鏈上治理：從原始遊戲到 Futarchy 的預測智慧

儘管鏈上治理具有複雜協議的潛力，但仍面臨女巫攻擊等挑戰。 Futarchy 由 Robin Hanson 提出，引入預測市場，根據組織的福利指標來確定決策，激勵參與者做出預測並給予相應獎勵。透過押注福利措施的未來價值取決於具體政策，futarchy 旨在消除情緒並促進基於「群體智慧」的理性決策。實施預測市場需要自動做市商 (AMM) 來解決流動性問題，而 Gnosis 的 CFMM 實施的 LMSR 可以平衡流動性和預測準確性。

On-Chain Governance: From Games to Futarchy

鏈上治理：從遊戲到 Futarchy

The discourse surrounding on-chain governance has been marked by contentious debate. While off-chain governance often suffers from cumbersome inefficiencies, on-chain governance has empowered developers to construct increasingly intricate protocols that grant users substantial sway in shaping network trajectories. Yet, these mechanisms are fundamentally akin to games that, when misconfigured or misaligned with appropriate incentives, possess the potential to steer the governed entity towards calamitous outcomes.

圍繞鏈上治理的討論一直充滿爭議。雖然鏈下治理常常面臨效率低下的問題，但鏈上治理使開發人員能夠建立日益複雜的協議，使用戶在塑造網路軌跡方面擁有巨大的影響力。然而，這些機制從根本上類似於遊戲，當配置錯誤或與適當的激勵措施不一致時，有可能引導受治理實體走向災難性的結果。

In her seminal work, "What is Futarchy? — Trading the Future," Freiderike Ernst, co-founder of Gnosis, elucidates prevalent methodologies underpinning on-chain voting. Given that the "one-person, one-vote" paradigm is susceptible to Sybil attacks on permissionless networks, whereby a single individual may fragment their capital across multiple accounts to cast a disproportionate number of votes, a user's voting power is typically weighted in proportion to their token holdings. Lotteries and token curated registries employ analogous techniques to mitigate Sybil attacks.

Gnosis 聯合創始人 Freiderike Ernst 在她的開創性著作《什麼是 Futarchy？—交易未來》中闡明了支持鏈上投票的流行方法。鑑於「一人一票」範式很容易受到無許可網路上的 Sybil 攻擊，即單個人可能會將其資金分散到多個帳戶中以投出不成比例的選票，因此用戶的投票權通常按比例加權他們持有的代幣。彩票和代幣管理註冊中心採用類似的技術來減輕 Sybil 攻擊。

Robin Hanson has proposed an alternative governance model termed "futarchy," which centers decision-making not on votes, but rather on the outcomes of prediction markets that evaluate the welfare measure of the organization, an indicator of the network's health or decline. Participants in these markets wager on the future value of the welfare measure.

漢森（Robin Hanson）提出了一種名為「futarchy」的替代治理模型，該模型的決策重點不是投票，而是評估組織福利指標（網路健康或衰退的指標）的預測市場的結果。這些市場的參與者押注於福利措施的未來價值。

Betting is typically facilitated using outcome tokens, each of which represents a discrete outcome of the market and whose monetary value is contingent on the eventual welfare measure. Accurate predictions are rewarded, while erroneous predictions incur losses.

投注通常使用結果代幣來促進，每個結果代幣代表市場的離散結果，其貨幣價值取決於最終的福利措施。準確的預測會獲得獎勵，而錯誤的預測則會帶來損失。

Harnessing outcome tokens, participants can even wager on the value of the welfare measure conditioned upon the implementation of a specific policy. For instance, a participant could place a bet that yields a profit if the policy is implemented and the welfare measure subsequently increases by a predetermined threshold, while the bet is voided if the policy remains unimplemented.

利用結果代幣，參與者甚至可以根據特定政策的實施來押注福利措施的價值。例如，參與者可以進行投注，如果政策得到實施並且福利措施隨後增加預定閾值，則可以產生利潤，而如果政策仍未實施，則投注無效。

Consider the case of a publicly traded company that adopts its stock price as its welfare measure and contemplates the dismissal of its CEO. Futarchy would yield two predictions: the projected future stock price were the CEO to be terminated and the projected future stock price were the CEO to be retained. As illustrated in the accompanying chart, the decision that results in the highest possible welfare measure is executed. In this instance, since the projected stock price contingent on the CEO's dismissal exceeds that contingent on the CEO's retention, the CEO would be removed from the company's leadership. This approach removes the influence of emotions from the decision-making process, enabling the organization to make rational choices informed by the collective wisdom of its participants, thereby enhancing its overall value.

考慮一家上市公司的案例，該公司採用股價作為其福利衡量標準，並考慮解僱其執行長。 Futarchy 產生兩個預測：預計未來股價是要被解僱的首席執行官，預計未來股價是要保留的首席執行官。如附圖所示，執行導致最高可能福利措施的決策。在這種情況下，由於執行長被解僱所帶來的預期股價超過了執行長留任所帶來的預期股價，因此執行長將被解除公司領導職務。這種方法消除了決策過程中情緒的影響，使組織能夠根據參與者的集體智慧做出理性選擇，從而提高其整體價值。

Market Makers for Prediction Markets

預測市場的做市商

The introduction of a market maker to facilitate transactions between participants introduces certain challenges. When futarchy is employed to assess more complex contingencies, the number of tokens in the associated market can rapidly escalate into the hundreds. In such scenarios, the "thin market problem" emerges, whereby the number of participants is insufficient to effectively adjust the probabilities of numerous outcomes. The logical solution is an automated market maker (AMM).

引入做市商來促進參與者之間的交易帶來了一定的挑戰。當使用 futarchy 來評估更複雜的突發事件時，相關市場中的代幣數量可能會迅速增加到數百個。在這種情況下，就會出現“稀薄市場問題”，即參與者數量不足以有效調整眾多結果的機率。合理的解決方案是自動做市商（AMM）。

A straightforward approach involves implementing the cost function of the logarithmic market scoring rule. However, this implementation precludes dynamic adjustments of liquidity, often resulting in a market that is either too shallow to accommodate all participants or excessively deep to generate meaningful results. The liquidity-sensitive logarithmic market scoring rule (LS-LMSR) addresses this issue, but introduces new vulnerabilities, the most significant of which is an arbitrage vulnerability inherent to all scoring rule market makers except LMSR.

一種簡單的方法涉及實施對數市場評分規則的成本函數。然而，這種實施排除了流動性的動態調整，通常會導致市場要么太淺而無法容納所有參與者，要么太深而無法產生有意義的結果。流動性敏感的對數市場評分規則（LS-LMSR）解決了這個問題，但引入了新的漏洞，其中最重要的是除 LMSR 之外的所有評分規則做市商固有的套利漏洞。

Crypto stalwarts—constant function market makers (CFMMs)—such as Balancer, manage liquidity more effectively by enabling liquidity providers to dynamically deposit and withdraw liquidity, and are more familiar to cryptocurrency enthusiasts. However, CFMMs share the same limitations as LS-LMSR. Notably, during its involvement in prediction markets, Gnosis developed a CFMM implementation of the LMSR, which appears to combine the advantages of both approaches.

加密貨幣中堅力量——恆定函數做市商（CFMM）——例如 Balancer，透過使流動性提供者能夠動態存入和提取流動性來更有效地管理流動性，並且為加密貨幣愛好者所熟悉。然而，CFMM 與 LS-LMSR 具有相同的限制。值得注意的是，在涉足預測市場期間，Gnosis 開發了 LMSR 的 CFMM 實現，它似乎結合了兩種方法的優點。