Endgame Zero-Sum Game: In-Depth Research on Web3 Incentive Engineering and Odyssey Behavioral Dynamics

1. Preface ------ The "Singularity" of Odyssey

The incentive mechanism of Web3 is at a singularity moment, returning from the "illusion of traffic" to the "essence of value." Over the past few years, the Odyssey model has undergone a baptism from peak to bottleneck, revealing that simple model replication can no longer create ripples in the information-overloaded world of blockchain.

1.1 Paradigm Shift: Why Do Most Projects' Odysseys Yield Little?

Although the Odyssey model has created many wealth-building myths, as we enter 2026, developers find that simply imitating the leading paths is unlikely to generate an "out-of-circle effect." This unsatisfactory situation is fundamentally due to a deep fracture between incentive logic and user ecology.

• Increased Incentive Entropy Leads to Serious Homogenization and Involution

When 90% of projects in the market require users to repeatedly "cross-chain, stake, and forward" to earn almost identical "points," the marginal returns on user attention begin to decline sharply. This model imitation has led to increased incentive entropy— the scarcity of rewards is diluted by a plethora of homogenized projects.

Take Linea's "The Surge" and the subsequent emergence of various L2 point wars as an example. When users find they need to move liquidity between dozens of logically similar protocols, only to receive continuously shrinking inflation points, aesthetic fatigue turns into a passive "lying flat," and the incentive effect is exhausted in endless involution.

• Lack of Game Mechanisms in "Witch-like Growth" Creates a Lot of False Prosperity

Many project teams only learned the superficial aspects of the "task wall," neglecting the deeper anti-witch game, resulting in most incentives being siphoned off by automated scripts (Farmers) from professional studios. The experience of zkSync Era serves as a typical warning: despite having over 6 million active addresses on paper, data penetration reveals that the vast majority are mechanical interactions created for exploiting rewards.

This "paper prosperity" not only triggered a massive community governance crisis during the TGE phase but, more critically, 90% of addresses quickly returned to zero after airdrop distributions. Aside from incurring high customer acquisition costs, project teams did not gain real ecological retention.

• Disconnection Between Product Logic and Incentive Interaction Makes Participation Mechanical

The out-of-circle effect often stems from the deep coupling of a product's core functionality and its reward mechanism. If Odyssey tasks become "on-chain labor" unrelated to product value (for example, requiring privacy protocol users to publicly shout on Twitter), users cannot develop brand recognition.

Just like early DeFi projects that forcibly bundled social tasks on platforms like Galxe, they may have gained tens of thousands of followers in a short time, but this "demand mismatch" attracted mostly low-net-worth taskers, while genuine large-cap users were lost due to aversion to this Web2-style forced interaction. Once the tasks ended, TVL (Total Value Locked) often experienced a cliff-like drop within 24 hours, failing to create any emotional resonance or competitive barrier.

1.2 Defining Win-Win: Protocol Unit Economics

To break the "ineffective results" deadlock, the logic of win-win must shift from "buying traffic" to "building ecology." We need to find a balance point at the mathematical level:

1.2.1 Unit Marginal Returns on the Protocol Side

Project teams must realize that the essence of Odyssey is the precise customer acquisition cost (CAC).

Unit Margin = LTVuser - CACincentive

Only when the long-term fees, liquidity stickiness, or governance contributions (i.e., LTV) generated by users within the protocol exceed the rewards (Incentive) they receive, can Odyssey be seen as more than just "throwing money," but as sustainable capital expansion.

1.2.2 Total Utility Capture on the User Side

Users' pursuits in future Odysseys will be more rational. They will no longer be satisfied with "potentially worthless" points but will calculate the comprehensive return rate:

• Airdrop: Immediately redeemable token shares.
• Utility: Long-term rights in the protocol (such as lifetime fee waivers, RWA revenue shares).
• Reputation: On-chain credit assets. This is the core credential for access to future top project "whitelists."

1.3 Core Assumption: Incentives Are Not Just Tokens, But a Composite of Credit, Privileges, and Revenue Rights

In deep incentive design, we completely overturn the old assumption that "ERC-20 tokens are the only driving force." An Odyssey capable of generating a breakout effect must possess value support across the following three dimensions:

• Credit (Credit/Identity)

By using soulbound tokens (SBT) or on-chain identity systems, users' contributions are permanently solidified. Credit is not just a badge; it is an efficiency multiplier: high-credit users can unlock "no-deposit lending" or "task weight bonuses," giving real contributors an advantage over scripts.

• Privileges (Privileges/Utility)

Embedding rewards within the usage rights of products. For example, winners of the Odyssey can receive a "veto power medal" in protocol governance or "first mining rights" for other new projects within the ecosystem. Privileges transform users from "passersby" into "long-term holders" of the protocol.

• Revenue Rights (Revenue Rights/RWA)

With the advancement of compliance, the most attractive Odysseys in 2026 will begin to introduce underlying dividend logic. Rewards will no longer be mere inflationary air but will be anchored to the protocol's real income (such as RWA government bond interest, Dex fee sharing). This injection of real yield is the trump card for projects to stand out in a bubble and achieve true breakout.

2. User Behavior Spectrum: From "Wool Pullers" to "On-Chain Citizens"

In the future on-chain ecology, the traditional definition of "users" has collapsed. With the popularization of Chain Abstraction and AI Agents, the souls (or algorithms) behind addresses exhibit high differentiation. Understanding this spectrum is a prerequisite for designing win-win incentive mechanisms.

2.1 User Layering Model: Deep Profiling Based on Motivation and Contribution

We categorize participants in the Odyssey into three representative Greek letter tiers, with this layering no longer based solely on asset scale (TVL) but on behavioral entropy and protocol loyalty.

2.1.1 Player Layering

Gamma - Arbitrageurs (AI Bounty Hunters)

• Role Definition: AI bounty hunters pursuing extreme efficiency.
• Psychological Motivation: Highly rational. They have no interest in the project's vision; their only coordinate system is "risk-free interest rate" and "certain returns."
• Behavioral Performance: Typical script-driven interactions with extremely low latency. They congregate like migratory birds in gas fee valleys, with highly standardized and homogenized behavioral paths.

Beta - Explorers (Hardcore Players)

• Role Definition: Hardcore players deeply involved in the ecology.
• Psychological Motivation: Resonance-driven. They value deep product experiences, community identity recognition, and future long-term rights.
• Behavioral Performance: Actively participate in deep feature testing, taking pride in obtaining scarce badges (SBT). They provide high-quality feedback in the community, with interaction trajectories showing clear personal characteristics and subjective preferences.

Alpha - Builders (Ecological Pillars)

• Role Definition: The foundational support and interest community of the protocol.
• Psychological Motivation: Sovereignty-driven. Their goal is long-term governance rights, dividend rights, and building an unbreakable security moat.
• Behavioral Performance: Manifested as large capital long-term locking, submitting core code proposals, or running validation nodes. As stated in the text: "They do not produce noise; they produce credit."

2.1.2 Behavioral Characteristics and Quantitative Models

• Gamma's Survival Law: Ruthless Cost Estimation

For Gamma players, Odyssey is a game of precise calculations. They do not care about the project vision, only focusing on capital efficiency per unit time.

• Alpha's Moat Effect: Power Games

Alpha players disdain retweeting and liking on Twitter; their Odyssey is reflected in sovereign contributions. They are the "stabilizing force" of the protocol, and their large asset deposits and maintenance of technical nodes directly determine the protocol's market cap ceiling and risk resistance.

2.1.3 Identity Collapse and "Consensus Alchemy"

Identity is not lifelong but a dynamically evolving continuous spectrum. In excellent Odyssey design, user identity undergoes a "quantum leap":

• From "Arbitrage" to "Exploration": A Gamma player initially only aiming to exploit rewards may be moved by the protocol's exceptional product experience or robust technical logic during deep interactions. When they discover that the long-term holding returns exceed the immediate selling profits, they experience "identity collapse"—transforming from "in and out" to "deep holding."
• Project's "Consensus Capture Ability": This leap is essentially the "alchemy" performed by the project team on users. Low-quality projects can only attract and retain arbitrageurs, ultimately collapsing as incentives dry up; while high-quality projects possess a centripetal force that allows "bounty hunters" to settle into "forest guardians."

Core Insight: Incentive mechanisms are no longer rigidly divided but a process of selection, filtering, and transformation. They acknowledge the existence value of Gamma but their ultimate mission is to use incentive levers to induce users to complete a cross-level evolution from profit-seeking retail investors to value partners.

2.2 Behavioral Heatmap Analysis: Non-linear Characteristics of Mainstream Layer 2 Task Completion Paths

Before 2024, the task paths of Odyssey were linear (Step 1: Follow on Twitter; Step 2: Cross-chain; Step 3: Swap). However, in the future, the "intent-centric" design will make user behavior heatmaps exhibit significant non-linear, networked characteristics.

2.2.1 Path Forking from "Task-Driven" to "Intent-Driven"

Through the latest data mining of mainstream L2s like Arbitrum, Optimism, and Base, we find:

• Path Uncertainty: For the same Odyssey task, User A may complete it through "lending -> staking -> minting," while User B may achieve it with one click via "full-chain aggregator -> automated strategy pool."
• Cross-Chain Heat Anchor Points: Behavior is no longer limited to a single chain. User behavior on Layer 2 is often accompanied by immediate feedback on Layer 3 dedicated application chains. For example, 10 minutes after L2 interaction, the heatmap shows users quickly triggering automated yield distribution scripts on related AI chains.

2.2.2 Non-uniform Distribution of Behavioral Entropy

Monitoring data shows that the behavior heatmap of high-quality users (beta and alpha tiers) has higher "complex entropy."

• Gamma's Heatmap: Displays a high degree of mechanical regularity. Interaction points are concentrated within the minimum closed loop required by tasks, with short and repetitive paths.
• On-Chain Citizens' Heatmap: Exhibits dispersion and long-tail characteristics. In addition to completing established Odyssey tasks, they explore secondary pages of the protocol, read on-chain proof documents, or interact with other dApps within the ecosystem.

Insight: The most successful Odyssey projects have heatmaps that are not a straight line but a gravitational field. They can attract users to spontaneously stay within the ecosystem and generate "unplanned" interactions after completing established tasks.

Users are no longer satisfied with being seen as a "wallet address." In Odyssey 3.0, the end of the behavioral spectrum is "on-chain citizenship." This citizenship not only signifies reward distribution but also represents a form of identity endorsement in a multi-chain civilization.

3. Mechanism Design: Ensuring a "Win-Win" Mathematical Model and Game Balance

In the evolution of Web3, early Odysseys were often criticized for falling into "Ponzi deadlocks": project teams exchanged future inflation expectations for present false prosperity. To break out of this vicious cycle, the key lies in achieving incentive compatibility. This means we need to ensure that users' paths to maximizing their own interests completely align with the protocol's long-term healthy development through rigorous mathematical models.

3.1 Incentive Compatibility Equation (The IC Constraint): Reconstructing the Game of Costs and Benefits

In traditional airdrop models, the marginal cost of witch attacks (Sybil Attacks) is almost zero. To protect the interests of genuine contributors from dilution, future Odyssey designs will introduce an IC constraint equation based on game theory.

Core Game Model

Let R(c) be the comprehensive rewards obtained by honest users during genuine interactions, and C(c) be the hard costs they incur (including gas, slippage, and capital occupation time). Meanwhile, let E[R(s)] be the expected returns obtained by witch attackers through automated scripts simulating interactions, and C(s) be their attack costs (including servers, IP pools, anti-detection algorithms, and sunk costs after being cleaned).

To achieve a win-win Nash equilibrium point, the following must be satisfied:

Interventions and Evolution of the 2.0 Era:

1. Dramatically Increase C(s) (Attack Resistance): Future defenses will no longer rely on simple blacklists but will introduce AI behavioral entropy detection. The system will analyze the spatiotemporal distribution of interactions, the associated entropy of funding links, and the "human-like" nature of operations. For suspected accounts, the system will dynamically implement a "gas fee punitive coefficient," forcing them to pay higher transaction fees during non-peak hours, directly destroying the unit profitability of scripts.
2. Deeply Optimize R(c) (Return Structure): The reward pool will shift from "pure governance tokens" to "mixed rights packages." This includes: cash flow rights: direct distribution of protocol fees (Real Yield). Privileged assets: permanent fee waivers (Gas Rebate) or interest bonuses for cross-protocol lending. Governance leverage: granting governance weight bonuses to long-term holders, ensuring that "real participation" generates not only wealth but also power.

3.2 Dynamic Difficulty Adjustment Mechanism (DDA)

Future Odysseys will no longer be a static task list. Drawing on Bitcoin's difficulty adjustment algorithm, advanced protocols will begin to implement dynamic difficulty adjustment (DDA).

Operational Logic:

When the Odyssey enters an explosive period, the number of interacting addresses and total locked value (TVL) will surge in a short time, and the system will automatically sense "overload." At this point, the points capture algorithm will automatically trigger a difficulty increase:

• Increasing Capital Thresholds: The amount of interaction or liquidity lock-up period required to obtain the same points will increase accordingly.
• Upgrading Task Complexity: Transitioning from simple "one-click swaps" to "multi-protocol combination strategies" (e.g., lending on Protocol A, staking on Protocol B, hedging on Protocol C).

Win-Win Logic:

• For the protocol: DDA acts as a safety valve, preventing the sudden influx of speculative traffic from overwhelming liquidity pools, avoiding cliff-like crashes due to "reward depletion."
• For alpha citizens: It protects early, stable builders. High-difficulty tasks naturally filter out "wool pullers" lacking professional skills, ensuring that reward shares flow more accurately to high-net-worth genuine users.

3.3 Proof of Value Model (PoV)

In Odyssey 3.0, "number of addresses" is completely deemed a vanity metric. Project teams will fully shift to the PoV model, which focuses on measuring contribution density.

Contribution Density Formula:

We define contribution density D as:

D = ∑(Liquidity × Time) + γ × GovernanceActivity / TotalReward

• Liquidity (Capital Stickiness): Measures the "settlement duration" of user funds within the ecology, rather than "in and out."
• Gamma (Community Contribution Factor): This is a variable. For users actively participating in governance voting, writing technical documents, or generating genuine positive radiation on social networks, the gamma bonus can reach 2x or even higher.
• Total Rewards: As the denominator, it aims to balance inflation and ensure the value of unit rewards.

Deep Analysis of Win-Win:

Through the PoV model, project teams no longer receive a cold list of wallet addresses but a real map of ecological participants. Users find that due to the existence of the gamma factor, their "labor" rather than mere "capital" can also yield high returns. This mechanism achieves a harmonious resonance between capital efficiency and human creativity, ensuring that Odyssey is no longer a "digital game" but a genuine value co-creation process.

4. Technical Pillars: Behaviorally Perceptive ZK Incentive Underlying Protocol

In the future paradigm shift, Odyssey will no longer be a front-end "task wall," but a foundational protocol capable of automatically capturing, analyzing, and transforming user behavior. This protocol constructs a closed loop from behavioral perception to precise incentives through ZK technology and full-chain abstraction.

4.1 Behavioral Perception Engine: From "Passive Check-ins" to "Full-Chain Behavior Tracking"

The core function of this protocol is to serve as a full-chain data crawler and indexer. It no longer relies on users manually submitting task screenshots but automatically records users' deep interactions within DApps through underlying gateways.

• Comprehensive Behavioral Modeling: The protocol can capture users' liquidity depth, transaction frequency, governance participation, and even the duration of their stay on the product front end in real-time (through zero-knowledge off-chain proofs).
• Dynamic Weight Analysis: The protocol will perform multi-dimensional modeling of these behaviors, analyzing whether the user is a "long-term holder (HODL)," "high-frequency liquidity provider," or "deep governance participant." This analysis based on genuine interactions allows the Odyssey model to evolve from "mechanical tasks" to "behavioral badges."

4.2 ZK-Proof Driven Privacy Analysis and Filtering

After obtaining behavioral data, the protocol utilizes ZK-Proof (zero-knowledge proof) technology to achieve precise filtering without disclosing user wallet details and privacy data (PII).

• ZK-Credentials Credit Endorsement: Users do not need to "show their face" or expose asset details. Through this underlying protocol, users can present "high-net-worth user proof" or "senior DeFi player proof" generated by the protocol to project teams.
• Filtering Effect and Anti-Witch: The protocol allows project teams to set "high-level access thresholds." For example, by verifying users' non-repetitive interactions over the past 180 days through ZK-STARKs, it generates a "unique real human proof." This fundamentally locks out the space for automated scripts (Farmers) at the base level, ensuring that incentives flow only to those recognized by the protocol as exhibiting "high-quality behavior."

4.3 Intent-Driven Full-Chain Abstraction Incentives (Intent-centric & Abstraction)

This protocol not only records behavior but also simplifies participation paths through an intent engine (Intent Engine), enabling interaction to equal incentive.

• Intent-Driven Automatic Interaction: Users only need to express the intent "I want to participate in the liquidity incentives of this protocol," and the underlying protocol will automatically coordinate cross-chain asset transfers, gas fee balancing, and contract calls.
• Instant Transformation and Win-Win: This "seamless interaction, automatic incentives" model frees users from cumbersome on-chain steps; meanwhile, project teams capture users' most genuine core intentions through the underlying protocol, enhancing conversion rates and allowing the Odyssey model to truly return to the product's intrinsic value.

5. Future Evolution ------ From "Marketing Activities" to "Normalized Incentive Protocols"

The future Odyssey will completely bid farewell to "time-limited" characteristics, evolving into a permanent growth module at the protocol code level (Native Incentive Layer).

5.1 Embedded Incentives (GaaS: Growth-as-a-Service)

Odyssey will no longer be a webpage but a dynamic reward logic embedded in smart contracts.

• Evolution: As long as users create positive value with the protocol (e.g., reducing slippage, providing long-term liquidity), the contract will automatically recognize and allocate rewards in real-time. Odyssey becomes the "autopilot mode" of the protocol.

5.2 Cross-Protocol "Credit Lego" (Interoperable Incentives)

Future Odyssey points will possess portability. Your performance in the Odyssey of lending protocol A will be transformed into the initial level of social protocol B through ZK proof.

• Ultimate Form: A universally applicable "on-chain contribution score" will replace fragmented points. This cross-protocol linkage will facilitate the ultimate leap of the Web3 ecology from "stock mutual cutting" to "incremental co-construction," achieving a truly global on-chain republic.

6. Practical Execution Guide (The Executive Playbook)

Odyssey is no longer a "throw money and leave" game but an extremely precise ecological drainage and capital solidification project. For project teams, the core of execution lies in balancing the "explosive power of traffic" with the "system's resilience." Here are 10 execution principles and practical frameworks to ensure a win-win situation.

6.1 Paradigm Shift of Core KPIs: From "Vanity" to "Hardcore"

Do not be misled by Twitter follower counts and address numbers. In a future where the intent engine can low-cost simulate millions of addresses, these metrics are easily faked.

• Metric A: Sticking TVL (Sticky Capital Ratio). The calculation formula is:

Retention Ratio = TVLT + 90 / TVLPeak

If this ratio is below 20%, it indicates serious flaws in the incentive mechanism design.

• Metric B: Net Contribution Score. This is the ratio of the total protocol fees generated by a single address to the costs of incentives they received.
• Metric C: Governance Active Entropy. Measures the true depth of Odyssey users' participation in Snapshot or on-chain proposals, rather than mere vote padding.

6.2 Modular Task Design: Building a Tiered "Funnel"

The most successful Odysseys typically adopt a "three-tier" structure aimed at converting massive traffic into core citizens.

Base Layer (L1) ------ Breaking the Ice and Reaching Out

• Target Audience: New users / General Web3 players
• Core Tasks: Complete basic interactions (e.g., one-click swap, social sharing)
• Incentive Structure: Grant non-fungible badges (SBT), accumulate future airdrop points
• Retention Logic: Extremely low barriers. Establish the first touchpoint through SBT, leaving users with a "digital footprint" in the ecology.

Growth Layer (L2) ------ Liquidity Engine

• Target Audience: Active traders / Liquidity providers (LP)
• Core Tasks: Deep liquidity provision, portfolio management, cross-chain staking
• Incentive Structure: Native token rewards, real-time fee discount cards
• Retention Logic: Yield (APY) games. By locking funds with high efficiency, artificially increase the "opportunity cost" of withdrawing.

Ecological Layer (L3) ------ Core Sovereignty Faction

• Target Audience: Core contributors / Developers / Governance representatives
• Core Tasks: Write technical documents, submit code patches, initiate effective governance proposals
• Incentive Structure: Governance weighting factors, RWA revenue sharing rights, ecological whitelists
• Retention Logic: Grant "citizenship." Not just profit distribution, but long-term interest binding, making contributors the masters of the ecology.

6.3 Risk Control and "Circuit Breaker" Design

During the execution of Odyssey, it is easy to encounter "wool pullers' raids" due to severe market fluctuations or mechanism loopholes.

• Dynamic Incentive Coefficient Adjustment: Establish a dynamic adjustment system based on on-chain congestion. When daily interaction volume triggers a threshold (e.g., exceeding 500% of the baseline), the system automatically reduces the point coefficient for that period, preventing scripts from violently inflating volume during low-cost phases.
• Preemptive Anti-Witch Measures: Reject "post-cleaning." On the first day of the event, use AI behavioral fingerprint systems to "shadow tag" suspicious addresses. These addresses can complete tasks normally but can only enter a "low-yield pool."
• Liquidity Mitigation Mechanism: All rewards should not be released in one go at TGE. Introduce a yield smoothing mechanism (e.g., rewards unlocked over 6-12 months based on users' continued activity after the Odyssey), enforcing "long-term incentive compatibility."

6.4 Preemptive Community Governance Experiments

Do not wait until after the token launch to start DAO governance.

• Simulated Voting Tasks: During the Odyssey phase, set "simulated voting on a proposed improvement to a protocol parameter" as a high-weight task.
• Purpose: This can not only filter out alpha citizens who genuinely care about protocol development but also cultivate the community's governance habits in advance, reducing communication costs during actual governance in the future.

6.5 Execution Check-list (Must Read Before Launch)

1. Value Closed Loop Check: Does the source of rewards include the protocol's own income (Real Yield)?
2. Anti-Witch Depth: Is there integration with ZK-ID or real-person identification systems (e.g., World ID / Gitcoin Passport)?
3. Capital Stickiness: Do tasks require funds to stay in the protocol for more than 14 days?
4. Technical Redundancy: Can the protocol contract withstand instantaneous calls 100 times the daily volume?
5. Emotional Value: Does the task narrative possess social dissemination attributes, rather than being merely "digital transportation"?

Conclusion ------ From "Game Confrontation" to "Value Symbiosis"

The Odyssey model is, at its core, a revolution in selection efficiency. When we introduce the "incentive compatibility equation" and "behavioral entropy analysis" in this text, the goal is not only to defend against witch attacks but also to establish a precise value measurement system in a decentralized anonymous network.

In this new paradigm, project teams and users are no longer zero-sum adversaries. Through dynamic difficulty adjustment (DDA) and the proof of value (PoV) model, we have successfully transformed mere capital interactions into quantifiable contribution density. This shift brings about a crucial byproduct—on-chain credit.

Credit does not arise from thin air; it is the "digital residue" accumulated by users through countless high-entropy interactions, long-term locking, and governance participation. In the future ecology, incentive mechanisms will no longer be just tools for distributing tokens but will become crucibles for forging credit. They will ensure that every genuine contribution is remembered by code, making "credibility" a more scarce passport than capital.

Ultimately, the end of Odyssey is not the conclusion of a single airdrop but the starting point of the contractual relationship between the protocol and its citizens. When we dispel the bubble of traffic with mathematics and technology, the solid foundation of credit that remains is the fundamental guarantee for Web3's transition from a "speculative wasteland" to a "value civilization."