Bitcoin Hivemind — originally Truthcoin —  is an open-source, P2P Oracle protocol and conditional prediction market (PM) proposed by Paul Sztorc (Truthcoin) and designed as a Bitcoin sidechain. Hivemind specifically focuses on governance by addressing problems with multi-factor decision making through a conditional PM.

The protocol targets a primary use within the voting system by reducing the problems of multi-factor decision making among a group of people coming to a consensus on a decision. The concept is highly ambitious but provides some valuable insights into governance and the capabilities of PMs.

Bitcoin Hivemind

Prediction Markets, Information, and Governance

Sztorc gave a presentation on Hivemind at the TAB Conference 2018 in Atlanta earlier this year that is very helpful in understanding the more general vision for the protocol. Sztorc identifies that many blockchain applications do not address a real-world problem, and is ultimately the root cause of their inability to remain relevant or practical.

The primary problem that Hivemind addresses is the concept of information aggregation, and the lack of viable means to adequately aggregate information in the Internet era. Information aggregation does not scale without markets, and PMs — specifically InTrade — have proven their economic viability even before blockchains were available. With blockchains, the repository of information is censorship-resistant and transparent. Further, Hivemind is a Bitcoin sidechain, which crucially transfers the established monetary network effects of the legacy cryptocurrency to the project.

PMs are valuable tools for reaching a decision that is determined by market forces while concurrently removing much of the noise and ambiguity that plagues the decision-making process. Drawing on a similar notion of the “Wisdom of the Crowd” as Augur and Gnosis, Bitcoin Hivemind is empirically a crowdsourcing method for determining the probable outcomes of events that is conferred several benefits of Bitcoin including transparency, robustness, censorship-resistance, and monetary effects.

Practical applications of PMs range from sports gambling to complex governance decisions. While the information that PMs provide is not perfect, it is the least bad as it aggregates data from a multitude of sources, many of whom identify as ‘experts’ and others who just provide useful market information (i.e., financially motivated) leading to insights that would not otherwise be accounted for.

One of the driving concepts of a PM merely is that if you disagree with what the market is revealing, then you are free to take advantage of those margins and bet against the market. A corollary effect of the data aggregated into the blockchain is that it is from sources who are willing to financially stake their opinion or knowledge, cutting through the BS.

PMs are inherently machines for minimizing trust, so their integration with blockchains — which afford the same property — is a natural fit. As such, Sztorc lays out numerous applications of PMs from encouraging whistleblowing to P2P governance structures.

PMs also filter poor information. Users who provide bad information because it does not accurately reflect the result are relieved of their ability to influence the market through substantial financial losses. Users who can curate information precisely and contribute useful data to the market are subsequently rewarded. Importantly, the market handles this correction naturally and there is no need for third parties or coercive practices to affect decision making.

Governance is flawed from the perspective of multi-factor decision making. Multi-factor decision making produces inefficiencies and conflicting criteria for coming to decisions by the influence of taking other perspectives into account. Further, the concept of the arrow impossibility in voting results leads to strategic voting and ‘electability.’ With so much information available, properly aggregating and converging on decisions based on the data is a method for improving the decision-making process.

Overall, Sztorc’s ambitious platform is tied to his strong belief in the power of PMs and how they can provide a method for evolving outdated governance structures.

How Bitcoin Hivemind Works

Hivemind is an exceptionally extensive and sophisticated protocol. For context, the whitepaper is a highly technical 81 pages, and both Andrew Poelstra and Gregory Maxwell were independently hired to review the protocol to evaluate its viability. That being said, we will only overview the concept from a more general standpoint and the primary mechanisms of its functionality.

Sztorc envisions Hivemind as eventually becoming the mechanism for deciding on political and governance issues. However, that process will take time to unfold as users want assurances that PM markets are accurate and reliable. Regardless, Sztorc identifies the three primary properties that are needed to solve governance:

  1. A cheap, reliable source of information.
  2. A method of crunching multi-factorness, specifically electability, back into a single factor.
  3. A way to prevent capture of the above processes by malicious third parties.

 

  1. The cheap and reliable source of information is the prediction market itself. PMs force a clear definition of ambiguous and general topics such as climate change, and converge on a probability, as defined by market pricing. Markets accurately aggregate and curate data for the users. The information is broadcast to everyone online, so it is widely available, and it is free to use.
  2. This is a more complex property to address but is how multi-dimensionality of the PM can reduce the multi-factorness of decision making through increasing relationships and forecasts between probability events, effectively gauging their influence on each other.
  3. This property is essentially censorship-resistance. Hivemind is a Bitcoin sidechain that is merge-mined with Bitcoin, so, the properties of immutability and censorship-resistance are conferred to Hivemind. Moreover, the native and borderless Bitcoin provides a unique medium of value outside of the conventional financial system for the PM.

Hivemind is a Bitcoin sidechain that uses a dual token scheme with Bitcoin functioning as the user layer and VoteCoins as the reputation/employee layer. The value of Bitcoin reflects exactly what it does — a store of value — while VoteCoins are used to indicate user reputation on the platform.

Decisions

Decisions (markets) on the PM must be resolved by voters. In Hivemind, decisions are either boolean or scalar. Voters have to agree on the decision of the outcome using the VoteCoins. The process is very similar to the use of Reputation tokens (REP) in Augur for resolving the outcomes of markets.

Similarly, voters are punished for reporting inaccurate results and rewarded for issuing accurate results.

Markets

The PM is the primary component of the system. Users can buy and sell ‘states’ of the world with Bitcoin leading to speculation and P/L on positions about future events. States are mutually exclusive, a vital consideration of removing market information ambiguity. Markets can either be ‘trading’ or ‘closed,’ and buying and selling of positions can be performed with an automated bookmaker.

The multi-dimensionality of Hivemind’s PM enables users to trade on both the probability of each state and the relationship between dimensions, such as an elected official and the implementation of a specific trade policy later.

Market decisions are divided into branches which consist of their own parameters and VoteCoins. Ballots are all of the matured decisions on a specific branch and make up the voter matrix which is a stack of the ballots for each voting cycle.

The outcome is the calculated and final result for each decision as determined by the algorithm underlying the process in the market. Reputation-based coins (VoteCoins) are then re-distributed based on the results of the round of voting within a branch.

The white paper subsequently dives into temporal economics, coordination games, and single value decomposition as part of voting strategies, which are out of the scope of this article.

Mining

Hivemind is merge-mined with Bitcoin, granting it the use of Bitcoin’s robust infrastructure. Miners can actually mine Hivemind at virtually no additional costs, making it an easy choice for miners to secure the sidechain. Further, miners cannot censor the creation of markets or votes on the platform.

Authoring Activity

Any user is capable of creating a prediction market if they can pay for it in BTC. There are two primary phases to creating a market:

  1. Authoring Decisions
  2. Adding The Market

All the decisions are added to the blockchain independently. Authors subsequently need to provide seed capital to provide initial market liquidity and “make the market.” Authors benefit from the market’s creation and its use but are also responsible for enforcing the market and all resource costs associated with making it.

Trading Activity

Trading activity should theoretically converge on the market price of an event’s likely “state,” but such accuracy requires a highly liquid and active market, something which takes time to develop — particularly when it is a PM built on a novel technology like Bitcoin.

Trading is confidential and censorship-resistant, and traders can even transfer shares to other addresses.

The rest of the paper focuses on the ‘scalability and customizability via branching’ and ‘implementation details,’ which are out of the scope of this article as well but you can find more information on here (articles 3 and 4).

Prediction Market Hurdles

Luckily, Augur provides a valuable live use case for evaluating the viability of decentralized prediction markets. The primary advantage that decentralized prediction markets have over centralized markets is censorship-resistance. Traditional markets — such as InTrade — were censored, and buying of positions were not confidential, as they are in Hivemind.

Augur

Read: What is Augur?

Censorship-resistance is vital for numerous reasons, although concerns about Deadpools were realized when they started popping up with prominent public figures on Augur because nobody controls markets that are made. Such is the trade-off for censorship-resistance.

One of the problems Augur has faced is liquidity. The volumes simply are not enough to match centralized services, yet. Much of this can be attributed to the novel nature of cryptocurrencies and the high barrier to entry but, liquidity problems are an established issue among PMs. Liquidity is problematic to address because it requires fostering adoption of not only a novel technology but the concept of PMs becoming a ubiquitous means of decision making. A transition of that size is likely far-fetched to a considerable portion of the population.

Interestingly, Sztorc addresses concerns about adoption and why people should use prediction markets in his in-depth FAQ section. He states:

“Firstly, Authors (who bear the economic cost of Market-Creation) are rewarded with a slice of transaction volume. Recreational speculation is likely in markets covering sports and politics, arbitrage transactions are likely in markets tracking a price index, and in many cases, individuals will just disagree with each other passionately enough to begin wagering (global warming, gun control, etc.).”

He also cites an essay by Robin Hanson detailing how the public might be interested in paying for useful information. Additionally, Sztorc argues that market revelations may be privately beneficial to individuals and collaboration among them can lead to assurance contracts for pooling info-demand.

Finally, one of the most significant hurdles — not just with PMs — in the larger cryptocurrency and blockchain space is the Oracle Problem. How do you map real-world information into a blockchain through a trust-minimized source in a scalable manner?

Unfortunately, the Oracle Problem is still a problem, and it is clearly a complicated issue that may take some bright minds significant time to eventually iron out, if at all possible.

Conclusion

Hivemind is an intriguing project that has been around for a while, albeit under the name Truthcoin. Sztorc is also behind Drivechains, something he recently announced the test version of for Bitcoin.

Prediction markets are powerful, and coupled with the sustainable and novel legacy cryptocurrency, Bitcoin, there is some serious potential to enhance governance mechanics and decision making among the general public.


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Posted by Brian Curran

Blockchain writer, web developer, and content creator. An avid supporter of the decentralized Internet and the future development of cryptocurrency platforms.


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