algorithmic Recommendations and Human Discretion," Rev. Econ. Stud. 2025. Identifies which overrides add value. link
The gap we fill: the token-economy canon is dominated by tradeable crypto tokens and consumer loyalty points. Fengdou is neither — a closed-loop, non-tradeable incentive currency with dual marginal cost (free virtual / budget-consuming real) and a two-sided budget target under a use-it-or-lose-it ratchet. The human–AI layer is a second contribution on top of that design.
Targeting a UTD-24 OR/OM outlet (Management Science / Operations Research).
Formalizes the closed-loop, dual-cost, budget-targeted incentive currency — distinct from tradeable crypto tokens and consumer loyalty points.
A separation result — issuance for incentives, relative virtual/real pricing for the budget — and a two-sided, newsvendor-type targeting policy.
Token-supply management as a float band, and how the multi-year use-it-or-lose-it ratchet distorts the static optimum.
Using the recommendation-vs-override trace, identifies which operator deviations from the algorithmic levers improve budget adherence and incentive value.
Full-granularity SF Express panel — issuance, balances, prices, individual redemptions (and the algorithm-recommendation-vs-human-decision trace), July 2024 → present.
Analytical-normative token-economy model with theorems, calibrated on the panel; dynamic (multi-period) float and ratchet. Human–AI overrides as a second, empirically-identified layer.
Active. Model formulation and calibration in progress; counterfactual policy experiments to follow.