“The Tao is the secret of all things, the treasure of good people, and the protection of bad people”
– “Lao Tzu” Chapter 62
“Decentralized Society: Finding the Soul of Web3” is the latest paper by Vitalik et al. This paper describes how to achieve a richer and more diverse ecosystem through soul-bound tokens, namely “Decentralized Society (DeSoc) and Critical Decomposable Property Rights and Enhanced Governance Mechanisms in a Decentralized Society”. Therefore, the DAOrayaki community translated this article and organized multiple Podcasts for in-depth analysis. Because the full text is too long, we will distribute it in three parts: first, middle and second.
For the last article, please check: DAOrayaki | Vitalik and other latest papers: Decentralized Society – Finding the Soul of Web3 (Part 1)
4.8 From private and public goods to composite network goods
More broadly, SBTs allow us to effectively represent and manage any asset or commodity that is somewhere between completely private and completely public. In reality, even goods consumed by individuals have positive spillover effects, just as they enable consumers to better contribute to households or communities, and even the most globally available public goods (e.g. climate) are unavoidable Land is more useful to some people than others (eg Seychelles to Siberia). Likewise, human motivations are rarely completely selfish or completely altruistic, and there will be many pre-existing modes of cooperation that are less in some communities and more in others.
However, today’s mechanism designs assume atomic, selfish agents with no pre-existing cooperation, which often leaves the mechanism vulnerable to innocent over-coordination . Worst-case scenario is the deliberate collusion of already cooperating groups. Consequently, even the best public financing models, including quadratic financing (QF), cannot scale.QF encourages coordination by providing a reduced reward for the concentrated action of the minority, while providing an increased reward for the collective action of the majority. 10 people split the $1 for a $99 match, resulting in a total of $100, while the $10 donated by one person is not matched. Mathematically, this is done with funds that match the square of the sum of the square roots of individual contributions (which we elaborate further on in the appendix). However, even a weak partnership between large groups like (e.g. most citizens of China) (eg donating $1 to a cause) dominates the system and absorbs all its matching funds because QF has The number of unique contributors gives a premium. Just like now, QF does not discount the coordination between related special interests, which will not only cause the QF mechanism to malfunction, but also the relevant special interests will be rewarded.
But rather than viewing pre-existing cooperation as a mistake we should “rewrite”, the key is to acknowledge that it actually reflects a part of cooperation that we should capitalize on and compensate for. After all, we are in a business that encourages collaboration. The trick is to make quadratic mechanisms work with pre-existing cooperative networks, correcting their biases and tendency to over-coordinate. SBTs provide a natural way for us to tip the balance in favor of cooperation across differences. As Nobel laureate Eleanor Ostrom has emphasized, the question is not about coordinating public goods per se, but how to help communities of imperfectly cooperative but socially connected individuals overcome them social differences and scale coordination across wider networks.
If SBTs represent community membership relationships that reflect Souls/Soul cluster bias, then favoring collaboration across differences simply means discounting collaboration rewards for similar or related Souls/Soul clusters that are driven by their shared SBTs measure. The assumption is that consensus among different allies is better at creating composite goods that apply to a wider network, while consensus among similar allies is more likely to serve only over-coordination (or collusion) of narrower interests. )s product.
By revealing membership between different Souls/Soul Sets, SBTs allow us to discount pre-existing collaborations and expand quadratically in emerging networks, empowering composite items to a wider group of interests, And by the consent of diverse members, not by innocent over-coordinated (or deliberately complicit) special interest groups that give items narrow meanings. The exact formula for the “best” correlation discount depends on the model details and has not been studied, but we provide first-hand data from experiments in the appendix for further study.
§5 Compound meaning construction
An example of a variety of online goods that are increasingly prominent in the digital world are predictive models built on user data. Both artificial intelligence (AI) and prediction markets attempt to predict future events based on data obtained primarily from users. But both paradigms are limited in different and almost opposite ways. The dominant paradigm in AI eschews incentives and instead collects data feeds (public or privately surveilled) and synthesizes them into predictions through proprietary large-scale nonlinear models, which consistently leverage the default web2 pair “usus” There is a monopoly and no “fructus” is attributed to the data workers.
Prediction markets take the opposite approach, with people placing bets on outcomes in the hope of financial gain, relying entirely on the economic incentives of financial speculation (“fructus”), without comprehensively analyzing bettors’ beliefs to produce composable models. At the same time, the conclusions produced by both paradigms are described as “objective” truths. AI models are described as “universal” or “universal intelligence,” while prediction markets are described as summarizing all the beliefs of market participants into one number: the equilibrium price.
A more productive paradigm is to eschew these extremes and draw on the strengths of both, while making up for their weaknesses to make them richer in breadth. We propose to combine the complexity of nonlinear AI models with the market incentives of prediction markets to transform passive data workers into active data creators. With this rich information rooted in the sociality of the data creators, DeSoc is able to unleash a composite intelligence network that is more powerful than either method.
5.1 From prediction markets to compound predictions
Prediction markets are designed to aggregate beliefs based on wealth and risk appetite from those willing to bet. But this “survival of the fittest” is not an ideal way to aggregate beliefs. In a zero-sum game, where one trader’s gain is another’s loss, it assumes that a general predictive power is fought against “smart people” rather than “dumb people.” While wealth may be a proxy for certain abilities and expertise, predictions of other forms of related expertise may be more reliable.Participants who lost bets in one area may have more accurate beliefs in another area. But prediction markets have the unfortunate effect of creating belief in those with a propensity to gamble, making those who win bets rich, making others poor, and preventing widespread participation by the risk-averse.
There are better ways to inspire belief. Research has shown that while prediction markets often outperform simple surveys, they are not better than complex team prediction surveys, which give people an incentive to share and discuss information. Under the team deliberation model, where members can weigh based on factors such as past performance and peer reviews, the team engages in semi-structured discussions to bring together information that cannot simply be encapsulated in a buy-sell contract. Such team deliberation models can be further improved by the quadratic rule in order to obtain accurate probability estimates from all participants (in contrast to prediction markets, which can only obtain up and down views about the current price balance) . It has been shown that the number of contracts people are motivated to buy reflects their subjective assessment of probability.  Such a market would also distribute the gains from participation more equally, rewarding the right people without bankrupting others, making everyone a participant in future rounds.
SBTs can unlock a new class of rich models and experiment with predictive power and relative expertise. Prediction markets come up with just one number, the price of the contract, and quadratic voting gives each participant’s exact belief in the probability of an event. SBTs are able to further calculate these beliefs in the social context of participants’ educational credentials, membership, and general sociality to develop better weighted (or non-linearly integrated) predictive models, likely in new, unforeseen circumstances A new generation of expert forecasters emerges at the intersection. So even if polls do not do a good job of clustering beliefs, polls can be studied retrospectively to reveal the characteristics of “more correct” participants, and to call in more targeted “experts” in future polls , perhaps in the context of a deliberative team. These mechanisms are closely related to those we advocate in this paper. A quadratic mechanism discounted by correlation scores can transform poorly coordinated top-down public goods into powerful, bottom-up composite network goods. Likewise, they can transform governance systems based on zero-sum prediction markets into more positive-sum decision-making, encouraging the revealing and synthesis of new, better information.
5.2 From artificial intelligence to compound intelligence
Large-scale nonlinear “neural network” models such as BERT and GPT-3 can also be transformed by SBTs. Such models leverage large amounts of publicly or privately monitored data to generate rich models and predictions, such as codes based on natural language cues. Most surveilled data creators are unaware of their role in creating these models, retain no residual rights for themselves, and are seen as “incidental” rather than key players. Furthermore, data collection takes models out of their social context, which masks their biases and limitations and diminishes our ability to compensate for them. These contradictions have come to the fore as demands for data availability have grown, and new initiatives, such as “data collection tables” that document the provenance of data, and privacy-preserving laws for machine learning, need to provide access to those who produce the data. With meaningful economic and managerial benefits, and incentivize them to collaborate to produce models that are more powerful than the models they built alone.
SBTs provide a natural way to develop economic incentive programs for richly sourced data, while giving data creators residual governance over their data. In particular, SBTs allow for careful and targeted incentivization of their data (and data quality) according to the characteristics of individuals and communities. At the same time, model makers can track the characteristics of the collected data and its social context, as reflected in SBT, helping to find contributors who can counteract bias and compensation limitations. SBTs can also provide data creators with bespoke stewardship, allowing them to form cooperatives, pool data and negotiate its use. This bottom-up programmability of data creators enables future compound intelligence, where model makers compete to negotiate how to use the same data to build different models. So we move away from a stand-alone, monolithic “artificial intelligence” paradigm that is decoupled from human origins, centralizing surveillance data without provenance, and instead employ collaboratively constructed composites
Intelligent Cambrian, these intelligences are rooted in society and governed by Souls/soul clusters.
Over time, just as SBTs individualize Soul/Soul clusters, they also individualize models. Embed data provenance, governance and economic rights directly into the code of the model. So multiagents, like humans, build a soul embedded in human sociality, and over time humans are embedded in multiagents, each with a unique soul that complements the others and cooperation. And, at this point, we see a fusion of prediction markets and AI paradigms, moving together in the direction of compound meaning construction. Combining widely distributed incentives and careful tracking of social context creates diverse models that combine the best of both approaches into a technological paradigm that is more powerful than either.
5.3 Programmable Composite Privacy
Composite agents raise important questions about data privacy. After all, building such powerful agents requires pooling data across individuals from large datasets (such as health data), or capturing data that is not interpersonal but shared (such as social graphs). Advocates of “autonomous identity” tend to treat data as private property: because the data for this interaction is mine, I should be able to choose when to disclose it to whom. In terms of simple private property, however, the data economy is even less understood than the real economy. In a simple two-way relationship, such as an extramarital affair, the right to disclose information is usually symmetric and usually requires the permission and consent of both parties. As the scholar Helen Nissenbaum has emphasized, the concern is not “privacy” per se, but rather the lack of a complete understanding of the context in which the information is shared during information sharing. The “Cambridge Analytica” scandal is mainly about people leaking their social graph attributes and friend information without their friends’ consent.
Rather than thinking of privacy as a transferable property right, a more promising approach is to think of privacy as a programmable, loosely coupled bundle of rights that allows information to be accessed, changed, or profited from. Under such a paradigm, each SBT (such as an SBT representing a credential or accessing a data store) would ideally also have implicit programmable property rights and be able to control some of the basic information that constitutes the SBT, such as the holder, their agreements between them, shared property (such as data), and obligations to third parties. For example, some issuers will choose to make SBTs fully public, but some SBTs, such as passports or health records, will be private in the sense of self-sovereignty, and Souls/soul clusters carrying SBTs have the right to unilateral disclosure. Others, such as SBTs that reflect membership in data cooperatives, involve multi-signature or more complex community voting rights, and all or most SBT holders must agree to make disclosures.
While there are currently technical questions such as (Can SBTs be programmed in this way?), and important questions around incentive compatibility (explored further in Section 7), we still believe that programmable composite privacy is worthwhile Further research, and provides key advantages of an alternative to the existing paradigm. According to our approach, SBTs have the potential to make privacy a programmable, composable right that can be mapped to the complex set of expectations and protocols we have today. Furthermore, this programmability can help us re-architect new configurations, as there are countless ways in which privacy as a right to allow access to information can be combined with “usus”, “abusus” and “fructus” to create a subtle access rights cluster. For example, SBTs can allow computations on data stores (possibly owned and managed by multiple Souls/Soul clusters) using specific privacy-preserving techniques. Some SBTs may even allow access to data in a way that does certain calculations, but the results cannot be proven to a third party. A simple example is voting: the voting mechanism needs to count the votes for each Souls/Souls cluster, but the votes should not be proven to anyone else to prevent the purchase of votes.
Communication is perhaps the most typical form of sharing data. However, today’s communication channels both lack user control and management (“usus” and “abusus”) while auctioning off the user’s attention (“fructus”) to the highest bidder, even a bot. SBTs have the potential to manage a healthier “attention economy”, giving Souls/soul clusters the ability to filter spam from outside their social graph, possibly even bots, while enhancing communication from real communities and desired intersections. Listeners can more clearly know who they are listening to and can better assign credit to works that inspire insight. This economic model is not optimized for maximizing user stickiness, but for creating more valuable common goals through positive-sum collaboration. This communication channel is also important for security. As mentioned above, “high-bandwidth” communication channels are critical to helping communities build a secure foundation.
§6 Decentralized Society
Web3 hopes to transform society broadly, not just the financial system. However, today’s social structures, such as the key words family, church, team, corporation, civil society, celebrity, democracy, etc., if in the virtual world (often referred to as the “Metaverse”) of the wider relationships they support, Aboriginal people have nothing to represent the human soul, so all this is meaningless. If Web3 eschews persistent identity, trust and cooperation models, and composable rights and permissions, we will see all of these over-financialization trends, respectively, Sybil attacks, collusion, and fully transferable private property in limited economic domains .
To avoid over-financialization while unleashing exponential growth, we propose to enhance and bridge our sociality in both virtual and physical reality, endow Souls/soul clusters and Communities/community clusters with richer coded social and economic relationships. However, it is not enough to build on trust and cooperation. Correcting biases and over-coordination (or collusion) tendencies in trust networks is critical to encouraging more complex and diverse social relationships than ever before. We call this a “Decentralized Society (DeSoc)”: a co-determined sociality in which Souls/Soul Clusters and Communities Clusters are brought together bottom-up as emerging properties of each other, spawned at different scales Composite web items.
We emphasize that composite network items are a feature of DeSoc, because the network is the most powerful engine of economic growth, but also the most easily captured by private actors (such as Web2) and powerful governments. Most significant economic growth comes from increased network revenue, where each additional unit of input produces more output. Examples of simple physical networks include roads, power grids, cities, and other forms of infrastructure, which are built with labor and other capital inputs. Examples of powerful digital networks include markets, predictive models, and composite intelligence built on data. In both cases, network economics differs markedly from neoclassical economics, which emphasizes diminishing returns, that is, with each additional unit of input, output diminishes, and private property yields the most efficient outcomes. The use of private property in the case of increasing returns will have the opposite effect, resulting in the phenomenon of restricting the development of the network by extracting rents. A road between two cities could unlock increasing returns from trade gains. But the same private ownership of roads can stifle growth if landlords choose to extract rents in trade between the two cities. Public ownership of the network has its own perils of being caught by regulators or underfunded.
Having increasing returns is most effective when the network is viewed neither as a purely public nor purely private good, but as a partial and composite public good. DeSoc provides a social basis for disaggregating and reconfiguring rights – the right to use (“usus”), the right to consume or destroy (“abusus”), and the right to benefit (“fructus”) – and to make these rights effective Governance mechanisms can enhance trust and cooperation while checking for collusion and capture. We explore several mechanisms in this paper, such as community-based SALSA and discounted quadratic funding (and voting) for related scores. This act of making compound ownership a third way avoids Charybdis for private rent and Scylla for public regulation.
In many ways, DeFi today is a private property model of diminishing returns transformed into a network of increasing returns. Built on a premise of distrust, DeFi is inherently limited to the realm of fully transferable private property (e.g., transferable tokens), which are largely tied to “usus,” “busus,” and “fructus.” At best, DeFi risks stifling network growth by charging rents, and at worst, it could lead to a dystopian surveillance monopoly, dominated by “whales” who harvest and absorb in a race to the bottom data, just like Web2.
DeSoc turns the DeFi race to control and speculate on network value into bottom-up coordination to build, participate, and govern the network. At the very least, the social foundation of DeSoc can make DeFi anti-witch (supporting community governance), anti-vampire (internalizing positive externalities to build open-source networks), and anti-collusion (keeping the network decentralized). With DeSoc’s structural revisions, DeFi can support and expand diverse networks, broadly conferring benefits, as most different members agree, rather than further consolidating networks dominated by narrow interests.
However, DeSoc’s greatest strength is the composability of its network. The ever-increasing returns and growth of the network do not simply avoid the danger of extracting rents, but also encourage the proliferation and intersection of nested networks. A road may form a network between two cities. But if cut off from the broader partnership, the two cooperating cities would end up with a ceiling of diminishing returns — either because of congestion (roads and housing) or depletion (reaching the limits of the people they could serve). Only through technological innovation and increasingly extensive cooperation, even if this cooperation is loose. The value can only continue to grow exponentially when adjacent networks collaborate to gain new sources of return. Some cooperation will be tangible, gradually expanding physical trade across space. But more connections will be informational and digital. Over time, we will see new matrices of cooperation between physical and digital networks, relying on and extending the social interconnection they create. It is this intersecting, partially nested, growing network of collaborative structures that spans the digital and physical worlds that DeSoc enables.
By forming networks and coordinating, DaSoc emerges at the intersection of politics and markets, enhancing both with sociality. DeSoc gave JCR Licklider (founder of ARPANET, which created the Internet) a vision of “human-machine symbiosis” in the “interplanetary computer network” and significantly increased social vitality on the basis of trust. Rather than building on the trustless premise of DeFi, DeSoc encodes the networks of trust that underpin today’s real economy and enables us to leverage them to generate multiple network goods that are resistant to capture, extraction, or domination. Through this enhanced sociability, web3 can eschew short-term over-financialization in favor of unlimited future benefit increases across social distancing.
6.1 Souls/soul clusters can go to heaven or hell
While we’ve selectively highlighted what we believe has potential to be hopefully unlocked by DeSoc, it’s more important to remember that almost any technology with this transformative potential will have a similar potential for disruptive change: flames burn, wheels Rollover, TV brainwashing, car pollution, credit card framing debt, etc. Here, SBT, which can be used to compensate for in-group dynamics and enable cooperation across differences, can also be used to automatically redline unwelcome social groups, and even target them with cyber or physical attacks, restrictive immigration policies, or predatory behavior. loan. Such possibilities are not prominent in the current web3 ecosystem, as they are not meaningful concepts on their current basis. The benefits of enabling DeSoc also enable these harms. Just as the disadvantage of having a heart is that it can be broken, the disadvantage of having a soul is that it can go to hell, so the disadvantage of having a society is that society is often driven by hatred, prejudice, violence, and fear. Humanity is a great and often tragic experiment.
As we ponder the possible utopias of DeSoc, we should also place these possibilities in the context of other tech-enabled dystopias. Web2 is an architecture of opaque authoritarian surveillance and social control that typically relies on top-down artificial bureaucracies to grant identities (“driver’s licenses”), whereas DeSoc relies on horizontal (“peer-to-peer”) social proofs. DeSoc empowers Souls to encode their own relationships and co-create multiple properties, while web2 mediates or monetizes social relationships through opaque algorithms that can create polarization, division, and misinformation.DeSoc eschews a top-down, opaque social credit system. Web2 forms their foundation. DeSoc sees Souls/soul clusters as proxies, while web2 sees Souls/soul clusters as objects.
Using DeFi for social control (without any identity basis) is less risky, at least in the short term. But DeFi has its own dystopia. While DeFi overcomes explicit forms of centralization—that is, specific actors have a super-level of formal power in a system—it has no built-in way to overcome implicit centralization through collusion and market power. Monopolies didn’t always emerge as Standard Oil did in the past. Collusion can even occur at higher and farther levels of an ecosystem. We can see this today with the rise of a slew of institutional asset managers. Pioneer, BlackRock, State Street, Fidelity, etc. are the largest shareholders of all major banks, airlines, auto companies and other major industries. Since these asset managers hold stakes in all competitors in an industry (e.g. in every major airline), their motivation is to make their holdings look like a competitive industry, but their Acts like a monopoly, maximizing profits and securing the entire industry at the expense of consumers and the public. 
The same is true in DeFi, where the same “whales” and VCs accumulate larger stakes at each level of the stack and among competitors within the stack, perhaps voting in token governance, or delegating it to the same Class representatives, they also have similar correlations across the network. Without any social foundation against witches and the associated discounts of enforcing decentralization of functions, we would see more monopolies financed by whales, as monopolies increasingly become the largest pool of available investment capital. As the “money class” and users diverge, we should see (and have seen) increasing incentive misalignment and rent extraction. If DeFi apps emerge that deal with private data, we’re likely to see similar dynamics, such as apps encouraging bidding wars between multiple people who “own” data that are actually relationships, such as their social graphs, to build A single private AI, competing with humans, avoids competing multiple AIs in the future, thereby augmenting human capabilities.
So DeSoc doesn’t need to be perfect to pass the test of acceptable non-utopia. To be a paradigm worth exploring, it just needs to be better than existing alternatives. While DeSoc has the potential to guard against dystopian scenarios, web2 and existing DeFi are falling into an inevitable dystopian mode, concentrating power in the hands of elites who determine social outcomes or hold most of the wealth. The direction of web2 is deterministic authoritarianism, accelerating top-down surveillance and behavioral manipulation capabilities. The direction of DeFi today is nominally anarcho-capitalist, but has fallen into network effects and monopoly pressures that threaten to turn its mid-term path into authoritarianism in the same way.
In contrast, DeSoc is a random social pluralism, a network of individuals and communities, as emerging attributes of each other, that together determine their own futures. From a web2 perspective, the development of DeSoc can be likened to the rise of participatory government that has prevailed in centuries of monarchy. Participatory government did not necessarily lead to democracy, it also led to the rise of communism and fascism. Similarly, likewise, SBTs do not make digital infrastructure inherently democratic, but are compatible with democracy based on what Souls/Souls and Communities/Communities collectively decide. Opening up the space for this possibility is a clear improvement compared to the authoritarianism of Web2 and the anarcho-capitalism of DeFi.
 We say “innocent” because highly cooperative groups will naturally seek to advance their interests, likely to influence their collective interests.
 Under the quadratic rule, a team member can purchase a contract that pays $X under the condition that the event occurs, but at a cost of (X^2)/$2. For example, if an event occurs, an individual who sets X = 0.5 will receive $0.5, the amount paid by the voter, and in any case at least $0.125.
 If a person evaluates the probability p, their expected reward Λ is pX and the cost is X^2/2. Derivative with respect to X, the optimal condition is p=X, assuming risk neutrality, which is reasonable for small stakes (both reward Λ and cost can be reduced or increased arbitrarily, the same argument still holds).
 See Eric Posner, Glenn Weir, Radical Markets: Eradicating Capitalism and Democracy for a Just Society, Princeton University Press, 2018.
Original Authors: E. Glen Weyl, Puja Ohlhaver, Vitalik Buterin
Posted by:CoinYuppie，Reprinted with attribution to:https://coinyuppie.com/vitalik-and-other-latest-papers-decentralized-society-finds-the-soul-of-web3-part-2/
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