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DAO Governance

2023-04-27T10:39:52Z

Aghaee: DAO Governance

{{DISPLAYTITLE: DAO Governance}}

Written by [[Alireza_aghaee|Alireza Aghaee]].

<span id="introduction"></span>
= Introduction =

This is a summary of “DAO Governance” by Han, Jungsuk, Jongsub Lee, and Tao Li, published on SSRN on Feb 2023. [[Decentralized_Autonomous_Organization|Decentralized autonomous organizations (DAOs)]] are non-centralized organizations that run according to a set of rules for making decisions that are encapsulated in smart contracts and implemented using blockchain technology. In (Han, Lee, and Li 2023), the authors create a theoretical model of [[Decentralized_Autonomous_Organization|DAO]] governance with token-based [[Voting Mechanisms in DAO|voting]] and strategic token trading to examine potential conflicts of interest between a single large participant, a Whale, and numerous small participants. The findings indicate that ownership concentration has a negative relationship with platform growth, but this relationship will be weakened by platform size, token illiquidity, and long-term incentives.

<span id="model"></span>
= Model =

<span id="setup"></span>
== Setup ==

There is an infinite-horizon, discrete-time model with a platform that facilitates user transactions. The platform is a [[Decentralized_Autonomous_Organization|DAO]] run by its token holders, and to use the platform, one needs tokens. These token holders can [[Voting Mechanisms in DAO|vote]] on platform changes using smart contracts. Participants may disagree with a platform service change proposal due to potential conflicts of interest from the proposed changes’ benefits and costs. According to a rule, a [[Voting Mechanisms in DAO|vote]] will decide the platform’s final decision.

The timeline of the model is as follows. At <math display="inline">t=0</math>, the platform issues one unit mass of the token. The initial cost determines participation. In <math display="inline">t = 1</math>, token owners [[Voting Mechanisms in DAO|vote]] on the proposal. [[Voting Mechanisms in DAO|Votes]] determine implementation. From <math display="inline">t = 1</math>, the platform gives token owners utility flows.

The formal setup of the model is as follows:

* '''Participants:''' Two types of participants: small participants, referred to as “users,” and a larger participant, referred to as the “whale.” Both types are risk neutral, with a discount factor of <math display="inline">\delta</math>. The risk-free rate is <math display="inline">r_f = \frac{1}{\delta} - 1</math>.
* '''Participation:''' There is a continuum of potential users uniformly distributed on the interval <math display="inline">[0,1]</math>. To participate, a user indexed by <math display="inline">i</math> must pay a one-time participation cost of <math display="inline">\phi_i>0</math> in <math display="inline">t=0</math> and purchase tokens at an exogenously given initial offering price of <math display="inline">\bar{P}</math> with no transaction costs. <math display="inline">\mathcal{U}</math> is the set of participating users, and <math display="inline">x_{i, t}</math> is the unit of tokens held by user <math display="inline">i</math> in period <math display="inline">t</math>.
* '''The Whale:''' The whale receives <math display="inline">y_0</math> units of the tokens at this initial stage, and <math display="inline">y_t</math> is the unit of tokens held by the whale in period t. The Whale is myopic or “short-termist”; it must liquidate its position before a finite horizon of <math display="inline">T \geq 2</math>.
* '''Utility:''' A user or whale holding <math display="inline">X_t</math> tokens in period <math display="inline">t \geq 1</math> derives platform utility as <math display="block">U\left(X_t\right)=A(a) N X_t</math> where <math display="inline">N</math> represents the total number of participating users given by <math display="block">N=\int_0^1 \mathbb{1}(i \in \mathcal{U}) d i</math> that captures the network effect, and <math display="inline">A(a)</math> captures the technology (or efficiency) component. The technology component <math display="inline">A(a)</math> is determined by the action <math display="inline">a \in\{R, I\}</math> where <math display="inline">a=</math> <math display="inline">I</math> means that the proposal is implemented, and <math display="inline">a=R</math> means it is rejected.
* '''Vote:''' In <math display="inline">t=1</math>, the platform implements the proposal <math display="inline">(a=I)</math> if the total mass of [[Voting Mechanisms in DAO|votes]] in favor of its implementation exceeds the minimum exogenous threshold of <math display="inline">\bar{x}</math> : <math display="block">\mathbb{1}\left(a_w=I\right) y_1+\int_{\mathcal{U}} x_{i, 1} \mathbb{1}\left(a_i=I\right) d i \geq \bar{x}</math>
* '''Conflict of interests:''' implementing the proposal would destroy value for users if <math display="inline">A(R)>A(I)=(1-\theta) A(R)</math>. If approved, the whale benefits privately, and this private benefit increases with its holding. The whale’s benefit from implementing the proposal is <math display="inline">B y_0</math>, where <math display="inline">B</math> is a random variable that is either <math display="inline">\bar{B}>0</math> or zero with probability <math display="inline">1-\mu</math>. Users learn <math display="inline">B</math>’s value at <math display="inline">t=1</math>.
* '''Price/Value:''' <math display="inline">P(a)</math> is the intrinsic value of the tokens to users given the status of the proposal implementation <math display="inline">a</math> and the mass of participating users <math display="inline">N</math>. It is given by the present value of utility flows per unit of tokens. Without the whale, the price would converge to this intrinsic value. <math display="block">P(a)=\sum_{s=0}^{\infty} \delta^s A(a) N=\frac{A(a) N}{1-\delta} .</math>
* '''Trades:''' Tokens can be traded with trading costs that are convex in volume to capture illiquidity consequences. Authors assume a quadratic function for trading costs as a function of the number of tokens traded, <math display="inline">\Delta X</math>: <math display="block">C(\Delta X)=\frac{\lambda}{2}(\Delta X)^2</math> where <math display="inline">\lambda>0</math> is a parameter that captures the severity of illiquidity problem. Short sales are not allowed.

<span id="problems"></span>
== Problems ==

* '''User Problem:''' Given the platform’s action <math display="inline">a</math> in <math display="inline">t=1</math>, a representative user’s value in period <math display="inline">t \geq 1</math> can be represented in a recursive form as: <math display="block">V_t^a\left(x_{t-1}\right)=\max _{\Delta \tau_t} A(a) N\left(x_{t-1}+\Delta x_t\right)-P_t^a \Delta x_t-\frac{\lambda}{2} \Delta x_t^2+\delta V_{t+1}^a\left(x_{t-1}+\Delta x_t\right),</math> subject to the constraints: <math display="block">\begin{aligned}
& x_t=x_{t-1}+\Delta x_t \\
& x_t \geq 0 .
\end{aligned}</math> The first term in the equation is the utility flows given the token holdings at the end of the period, the second term is the cost of acquisition (or the proceeds from selling), the third term is trading costs, and the fourth term is the continuation value given the choice.
* '''User Solution:''' The value function of a user in period <math display="inline">t \geq 1</math> with the token holdings <math display="inline">x_{t-1}</math> at the beginning of period <math display="inline">t</math> can be written as <math display="block">V_t^a\left(x_{t-1}\right)=\alpha_t+\beta x_{t-1}</math> where <math display="inline">\alpha_t</math> is the present value of future trading gains (pinned down in the paper) and <math display="inline">\beta</math> is the marginal value of tokens: <math display="inline">\beta=P(a)</math>. The optimal trading strategy of tokens in period t given price <math display="inline">P_t^a</math> is <math display="inline">\Delta x_t=\frac{P(a)-P_t^a}{\lambda}</math>
* '''Token Price''': The market clearing condition, besides the optimal trading of all users (that is inevitably the inverse of the Whale’s trades,) implies the equilibrium token price as: <math display="block">P\left(\Delta y_t ; a\right)=P(a)+\frac{\lambda}{N} \Delta y_t</math>
* '''Whale’s Problem:''' The whale’s value, given <math display="inline">y_{t-1}</math>, in a recursive form is as follows: <math display="block">\begin{aligned}
V_{w, t}^a\left(y_{t-1}\right)=\max _{\Delta y_t} A(a) N\left(y_{t-1}+\Delta y_t\right)-P\left(\Delta y_t ; a\right) \Delta y_t\\
-\frac{\lambda}{2} \Delta y_t^2+\delta V_{w, t+1}^a\left(y_{t-1}+\Delta y_t\right),

\end{aligned}</math> subject to the constraints: <math display="block">\begin{aligned}
& y_t=y_{t-1}+\Delta y_t \\
& y_t \geq 0
\end{aligned}</math> and the boundary condition: <math display="block">y_T=0</math> The boundary condition ensures that the holdings are completely liquidated by period <math display="inline">T</math>. Optimally reducing its position over the investment horizon allows the Whale to maximize its expected utility, considering the trade-offs between token payoffs and trading costs. The paper shows that in equilibrium, the Whale will optimally liquidate its position gradually through time and not all at once. This is because of the illiquidity-induced convex trading costs.

<span id="equilibrium"></span>
== Equilibrium ==

At <math display="inline">t=0</math>, each user maximizes their expected utility by choosing whether to participate or not and buying the tokens at the initially-offered price of <math display="inline">\bar{P}</math>. That is, user <math display="inline">i</math> solves <math display="block">\max \left(V_0, \phi_i\right)</math> where <math display="inline">V_0</math> is the ex-ante value of participating in the platform’s activities: <math display="block">V_0=\max _{x_0 \geq 0}-\bar{P} x_0+\delta \mathrm{E}\left[V_1^a\left(x_0\right)\right]</math>

The paper carefully solves the model and derives both players’ equilibrium actions and values given the state realization. Based on the derived equilibrium, the authors make four theoretical predictions:

* '''Prediction 1''' The growth rate of platforms is negatively correlated with their ownership concentrations, i.e., <math display="block">\frac{\partial V_0}{\partial y_0}<0</math>
* '''Prediction 2''' The higher service value of platforms reduces the negative correlations between the growth rate of platforms and their ownership concentrations, i.e., <math display="block">\frac{\partial^2 V_0}{\partial y_0 \partial A(R)}>0</math>
* '''Prediction 3''' The illiquidity of tokens reduces the negative correlations between the total value of platforms and their ownership concentrations, i.e., <math display="block">\frac{\partial^2 V_0}{\partial y_0 \partial \lambda}>0</math>
* '''Prediction 4''' Long-term incentives of the Whale reduce the negative correlations between the growth rate of platforms and their ownership concentrations, i.e., <math display="block">\frac{\partial^2 V_0}{\partial y_0 \partial T_L}>0</math>

<span id="empirical-analysis"></span>
= Empirical Analysis =

<span id="data"></span>
== Data ==

The study uses [[Voting Mechanisms in DAO|voting]] platform data to examine [[Decentralized_Autonomous_Organization|DAO]] investors’ voting records from July 20, 2020, through July 31, 2022. They acquired [[Voting Mechanisms in DAO|voting]] data for 460 [[Decentralized_Autonomous_Organization|DAOs]] with at least 650 votes that were most likely to have an existing underlying business. The data includes the voting token name, symbol, contract address, proposal information, start and deadline dates, voter address, vote date, and the number of votes.

The researchers manually searched [[Decentralized_Autonomous_Organization|DAO]] voting token contracts to establish whether investors used governance tokens or staked tokens, including [[Voting Mechanisms in DAO|vote]] escrowed/locked tokens. They manually searched CoinMarketCap for [[Decentralized_Autonomous_Organization|DAO]]-related coins and downloaded their daily price and volume data. The final dataset comprised 207 [[Decentralized_Autonomous_Organization|DAOs]] with non-missing price, volume, and TVL data.

The total value locked (TVL) in the sample of platforms over time shows a clear boom-and-bust cycle in the DeFi industry as a whole, peaking around the end of 2021. The average platform in the sample has a TVL of $1.2 billion, but the median TVL is only $103 million, indicating a highly skewed distribution. The average and median weekly TVL growth are both negative, and the weekly returns of associated tokens are even more negative. The authors note that the market for governance tokens is highly concentrated, with the largest three whales commanding almost two-thirds of voting power on average. The average number of platform participants is 212, and the average platform age is six months. Finally, the authors report that the distribution of the illiquidity measure is also highly skewed, with an average of 0.112 and a median of 0.017.

<span id="empirical-findings"></span>
= Empirical Findings =

To test their theoretical predictions about the relationship between platform growth and proxies for voting power concentration, the authors use weekly panel regressions on the collected sample of [[Decentralized_Autonomous_Organization|DAOs]]. Voting data is converted to weekly series, and weekly averages of voting power concentration are used when multiple proposals exist in a particular week. The Herfindahl-Hirschman Index of voting power and the top three voters’ fraction of total votes are used as concentration measures. These measures were lagged by one week when used as the independent variables.

The study identifies a negative correlation between TVL growth and the HHI of voting power. Particularly, a one standard deviation increase in HHI is associated to a 1.1 percentage-point decrease in weekly TVL growth. The impact is economically substantial as the average weekly TVL growth is only -0.9%. Additionally, the top three voters’ ownership negatively affects platform growth, with the marginal effect being significantly greater than the average weekly TVL growth. These findings align with the study’s first theoretical prediction that more decentralized voting power increases platform growth.

The study tests two additional predictions. Firstly, it is hypothesized that a platform with a broader user group and higher network value would reduce the negative effect of the HHI of voting power on TVL growth. Secondly, the study expects token illiquidity to have a similar dampening effect, as whales would suffer significant price impacts when attempting to amass a large stake. Token illiquidity is measured using the (Amihud 2002) illiquidity ratio. Whales may accumulate significant voting power to pass proposals that benefit them but harm other investors. This is more costly when tokens are illiquid, prompting them to align their incentives with minority token holders.

The study adds an interaction term between the HHI of voting power and platform size (proxied by lagged TVL) to the baseline regression specification to test the latter predictions. The results show a positive and statistically significant coefficient on the interaction term, indicating that a higher valuation reduces the negative relationship between platform growth and ownership concentration. A similar result is obtained using an interaction term of HHI and token illiquidity instead.

The study tests its last prediction by examining events where platforms transitioned from the one-token-one-vote model to a staking model, which assigns [[Voting Mechanisms in DAO|vote]] weights and yields proportional to a “locking period." Using an event-study framework, the TVL growth of a set of [[Decentralized_Autonomous_Organization|DAOs]] that switched to a staking model during the sample period is compared to that of a control group of [[Decentralized_Autonomous_Organization|DAOs]] that did not adopt staking models. The treated platforms exhibit an 8.3 percentage-point higher growth rate during the event window than control platforms, which is economically significant considering the average weekly growth rate of the sample [[Decentralized_Autonomous_Organization|DAOs]] is slightly negative.

<span id="conclusion"></span>
= Conclusion =

The research paper fills a significant gap in the literature on [[Decentralized_Autonomous_Organization|DAO]] governance by incorporating micro-foundations of the conflicts of interest among different token holders. The paper develops a theoretical model that explains how whales, large token holders in a [[Decentralized_Autonomous_Organization|DAO]], may disrupt the long-term growth of the platform through “rug pulls," in which they inflate token prices before unwinding their positions. The model predicts a negative correlation between whales’ voting power concentration and [[Decentralized_Autonomous_Organization|DAO]] growth, which will be significantly alleviated for larger platforms and alternative voting mechanisms, including staking and [[Voting Mechanisms in DAO|vote]] escrow models. The empirical evidence strongly supports these predictions, providing insights into alternative voting mechanisms to improve the effectiveness of this new type of digital organization.

<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-amihud2002illiquidity" class="csl-entry">
Amihud, Yakov. 2002. <span>“Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”</span> ''Journal of Financial Markets'' 5 (1): 31–56.
</div>
<div id="ref-han2023dao" class="csl-entry">
Han, Jungsuk, Jongsub Lee, and Tao Li. 2023. <span>“Dao Governance.”</span> ''https://ssrn.com/Abstract=4346581''.
</div>
</div>

Smart Contracts and DeFi

2023-03-16T18:06:53Z

Aghaee: SMART CONTRACTS AND DEFI

{{DISPLAYTITLE: SMART CONTRACTS AND DEFI}}

Summarised by [[Alireza_aghaee|Alireza Aghaee]].

<span id="introduction"></span>
= Introduction =

This is a summary of “Smart Contracts and Decentralized Finance”, a working paper by Kose John, Leonid Kogan, and Fahad Saleh that can be found with this [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4222528 link] in SSRN (John, Kogan, and Saleh 2022). This paper describes smart contract mechanics. They highlight smart contracts’ benefits, such as overcoming commitment issues and explore constraints like their inability to access information beyond the blockchain and the difficulties of combining smart contract code with traditional legal enforcement. The paper also shows how blockchain application implementation costs are higher without a trustworthy intermediary. They finish with a study of the most prominent smart contract applications in Decentralized Finance: token issuance, decentralized exchanges, and protocols for loanable funds.

<span id="smart-contracts"></span>
= Smart contracts =

A smart contract is computer code comprising state variables and functions uploaded to a blockchain. The state variables represent the state of the contract and the functions are used to transition from one state to another. Users interact with smart contracts by creating transactions identifying themselves as senders, the smart contract as the receiver, and the function they wish to execute. Validators, who execute all blockchain transactions, are not obligated to execute any particular transaction, but users can incentivize them with a fee. Once a transaction is executed and recorded on a block of the blockchain, the blockchain state updates to reflect the state transition. Token smart contracts enable payments on a blockchain, as they have a state variable that stores the token holdings of each user, and token transfers are implemented as functions within the contract.

Smart contracts can affect not only their own state but also the states of other smart contracts because a function in a smart contract may initiate the execution of a function within another smart contract. However, smart contracts cannot directly initiate transactions with other smart contracts as they lack agency. This ability to interact with other smart contracts enhances economic exchanges feasible on a blockchain, allowing for contingent payments if a token smart contract interacts with another smart contract that identifies whether particular contingent events have occurred. The difficulties associated with executing contingent payments on a blockchain are discussed later on in the paper.

<span id="benefits-and-limitations"></span>
= Benefits and Limitations =

A function within a smart contract can be declared as public, which implies that any user may initiate an execution of that function at any time. Once a smart contract is deployed, the entity deploying the smart contract cannot preclude a transition in the blockchain state implied by any function that she declared as public.

The paper brings an economic transaction example to illustrate how a smart contract could enable contingent payments. A user can commit a payment to another user based on a particular contingent event via a smart contract. The smart contract would include a public function examining the relevant contingency condition and then transferring funds to the other user only if the contingency condition is realized. If the contingency condition is realized, the other user could retrieve their payment from the smart contract by creating a transaction that identifies the smart contract as the recipient of the transaction and also identifies the aforementioned public function as the function that the user wishes to have executed. The execution of this transaction would lead to an inspection of whether the contingency condition is true, and if so, then the transfer of funds to the other user would be implemented as a transition in the state of the blockchain. This contractual structure enables the user to overcome their inability to commit credibly to paying the other user.

A smart contract can also serve as a mechanism to overcome the problem of asymmetric information for new entrant firms in a market. By offering a contingent contract executed through a smart contract, the high-quality entrant can signal their type to potential partners or investors, thereby overcoming the lemons problem. This is because the smart contract can be designed to automatically verify that the entrant has successfully delivered their product or service before payment is released, which provides a credible signal of their quality.

The paper then continues by listing the limitations of smart contracts. In particular, they discuss three sorts of limitations that are limitations regarding conditioning events, limitations regarding outcomes, and economic limitations.

Smart contracts are only allowed to use data already part of the blockchain’s state, which ensures an inductive property that validators will continue to agree on the blockchain’s state after processing the same additional transaction. This consensus is crucial for the blockchain’s functionality. The data restriction is necessary to ensure this property. As for the limitations regarding conditioning events, the paper then explains why using an external data source could preclude the necessary inductive property. External data sources can alter the data they store, and two validators querying the same data from the external source may receive different data in response. If different data is received by two validators processing the same transaction, they will disagree on the blockchain state even if they agreed beforehand. This would break the necessary inductive property and compromise the consensus that underlies the blockchain.

The problem that induces the referenced data restriction is called the Oracle Problem. The oracle problem refers to the challenge of obtaining reliable and trustworthy external data for a smart contract on a blockchain. Since a smart contract is limited to invoking data already part of the blockchain’s state, it cannot access external data sources directly. Instead, it must rely on a third-party service or entity, called an oracle, to provide the external data. The oracle problem arises because the oracle may be compromised or provide inaccurate data, which can lead to incorrect or malicious outcomes in the smart contract. Solving the oracle problem is critical for encouraging smart contracts in real-world applications that require reliable and up-to-date external data.

The limitations in achieving outcomes are discussed next in this text, specifically concerning transfers of physical assets and transfers of stable value. It is highlighted that smart contracts alone cannot enforce ownership of physical assets, and they must be integrated with traditional legal structures to ensure recognition of ownership by law enforcement. As a result, smart contracts are not perfect substitutes for traditional legal agreements.

The transfer of stable value on a blockchain is limited by the expediency of ensuring price stability. There is interest in developing a stablecoin that could serve as a stable medium of exchange on a blockchain, but it remains unclear whether there is a theoretical methodology for ensuring such price stability. Some existing stablecoins, such as USDT, claim to maintain par value relative to USD, but the asset holdings supporting them cannot be directly verified from the blockchain. According to a formal examination, even over-collateralization does not ensure price stability. Despite this, stablecoins such as USDT, USDC, and BUSD have maintained large trading volumes and relative stability.

Lastly, the paper discusses the economic limitations associated with implementing economic exchanges through smart contracts. Two such costs are the cost of collateral and the cost arising from redundant behavior across blockchain validators. Collateral is generally required to implement contingent payments between two parties through smart contracts since the funds for the contingent payment must be out of the control of both the sender and the receiver until the contingent event has either occurred or failed. This collateral is held by the smart contract to resolve the commitment problem, which means that the sender forgoes the opportunity to invest the collateral and incurs an associated economic cost. Collateral is also needed to implement purchase agreements, where the buyer pays the seller only upon successful delivery. Both the buyer and the seller face an opportunity cost associated with posting collateral.

Redundant validator behavior is another cost of implementing economic exchanges through smart contracts. Blockchain entails redundant computation because each validator should execute each transaction separately, ensuring that each validator can separately verify that all transactions are executed correctly. The data storage costs for a blockchain are also borne separately by each validator, implying that the overall storage cost for a blockchain is several multiples of the storage cost for a centralized system where the storage cost is borne only once. The excess cost that arises for a blockchain due to redundancy constitutes an economic limitation because it implies that economic exchanges that are technically feasible might not be implemented for economic reasons. This economic limitation has practical implications in restricting the economic applications on blockchains.

<span id="existing-applications"></span>
= Existing Applications =

The paper discussed three cases that are the most popular uses of smart contracts: Token Issuance, Decentralized Exchanges, and Protocols for Loanable Funds.

<span id="token-issuance"></span>
== Token Issuance ==

The smart contract acts as the central authority for issuing and transferring tokens on the blockchain. It contains a state variable that maintains a record of the token balances for each user and various functions that allow modifications to this state variable with the token balances. For instance, a transfer function could be called to transfer tokens from one user to another by reducing the balance of the sender’s account and increasing the balance of the recipient’s account. Ultimately, any change to the state variable corresponds directly to the final settlement of the transfer of units of the token. A fungible token is a type of digital asset on a blockchain that is interchangeable with any other unit of the same token. This means that each token unit has the same value and is indistinguishable from any other unit of the same token. Examples of fungible tokens include cryptocurrencies such as Bitcoin and Ethereum and stablecoins like Tether and USD Coin. On the other hand, a non-fungible token (NFT) is a digital asset on a blockchain that represents a unique item or asset and is not interchangeable with any other unit of the same token. NFTs are often used to represent digital art, collectibles, and other unique items. Each NFT is distinct and unique, often determined by the rarity or uniqueness of the item it represents.

<span id="decentralized-exchange"></span>
== Decentralized Exchange ==

A decentralized exchange (DEX) consists of smart contracts that enable trading fungible tokens on the same blockchains. Liquidity pools are smart contracts within the DEX that own two types of tokens and allow users to purchase one type of token by paying units of the other type, where the exchange rate is determined by an Automated Market Maker (AMM) function. The AMM function satisfies an equation requiring evaluating the same value before and after a trade, ensuring fair exchange.

A liquidity pool at a DEX generates token supply in equilibrium, which arises from investors transferring ownership of their tokens to the pool in exchange for an ownership share. The ownership share is proportional to the tokens transferred relative to the total tokens in the pool after the transfer and allows the investor to withdraw tokens from the pool. (Capponi and Jia 2021) found that sufficient volatility in the exchange rate between tokens in a liquidity pool can lead to a liquidity freeze, where the equilibrium pool quantity is zero. Therefore, token pairs with high exchange rate volatility are unsuitable for trading through liquidity pools.

The result that high volatility in the exchange rate between two tokens in a liquidity pool leads to a liquidity freeze can be intuitively explained by arbitrage opportunities. When there is a shock to the exchange rate, an arbitrageur can exploit this by trading with the liquidity pool in a way that generates a profit for themselves but a loss for the pool. This expected loss is accounted for ex-ante, meaning investors will not acquire an ownership share of the liquidity pool when the expected arbitrage profit is high enough. As volatility increases, so do expected arbitrage profits, leading to zero token supply of the liquidity pool.

Investors are incentivized to invest in liquidity pools despite the risk of arbitrage trading due to the proportional trading fees charged by the pools, which can increase the overall value of the pool and the value of each investor’s ownership share. (Hasbrouck, Rivera, and Saleh 2022) show that increasing the trading fee level can increase overall investment in the liquidity pool, reduce trading price impact, and increase trading volume.

The sustainability of DEXs as alternatives to CEXs that are centralized exchanges such as Binance remains an open question. (Barbon and Ranaldo 2021) find that while CEX transaction costs are generally lower than DEX transaction costs, the most significant component of DEX trading costs arises due to fees paid to blockchain validators. However, these fees endogenously decline as the blockchain’s throughput increases. Various solutions have been developed to improve blockchain throughput, suggesting that DEXs might become more competitive with CEXs.

<span id="protocols-for-loanable-funds"></span>
== Protocols for Loanable Funds ==

A Protocol for Loanable Funds (PLF) consists of a set of smart contracts that define a loan system where funds hold a single type of cryptocurrency. Investors can invest in a particular fund by selling their units of the associated cryptocurrency to the fund in exchange for a pro-rata equity share. Each fund creates a fungible token representing a pro-rata share of its cryptocurrency, which investors receive in exchange for selling their cryptocurrency to the fund. The cryptocurrency in the fund is then available for borrowing by any borrower at a given interest rate, denominated in terms of the fund’s cryptocurrency. Interest payments must be made in units of the fund’s cryptocurrency, increasing the number of cryptocurrencies held by the fund and, therefore, the value of each investor’s equity stake.

For example, Compound PLF is PLF with multiple cryptoassets, such as DAI, and USDC. The mechanics for investing in and borrowing from these funds are similar to ETH funds. When borrowing from a Compound fund, borrowers must post collateral above the amount they wish to borrow. The amount of collateral required depends on the collateral factor assigned to the particular cryptoasset being used as collateral. For example, if the collateral factor for ETH is 75%, then a borrower would need to post <math display="inline">1/0.75= 1.33</math> times the amount they wish to borrow in ETH as collateral. If the value of the collateral falls below a certain threshold, called the liquidation threshold, then the borrower’s position may be liquidated to ensure that lenders are protected. When a position is liquidated, the collateral is sold off and used to repay the loan, along with any outstanding interest owed. If the value of the collateral is not enough to repay the loan and interest, then the borrower may be subject to additional fees and penalties.

PLFs, like Compound, provide a decentralized way for investors to earn interest on their cryptoassets by lending them to borrowers. While this can be a lucrative way to earn passive income, investors must also be aware of the risks involved, including price fluctuations of the underlying cryptoassets, borrower default, and the potential for liquidation if the value of the collateral falls below the liquidation threshold. Investors in a PLF fund are not debtholders and do not receive direct interest payments. Instead, they receive interest in appreciating the fund’s fungible token regarding its cryptoasset, assuming borrowers repay their loans. PLFs do not involve human discretion in lending, and borrower interest rates are specified according to an exogenous function that is typically an increasing function of the fund’s utilization rate. Loans in a PLF are generally floating-rate loans, and borrowers can repay them anytime.

The Oracle problem is also present in the context of PLFs. Here, it refers to the difficulty of obtaining accurate pricing data for the collateral used to secure loans, as this data is not directly available on the blockchain. PLFs typically rely on oracles, trusted third-party services that provide off-chain data to smart contracts to obtain pricing data. However, oracles can be subject to manipulation or failure, leading to inaccurate pricing data and potentially compromising the solvency of the PLF. To mitigate this risk, PLFs may use multiple oracles and implement various mechanisms to ensure the accuracy and reliability of the data provided by these oracles.

The limited set of acceptable collateral on a PLF has significant implications for economic activity on the platform. It makes traditional borrowing through physical collateral impossible and instead focuses on leveraged long trading and short selling of risky cryptoassets. These activities are mainly executed with ETH since PLFs operate primarily on the Ethereum blockchain. Leveraged long trading involves purchasing ETH, investing it at the PLF ETH fund, pledging it as collateral, borrowing stablecoin from the PLF stablecoin fund against the collateral, and then selling the borrowed stablecoin at a cryptoasset exchange for more ETH. This results in leveraged gains as ETH appreciates relative to USD. On the other hand, short selling of ETH involves purchasing stablecoin, investing it at the PLF stablecoin fund, pledging it as collateral, borrowing ETH from the PLF ETH fund against the collateral, and then selling the borrowed ETH at a cryptoasset exchange for more stablecoin. As the price of ETH declines, the investor profits since the fall in ETH prices reduces the USD-equivalent value of their borrowings from the PLF.

<span id="conclusion"></span>
= Conclusion =

It is worth noting that the adoption and development of smart contracts have been relatively slow despite their early conceptualization. However, the emergence of Decentralized Finance (DeFi) applications has started unlocking smart contracts’ economic potential. DeFi has attracted large attention and investment recently. Despite challenges, such as regulatory clarity, the potential for smart contracts to integrate with traditional legal structures could lead to further growth in their economic value. Overall, smart contracts and DeFi are still in their early stages of development, and there is significant potential for growth in the future.

=References=

<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-barbon2021quality" class="csl-entry">
Barbon, Andrea, and Angelo Ranaldo. 2021. <span>“On the Quality of Cryptocurrency Markets: Centralized Versus Decentralized Exchanges.”</span> ''arXiv Preprint arXiv:2112.07386''.
</div>
<div id="ref-capponi2021adoption" class="csl-entry">
Capponi, Agostino, and Ruizhe Jia. 2021. <span>“The Adoption of Blockchain-Based Decentralized Exchanges.”</span> ''arXiv Preprint arXiv:2103.08842''.
</div>
<div id="ref-hasbrouck2022need" class="csl-entry">
Hasbrouck, Joel, Thomas J Rivera, and Fahad Saleh. 2022. <span>“The Need for Fees at a Dex: How Increases in Fees Can Increase Dex Trading Volume.”</span> ''Available at SSRN''.
</div>
<div id="ref-john2022smart" class="csl-entry">
John, Kose, Leonid Kogan, and Fahad Saleh. 2022. <span>“Smart Contracts and Decentralized Finance.”</span> ''Available at SSRN''.
</div>
</div>

Tokenization

2023-03-04T10:52:37Z

Aghaee: Tokenization

{{DISPLAYTITLE: Tokenization}}

Summarised by [[Alireza_aghaee|Alireza Aghaee]].

<span id="introduction"></span>
= Introduction =

This is a summary of “Decentralization through Tokenization”<ref> SOCKIN, M. and XIONG, W. (2023), Decentralization through Tokenization. J Finance, 78: 247-299. https://doi.org/10.1111/jofi.13192 </ref> published in the Journal of Finance in February 2023. This study investigates the decentralization of digital platforms through tokenization as a novel approach to resolving the tension that exists between platforms and users. Tokenization via utility tokens functions as a commitment mechanism that stops a platform from abusing its users by transferring control to them. This commitment comes at the expense of not having an owner with an equity investment who, in traditional platforms, would subsidize participation to increase the platform’s network effect. Because of this trade-off, utility tokens are a more enticing funding strategy than equity for platforms with bad fundamentals. The conflict resurfaces when non-users, such as investors and validators, participate on the platform.

<span id="model"></span>
= Model =

The model has three dates and a developer who wants to develop a generic platform that supports bilateral transactions among a group of users. At t = 0, the platform developer chooses a funding scheme based on a prior belief about the platform’s fundamentals. Each potential user decides whether to join the platform at t = 1. After joining the platform, a user can randomly match with another user to execute mutually beneficial transactions at t = 1 and t = 2. There are multiple funding schemes to consider. The equity-based platform owner can commercialize customers’ private data at t = 2. Potential users’ decisions to join the platform may be influenced by their expectations of the owner’s lack of commitment. The setting of the model are as follows:

* At t = 1, there is a continuum of prospective users with a measure of one unit, indexed by <math display="inline">i \in [0, 1]</math>. These potential users can trade products in two rounds at t = 1, 2 on the platform.
* To join the platform, each user incurs a personal cost of <math display="inline">\kappa > 0</math>.
* There is an entry fee <math display="inline">c</math> to the platform that users pay. This fee can also be negative if owners subsidize entry.
* If the platform is funded by a token-based scheme, a user must pay the cost of acquiring a token to join. If the platform is funded by an equity-based scheme, the owner may subsidize each user’s initial involvement.
* <math display="inline">X_i = 1</math> if user <math display="inline">i</math> joins the platform, and <math display="inline">X_i = 0</math> otherwise.
* User i has a unique good and a randomly matched trading partner, user j. Both i and j must be on the platform to trade products at t = 1 and t = 2.
* User i has a Cobb-Douglas utility function <math display="inline">U_i\left(C_i, C_j\right)=\left(\frac{C_i}{1-\eta_c}\right)^{1-\eta_c}\left(\frac{C_j}{\eta_c}\right)^{\eta_c}</math>. Both goods are needed for a user to derive utility from consumption. If one of them is not on the platform, there is no transaction and each of them gets zero utility.
* User <math display="inline">i</math> has a goods endowment of <math display="inline">e^{A_i}</math>, which is equally divided across <math display="inline">t=1</math> and <math display="inline">t=2</math>. User <math display="inline">i</math> ’s fundamental, <math display="inline">A_i</math>, comprises a component <math display="inline">A</math> common to all users and an idiosyncratic component,<math display="inline">A_i=A+\tau_{\varepsilon}^{-1 / 2} \varepsilon_i.</math> with niid <math display="inline">\varepsilon_i</math> such that <math display="inline">\varepsilon_i \sim \mathcal{N}(0,1)</math>.
* The common component <math display="inline">A</math> represents the platform’s demand fundamental, which is publicly observed by all users and the developer only at <math display="inline">t=1</math>. At <math display="inline">t=0</math>, the developer has a prior over <math display="inline">A, A \sim G\left(\bar{A}, \tau_A^{-1}\right)</math>, and chooses the platform’s funding scheme based on this prior belief.

Solving the consumer transaction problem yields that when user <math display="inline">i</math> is paired with another user <math display="inline">j</math> on the platform, they simply swap their goods, with user <math display="inline">i</math> using <math display="inline">\eta_c e^{A_i}</math> units of good <math display="inline">i</math> to exchange for <math display="inline">\eta_c e^{A_j}</math> units of good <math display="inline">j</math>. Consequently, both users are able to consume both goods, with user <math display="inline">i</math> consuming <math display="block">C_i(i)=\left(1-\eta_c\right) e^{A_i}, C_j(i)=\eta_c e^{A_j},</math> and user <math display="inline">j</math> consuming <math display="block">C_i(j)=\eta_c e^{A_i}, C_j(j)=\left(1-\eta_c\right) e^{A_j}</math>

'''First Best''' The paper shows that in the first best equilibrium, there exists a threshold <math display="inline">A_*^{FB}</math>, that if <math display="inline">A \geq A_*^{F B}</math>, then all users participate on the platform and a social planner can implement this outcome by imposing transaction fees proportional to users’ transaction gain at a sufficiently high rate and redistributing the fees equally back to all users. If <math display="inline">A<A_*^{F B}</math>, then the platform shuts down because the social surplus is negative.

'''Discussion''' First best equilibrium shows a network impact in which all users join the platform when the social surplus is positive, even when individuals with low endowments cannot cover their participation costs from their transaction gains because their involvement boosts the transaction gains of other users. Given that users with high endowments earn more profits from transactions and pay higher fees, the redistribution of fees offers a cross-subsidy from users with high endowments to those with low endowments. A high transaction cost ensures full user participation.

<span id="equity-financed-platform"></span>
== Equity-Financed platform ==

Assume at t = 0 the developer chooses to set up a conventional equity-based scheme to fund the platform. Therefore, the developer issues equity, fully or partially sold to outside investors. All equity holders together comprise the owners of the platform. Owners can provide an entry subsidy (i.e., a negative <math display="inline">c</math>) at <math display="inline">t=1</math> and then charge each user a fraction <math display="inline">\delta</math> of his utility surplus <math display="inline">U_{i, t}</math> from the transaction in each period <math display="inline">t=1,2</math>. The subsidy has a cap to prevent opportunistic individuals from joining the platform: <math display="block">c \geq-\alpha \kappa .</math> This cap ensures the total cost of participation for users, that is <math display="inline">\kappa + c</math>, remains positive. The owners can take a subverting action <math display="inline">s \in\{0,1\}</math> at <math display="inline">t=2</math>. If the owner chooses <math display="inline">s=1</math>, this action benefits the owner by an amount proportional to the number of users on the platform, <math display="inline">\gamma \int_0^1 X_i d i</math>, at the expense of the users. This action prevents any transaction on the platform and imposes a utility cost of <math display="inline">\gamma>\alpha \kappa</math> on each user. The owner, therefore, sets fees at <math display="inline">t=1</math> to maximize their total expected profit <math display="block">\Pi^E=\sup _{\{c, \delta, s\}} E\left[\int_0^1\left(c+\delta U_{i, 1}\right) X_i d i+\int_0^1\left((1-s) \delta U_{i, 2}+s \gamma\right) X_i d i \mid \mathcal{I}_1\right],</math> where <math display="inline">\mathcal{I}_1=\{A\}</math> is the owners’ information set at t=1. The owner chooses subversive action <math display="inline">s \in\{0,1\}</math> at <math display="inline">t=2</math> to maximize its profit <math display="block">s=\arg \max \int_0^1\left(\delta U_{i, 1}(1-s)+\gamma s\right) X_i d i</math>

Noteworthy, the owner may prefer to commit to not subverting at t = 1 to maximize user participation, but such commitment is not credible under the equity-based scheme.

User <math display="inline">i</math> decides on participation based on her expected utility as follows: <math display="block">\max _{X_i \in\{0,1\}} E\left[(1-\delta)\left(U_{i, 1}+(1-s) U_{i, 2}\right)-\kappa-c-\gamma s \mid \mathcal{I}_i\right] X_i,</math> where <math display="inline">\mathcal{I}_i=\left\{A, A_i\right\}</math> is the information set of user <math display="inline">i</math> at t=1. Solving the user problem will result in a cutoff equilibrium, in which only users with endowments above a critical level, <math display="inline">\hat{A}^E</math>, participate in the platform.

'''Equilibrium:''' Under the equity-based funding scheme, there is a unique cutoff equilibrium with the following properties:

* If <math display="inline">A>A_*^E</math>, the owner does not subvert the platform at <math display="inline">t=2</math>, which leads to the following outcomes at <math display="inline">t=1</math> :
** The owner provides the maximum entry subsidy, <math display="inline">c=-\alpha \kappa</math>.
** The owner sets a positive transaction fee.
** Each user <math display="inline">i</math> adopts a cutoff strategy to join the platform if <math display="inline">A_i</math> is higher than <math display="inline">\hat{A}_{N S}^E</math>, where <math display="inline">\hat{A}_{N S}^E</math> is decreasing in <math display="inline">A</math>.
* If <math display="inline">A \in\left[A_{* *}^E, A_*^E\right]</math>, the owner subverts the platform at <math display="inline">t=2</math>, which leads to the following outcomes at <math display="inline">t=1</math> :
** The owner provides the maximum entry subsidy, <math display="inline">c=-\alpha \kappa</math>.
** The owner sets a positive transaction fee.
** Each user <math display="inline">i</math> follows a cutoff strategy to join the platform with the cutoff <math display="inline">\hat{A}_{S V}^E</math>, which is decreasing in <math display="inline">A</math>.
* If <math display="inline">A<A_{* *}^E</math>, the platform breaks down with no user participation at <math display="inline">t=1</math>.

Values of <math display="inline">A_{* *}^E</math>, <math display="inline">A_*^E</math>, <math display="inline">\hat{A}_{N S}^E</math> and <math display="inline">\hat{A}_{S V}^E</math> are pined down in the paper.

'''Discussion:''' The equity cash ﬂows incentivize the owner to internalize the network effect and subsidize the entry fee to maximize user participation. Therefore, the owner always chooses the maximum entry subsidy, <math display="inline">c = -\alpha \kappa</math>, to attract the marginal user. Nevertheless, the cap on the entry subsidy constraints user participation from reaching the first-best level. Furthermore, Anticipating the subversion and the resulting damage to users, potential users are reluctant to join the platform at t = 1. Their reluctance forces the owner to reduce the transaction fee, and, despite the reduced fee, platform participation by users remains lower than the level in the absence of the subversion. The paper also shows that under the equity-based scheme, when the subversion equilibrium occurs, that is, when <math display="inline">A \in\left[A_{* *}^E, A_*^E\right]</math>, user participation, owner profit, and social surplus all decrease with the degree of user abuse <math display="inline">\gamma</math>, while the boundary of platform breakdown <math display="inline">A_{* *}^E</math> increases with <math display="inline">\gamma</math>.

<span id="token-financed-platform"></span>
== Token-Financed platform ==

When the platform is funded through tokenization, by giving control to users, users can protect themselves from nonusers who would take subversive actions. Under this scheme, a user must purchase a token to join the platform. Buying a token also entitles users to vote on issues related to the platform at t = <span>1, 2</span>. A utility token, therefore, conveys control rights to holders. The main tradeoff between token-funded and equity-funded platforms is that decentralization leads to a commitment not to exploit users at the expense of not having an owner with a stake in the platform’s profit which has the incentive to subsidize user participation. The lack of entry subsidy implies that the token-based scheme cannot accomplish the full user participation required by the ﬁrst-best equilibrium.

Under the token-based scheme, the developer has a simple choice at <math display="inline">t=1</math> of setting the token price <math display="inline">P</math> to maximize his revenue from token issuance, <math display="block">\Pi^T=\max _P \int_0^1 P X_i\left(\mathcal{I}_i\right) d i,</math> The developer faces a trade-off between a higher token price and a smaller user base. Similar to the equity-based scheme, at <math display="inline">t=1</math>, each user chooses whether to join the platform by evaluating whether his expected transaction surplus with another matched user on the platform is sufficient to cover the costs of participation, which is now the fixed cost and the purchase of a token, <math display="block">\max _{X_i \in\{0,1\}} E\left[U_{i, 1}+U_{i, 2}-\kappa-P \mid \mathcal{I}_i\right] X_i</math>

'''Equilibrium''' Under the utility token-based funding scheme, the platform breaks down with no user participation if <math display="inline">A<A_{* *}^T</math>, and there is a cutoff equilibrium with the following properties if <math display="inline">A \geq A_{* *}^T</math> :

* Each user <math display="inline">i</math> adopts a cutoff strategy in purchasing the token to join the platform <math display="block">X_i= \begin{cases}1 & \text { if } A_i \geq \hat{A}^T \\ 0 & \text { if } A_i<\hat{A}^T\end{cases}</math>
* The token price <math display="inline">P</math> is given by <math display="block">P=e^{\left(1-\eta_c\right)_{\varepsilon}^{-1 / 2} z^T+A+\frac{1}{2} \eta_{\varepsilon}^2 \tau_{\varepsilon}^{-1}} \Phi\left(\eta_c \tau_{\varepsilon}^{-1 / 2}-z^T\right)-\kappa,</math> where <math display="inline">\boldsymbol{z}^T=\sqrt{\tau_{\varepsilon}}\left(\hat{A}^T-A\right)</math>.

Values of <math display="inline">A_{* *}^T</math> and <math display="inline">\hat{A}^T</math> are pinned down in the paper.

'''Discussion:''' The token price P depends on the marginal user’s platform participation. The equity price under the equity-based scheme is set by the transaction fee collected from the average user, who gains more from participation in the platform than the marginal user due to the network effect. This disparity has numerous major ramifications. First, an equity offering is superior to token issuance for fundraising since marginal users have lower transaction surpluses due to the network effect than average users. Second, token values are more volatile than equity prices due to the platform’s network effect.

'''Comparison:''' Token-based scheme vs. equity-based scheme:

* For a given level of <math display="inline">\gamma</math>, the utility token-based scheme leads to lower user participation, developer proﬁt, and social surplus if the platform fundamental A is sufﬁciently high.
* For a given level of <math display="inline">A</math>, the utility token-based scheme leads to higher user participation, developer profit, and social surplus if the degree of user abuse <math display="inline">\gamma</math> is sufficiently high.
* Given his prior belief distribution about A, the developer chooses the utility token-based scheme when his prior is that A is weak.

<span id="mixed-platform"></span>
== Mixed platform ==

The additional cost of decentralization motivates hybrid schemes that combine features of equity and utility tokens. The cash flows from the equity tokens provide a channel to subsidize marginal users. Such cash flows, however, may also incentivize nonusers to acquire equity tokens as a ﬁnancial investment. The paper separates the two cases relating to whether non-user investors can hold the tokens.

In the case without investors, since the decision makers are the users, they never choose the subversive action. The paper shows that this hybrid equity token-based scheme can achieve the first-best equilibrium. If the platform fundamental is higher than <math display="inline">A_*^{FB}</math>, there is full user participation, and the developer can extract the entire transaction surplus through the token sale. If the platform fundamental is below the threshold, it breaks down as it does not lead to any social surplus. Therefore, without investors, equity tokens improve traditional equity financing and can achieve the first-best outcome on the platform.

In the case with investors, however, the presence of nonusers who can acquire a sufficient quantity of equity tokens may reintroduce the commitment problem, albeit through a modiﬁed form. For instance, the investor can alter the platform’s terms of service and use privileged information about users to harvest blockchain transactions for ad targeting.

The paper shows that under the equity token-based funding scheme with a large investor, there is an equilibrium in which the investor acquires a majority share of tokens and subverts the platform when the platform fundamental, A, is sufficiently weak. Moreover, the developer’s profit, the token price, and user participation are lower than in the absence of the investor. The key shortcoming of equity tokens is that the platform’s developer and precoded governance algorithms cannot distinguish which token holders are users or investors.

<span id="conclusion"></span>
= Conclusion =

This study examines how tokenization can decentralize online platforms to alleviate conflicts of interest between platforms and users. Tokenization prevents platforms from abusing consumers by giving them authority over preprogrammed smart contracts. The research indicates that this commitment comes at the cost of not having an equity-stake owner who is encouraged to subsidize user involvement to optimize the platform’s network impact. Utility tokens may not always be better than equity for funding platforms. Utility tokens appeal to platforms with weak fundamentals because they worry more about user exploitation.

This analysis reveals a high-level trade-off that can guide platform design in general and platform financing, in particular, using traditional control and cash flow rights allocations. It shows that token values are based on the marginal user’s convenience yield, while equity prices are based on the average user’s transaction fees. Moreover, although users will never undermine the platform, they have no motive to subsidize platform participation, even when it is socially optimal. Lastly, if tokens contain cash flow and control rights, users or outsiders may have the motive to centralize the platform by amassing tokens, which would reintroduce the commitment problem, especially if the token price is low and the platform is open to subversion.

=References=

Open Source

2023-02-13T10:07:29Z

Aghaee: Created the entire page

{{DISPLAYTITLE: Open Source}}

Written by [[Alireza_aghaee|Alireza Aghaee]].

<span id="introduction"></span>
=Introduction=

open-source software development refers to the practice of creating software that is freely available to the public and can be modified, used, and distributed by anyone. This approach to software development is based on the principles of transparency, collaboration, and sharing.

In an open-source development model, the source code of the software is openly available to anyone who wants to view or contribute to it. This means that developers from around the world can work together to build, improve, and maintain the software, leading to a more robust and high-quality product.

Examples of popular open-source software include the operating system Linux, the web server Apache, and the database management system MySQL. These and many other open-source software projects have had a significant impact on the technology industry, and continue to shape the way that software is developed and used today.

This article brings a review to open-source development in general and the economic aspects of it in particular. The article starts with a brief history of the open-source movement and reviews four case studies of some successful open-source projects. It then recapitulates the licenses in the open-source environment which are at the heart of keeping the open source open and preventing exploitations. Finally, the articles review some of the scholarly contributions to the economics of open-source development.

<span id="history"></span>
=History=

The history of open-source software development dates back to the late 1960s and early 1970s, when the concept of “free software" was first introduced by computer programmer Richard Stallman. Stallman was frustrated by the proprietary software models that dominated the computer industry at the time, and he believed that software should be freely available to everyone so that they could use, study, and modify it as they saw fit.

In 1983, Stallman founded the [https://en.wikipedia.org/wiki/Free_Software_Foundation Free Software Foundation (FSF)], an organization dedicated to promoting the use of free software. Perhaps one of the biggest achievements of stallman’s movement was the development of GNU. GNU is a large collection of free software that can be utilized as an operating system or in conjunction with other operating systems. He also created the GPL (GNU General Public License), a widely used open-source license that ensures that software remains free and open for anyone to use, modify, or distribute. Through the FSF, Stallman worked to create a community of software developers and users who shared his vision of a world where software was freely available and modifiable by all.

Despite Stallman’s efforts, the concept of free software was slow to catch on in the broader technology industry. It wasn’t until the late 1990s that a group of software developers and companies came together to promote the concept of open-source software as a viable alternative to proprietary software. This group included leaders in the tech industry, such as Eric Raymond, Tim O’Reilly, and Linus Torvalds, who is famous for creating the Linux operating system.

The term "open-source" was first used in 1998, when this group of developers and companies gathered to promote the concept of open-source software. They argued that the transparency and collaboration inherent in the open-source development model would lead to better software, and they set out to convince the tech industry of the value of open-source.

Their efforts were successful, and the adoption of open-source software quickly gained momentum. Many of the largest tech companies in the world, including IBM, Google, and Microsoft, embraced open-source and began contributing to open-source projects. The use of open-source software also spread beyond the tech industry, with governments, schools, and other organizations adopting open-source solutions for a wide range of applications.

One of the key benefits of the open-source development model is the ability for developers from around the world to collaborate and contribute to software projects. This has led to the creation of many high-quality and widely used open-source software projects, such as the Apache web server, the MySQL database management system, and the Python programming language. In the next section, we overview some of these projects.

<span id="cases"></span>
=Cases=

<span id="linux"></span>
==Linux==

Linux is a free and open-source operating system that was first released in 1991 by Linus Torvalds, a computer science student at the University of Helsinki in Finland. At the time, Torvalds was frustrated with the limitations of the available operating systems and decided to develop his own.

The initial version of Linux was based on the Unix operating system and was designed to run on personal computers. It quickly gained popularity among programmers and hobbyists, who appreciated its open-source nature and its ability to run on a wide range of hardware. Over time, Linux grew into a full-fledged operating system that could compete with commercial alternatives like Microsoft Windows and Apple’s macOS.

One of the key factors that has contributed to Linux’s success is its open-source nature. This means that anyone can access the source code and make changes to it, as long as they follow the terms of the GNU General Public License. This has led to a vibrant community of developers and users who contribute to the development of the operating system and create a wide range of applications that run on it.

In the late 1990s and early 2000s, Linux began to gain traction in the enterprise market, particularly in server and cloud computing. Today, Linux is widely used in data centers, web servers, and supercomputers; According to [https://truelist.co/blog/linux-statistics/ truelist] it is estimated to power over 96.3% of the top one million web servers. It is also widely used in embedded systems, such as mobile phones and networking equipment.

One of the most significant events in the history of Linux was the introduction of the Linux kernel in 1991. This is the core of the operating system, and it is what makes Linux a Unix-like system. Since its introduction, the Linux kernel has gone through many changes and has become more reliable, secure, and scalable.

<span id="python"></span>
==Python==

Python is a high-level, interpreted programming language that was first released in 1991 by Guido van Rossum. At the time, van Rossum was working at the National Research Institute for Mathematics and Computer Science in the Netherlands, and he created Python as a hobby project to help him keep track of his work.

The name "Python" was inspired by Monty Python’s Flying Circus, a popular British comedy series from the 1970s. The language was designed with readability and simplicity in mind, and it is known for its use of indentation to delimit blocks of code, which makes it easier to read and write than many other programming languages.

In the early years of its development, Python was primarily used for scripting and small, specialized applications. However, as the language grew in popularity, more and more developers began to use it for larger projects. Over time, Python has become one of the most widely used programming languages in the world, and it is now used in a wide range of applications, including scientific computing, data analysis, web development, and machine learning.

One of the key factors that has contributed to Python’s success is its ease of use and versatility. The language has a large and active community of developers who have created a vast ecosystem of libraries and tools that make it easier to develop and deploy applications. Additionally, Python’s readability and simplicity make it a popular choice for teaching programming, and it is now used as an introductory programming language in many universities and schools.

<span id="mysql"></span>
==MySQL==

MySQL is a widely used open-source relational database management system that was first released in 1995. It was created by Michael Widenius and Allan Larsson, two Swedish computer scientists who wanted to provide a high-performance database system that was both easy to use and cost-effective.

In the early years of its development, MySQL was primarily used for small web applications and as an alternative to proprietary database systems like Oracle and Microsoft SQL Server. Over time, as the web grew in popularity and the demand for powerful, scalable databases increased, MySQL grew in popularity as well.

In the late 1990s and early 2000s, MySQL was acquired by several companies, including Sun Microsystems, which was later acquired by Oracle. Despite these changes, MySQL continued to grow in popularity and became one of the most widely used databases in the world. Today, MySQL is the second most popular database management systems worldwide<ref>https://www.statista.com/statistics/809750/worldwide-popularity-ranking-database-management-systems/</ref>, and it is used by a wide range of organizations, from small startups to large enterprises.

One of the key factors that has contributed to MySQL’s success is its scalability. The database is designed to handle large amounts of data and is used by many of the largest websites and applications in the world such as Facebook, Twitter, Netflix, Uber, Airbnb, Shopify, and Booking.com. Additionally, its support for SQL (Structured Query Language) makes it a popular choice for developers, as SQL is a widely used language for database management.

<span id="apache"></span>
==Apache==

The Apache HTTP Server, commonly referred to as Apache, is a widely used open-source web server that was first released in 1995. It was developed by a group of volunteers who wanted to create a high-performance web server that was both easy to use and cost-effective.

Apache quickly became popular among web developers, and it soon became the most widely used web server on the Internet. This was due in part to its ease of use, as well as its scalability and support for a wide range of platforms and operating systems. Additionally, its modular design made it easy for developers to extend its functionality by creating new modules that could be easily added to the server.

In the late 1990s and early 2000s, Apache became the foundation for the development of many other open-source projects, including the Apache Foundation, which was created to oversee the development of a range of open-source software projects, including Apache. Today, Apache is still one of the most widely used web servers in the world, mostly indebted to its open-source nature allowing for scalability, and support for a wide range of platforms and technologies.

<span id="licenses"></span>
=Licenses=

Open-source software is developed and distributed under a wide variety of licenses, each with its own set of terms and conditions that govern how the software can be used, modified, and distributed. The main difference between these licenses is the degree of restrictions they entail. These licenses are critical to the open-source ecosystem, as they help to ensure that software remains free, open, and accessible to everyone. In this section, we’ll take a closer look at some of the most common licenses used in the open-source environment.

In general, licenses fall into two groups viral or non-viral (permissive) licenses. Viral licenses require that modifications to the program also be licensed under the same license as the original work. Examples of this type include GPL and LGPL. Permissive licenses, on the other hand, allow for redistribution under a smaller set of conditions. The program can be modified under these licenses without making the new source code publicly available as long as due acknowledgment is given. Examples of this sort of license are BSD, Apache license, and MIT license. Mozilla public license falls in between the two groups. In what follows we briefly review these licenses, starting with the viral ones.

GPL (GNU General Public License): The GPL is a copyleft<ref>Copyleft is a concept in copyright law that is used in the free and open-source software communities. Under the terms of a copyleft license, anyone who modifies or distributes the software must make the source code available to others, and must also license the software under the same copyleft license. This ensures that the software remains free and open for everyone to use, modify, and distribute, even if it is modified or incorporated into other software.</ref> open-source license that requires all modifications and distributions of the software to be released under the same license. The GPL is often used for software projects that are intended to be free and open for everyone to use, modify, and distribute. Some of the most widely used open-source software projects, including the Linux operating system, are licensed under the GPL.

LGPL (GNU Lesser General Public License): The LGPL is a copyleft open-source license that allows the software to be used, modified, and distributed without any restrictions, as long as the original copyright notice and disclaimer are included. Unlike the GPL, the LGPL allows the software to be used as a library in proprietary software, as long as the proprietary software does not modify the library.

MIT License: The MIT License is a permissive open-source license that allows software to be used, modified, and distributed without any restrictions, as long as the original copyright notice and disclaimer are included. The MIT License is one of the most widely used open-source licenses, and is often used for simple software projects where the author wants to ensure that the software is freely available to everyone.

Apache License: The Apache License is another permissive open-source license that allows the software to be used, modified, and distributed without any restrictions, as long as the original copyright notice and disclaimer are included. Unlike the MIT License, the Apache License includes a patent grant that provides patent protection for users of the software.

BSD (Berkeley Software Distribution) License: The BSD License is a permissive open-source license that allows the software to be used, modified, and distributed without any restrictions, as long as the original copyright notice and disclaimer are included. Unlike the GPL, the BSD License does not require modified versions of the software to be released under the same license.

Mozilla Public License: The Mozilla Public License is a copyleft open-source license that requires all modifications and distributions of the software to be released under the same license. Under the terms of the MPL, it allows the integration of MPL-licensed code into proprietary codebases but is conditional on those components remaining accessible.

(Lerner and Tirole 2005) use the database of open source projects available on SourceForge website<ref>https://sourceforge.net/</ref> to examine the effect of license choice. They found that open-source projects that operate on commercial operating systems and open-source projects aimed to be used mostly by developers prefer to use licenses with fewer restrictions, whereas open-source projects designed for end users tend to use licenses with more restrictions. Conforming to the natural mindset, (Comino, Manenti, and Parisi 2007) find that the degree of restriction that a license brings in is inversely proportional to the likelihood that the project will proceed to a more advanced level of development.

<span id="economics-of-opensource"></span>
=Economics of opensource=

In this review, we will summarize some of the key findings from the literature on the economics of open-source development. Some relevant questions in this literature are: Why do people participate? Why are there open-source projects in the first place? And how do commercial vendors react to the open-source movement?

One of the main topics of research in this area has been the motivations of developers to participate in open-source projects. Researchers have found that developers are motivated by a variety of factors, including the desire to improve their skills, the opportunity to collaborate with others, the desire to contribute to a cause they believe in, and the potential to receive recognition and status within the open-source community.

(Lerner and Tirole 2002) assert that seeking recognition in the developer society is perhaps one of the most important incentives of open-source development. on the other hand, using a web survey of 684 software developers in 287 Free or open-source projects, (Lakhani and Wolf 2003) find that enjoyment-based intrinsic motivation, namely how creative a person feels when working on the project, is the strongest and most pervasive driver. This is in line with the findings of the survey by (Ghosh et al. 2002) that argue that open-source development is more like a hobby than a paying job for developers. On the other hand, using a game theoric model, (Harhoff, Henkel, and Von Hippel 2003) argue that end users of open source benefit by sharing their innovations. they show that under realistic parameter constellations, the free revealing of proprietary information actually pays off the innovators.

In general, it seems that both theoretical contributions and survey results support the idea that recognition is at least one of the primary reasons to contribute to open-source projects. (Hann et al. 2004) empirically test this hypothesis. Using Apache HTTP Server Project, they empirically find that contributions are not correlated with higher earnings, although higher Apache Project rankings are, suggesting that the recognition from open source development is somehow priced in the software job market.

Another important area of research has been the impact of open-source software on the software industry. Many studies have found that the widespread adoption of open-source software has led to increased competition in the software market, lower prices for consumers, and increased innovation (August, Chen, and Zhu 2021). However, some studies have also raised concerns about the sustainability of open-source development, as the development of open-source software is often based on volunteer contributions and may not be financially viable in the long run (Nyman and Lindman 2013).

(Lerner and Tirole 2002) discuss that commercial software businesses may utilize one of two ways in reaction to the raging popularity of open-source. The first is to mimic some of the incentive elements of open-source procedures in a very closed-source setting. Another option is to combine open and closed source procedures to achieve the best of both worlds. One such approach is straightforward. It comprises commercially delivering complementary services and goods that the open-source community does not provide efficiently. This ‘reactive’ strategy is exemplified by Red Hat and VA Linux, for example.

The second method is to become more involved in the creation of open-source software. Companies can distribute existing proprietary code while also establishing some governance structure for the resulting open-source process. For example, Hewlett-Packard recently made available to the open-source community their Spectrum Object Model-Linker in order to assist the Linux community in porting Linux to Hewlett-RISC Packard’s architecture (Lerner and Tirole 2002). The reliance on unpaid development has evolved over time in the open-source ecosystem. Contributors who work for proprietary firms have done more of the work on open-source projects recently. Using a sample of 100 open source projects available on Sourceforge.com, (Lerner, Pathak, and Tirole 2006) shows that the share of corporate contributors is higher for larger open source projects.

(Glynn, Fitzgerald, and Exton 2005) examines the factors that influence the adoption of open-source software. Overall, their findings indicate that open-source has a large penetration with broad deployment in two business sectors – consultancy/software house and service/communication – and more limited deployment in the government/public sector. The existence of a coherent and planned IT infrastructure based on proprietary software, on the other hand, served to restrict open-source adoption. Finally, individual-relevant elements such as overall open-source ideology support and devoted personal open-source championing were revealed to be significant.

Additionally, research has explored the role of firms in open-source development. Many firms have embraced open-source development as a way to reduce development costs, improve the quality of their software, and engage with the developer community. However, some studies have also shown that the involvement of firms in open-source development can lead to the commoditization of open-source software, which can reduce the incentives for developers to contribute to open-source projects. There is also substantial evidence that open-source work may be a good stepping stone for securing access to venture capital (Lerner and Tirole 2002). Sun, Netscape, and Red Hat are examples in which the early founders signaled their talent firstly in the open-source world.

(Johnson 2000) proposes an open-source software theoretical model. In his model, individual user-programmers decide whether to devote their valuable time and effort to developing a software application that, if created, will become a public good. open-source code has the ability to enable the whole Internet community to pool its programming knowledge, creativity, and skill. On the other hand, a lack of profit motivation might lead to individual free-riding and, as a result, unrealized developments. He also studies the effect of modifying the population size of user/programmers seeking both finite and asymptotic conclusions. He shows that when applications have a "modular structure", whether or not the number of programs increases is determined by whether or not the developer base exceeds a certain size.

In general, both the volume and distribution of open-source development efforts are inefficient. (Johnson 2000) demonstrated that neither of the open-source and closed source paradigms corresponds to a restricted social optimum. While the open-source paradigm exhibits both inefficient levels and distribution of development, it benefits from the fact that individuals know their own preferences better than a firm does, as well as the fact that a larger skill set (that of the community of programmers as a whole) can be utilised. Closed source developers consider the aggregate enjoyment that consumers will derive from a programme, whereas free-riding open-source developers do not.

One interesting question in understanding the economic incentives behind the open-source movement is why some obviously useful software such as word processors does not get written in open-source, or if they get written, they still lack the quality in comparison to their closed source counterparts. In this regard, (Johnson 2000) contends that the open-source community has been able to build enormously complex software objects, such as operating systems, but has failed to build other useful applications, such as word processors of comparable quality to proprietary versions, because a natural correlation between human capital and production technology leads those most adept at building applications to build ones that are most useful in their own work.

=Notes=
<references />

=References=

<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-august2021competition" class="csl-entry">
August, Terrence, Wei Chen, and Kevin Zhu. 2021. <span>“Competition Among Proprietary and Open-Source Software Firms: The Role of Licensing in Strategic Contribution.”</span> ''Management Science'' 67 (5): 3041–66.
</div>
<div id="ref-comino2007planning" class="csl-entry">
Comino, Stefano, Fabio M Manenti, and Maria Laura Parisi. 2007. <span>“From Planning to Mature: On the Success of Open Source Projects.”</span> ''Research Policy'' 36 (10): 1575–86.
</div>
<div id="ref-ghosh2002free" class="csl-entry">
Ghosh, Rishab A, Ruediger Glott, Bernhard Krieger, and Gregorio Robles. 2002. <span>“Free/Libre and Open Source Software: Survey and Study.”</span>
</div>
<div id="ref-Glynn2005" class="csl-entry">
Glynn, E., B. Fitzgerald, and C. Exton. 2005. <span>“Commercial Adoption of Open Source Software: An Empirical Study,”</span> 10 pp.–. https://doi.org/10.1109/ISESE.2005.1541831.
</div>
<div id="ref-hann2004empirical" class="csl-entry">
Hann, I, Jeff Roberts, Sandra Slaughter, and Roy Fielding. 2004. <span>“An Empirical Analysis of Economic Returns to Open Source Participation.”</span> ''Unpublished Working Paper, Carnegie-Mellon University''.
</div>
<div id="ref-harhoff2003profiting" class="csl-entry">
Harhoff, Dietmar, Joachim Henkel, and Eric Von Hippel. 2003. <span>“Profiting from Voluntary Information Spillovers: How Users Benefit by Freely Revealing Their Innovations.”</span> ''Research Policy'' 32 (10): 1753–69.
</div>
<div id="ref-johnson2000some" class="csl-entry">
Johnson, Justin Pappas. 2000. <span>“Some Economics of Open Source Software.”</span> ''Unpublished Working Paper''.
</div>
<div id="ref-lakhani2003hackers" class="csl-entry">
Lakhani, Karim R, and Robert G Wolf. 2003. <span>“Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects.”</span> ''Open Source Software Projects (September 2003)''.
</div>
<div id="ref-lerner2006dynamics" class="csl-entry">
Lerner, Josh, Parag A Pathak, and Jean Tirole. 2006. <span>“The Dynamics of Open-Source Contributors.”</span> ''American Economic Review'' 96 (2): 114–18.
</div>
<div id="ref-lerner2002some" class="csl-entry">
Lerner, Josh, and Jean Tirole. 2002. <span>“Some Simple Economics of Open Source.”</span> ''The Journal of Industrial Economics'' 50 (2): 197–234.
</div>
<div id="ref-lerner2005scope" class="csl-entry">
———. 2005. <span>“The Scope of Open Source Licensing.”</span> ''Journal of Law, Economics, and Organization'' 21 (1): 20–56.
</div>
<div id="ref-nyman2013code" class="csl-entry">
Nyman, Linus, and Juho Lindman. 2013. <span>“Code Forking, Governance, and Sustainability in Open Source Software.”</span> ''Technology Innovation Management Review'' 3 (1).
</div>
</div>

Voting Mechanisms in DAO

2023-02-06T12:51:49Z

Aghaee: Created the entire page

{{DISPLAYTITLE: Voting Mechanisms in DAO}}

Written by [[Alireza_aghaee|Alireza Aghaee]].
<span id="introduction"></span>
=Introduction=

[[Decentralized_Autonomous_Organization|Decentralized autonomous organizations (DAOs)]] are a new type of organizational structure that is built on blockchain technology. DAOs operate without a central authority and are governed by a community of members who make decisions through a voting process. This process of decision-making is crucial to the functioning of DAOs, and various voting mechanisms have been developed to ensure that the process is fair and efficient. Voting mechanisms are used to make decisions within a DAO, and there are several different types of voting mechanisms that can be used, each with its own strengths and weaknesses. In this article, we will explore some of the most popular voting mechanisms used in DAOs and discuss the pros and cons of each.

One of the most basic voting mechanisms is called “one member, one vote," also known as the plurality vote, where each member has one vote, and they can cast that vote for one candidate or proposal. This system is simple and easy to understand, but it can also be vulnerable to manipulation and can lead to outcomes that are not representative of the community’s preferences. For example, it can lead to a situation where a candidate or proposal wins with only a small percentage of the vote, which can be unfair to the other candidates or proposals. Furthermore, it is discouraging investment since holding a larger stake in the enterprise does not lead to a larger control power. That’s why this mechanism is used mostly in non-economic decisions.

Another type of voting mechanism that has been developed is known as “weighted voting." Members with more tokens in the DAO have more voting power under this system. This can be used to give members who have made significant contributions to the organization more power. Because these systems encourage investment, almost all DAOs use a variant of the weighted voting mechanism. In weighted voting systems, each member has a voting power that is proportional to a function of his token holding. The properties of this function can have interesting economic implications on the voting outcomes that will be discussed in the next section. Depending on the function we use to weigh votes, it can result in a concentration of power in the hands of a small number of members, which can be problematic. So weighted voting systems can either promote or discourage decentralization.

The first type of weighted voting mechanism is the traditional voting system, also known as one token one vote. In this system, each member has one vote, and they can cast that vote for one candidate or proposal. This system is simple and easy to understand, but it can be easily manipulated by a small number of members who hold a large number of tokens. Additionally, it does not take into account the intensity of preferences, meaning that a member’s vote for one candidate or proposal is worth the same as any other member’s vote.

A more advanced weighted voting mechanism used in the DAO ecosystem is Quadratic Voting. This system gives each member a certain number of voting tokens, and they can choose to use those tokens in any way they want. The cost of voting tokens increases quadratically with the number of tokens being used, so the more tokens a member uses to support a candidate, the more expensive it becomes to use more tokens for that candidate. This creates an incentive for members to be strategic about how they use their tokens and it also helps to prevent a small number of members from dominating the voting process. A proper mathematical analysis of the voting mechanism will be followed in the [[#sec: mechanism|2]]. In that section, we also show how these voting mechanisms promote decentralization.

There are also other innovations in the control mechanisms in DAO that are not separate voting mechanisms, but instead, come on top of it. One of these approaches is called "liquid democracy". This is a voting system that allows members to delegate their voting power to other members. For a retail token holder whose primary job is not investment, it may not be economical to read and engage on every proposal. Moreover, properly understanding many proposals requires a solid understanding of blockchains or other technical aspects and it may be beyond the average investor’s expertise. Vote delegation promotes efficiency by allowing investors who are not willing or unable to vote, to delegate them to other stakeholders that have the expertise and willingness to strive for improvement. On the other hand, liquid democracy opens doors to exploitation. A proper discussion about these other innovations in the voting systems in the DAO ecosystem will be followed in section [[#sec: Innovations|3]].

<span id="sec: mechanism"></span>
=Voting Mechanism=

Almost all economic enterprises incorporate a method of weighted voting mechanisms, in which the voting power is proportional to a function of one’s stake. In traditional public firms, for example, a simple affine function is used for weighing, so that the voting power is proportional to the shares holding. In the DAO ecosystem, however, other voting schemes are becoming increasingly popular, predominately to promote decentralization. Promoting decentralization mainly stems from control rights that are no more proportional to cashflow rights.

One of the decentralization-promoting mechanisms in the DAO ecosystem is quadratic voting. In this system, each member has a certain number of voting tokens, and they can choose to use those tokens in any way they want. For example, a member could use all of their tokens to support one proposal, or they could spread their tokens out among several alternatives. Additionally, the cost of voting tokens increases quadratically with the number of tokens being used. So, the more tokens a member uses to support a proposal, the more expensive it becomes to use additional tokens for that proposal. This creates an incentive for members to be strategic about how they use their tokens, and it also helps to prevent a small number of members from dominating the voting process. Quadratic voting also allows members to express the intensity of their preferences in addition to the direction, by using more tokens to vote for the options they like more. We will see in the rest of this section that quadratic voting is just a special case of weighted voting schemes.<ref name="code2" />

To formalize the notation, and shed light on the difference between cashflow rights and control rights, imagine a DAO with S tokens outstanding, collectively held by <math display="inline">n</math> token holders. Holding a token in the DAO entitles the holders to two kinds of rights: cashflow rights and control rights. For example, if you own one UNI token from Uniswap, you are entitled to some remuneration from the transaction fees that the system collects from the people swapping their tokens. Moreover, this token entitles you to cast your vote when there is a proposal on the Uniswap system. The cash flow rights of shareholder <math display="inline">i</math>, denoted by <math display="inline">C_i</math>, are directly proportional to his percentage holding <math display="inline">S_i</math>. However, her control rights, <math display="inline">V_i</math>, is pinned down by a voting function <math display="inline">v: \mathcal{N} \bigcup \{0\} \to \mathcal{R}</math> such that

<math display="block">\begin{aligned}
C_i &= \frac{S_i}{\sum_{j = 1}^n S_j},[1] \\
V_i &= \frac{v(S_i)}{\sum_{j = 1}^n v(S_j)},[2]

\end{aligned}</math> Where <math display="inline">v</math> is an increasing function (larger stake, larger say) that passes through the origin (<math display="inline">v(0) = 0</math>, no token no say).

Firstly, observe that for any linear voting function <math display="inline">v(x) = ax</math>, the voting power <math display="inline">V</math> reduces down to the common one-token-one-vote system. Since for these specific voting functions, the denominator is fixed (<math display="inline">=aS</math>), we will have <math display="inline">\partial V_i/ \partial S_i = 1/S</math>. The proportionality of control right can be demonstrated mathematically as <math display="block">\frac{\partial V_i/ \partial S_i}{\partial C_i/ \partial S_i} = 1.</math>

This voting function is also insensitive to the number of shareholders and their diversity. Formally, <math display="inline">\partial C_i/ \partial n = 0</math>. A voting function that promotes decentralization has to have the following two conditions:

*It should value an additional vote from an additional mind more than the identical additional vote from a token holder who already has cast his votes. In other words, it has to exhibit a diminishing marginal increase in voting power with respect to token holdings. Formally, it should be the case that <math display="inline">V_{SS} < 0</math>. A direct consequence of this property would be that the marginal improvement in control rights is always smaller than the corresponding marginal improvement in cash flow rights, which is proportional to the reciprocal of total circulating tokens, <math display="inline">1/S</math>.
*It should decrease one’s voting power when the rest of the tokens are redistributed in a more decentralized fashion. Particularly, for a fixed sum of tokens, two tokenholders should collectively have more voting power than if one of them holds it all. Formally, <math display="inline">V(S_1) + V(S_2) > V(S_1 + S_2)</math>. Consequently, for a fixed total supply of tokens, voting power is generally decreasing in the number of token holders, <math display="inline">{\partial V_i}/{\partial n} < 0.</math>

The following theorem shows that the concavity of the voting function <math display="inline">v</math> is sufficient for voting power <math display="inline">V</math> to satisfy the two conditions above.

<div id="theory: 1" class="theorem">

'''Theorem 1'''. ''A concave voting function <math display="inline">v</math> is sufficient to promote decentralization. Formally, if voting function <math display="inline">v</math> is concave, then <math display="block">\begin{aligned}
V_{SS} &< 0, [3]\\
\frac{\partial V}{\partial n} &< 0, \text{ or equivalently, } V(S_i)|n = N < V(S_i)|n = N +1 . [4]

\end{aligned}</math>''

</div>
<div class="proof">

''Proof.'' Take the tokenholder <math display="inline">i</math> and fix other investors’ tokens as <math display="inline">\{S_j\}_{j \neq i}</math>. We have to show that <math display="inline">v''(S) < 0</math> results in <math display="inline">V_{SS} < 0</math>. According to Equation [2], <math display="block">V_{S}(S_i) = \frac{v'(S_i)(\sum_{j = 1}^n v(S_j)) - v(S_i)v'(S_i)}{(\sum_{j = 1}^n v(S_j))^2} = \frac{v'(S_i)(\sum_{j \neq i}^n v(S_j))}{(\sum_{j = 1}^n v(S_j))^2},</math> <math display="block">\implies V_{SS}(S_i) = (\sum_{j \neq i}^n v(S_j)) \frac{v''(S_i)(\sum_{j = 1}^n v(S_j))^2 - v'(S_i)(2((\sum_{j = 1}^n v(S_j)))v'(S_i)}{(\sum_{j = 1}^n v(S_j))^4} =</math> <math display="block">\implies V_{SS}(S_i) = (\sum_{j \neq i}^n v(S_j)) \frac{v''(S_i)(\sum_{j = 1}^n v(S_j)) - 2(v'(S_i))^2}{(\sum_{j = 1}^n v(S_j))^3}.</math> Equation [3] is directly proven here since the denominator in <math display="inline">V_{SS}</math> is always positive and the nominator is always negative. To prove the second part of the theorem, notice that we only have to concern the denominator in [2] since the nominator remains constant. To that end, we have to show that conditional on a fixed total number of tokens, the denominator grows as the number of holders grows. Assume the marginal token holder buys m tokens from the last token holder indexed by N, who originally had k tokens. Then we only have to prove that for integers <math display="inline">m<k</math>, <math display="block">v(k) < v(k- m) + v(m).
[5] </math>

Let <math display="inline">l = k-m > 0</math>, then the inequality reduces to <math display="inline">v(l+m) < v(l) + v(m)</math> for arbitrary integers <math display="inline">l</math> and <math display="inline">m</math> and the concave function <math display="inline">v</math> that passes through the origin. Fix <math display="inline">l</math> and Define <math display="inline">g(x) = v(l) + v(x) - v(l+x)</math>. Observe that <math display="inline">g(0) = 0</math>, and <math display="inline">g'(x) = v'(x) - v'(x+l)</math>. Since <math display="inline">v</math> is concave, <math display="inline">v'</math> is a decreasing function, and therefore <math display="inline">g'(x) \geq 0</math>. So the inequality in [5] is proven. ◻

</div>
Theorem [[#theory: 1|1]] states that with a concave voting function <math display="inline">v</math>, even with a fixed total number of tokens, an additional token holder reduces the power of other incumbent token holders with unchanged holding. Furthermore, the marginal control right associated with buying one more token is decreasing, while its marginal cashflow right is fixed.

With this analysis, we can see that quadratic voting is just a special case of weighted voting with a voting function defined as <math display="inline">v(S) = \sqrt(S)</math>. Note that in simple terms the cost of votes is proportional to the tokens one holds, and hence is proportional to the cashflow rights. So the quadratic cost of the vote translates in our setting to a square root voting function.

One of the most significant drawbacks of quadratic voting is that there are no measures in place to deal with instances of cheating, here mostly referred to as “Sybil attacks"<ref name="Miller">[https://aragon.org/how-to/set-your-dao-governance aragon.org/how-to/set-your-dao-governance]</ref>. In simple terms, the problem arises from the fact that if a token holder divides her holding into two wallets and votes the same proposal with both, she will receive more voting power in comparison to the case that she votes with only one wallet or identity. So Sybil attacks make use of fake identities to sway community-based decisions and tilt them in the attackers’ favor. In quadratic voting, the prevention of Sybil attacks is a crucial objective in order to ensure the system’s security. In order to successfully execute extensive quadratic voting, an anti-sybil identification program such as a KYC<ref group="footnotes">Know Your Customer</ref> is required.

<span id="sec: Innovations"></span>
=Other Innovations in Voting=

The previous chapter demonstrated that the majority of differences in voting mechanisms result from a twist in the voting function used to weigh votes. This section, on the other hand, discusses innovations that cannot be expressed as voting function twists. Much of this innovation is not truly novel, innovative, or unique to the DAO ecosystem. Nevertheless, they are relevant for this article because they are widely used in the DAO ecosystem.

<span id="liquid-democracy"></span>
==Liquid Democracy==

DAO members may be apathetic to proposals because they lack the mental bandwidth to comprehend the issues at hand. Particularly if the abovementioned persons consider participation in a DAO to be a part-time activity, they may be unwilling to dedicate the time required to make significant decisions. Delegated voting, often known as “liquid democracy," is one possible solution to such challenges. Delegated voting is simply the act of appointing someone to vote on your behalf. In this situation, the delegate is most likely a reputable member of the DAO or somebody with shown competence on the problems at hand.<ref name="limechain">[https://limechain.tech/blog/dao-voting-mechanisms-explained-2022-guide/ limechain.tech/blog/dao-voting-mechanisms-explained-2022-guide/]</ref>

When liquid democracy or vote delegation is possible, a DAO designates specialists to serve on an electorate with the authority to make decisions on behalf of DAO members. Members delegate their votes to trusted experts of their choice, who are better equipped to make sound decisions on the DAO’s future. This technique is more centralized than others, but DAO members have the authority to transfer delegation and assign new participants to the electorate at any moment. The advantages of this type of DAO voting mechanism are that smarter and more informed judgments in the best interests of the DAO are more likely. However, as in our world’s political democracies, bribery and corruption could be used to influence decision-making.<ref>[https://businesstechguides.co/dao-voting-mechanisms#heading-1-delegated-votingliquid-democracy businesstechguides.co/dao-voting-mechanisms#heading-1-delegated-votingliquid-democracy]</ref>

<span id="quorum-voting"></span>
==Quorum Voting==

The token-based quorum is a fundamental method for voting in a DAO (Decentralized Autonomous Organization). In order for a proposal to be approved, a specific number of members must take part in the voting process. If the required number of participants is reached, the proposal with the most votes will be accepted. However, if the threshold is not met, the proposal will not be passed.

Quorum voting introduces a minimum level of absolute majority on top of the relative majority in the voting mechanism. In the absence of any Quorum, only the number of voters who voted ‘for’ and ‘against’ a proposition matters in the result even if only a minority have participated in it. The described voting procedure is simple and straightforward, making it less expensive and less demanding. However, the approach permits a single DAO member to amass too much power and decide how to administer DAO funds. Furthermore, the method makes proposal passing a dangerous endeavor because it is a simple process that does not require much attention from other members.

While token-based quorum voting attempts to promote active participation and consider the views of the majority, it also poses certain challenges. One issue is determining the appropriate quorum. A higher number of required voters may lead to a large number of proposals failing due to low participation. On the other hand, a low quorum could lead to bad decisions and a deviation from the intended direction of the DAO.

Regardless of the quorum, encouraging members to take part in the voting process is a significant and costly challenge for most DAOs. Some members may choose to remain inactive in order to prevent a proposal from passing or may not be interested in the decision-making process. Additionally, the token-based model ties voting power to financial stability, allowing members with more tokens to influence and bribe others, potentially turning the voting process into a political activity.

<span id="rage-quitting"></span>
==Rage Quitting==

One example of how sponsorship has been used to boost security in the DAO voting process is the rage quitting voting method. This approach may be able to provide a solution to the relative majority of challenges. Members must sponsor a proposal before it can be voted on. If the plan is approved by a majority, it enters a grace period during which voters can reconsider and withdraw their support for the vote or the DAO. If the idea receives insufficient support after this step, it is dropped.<ref name="limechain" />

The key advantage of this voting process is that it prevents majority voters from acquiring an advantage over minority voters. However, the voting process is exceedingly lengthy, which may make it unsuitable for all DAOs.

<span id="conviction-voting"></span>
==conviction voting==

Conviction voting is another novel DAO voting technique. It is based on the community’s aggregated preference and uses time as a utility. Members can vote on various in-progress initiatives, and the longer their vote remains unchanged, the more powerful the vote gets. The voting utility’s growth slows progressively as it approaches a predetermined maximum. Voters can modify their vote at any time, in which case the voting utility of their previous vote will drop over time.

This approach is an excellent way to demonstrate how interested voters are in a proposal and how their opinions may be swayed by internal or external forces. A majority vote is not required to advance a proposal; rather, the beliefs of the community are at the heart of decision-making. It’s also an effective approach to avoid new DAO members having too much influence on the DAO protocol.

On the disadvantage, the method takes a long time to reach a conclusion, making it unsuitable for DAOs that demand quick judgments. If more DAOs implement conviction voting, it is likely that it will be used in conjunction with another, faster process.

<span id="multi-sig-voting"></span>
==Multi-sig voting==

Multisig voting is a DAO voting technique that aims to create a balance in an organization between central authority and decentralization. In this concept, DAO members can signal on suggestions, while centralized and predefined committee votes on the proposal. This model is one of the fastest voting processes and maybe suited in circumstances where immediate action is critical to the DAO’s existence. However, there is a risk of the centralized authority abusing its position and voting in a way that is no longer in the best interests of the majority of the DAO community. <ref name="code2" />

<span id="holographic-consensus"></span>
==Holographic Consensus==

One goal in the DAO ecosystem is to raise the global adoption of the DAO concept, which is the theoretical point of reference denoting the ultimate decision that would be made under ideal conditions while devoting the fewest possible resources to achieve this goal. There are two difficulties that need to be addressed in this situation: the scalability of the voting process (frequency), and the degree to which the outcome represents the collective opinion of the DAO. The term "scalability-resilience paradox" was used to describe this situation. The scalability of the voting process might be negatively impacted by the measures that are used to ensure high levels of voter participation. On the other hand, if there is insufficient involvement, this could lead to unfavorable suggestions being approved.<ref name="code2">[https://www.code2.io/blog/web3-dao-voting-mechanisms/ www.code2.io/blog/web3-dao-voting-mechanisms/]</ref>

In order to achieve maximum scalability, it is necessary for the global opinion of the DAO to be attained with the least amount of voting power being mobilized as possible. Holographic consensus, or HC for short, is a strategy that seeks to alleviate this problem by having highly representative decisions made at the local level. A prediction market is used to determine the outcomes of these decisions. While the DAO is the ultimate wager on each proposal, individual DAO members bet money on those ideas they believe will emerge victorious. The backers of the winning proposals receive monetary rewards, and the money that the DAO gives them is categorized as some form of administrative expense to ensure that the DAO operates well. HC is quite difficult and not very democratic because the cost of involvement can be high for some people, depending on their location. Despite the fact that it improves scalability and robustness, it is quite complicated.

<span id="conclusion"></span>
=Conclusion=

Voting mechanisms in DAOs are a crucial aspect of decision-making within these organizations. Weighted voting systems, such as one token one vote, can give members with more tokens in the DAO more voting power, but can also result in a concentration of power in the hands of a small number of members. Quadratic Voting is a more advanced system that promotes decentralization and prevents a small number of members from dominating the voting process. Furthermore, innovations such as liquid democracy can be used in conjunction with voting mechanisms to promote efficiency and decentralization. It is important for DAOs to carefully consider the pros and cons of each voting mechanism and to choose the one that best fits their specific needs for their specific use case.

==Notes==
<references group="footnotes" />

==References==
<references />

VOTING MECHANISMS IN DAO

2023-02-01T12:41:30Z

Aghaee: Created page with "= Voting Mechanisms in DAO = Written by Alireza Aghaee. <span id="introduction"></span> = Introduction = Decentralized autonomous organizations (DAOs) are..."

= Voting Mechanisms in DAO =
Written by [[Alireza aghaee|Alireza Aghaee]].
<span id="introduction"></span>
= Introduction =

Decentralized autonomous organizations (DAOs) are a new type of organizational structure that is built on blockchain technology. DAOs operate without a central authority and are governed by a community of members who make decisions through a voting process. This process of decision-making is crucial to the functioning of DAOs, and various voting mechanisms have been developed to ensure that the process is fair and efficient. Voting mechanisms are used to make decisions within a DAO, and there are several different types of voting mechanisms that can be used, each with its own strengths and weaknesses. In this article, we will explore some of the most popular voting mechanisms used in DAOs and discuss the pros and cons of each.

One of the most basic voting mechanisms is called “one member, one vote," also known as the plurality vote, where each member has one vote, and they can cast that vote for one candidate or proposal. This system is simple and easy to understand, but it can also be vulnerable to manipulation and can lead to outcomes that are not representative of the community’s preferences. For example, it can lead to a situation where a candidate or proposal wins with only a small percentage of the vote, which can be unfair to the other candidates or proposals. Furthermore, it is discouraging investment since holding a larger stake in the enterprise does not lead to a larger control power. That’s why this mechanism is used mostly in non-economic decisions.

Another type of voting mechanism that has been developed is known as “weighted voting." Members with more tokens in the DAO have more voting power under this system. This can be used to give members who have made significant contributions to the organization more power. Because these systems encourage investment, almost all DAOs use a variant of the weighted voting mechanism. In weighted voting systems, each member has a voting power that is proportional to a function of his token holding. The properties of this function can have interesting economic implications on the voting outcomes that will be discussed in the next section. Depending on the function we use to weigh votes, it can result in a concentration of power in the hands of a small number of members, which can be problematic. So weighted voting systems can either promote or discourage decentralization.

The first type of weighted voting mechanism is the traditional voting system, also known as one token one vote. In this system, each member has one vote, and they can cast that vote for one candidate or proposal. This system is simple and easy to understand, but it can be easily manipulated by a small number of members who hold a large number of tokens. Additionally, it does not take into account the intensity of preferences, meaning that a member’s vote for one candidate or proposal is worth the same as any other member’s vote.

A more advanced weighted voting mechanism used in the DAO ecosystem is Quadratic Voting. This system gives each member a certain number of voting tokens, and they can choose to use those tokens in any way they want. The cost of voting tokens increases quadratically with the number of tokens being used, so the more tokens a member uses to support a candidate, the more expensive it becomes to use more tokens for that candidate. This creates an incentive for members to be strategic about how they use their tokens and it also helps to prevent a small number of members from dominating the voting process. A proper mathematical analysis of the voting mechanism will be followed in the [[#sec: mechanism|2]]. In that section, we also show how these voting mechanisms promote decentralization.

There are also other innovations in the control mechanisms in DAO that are not separate voting mechanisms, but instead, come on top of it. One of these approaches is called "liquid democracy". This is a voting system that allows members to delegate their voting power to other members. For a retail token holder whose primary job is not investment, it may not be economical to read and engage on every proposal. Moreover, properly understanding many proposals requires a solid understanding of blockchains or other technical aspects and it may be beyond the average investor’s expertise. Vote delegation promotes efficiency by allowing investors who are not willing or unable to vote, to delegate them to other stakeholders that have the expertise and willingness to strive for improvement. On the other hand, liquid democracy opens doors to exploitation. A proper discussion about these other innovations in the voting systems in the DAO ecosystem will be followed in section [[#sec: Innovations|3]].

<span id="sec: mechanism"></span>
= Voting Mechanism =

Almost all economic enterprises incorporate a method of weighted voting mechanisms, in which the voting power is proportional to a function of one’s stake. In traditional public firms, for example, a simple affine function is used for weighing, so that the voting power is proportional to the shares holding. In the DAO ecosystem, however, other voting schemes are becoming increasingly popular, predominately to promote decentralization. Promoting decentralization mainly stems from control rights that are no more proportional to cashflow rights.

One of the decentralization-promoting mechanisms in the DAO ecosystem is quadratic voting. In this system, each member has a certain number of voting tokens, and they can choose to use those tokens in any way they want. For example, a member could use all of their tokens to support one proposal, or they could spread their tokens out among several alternatives. Additionally, the cost of voting tokens increases quadratically with the number of tokens being used. So, the more tokens a member uses to support a proposal, the more expensive it becomes to use additional tokens for that proposal. This creates an incentive for members to be strategic about how they use their tokens, and it also helps to prevent a small number of members from dominating the voting process. Quadratic voting also allows members to express the intensity of their preferences in addition to the direction, by using more tokens to vote for the options they like more. We will see in the rest of this section that quadratic voting is just a special case of weighted voting schemes.

To formalize the notation, and shed light on the difference between cashflow rights and control rights, imagine a DAO with S tokens outstanding, collectively held by <math display="inline">n</math> token holders. Holding a token in the DAO entitles the holders to two kinds of rights: cashflow rights and control rights. For example, if you own one UNI token from Uniswap, you are entitled to some remuneration from the transaction fees that the system collects from the people swapping their tokens. Moreover, this token entitles you to cast your vote when there is a proposal on the Uniswap system. The cash flow rights of shareholder <math display="inline">i</math>, denoted by <math display="inline">C_i</math>, are directly proportional to his percentage holding <math display="inline">S_i</math>. However, her control rights, <math display="inline">V_i</math>, is pinned down by a voting function <math display="inline">v: \mathcal{N} \bigcup \{0\} \to \mathcal{R}</math> such that

<math display="block">\begin{aligned}
C_i &= \frac{S_i}{\sum_{j = 1}^n S_j}, \\
V_i &= \frac{v(S_i)}{\sum_{j = 1}^n v(S_j)}, [1]

\end{aligned}</math> Where <math display="inline">v</math> is an increasing function (larger stake, larger say) that passes through the origin (<math display="inline">v(0) = 0</math>, no token no say).

Firstly, observe that for any linear voting function <math display="inline">v(x) = ax</math>, the voting power <math display="inline">V</math> reduces down to the common one-token-one-vote system. Since for these specific voting functions, the denominator is fixed (<math display="inline">=aS</math>), we will have <math display="inline">\partial V_i/ \partial S_i = 1/S</math>. The proportionality of control right can be demonstrated mathematically as <math display="block">\frac{\partial V_i/ \partial S_i}{\partial C_i/ \partial S_i} = 1.</math>

This voting function is also insensitive to the number of shareholders and their diversity. Formally, <math display="inline">\partial C_i/ \partial n = 0</math>. A voting function that promoted decentralization has to have the following two conditions:

* It should value an additional vote from an additional mind more than the identical additional vote from a token holder who already has cast his votes. In other words, it has to exhibit a diminishing marginal increase in voting power with respect to token holdings. Formally, it should be the case that <math display="inline">V_{SS} < 0</math>. A direct consequence of this property would be that the marginal improvement in control rights is always smaller than the corresponding marginal improvement in cash flow rights, which is proportional to the reciprocal of total circulating tokens, <math display="inline">1/S</math>.
* It should decrease one’s voting power when the rest of the tokens are redistributed in a more decentralized fashion. Particularly, for a fixed sum of tokens, two tokenholders should collectively have more voting power than if one of them holds it all. Formally, <math display="inline">V(S_1) + V(S_2) > V(S_1 + S_2)</math>. Consequently, for a fixed total supply of tokens, voting power is generally decreasing in the number of token holders, <math display="inline">{\partial V_i}/{\partial n} < 0.</math>

The following theorem shows that the concavity of the voting function <math display="inline">v</math> is sufficient for voting power <math display="inline">V</math> to satisfy the two conditions above.

<div id="theory: 1" class="theorem">

'''Theorem 1'''. ''A concave voting function <math display="inline">v</math> is sufficient to promote decentralization. Formally, if voting function <math display="inline">v</math> is concave, then <math display="block">\begin{aligned}
V_{SS} &< 0, [2] \\
\frac{\partial V}{\partial n} &< 0, \text{ or equivalently, } V(S_i)|n = N < V(S_i)|n = N +1

\end{aligned}</math>''

</div>
<div class="proof">

''Proof.'' Take the tokenholder <math display="inline">i</math> and fix other investors’ tokens as <math display="inline">\{S_j\}_{j \neq i}</math>. We have to show that <math display="inline">v''(S) < 0</math> results in <math display="inline">V_{SS} < 0</math>. According to Equation [1], <math display="block">V_{S}(S_i) = \frac{v'(S_i)(\sum_{j = 1}^n v(S_j)) - v(S_i)v'(S_i)}{(\sum_{j = 1}^n v(S_j))^2} = \frac{v'(S_i)(\sum_{j \neq i}^n v(S_j))}{(\sum_{j = 1}^n v(S_j))^2},</math> <math display="block">\implies V_{SS}(S_i) = (\sum_{j \neq i}^n v(S_j)) \frac{v''(S_i)(\sum_{j = 1}^n v(S_j))^2 - v'(S_i)(2((\sum_{j = 1}^n v(S_j)))v'(S_i)}{(\sum_{j = 1}^n v(S_j))^4} =</math> <math display="block">\implies V_{SS}(S_i) = (\sum_{j \neq i}^n v(S_j)) \frac{v''(S_i)(\sum_{j = 1}^n v(S_j)) - 2(v'(S_i))^2}{(\sum_{j = 1}^n v(S_j))^3}.</math> Equation [2] is directly proven here since the denominator in <math display="inline">V_{SS}</math> is always positive and the nominator is always negative. To prove the second part of the theorem, notice that we only have to concern the denominator in [1] since the nominator remains constant. To that end, we have to show that conditional on a fixed total number of tokens, the denominator grows as the number of holders grows. Assume the marginal token holder buys m tokens from the last token holder indexed by N, who originally had k tokens. Then we only have to prove that for integers <math display="inline">m<k</math>, <math display="block">v(k) < v(k- m) + v(m).[3]</math>

Let <math display="inline">l = k-m > 0</math>, then the inequality reduces to <math display="inline">v(l+m) < v(l) + v(m)</math> for arbitrary integers <math display="inline">l</math> and <math display="inline">m</math> and the concave function <math display="inline">v</math> that passes through the origin. Fix <math display="inline">l</math> and Define <math display="inline">g(x) = v(l) + v(x) - v(l+x)</math>. Observe that <math display="inline">g(0) = 0</math>, and <math display="inline">g'(x) = v'(x) - v'(x+l)</math>. Since <math display="inline">v</math> is concave, <math display="inline">v'</math> is a decreasing function, and therefore <math display="inline">g'(x) \geq 0</math>. So the inequality in [3] is proven. ◻

</div>
Theorem [[#theory: 1|1]] states that with a concave voting function <math display="inline">v</math>, even with a fixed total number of tokens, an additional token holder reduces the power of other incumbent token holders with unchanged holding. Furthermore, the marginal control right associated with buying one more token is decreasing, while its marginal cashflow right is fixed.

With this analysis, we can see that quadratic voting is just a special case of weighted voting with a voting function defined as <math display="inline">v(S) = \sqrt(S)</math>. Note that in simple terms the cost of votes is proportional to the tokens one holds, and hence is proportional to the cashflow rights. So the quadratic cost of the vote translates in our setting to a square root voting function. Quadratic voting has other additional features on top of what is discussed here. Lalley, S. P., & Weyl, E. G. (2016)<ref name="Lalley & Weyl" /> show that if individuals take the chance of a marginal vote being pivotal as given, like a market price, quadratic voting is the unique pricing rule that is always efficient.

One of the most significant drawbacks of quadratic voting is that there are no measures in place to deal with instances of cheating, here mostly referred to as “Sybil attacks". In simple terms, the problem arises from the fact that if a token holder divides her holding into two wallets and votes the same proposal with both, she will receive more voting power in comparison to the case that she votes with only one wallet or identity. So Sybil attacks make use of fake identities to sway community-based decisions and tilt them in the attackers’ favor. In quadratic voting, the prevention of Sybil attacks is a crucial objective in order to ensure the system’s security. In order to successfully execute extensive quadratic voting, an anti-sybil identification program such as a KYC<ref group="footnotes">Know Your Customer</ref> is required.

<span id="sec: Innovations"></span>
= Other Innovations in Voting =

The previous chapter demonstrated that the majority of differences in voting mechanisms result from a twist in the voting function used to weigh votes. This section, on the other hand, discusses innovations that cannot be expressed as voting function twists. Much of this innovation is not truly novel, innovative, or unique to the DAO ecosystem. Nevertheless, they are relevant for this article because they are widely used in the DAO ecosystem.

<span id="liquid-democracy"></span>
== Liquid Democracy ==

DAO members may be apathetic to proposals because they lack the mental bandwidth to comprehend the issues at hand. Particularly if the abovementioned persons consider participation in a DAO to be a part-time activity, they may be unwilling to dedicate the time required to make significant decisions. Delegated voting, often known as “liquid democracy," is one possible solution to such challenges. Delegated voting is simply the act of appointing someone to vote on your behalf. In this situation, the delegate is most likely a reputable member of the DAO or somebody with shown competence on the problems at hand.<ref name="Grace"></ref>

When liquid democracy or vote delegation is possible, a DAO designates specialists to serve on an electorate with the authority to make decisions on behalf of DAO members. Members delegate their votes to trusted experts of their choice, who are better equipped to make sound decisions on the DAO’s future. This technique is more centralized than others, but DAO members have the authority to transfer delegation and assign new participants to the electorate at any moment. The advantages of this type of DAO voting mechanism are that smarter and more informed judgments in the best interests of the DAO are more likely. However, as in our world’s political democracies, bribery and corruption could be used to influence decision-making.<ref name="Code3"></ref>

<span id="quorum-voting"></span>
== Quorum Voting ==

The token-based quorum is a fundamental method for voting in a DAO (Decentralized Autonomous Organization). In order for a proposal to be approved, a specific number of members must take part in the voting process. If the required number of participants is reached, the proposal with the most votes will be accepted. However, if the threshold is not met, the proposal will not be passed.<ref name="Grace"></ref>

Quorum voting introduces a minimum level of absolute majority on top of the relative majority in the voting mechanism. In absence of any Quorum, only the number of voters who voted ‘for’ and ‘against’ a proposition matters in the result even if only a minority have participated in it. The described voting procedure is simple and straightforward, making it less expensive and less demanding. However, the approach permits a single DAO member to amass too much power and decide how to administer DAO funds. Furthermore, the method makes proposal passing a dangerous endeavor because it is a simple process that does not require much attention from other members.

While token-based quorum voting attempts to promote active participation and consider the views of the majority, it also poses certain challenges. One issue is determining the appropriate quorum. A higher number of required voters may lead to a large number of proposals failing due to low participation. On the other hand, a low quorum could lead to bad decisions and a deviation from the intended direction of the DAO.

Regardless of the quorum, encouraging members to take part in the voting process is a significant and costly challenge for most DAOs. Some members may choose to remain inactive in order to prevent a proposal from passing or may not be interested in the decision-making process. Additionally, the token-based model ties voting power to financial stability, allowing members with more tokens to influence and bribe others, potentially turning the voting process into a political activity.

<span id="rage-quitting"></span>
== Rage Quitting ==

One example of how sponsorship has been used to boost security in the DAO voting process is the rage quitting voting method. This approach may be able to provide a solution to the relative majority of challenges. Members must sponsor a proposal before it can be voted on. If the plan is approved by a majority, it enters a grace period during which voters can reconsider and withdraw their support for the vote or the DAO. If the idea receives insufficient support after this step, it is dropped.

The key advantage of this voting process is that it prevents majority voters from acquiring an advantage over minority voters. However, the voting process is exceedingly lengthy, which may make it unsuitable for all DAOs.

<span id="conviction-voting"></span>
== conviction voting ==

Conviction voting is another novel DAO voting technique. It is based on the community’s aggregated preference and uses time as a utility. Members can vote on various in-progress initiatives, and the longer their vote remains unchanged, the more powerful the vote gets. The voting utility’s growth slows progressively as it approaches a predetermined maximum. Voters can modify their vote at any time, in which case the voting utility of their previous vote will drop over time.

This approach is an excellent way to demonstrate how interested voters are in a proposal and how their opinions may be swayed by internal or external forces. A majority vote is not required to advance a proposal; rather, the beliefs of the community are at the heart of decision-making. It’s also an effective approach to avoid new DAO members having too much influence on the DAO protocol.<ref name="Code2"></ref>

On the disadvantage, the method takes a long time to reach a conclusion, making it unsuitable for DAOs that demand quick judgments. If more DAOs implement conviction voting, it is likely that it will be used in conjunction with another, faster process.

<span id="multi-sig-voting"></span>
== Multi-sig voting ==

Multisig voting is a DAO voting technique that aims to create a balance in an organization between central authority and decentralization. In this concept, DAO members can signal on suggestions, while centralized and predefined committee votes on the proposal. This model is one of the fastest voting processes and maybe suited in circumstances where immediate action is critical to the DAO’s existence. However, there is a risk of the centralized authority abusing its position and voting in a way that is no longer in the best interests of the majority of the DAO community.

<span id="holographic-consensus"></span>
== Holographic Consensus ==

One goal in the DAO ecosystem is to raise the global adoption of the DAO concept, which is the theoretical point of reference denoting the ultimate decision that would be made under ideal conditions while devoting the fewest possible resources to achieve this goal. There are two difficulties that need to be addressed in this situation: the scalability of the voting process (frequency), and the degree to which the outcome represents the collective opinion of the DAO. The term "scalability-resilience paradox" was used to describe this situation. The scalability of the voting process might be negatively impacted by the measures that are used to ensure high levels of voter participation. On the other hand, if there is insufficient involvement, this could lead to unfavorable suggestions being approved.<ref name="Code2"></ref>

In order to achieve maximum scalability, it is necessary for the global opinion of the DAO to be attained with the least amount of voting power being mobilized as possible. Holographic consensus, or HC for short, is a strategy that seeks to alleviate this problem by having highly representative decisions made at the local level. A prediction market is used to determine the outcomes of these decisions. While the DAO is the ultimate wager on each proposal, individual DAO members bet money on those ideas they believe will emerge victorious. The backers of the winning proposals receive monetary rewards, and the money that the DAO gives them is categorized as some form of administrative expense to ensure that the DAO operates well. HC is quite difficult and not very democratic because the cost of involvement can be high for some people, depending on their location. Despite the fact that it improves scalability and robustness, it is quite complicated.

<span id="conclusion"></span>
= Conclusion =

Voting mechanisms in DAOs are a crucial aspect of decision-making within these organizations. Weighted voting systems, such as one token one vote, can give members with more tokens in the DAO more voting power, but can also result in a concentration of power in the hands of a small number of members. Quadratic Voting is a more advanced system that promotes decentralization and prevents a small number of members from dominating the voting process. Furthermore, innovations such as liquid democracy can be used in conjunction with voting mechanisms to promote efficiency and decentralization. It is important for DAOs to carefully consider the pros and cons of each voting mechanism and to choose the one that best fits their specific needs for their specific use case.

= Notes =
<references group="footnotes" />

= References =
<references>
<ref name="Lalley & Weyl">Lalley, S. P., & Weyl, E. G. (2016). Quadratic voting. Available at SSRN.</ref>
<ref name="Grace">https://limechain.tech/blog/dao-voting-mechanisms-explained-2022-guide/</ref>
<ref name="Code2">https://www.code2.io/blog/web3-dao-voting-mechanisms/</ref>
<ref name="Code3">https://businesstechguides.co/dao-voting-mechanisms#heading-1-delegated-votingliquid-democracy</ref>
</references>

Decentralized Autonomous Organization

2022-08-10T20:38:56Z

Aghaee: Created the entire page

=Decentralized Autonomous Organizations<ref>Written by Alireza Aghaee Shahrbabaki, PhD student at Bocconi University, Milan.</ref>=
=What is DAO?=
A DAO is a company or organization that automates its operations in accordance with agreed-upon rules and principles stated in programming codes. The DAOs’ rules and transactions are maintained on a blockchain, which promotes transparency among stakeholders, while computer code controls the implementation of the DAOs’ regulations.

Tokens or NFTs that give voting capabilities are used to organize DAO governance. People who have verified possession of these governance tokens in a cryptocurrency wallet are qualified to participate in the DAO’s operation and voting. Similar to any other digital asset, DAO membership can be traded. Governance is carried out by a series of proposals that members vote on through the blockchain, and having more governance tokens typically equates to having more voting power. Members’ contributions to a DAO’s organizational objectives may occasionally be recorded and internally paid.

While it is not hard to define DAOs in technical terms or from an economic point of view, such definition from a legal or social standpoint is proven to be challenging. Like most other new technologies, legislators and governments wait for a proper establishment of these entities before they take a firm position towards them, whether friendly or hostile. The lack of legal jurisdiction positions towards DAOs worldwide has resulted in attempts to fit DAOs in the existing legal formats when applicable, such as associations, foundations, or general partnerships. Hassan and Filippi (2021)<ref name="Hassan2021">Hassan, S. and Filippi, P. D. (2021). Decentralized autonomous organization. Internet Policy Review, 10(2).</ref> lists and discusses the different definitions of DAOs used both in academics and industry. They assert that most academic definitions include the following distinctive characteristics:
*DAOs enable people to coordinate and self-govern online. Although no minimum group size is specified, the term “organization" is commonly understood to refer to a group of people working together toward a shared objective rather than a legally recognized organization.
*deployment on a blockchain, arguably always a public blockchain.
*The smart contract code for a DAO specifies the rules for human interaction — however, it is unclear to what degree other governance mechanisms may affect or override such code.
*Self-execution of smart contracts, independent of the will of involved parties.
*independence from central control.
*transparency, cryptographic security, decentralization.
DAO may be used to do a wide variety of purposes. A DAO may be used to construct a virtual entity that acts as a crowd-funding platform, a ride-sharing platform, a completely automated firm, or a fully automated decision-making mechanism, among other things. It is critical to recognize that a DAO is not a specific business model or organization but rather a term that may relate to a broad range of things.

Classic agency theory depicts an ideal scenario in which a single “entrepreneur-manager" makes optimum choices and then implements them, functioning as both principle and agent for his benefit. If there is a separation between ownership and management, agents will pursue their own interests independent from the interests of the principals in the absence of proper incentives (Fama and Jensen, (1983)<ref name="famajensen">Fama, E. F. and Jensen, M. C. (1983). Separation of ownership and control. The Journal of Law and Economics, 26(2):301–325.</ref>; Shapiro, (2000)<ref name="shapiro">Shapiro, S. P. (2005). Agency theory. Annual Review of Sociology, 31(1):263–284.</ref>). The DAO suggests a “next-best-case" of Agency Theory, in which numerous entrepreneur-managers have no need to trust one another but can nonetheless act as a single-minded entrepreneur-manager (Morrison et al., (2020)<ref>Morrison, R., Mazey, N. C. H. L., and Wingreen, S. C. (2020). The dao controversy: The case for a new species of corporate governance? Frontiers in Blockchain, 3.</ref>). Because the blockchain became the vehicle for managing trust, players don’t have to trust anybody else except the system.

The Ethereum blockchain gave birth to the Ethereum virtual machine (EVM), which can be used to run various decentralized applications (dAPPs) on top of it. These dApps incorporate advanced smart contracts, which might be produced by developers outside the Ethereum core team, extending the technology’s reach. In this sense, a DAO can be regarded as a complex dynamic collection of different but interwoven smart contracts. Since the introduction of Ethereum, a seemingly unending stream of dApps have been created, many of which have their own native unit, often referred to as cryptotokens or appcoins (Burniske and Tatar (2018))<ref>Burniske, C. and Tatar, J. (2018). Cryptoassets: The innovative investor’s guide to bitcoin
and beyond. McGraw-Hill Education New York.</ref>). While Ethereum blockchain seems to be the most popular platform for dApps and hence DAOs, many other DAOs work on different blockchains. For instance, Rootstock, a host for smart contracts, runs on the Bitcoin blockchain.

Speaking about DAOs, one should be cautious to avoid mistaking DAO as an umbrella term to refer to all Decentralized Autonomous Organizations with “The DAO," which was the first DAO established in 2016 on the Ethereum blockchain. The DAO has an informative story behind it that led to the creation of the Ethereum classic. In a nutshell, Only two months after its introduction, a hacker exploited a flaw in the code of The DAO and stole 3.6 million ether, which was valued at roughly $70 million at the time.

Despite the early mishap, DAOs have continued to proliferate. Some DAOs are in charge of stable coins like DAI (managed by MakerDAO), which is tied to the US dollar. Owners of tokens vote on how interest rate mechanisms function that helps keep the price stable and fine-tune supply and demand. Some others such as SpiceDAO buy tangible assets. Another group of DAOs such as PleasrDAO, speculate on cryptocurrencies and non-fungible tokens (NFTs). Many DAOs seem to have been created to handle wagers in a virtual casino like Augur. The following table lists a few other famous DAOs.

+Some DAO instances
{| class="wikitable"
!Name!!Token!!Use cases!!Network!!Launch!!Status
|-
|The DAO||DAO||Venture capital||Ethereum||April 2016||<nowiki>\begin{tabular}[c]{@{}c@{}}Defunct late 2016</nowiki>
|-
|Dash||DASH||Anonymous transactions||Dash||May 2015||Operational
|-
|Augur||REP||Prediction market, betting||Ethereum||July 2018||Operational
|-
|Steem||STEEM||Data distribution, Social media||Steem||March 2016||Operational
|-
|Uniswap||UNI||Decentralized Exchange||Ethereum||November 2018||Operational
|-
|Maker||MKR||Stable Coin, DeFi||Ethereum||December 2017||Operational
|}

<references />

Alireza aghaee

2022-07-07T09:21:50Z

Aghaee: User Introduction

Alireza Aghaee Shahrbabaki

Ph.D. Student in Economics and Finance

Bocconi University

Via Roentgen, 1, 2.b2 05

20136, Milan, Italy

[https://drive.google.com/file/d/1w0NHiXFeZAIMbiy0rYO8yHUSaC9WyDq1/view CV (PDF)]

[https://sites.google.com/view/alirezaaghaee/home?authuser=0 Personal Webpage]