Web3 · Strategy
Tokenomics Design
Most tokenomics models hold up in the whitepaper and break under real participant behaviour. We simulate supply, emissions, and incentives against thousands of self-interested actors, then stress-test the economics until the design holds and is defended before launch, not patched after.
- ~$20MTVL engineerednsASTR on Astar
- 4Hacken audit roundsNeemo Finance
- 10,000Simulation trajectoriesper model run
In short
What is Tokenomics Design?
Tokenomics design is a quantitative modelling service for protocol founders building tokenised systems. Each engagement produces an agent-based simulation across 10,000 scenario trajectories, a tokenomics paper covering supply, distribution and emissions, a vesting schedule mapped to contract logic, and a sensitivity analysis. The simulation is what serious investors and DAOs read first.
What we deliver
Concrete artefacts, not capabilities
- 01
Tokenomics paper covering supply schedule, distribution, emissions, governance, fee accrual, and the vesting graph.
- 02
Agent-based simulation of holders, stakers, governance voters, and arbitrageurs across 10,000 scenario trajectories.
- 03
Vesting schedule with cliff and unlock calendar mapped to on-chain vesting-contract logic.
- 04
Sensitivity analysis ranking the parameters that destabilise supply, demand, or governance under stress.
Key concepts
Key terms, defined
- Supply curve
- A supply curve is the schedule that maps token supply to time. It captures initial circulating supply, scheduled emissions, vesting unlocks, and any sinks such as burns or buy-backs. Investors read the curve to estimate inflation, dilution, and unlock-driven sell pressure across the next 24 to 60 months.
- Vesting cliff
- A vesting cliff is a date before which no vested tokens are released. Tokens accrue through the cliff but cannot be claimed or sold until it passes. Cliffs are used for team and investor allocations to align long-term incentives and prevent immediate post-launch sell pressure.
- Emission schedule
- An emission schedule defines how new tokens enter circulation over time, typically through staking rewards, liquidity mining, or protocol-revenue distribution. Schedules can be fixed, decaying, or governance-controlled. The shape of emissions drives long-run inflation and is the primary lever for staking yield.
- Agent-based simulation
- An agent-based simulation models a token economy by simulating individual participants (long-term holders, mercenary stakers, governance voters, arbitrageurs) and observing emergent outcomes. Unlike closed-form models, it surfaces second-order effects such as governance capture, mercenary capital flight, and feedback loops between incentives and behaviour.
- Sensitivity analysis
- A sensitivity analysis tests how protocol outcomes change when input parameters move. In tokenomics it identifies the parameters most likely to destabilise the model: emission rates, staking yields, vesting cliffs, fee splits. The analysis surfaces which assumptions a launch is most exposed to.
How we work
Engagement phases
Intent & constraints
We write down what the token is for (coordination, governance, fee accrual, or distribution), then capture constraints: fundraise size, investor vesting, regulatory posture, and target-chain economics. The output is a one-page brief every model variant has to pass.
Model & stress-test
We build an agent-based simulation in Python with stylised actors: long-term holders, mercenary stakers, governance voters, and arbitrageurs. Supply curves, emissions, and fees run against 10,000 trajectories, naming where the protocol turns inflationary, where governance captures, and where staking yields collapse.
Paper, defence & handoff
We write the tokenomics paper and defend it in founder sessions against challenge questions: cliff-edge dumps, governance capture, runaway emissions. On sign-off we map emissions, vesting cliffs, and fee splits to the contracts that enforce them.
Tech stack
What we build on
- PythonModelling
- NumPyNumerics
- pandasAnalysis
- MesaAgent Sim
- MatplotlibPlotting
- JupyterIteration
- TypeScriptContracts
- FoundryVerification
- PythonModelling
- NumPyNumerics
- pandasAnalysis
- MesaAgent Sim
- MatplotlibPlotting
- JupyterIteration
- TypeScriptContracts
- FoundryVerification
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| You have a protocol concept and need the token's supply and incentive model defended before launch. | You want a one-page token summary copied from a recent launch with the parameters adjusted. |
| Investors or a DAO are asking for a tokenomics paper that holds up to stress-testing. | The token is decorative: no staking, no governance, no fee accrual, no real economic role. |
| The token launch is paired with smart-contract engineering we can deliver end-to-end. | Timeline pressure rules out a stress-test phase and the founders want a 'good enough' model. |
Related services
Adjacent engagements
- Web3
Smart Contract Development
Solidity, Vyper, and Move contracts engineered for third-party audit, with tests and monitoring.
- Web3
Token Launchpad Development
Token sale and distribution platforms: fair launch, vesting, bonding curves, and allowlists, engineered for audit.
- Web3
DeFi Protocol Development
Lending, perpetuals, yield, and vault infrastructure spec'd for third-party audit.
Frequently asked questions
No. A tokenomics paper without a simulation is a marketing document. The simulation is what surfaces failure modes: cliff-edge dumps, governance capture, runaway emissions, mercenary staking. Without it the paper makes claims it can't defend in an investor meeting or a DAO vote. We won't ship the paper standalone.
Most engagements run 4–8 weeks end-to-end. Intent and constraints take a week. Modelling and stress-testing run 2–4 weeks. Paper drafting and defence sessions take another 1–2 weeks. When tokenomics is bundled with smart-contract engineering, the modelling timeline lengthens because parameter choices feed directly into contract spec.
In practice the two terms are used interchangeably. Tokenomics is the practical economic design of a single token: its supply, distribution, vesting, emissions, fee accrual, and governance. Token economics is the broader academic discipline that studies token-based systems, including market structure, network effects, and incentive design. Founders shopping for a vendor are almost always asking for tokenomics work.
If you're raising or listing, yes. A tokenomics audit is the analytical companion to a smart-contract audit: it stress-tests the economic model against participant behaviour, so the supply curve, emission schedule, and vesting design defend themselves under attack. Investors and exchanges increasingly request it. The audit either confirms the existing design or surfaces the parameters that need to change before launch.
Cost follows scope. The main drivers are how many token mechanisms we model (staking, governance, fee accrual, vesting), how many scenario trajectories and stress dimensions the simulation covers, and whether the work is bundled with smart-contract engineering. We quote a fixed price after the intent-and-constraints week, once the model surface is known. We do not publish a flat rate because a single-mechanism token and a multi-mechanism protocol are different bodies of work.
Last reviewed · Reviewed by Metaborong engineering team
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