Tiledom Economic Whitepaper

A crypto-native simulation of scarce land, labor, capital and adaptive monetary policy

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Abstract

Tiledom is a crypto-economic simulation game in which every economic primitive—land, resources, labor, capital and money—has an explicit on-chain representation and a formal model behind it. The system is designed as a closed, instrumented laboratory for macroeconomics: we can observe token velocity, sink/issuance balance, treasury utilization and player behavior in real time, then let a DAO and, eventually, AI agents steer the parameters.

Unlike most GameFi experiments, Tiledom is not built around yield farming or inflationary rewards. Tokens are minted and burned according to transparent rules; capital assets depreciate over time; and economic activity is bounded by finite land endowments and worker energy. The long-term goal is to evolve from a pure-crypto world into a Matrix-style parallel economy that gradually leaks into real-life rails: fiat on- and off-ramps, real-world asset hooks and AI-proposed governance upgrades.

1. System Overview

The economy is defined over four primary sets:

  • Land: non-fungible tiles with heterogeneous resource capacities and building slots.
  • Resources: fungible commodities extracted from land (bronze ore, stone, wood, nutrients).
  • Labor: workers with energy and nutrition budgets who perform actions.
  • Capital: buildings and tools that multiply productivity but depreciate.

A protocol-controlled treasury manages the primary game token TILE, acts as a central bank and liquidity provider, and enforces an adaptive monetary policy based on observable indicators rather than discretionary interventions.

Formally, at time $t$:

$$ S(t+1) = S(t) + I(t) - B(t) $$

Where:

  • $S(t)$ = circulating supply of TILE at time $t$
  • $I(t)$ = newly issued tokens (inflation / rewards)
  • $B(t)$ = burned tokens (sinks)

Our design objective is to keep net inflation in a narrow, data-driven band while ensuring that productive investment (land, factories, lending) dominates passive hoarding.

2. Land NFTs and Resource Endowments

There are at most 1,000,000 land NFTs, released in generations:

  • Generation 0: ~50k–100k tiles (launch cohort)
  • Later generations: new regions with distinct resource distributions and yield curves

Each tile $i$ is modeled as a resource vector:

$$ Tile_i = (B_i, S_i, N_i) $$

Where:

  • $B_i$ = bronze ore capacity
  • $S_i$ = stone capacity
  • $N_i$ = nutrient capacity (drives crop yields)

Over time, extraction depletes these capacities; maintenance and capital investment can slow, but not fully negate, degradation. This creates path dependence and long-run valuation differences between tiles.

Each tile exposes four building slots that can be configured as:

  • Housing (huts, then higher tiers)
  • Farmland
  • Workshops and factories

Slots, not just raw land, become a scarce coordination surface between landowners and workers.

3. Resource Extraction and Production Chains

3.1 Energy-Gated Actions

Workers allocate energy to discrete actions such as:

  • Mine bronze on tile #i with 20 energy
  • Convert slot #j to farmland with 15 energy
  • Harvest potatoes on tile #k

Let $e_i(t)$ be effective energy allocated to land $i$ at time $t$, and $\alpha_{r,i}$ be the extraction coefficient for resource $r$ on tile $i$. Then aggregate supply of resource $r$ at time $t$ is:

$$ S_r(t) = \sum_{i=1}^{N} e_i(t) \cdot \alpha_{r,i}. $$ Capital goods such as tools and factories follow a depreciation model:

$$ K(t+1) = K(t) \cdot (1 - \delta), $$

where $K(t)$ is effective capital stock and $\delta$ is the depreciation rate. This enforces recurring demand for reinvestment and keeps resource and token sinks active.

4. Workers, Nutrition and Labor Markets

Each worker $j$ is a triple:

$$ Worker_j = (E_j, N_j, H_j) $$

Where:

  • $E_j$ = energy budget $[0, E_{\max}]$
  • $N_j$ = nutrition level $[0, N_{\max}]$
  • $H_j$ = housing tier (none, hut, advanced)

Energy evolves as:

$$ E_j(t+1) = \min\left(E_{\max}(N_j(t)),, E_j(t) - C_j(t) + R(N_j(t))\right), $$

with:

  • $C_j(t)$ = energy spent on actions
  • $R(N_j)$ = nutrition-dependent regeneration

Nutrition increases via food consumption (potatoes) and never directly destroys assets; instead, low nutrition implies soft penalties (reduced energy cap and regen), aligning UX with regulatory prudence.

4.1 Job Market

Landowners post jobs such as:

  • Build hut on tile #i; requires 20 energy; pays 15 TILE

If worker $j$ accepts, then:

$$ E_j(t+1) = E_j(t) - E_{\text{req}}, \quad Tokens_j(t+1) = Tokens_j(t) + W, $$

where $E_{\text{req}}$ is energy required and $W$ is wage. Matching is driven by effective wage per energy unit $W / E_{\text{req}}$ and local conditions like housing and guild relationships.

This labor layer turns Tiledom into a true factor market: land, labor and capital form a triangle where each side depends on the others.

5. Token System and Supply Dynamics

5.1 Primary Currency: TILE

The primary token serves as:

  • Medium of exchange (resource trades, rents, fees)
  • Unit of account (denomination of prices)
  • Settlement and governance asset

Launch parameters:

  • Initial supply: 1,000,000 TILE
  • Hard cap: 10,000,000 TILE
  • Launch philosophy: fair access, not fair launch

We avoid one-shot meme-style distributions. Instead we:

  • Allocate supply across community rewards, treasury, team and investors with on-chain vesting.
  • Gate external liquidity until sinks and real utility exist.

5.2 Issuance and Burns

Supply follows:

$$ S(t+1) = S(t) + I(t) - B(t). $$

We target gross monthly inflation $I(t)/S(t)$ in the band 3–8%, with token burns $B(t)$ aiming at 3–5% of supply, yielding 0–3% net inflation in a healthy state.

Minting flows:

  • 50% → active players (proportional to productive activity)
  • 30% → treasury reserves
  • 20% → conditional burn when token velocity is high (automatic deflationary offset)

Burn flows:

  • Crafting fees on tools and buildings
  • Land maintenance and repair
  • Withdrawal taxes on external cash-out (later phases)
  • Interest on treasury loans
  • New land auctions and guild creation costs

6. Monetary Policy: Adaptive, Rule-Based, DAO-Governed

We track three primary metrics:

  • Token velocity $V$:

    $$ V = \frac{\text{Total transaction volume in TILE}}{\text{Circulating supply}}. $$

  • Active player ratio $A$:

    $$ A = \frac{\text{Players with a transaction in last 7 days}}{\text{Total players}}. $$

  • Net inflation $\Delta S / S$ via the previous supply equation.

Policy rules (evaluated weekly):

  • If $V < 0.3$: increase rewards by ~20%, reduce certain fees/sinks by ~10%.
  • If $V > 0.7$: reduce rewards by ~20%, increase sink rates by ~10%.
  • If $A < 0.5$: add temporary incentive events and login streak bonuses (non-extractive).
  • If $A > 0.7$ and $0.4 \le V \le 0.6$: hold parameters stable.

These rules are encoded as on-chain policy parameters the DAO can adjust within bounded ranges. Over time, we envision AI policy advisors generating suggested parameter deltas that token holders can vote to adopt or reject, effectively creating a machine-assisted central bank under DAO oversight.

7. Treasury, Lending and Liquidity

The protocol-controlled treasury plays three roles:

  1. Lender of first resort for productive capital investment.
  2. Liquidity provider to AMM pools for TILE–resource and TILE–stable pairs.
  3. Emergency stabilizer when on-chain indicators breach safety bounds.

7.1 Collateralized Loans

Loans are strictly over-collateralized:

  • Max LTV: 50% (loan <= 0.5 * collateral_value)
  • Collateral: land NFTs, staked TILE, high-tier factories
  • APR: 10% (subject to future DAO tuning)
  • Tenors: 30 / 60 / 90 days

Example:

$$ \text{Loan} = 500, \quad \text{APR} = 10%, \quad T = 90\ \text{days} $$ $$ \text{Interest} \approx 500 \times 0.10 \times \frac{90}{365} \approx 12.3. $$

  • 500 TILE principal returns to the treasury.
  • 12.3 TILE are burned, adding deflationary pressure.

7.2 Treasury Risk Limits

  • At most 30% of treasury balance may be lent out.
  • Liquidation triggers when collateral value falls to 100% of loan value.
  • Warning threshold at 80% loan-to-collateral.

This keeps the treasury solvent and prevents cascading liquidations from nuking protocol health.

8. Markets, AMMs and MEV Considerations

All fungible resources and TILE pairs trade on AMM pools (Uniswap v2-style curves in early versions). The protocol seeds initial liquidity and may later introduce concentrated liquidity or curve adjustments tuned for game-specific volatility.

To mitigate MEV and manipulation:

  • Large orders can be routed through batch auctions rather than one-shot swaps.
  • Price feeds for policy use TWAP oracles across multiple blocks.
  • Sensitive actions (e.g., large treasury rebalances) may use private mempools or off-chain commit–reveal.

The goal is not to eliminate MEV entirely but to defend the core game loop from being a cheap playground for sophisticated extractors.

9. On-Chain / Off-Chain Architecture

We use a hybrid architecture:

  • On-chain:

    • TILE and resource token balances
    • Land NFT ownership and metadata
    • Treasury logic (mint, burn, lend, liquidate)
    • AMM pools and fee routing
    • Vesting contracts and DAO governance

  • Off-chain:

    • High-frequency game logic (energy ticks, crop timers, job boards)
    • Anti-cheat heuristics and Sybil detection
    • Analytics, dashboards, and simulation back-ends

  • Bridge layer:

    • Periodic settlement of off-chain state via Merkle commitments
    • Challenge windows for disputed state transitions

Early deployments will likely target Solana or an Ethereum L2 (Arbitrum/Base) depending on community preferences; both offer the throughput and fee profiles required for frequent micro-interactions.

10. Risk Landscape

10.1 Economic Risks

  • Hyperinflation if issuance outruns sinks and adoption.
  • Deflationary gridlock if sinks are too strong or liquidity too thin.
  • Whale capture of key resources or governance.

Mitigations:

  • Hard caps and automatic burn channels.
  • Data-driven policy with explicit emergency brakes (e.g., pause new issuance).
  • Transparent governance, position limits on specific pools if needed.

10.2 Technical Risks

  • Smart contract bugs in core primitives (token, treasury, AMM).
  • Off-chain cheating and timing exploits.
  • Chain-level congestion and fee spikes.

Mitigations:

  • Multi-stage audits and bug bounties before mainnet.
  • Limited blast radius of early-phase deployments.
  • Conscious choice of base chain and L2s with proven reliability.

10.3 Regulatory and Social Risks

  • Money transmitter or securities classification.
  • App store policy shifts blocking mobile distribution.
  • Misaligned expectations from speculative users.

We use a phased compliance approach with crypto-only flows in early phases, tight withdrawal limits, and conservative marketing that emphasizes utility and experimentation, not guarantees of financial return.

11. From Simulation to Real-Life Matrix

In the near term (2026–2027), Tiledom is best understood as a sandbox for crypto-native macroeconomics: a fully observable toy world where we can see how real humans and bots interact with scarcity, leverage, regulation and governance.

Longer term, the path is clear:

  1. Crypto-only economy with robust tokenomics and DAO.
  2. Integrated fiat rails and custodial paths for non-crypto users.
  3. AI co-governors proposing protocol changes, parameter sets and monetary regimes.
  4. Selective real-world hooks where successful patterns (lending, insurance, coordination) graduate from game to production finance.

If we succeed, Tiledom becomes a living reference implementation of what a transparent, programmable, on-chain economy can look like when it is allowed to evolve under the combined pressure of markets, community and machine intelligence.

12. Comparative Context and Design Anti-Patterns

Tiledom does not exist in a vacuum. Its design is informed by both successful virtual economies and high-profile failures in crypto and traditional finance.

12.1 Lessons from GameFi and Crypto Tokenomics

  • Axie Infinity (SLP): uncapped, weakly-sinked reward inflation produced a classic death spiral once new-user inflows slowed; SLP price collapsed as emissions overwhelmed demand.
    Design response: TILE issuance is parameterized via the supply identity (S(t+1) = S(t) + I(t) - B(t)) with hard caps, structurally strong sinks, and adaptive rules keyed to token velocity and active user ratios.

  • StepN (GMT/GST dual-token): value accrued primarily to the governance token while the utility/reward token served as an inflation sponge; this created unstable expectations and complex, fragile incentives.
    Design response: Tiledom keeps the token system minimal and treats reward flows, governance weight and sinks as parts of a single coherent balance sheet rather than siloed instruments.

  • pump.fun-style "fair launches": 100% of supply unlocked at inception with zero inherent utility led to extreme volatility and near-certain value destruction for late entrants.
    Design response: we pursue fair access, not fair launch: staged deployment after ghost-economy testing, gradual liquidity, and multi-year on-chain vesting for team and investors.

  • Terra/Luna, Celsius, BlockFi: opaque treasuries, under-collateralized exposure and maturity mismatches in lending created hidden fragility.
    Design response: the treasury operates with explicit LTV limits, lending caps (≤ 30% of reserves), and on-chain accounting of reserves and obligations; there is no off-balance-sheet leverage.

12.2 Economic Fallacies We Avoid

Several recurring fallacies shape how we parameterize Tiledom:

  • "Buy once, earn forever" capital: non-depreciating assets with perpetual yield break long-run equilibria. In Tiledom, capital goods obey (K(t+1) = K(t)\cdot(1-\delta)), making reinvestment a structural necessity.

  • First-mover extraction trap: if early players can exhaust all high-yield resources, latecomers face a barren economy. We avoid this by releasing land in generations, degrading yields on over-exploited tiles, and giving late entrants viable roles (specialized production, labor, trading).

  • Fixed-supply-money-is-always-good: hard caps like Bitcoin’s are attractive for store-of-value narratives but create deflationary pressure in economies where spending and investment are essential. Tiledom instead targets low, adaptive net inflation aligned with real in-game output, informed by quantity-theory and money-velocity thinking.

  • "Fair launch = fair outcome": equal starting allocations do not guarantee equitable or stable equilibria. We privilege transparent vesting, simulation-tested parameters and governance over cosmetics of launch mechanics.

12.3 Inspiration from Long-Lived Virtual Economies

We also draw on non-crypto virtual worlds:

  • EVE Online demonstrated that deep, player-driven economies can sustain themselves for decades when supported by rigorous monitoring (including an in-house economist and real-time dashboards for inflation, trade volumes and wealth distribution).

  • Second Life proved the viability of user-created value and real-money flows inside a virtual economy, while also highlighting the need for stronger monetary controls and clearer banking rules.

Tiledom aims to combine this lineage—EVE’s simulation depth and commitment to data, Second Life’s real-economy interfaces—with the programmability and transparency of on-chain infrastructure, while explicitly stress-testing against the failure modes outlined above.