This article discusses the medium of money through the lens of the trust context in which it operates. Historically, value exchange within a community, where trust among individuals is inherent, often relied on credit money based on accounting records and reputation. Whereas, outside the community, particularly in import-export trade with strangers, trust is lower and people commonly turn to commodity money, such as gold or silver, valued for its intrinsic worth and material weight. In the presence of an authority, where trust is intermediated, various materials from marked coins to paper banknotes have been used as money, as long as they are enforced for payments and tax collection. Modern nation-states employ fiat money, which is based on the public's trust in the state's ability to manage and repay its debt. Bitcoin and other decentralized crypto assets, fundamentally rooted in cryptography, represent virtual resources within the network, with intrinsic durability derived from the laws of physics. Consequently, cryptocurrencies function as commodity money similar to gold and are suitable for use in low-trust environments without the presence of an authority. Beyond functioning as commodity money, cryptocurrencies are programmable and offer communal databases of verifiable records without the need for intermediaries, and allow new forms of coordination and economic models. While states and banks around the world are trying to adapt to these developments, current implementations of credit mechanisms in crypto largely mimic traditional banking models. The potential of peer-to-peer credit systems facilitated by crypto protocols, remains largely unexplored and ripe for innovation.

Within a community, where trust is inherent among individuals, exchanges often rely on credit money. For example, I give you grains, you give me a token marked with a grain symbol. This token represents your debt, which I hold onto until you return my grains. Alternatively, you can deposit your grain in a secure storage facility and receive a grain token, while I do the same with my wine. Then, in the marketplace, we simply exchange these tokens, which can be redeemed for the actual goods in storage, settling our debt. This is how money worked 5000 years ago in Sumerian towns, as they began trading their agricultural surplus and used clay tokens first for counting, then for accounting, and eventually for administration.[1] These tokens were made of clay, often stored in clay envelopes for safekeeping. The contents of the envelope were marked as recordings on clay tablets for additional security. This practice was, in fact, an early form of double-entry bookkeeping.[2]

Credit money, based on trust between local community members or a network of traders, has been widely used for centuries in various forms such as certificates of debt, vouchers, notes, cheques, bills of exchange, and mutual credit across regions from Asia to Mesopotamia to Europe.[3] Whereas, outside of the community, when trading with strangers, a different form of money was necessary.

When we move outside of our immediate communities, engaging in import-export trade with strangers, trust levels are lower. In this case, people often turn to commodity money, materials with intrinsic value that can be weighed to ensure their worth without requiring trust in the counter-party. Initially, a specific amount of barley, measured in a unit known as a shekel, similar to pounds or kilograms, was used in exchange for a given amount of grain. Gradually, barley was replaced with materials like silver and gold, which were more durable, easier to carry and verify, and harder to forge due to their scarcity in nature. [4] [5]

Eventually, to avoid the repetitive task of weighing these materials, Anatolian traders began stamping a mark on the metal to certify its weight. Around 620 BC, the Lydian kings standardized the purity and circulation of coinage.[7] During this same period, rulers in China and India also began to adopt coinage. The practice of authorities sealing coins and inscribing numbers combined the process of accounting from high-trust environments with the use of materials with intrinsic value reliant upon weighing of species for low-trust verification. This innovation represents the midpoint on the trust spectrum, a point of intermediated trust by the government.

As towns grew into cities and states, authorities were established and people used various materials for money, including marked coins, paper notes, and silk, as long as they were permitted for payments and tax collection. In the presence of authority, trust between the sides of an exchange was intermediated. For example, the Sumerians used clay tokens as proof of goods stored in temple warehouses under royal control.[8] Similarly, from the 7th to the 12th century, Chinese rulers mandated paper banknotes, which originated from merchant receipts of deposited commodities in warehouses.[9]

Among the various media for recording monetary value, metal coins had the greatest legitimacy in fulfilling the functions of money, due to intrinsic value derived from rarity, durability, and ease of verification.[10] The advent of coinage facilitated efficient money circulation, from paying mercenary soldiers and slaves, to retail trade, to tax collection. This cyclic flow of coins became a vehicle for expanding the authority of empires, including the Lydians, Greeks, Persians, Romans and others.[11] For centuries, states around the world accumulated silver and gold reserves through trade, mining, and conquest, and minted coins based on these reserves.

States needing to stimulate the economy and stabilize the money markets would resort to debasement, reducing the amount of precious metal in each coin to mint a greater quantity. Over two hundred years, the silver content in the Roman Denarius was reduced from 90% to 5%, leading to hyperinflation and contributing to the demise of the Western Roman Empire.[12] Silver and gold coins were the lifeblood of medieval Europe’s economy, such that the availability of metals often caused periods of economic expansion or contraction.[13]

In the 7th century, to facilitate larger transfers of metal coins, the Chinese began using paper money. By the 10th century, during the Song dynasty, promises to pay were introduced to address a shortage of metal coins. Kublai Khan, the ruler of the Mongol Yuan dynasty in the 13th century, banned coinage altogether, mandating the exclusive use of paper money across a vast territory. This represented the first widely recognized paper fiat money not backed by a commodity reserve, a concept later introduced to Europe by Marco Polo.[15]

The relative dominance of credit or commodity money fluctuated over the centuries, particularly during periods with less stringent authority, or in places on the fringes of power. [16] Similar to Chinese merchants from the 7th century onwards, Indian and Muslim traders used hawala, cheques, and promissory notes for long-distance payments. [17] This mechanism of payments for import-export, involving trusted agents who periodically settle accounts among themselves, emerged as a safer alternative to transporting large sums of gold bullion and coins over insecure routes.

In the 14th to 15th centuries, before an influx of metals from the Americas, European merchants relied on promissory notes as receipts of gold or silver deposits. This practice was based on trust established over time through trade. Likewise, for import-export transactions, the use of bills of exchange was common, facilitated by a network of exchange bankers and trade fairs for clearing obligations. Bills of exchange served not only as a means of payment but also a form of credit.[18]

Over time, bills of exchange and cheques evolved to become negotiable instruments, allowing these obligations to themselves be used as payment via endorsement. This innovation enabled the creation of additional credit money among a chain of parties who trusted each other, thereby providing liquidity and expanding trade. While merchant banks and deposit banks efficiently facilitated such short-term credit, long-term credit relationships emerged in the early 14th century as rulers, such as Edward III, financed their wars by borrowing from private lenders, including Florentine bankers.[19]

In the 15th and 16th centuries in Europe, monarchs continued to borrow from private bankers to finance their military expenditures. By the 17th century, banknotes representing private deposits, like those issued by goldsmith bankers of London, began circulating as money.[21] These bankers issued more notes than they had in reserves, effectively using credit to expand the money supply. With the establishment of the Bank of England in 1694, for financing England's war with France, banknotes transitioned into government-backed money.

This ability for governments to issue debt, secure funds from lenders, and monetize their debts was pivotal to the formation of states. The establishment of new state institutions enabled governments to credibly commit to upholding property rights and preventing confiscation.[22] Notably, by limiting the monarchy with a representative parliament that included creditors, the United Kingdom was able to gain increased access to private credit.[23] As a result, states began to accumulate debt as treasury bonds and settle through periodic repayment. Governments increasingly spent more than the tax revenues they took in and covered deficits through borrowing.

In times of war, particularly during the early 20th century, states were compelled to borrow more money often lent by foreign countries. Military spending could drive countries to exceed their ability to borrow, forcing them to depeg their currencies from gold reserves, and weakening their currency as a result. After World War II, European countries were very aware of the role exchange rates had played in escalating conflict, particularly the repayment of foreign debt. Meanwhile, the U.S. held two-thirds of the world’s gold reserves, which set the stage for the Bretton Woods agreement in 1944.

Bretton Woods sought to stabilize foreign exchange rates by tying them indirectly to gold. Under this system, only the U.S dollar was backed by a fixed rate of gold, while other currencies would target an exchange rate to U.S. dollars by managing their dollar reserves. This made dollars as good as gold, while enshrining the U.S. dollar as the world’s primary reserve currency.

However, using a country’s national currency as international money presents a challenge known as Triffin’s dilemma: as the world economy expands and demand for that currency increases, the issuing country must run a deficit to supply it. This undermines the credibility of the currency as a sound means of exchange, leading to a situation where the gold reserves backing those dollars soon becomes insufficient.[25] In fact, this system was viable primarily because banks outside of the U.S. invented the eurodollar system, which refers to U.S. dollars deposited in banks outside the United States, often used for international transactions.[26] Indeed, within just two decades following the Bretton Woods agreement, the U.S.'s gold reserves experienced a significant decline. The depletion disrupted the established system of international trade, which was heavily reliant on gold exchange standards to balance trade flows. Ultimately, Richard Nixon would end the Bretton Woods system in 1971, delinking the dollar from gold, and converting to a fiat money system.[27] As a result, all national currencies that had been targeting an exchange rate with dollars were no longer pegged to gold.

The eurodollar market facilitated the continuous growth of dollar circulation globally, significantly expanding the international liquidity of the dollar and making it a dominant currency for global trade and finance. This status was further solidified by the petrodollar system. In the mid-1970s, an embargo by OPEC’s Arab nations led to increased oil prices, contributing to economic downturn and increased inflation in the U.S. This situation resulted in a deal—initially secret—with Saudi Arabia whereby in return for military aid, the Saudi government agreed to sell their oil exclusively in dollars and invest in the U.S. Treasury bonds, thereby financing America’s spending.[28] This arrangement would eventually play a significant role in the two Iraq wars, reinforcing the doctrine that oil should be traded in U.S. dollars. Through U.S. economic and military influence, most oil-producing nations were ultimately compelled to sell their oil in dollars, establishing the petrodollar system and reaffirming the dollar's status as the global reserve currency.[29]

As a result, most modern nation-states today hold dollar reserves, while their own fiat currencies are established as legal tender through government regulation. Domestically, these fiat systems rely on the state's ability to effectively manage its economy and service its debt. International trade continued to rely on floating exchange rates and joint intervention primarily by economically powerful countries to keep the rates stable.[30] In the decades following the introduction of the eurodollar and the petrodollar system, despite increased deregulation, globalization, and the growing complexity of financial networks, states and banks have consolidated their control over both the “money inside” and “money outside.”

At the heart of digital commodities like Bitcoin lies cryptography, which is fundamentally a relationship between thermodynamics and information theory. Much like brute-forcing a password involves attempting numerous combinations, consuming time and energy to access a piece of information, cryptographic primitives rely on strong randomness to ensure the security and integrity of transactions on the blockchain. Bitcoin’s decentralized network enables making payments over a communication channel without the need for a trusted intermediary.[31][32] It represents a new form of commodity money, drawing parallels to gold through its reliance on proof of work for value creation.[33] Its introduction has not only redefined "money outside" by providing a low-trust, globally accessible currency, but also catalyzed an unprecedented wave of monetary experimentation.

Ethereum's launch in 2015 significantly expanded the landscape of digital commodities and made it easier to develop and use programmable money through its smart contract system. This has enabled a reimagining of financial instruments and the emergence of decentralized finance (DeFi), extending the utility of blockchain technology beyond mere currency to encompass a wide range of applications, from governance to art.

However, challenges such as scalability and high transaction costs during peak periods pose barriers to mass adoption of blockchains. The development of “Layer 2” solutions for Ethereum and a new generation of blockchains like Cosmos, Polkadot, and Avalanche aims to address these issues, promising a more scalable and efficient "internet of blockchains."[35]

The trajectory of digital commodities suggests a future where financial transactions are more inclusive, transparent, and decentralized. With these advancements in blockchain technology, “money outside” has once again become free from centralized control. However, states and their creditors worldwide are keen to adapt to these changes and establish their own positions.

In response to the rise of blockchain-based digital commodities, states initially established regulations for taxation and the prevention of illicit uses. More recently they have largely focused on two main areas: classifying crypto tokens for consumer protection, control, and further taxation, and developing their own digital fiat currencies.

The classification of crypto tokens as securities or commodities remains unclear in the U.S., despite proposals aiming to define digital asset market structures based on decentralization.[37] Projects with centralized control are suggested to fall under security laws, whereas sufficiently decentralized ones may be considered commodities. In contrast, the European Union's Markets in Crypto-Assets (MiCA) framework, effective by the end of 2024, mandates token issuers to register as legal entities and follow their whitepapers closely, with exemptions for 'tokens without issuers' like Bitcoin.[38] This evolving regulatory landscape underscores the shift towards digital fiat currencies, marking a significant transformation in traditional finance and banking systems.

How might states develop a digital fiat currency? While Central Bank Digital Currencies (CBDC) offer efficiency benefits, those without robust encryption, guaranteed privacy, and anonymity raise serious concerns. A state-controlled wallet system for CBDCs, similar to China’s digital renminbi,[39] could result in full governmental control over private assets and lead to mass economic surveillance. A more viable option might be to adopt the stablecoins mechanism, where licensed entities issue digital fiat currency tokens backed by government bonds or fiat currencies held in their reserves.[40] Europe’s MiCA framework also addresses stablecoins, categorizing them as “asset-referenced tokens.” However, it's crucial for stablecoins to incorporate a strictly privacy-preserving mechanism to prevent widespread economic monitoring. Countries opting for the stablecoin model for their fiat currencies could significantly expand the global market for their debt.[41] Additionally, to diversify and manage risk, national reserves can hold sufficiently decentralized crypto tokens alongside other assets.

Just as the U.S. held a monopoly over gold reserves by the end of World War II, modern states might now contemplate acquiring significant holdings of bitcoins or other crypto tokens. However, the consensus mechanisms of blockchain networks possess inherent self-defense capabilities. These mechanisms ensure participation in the consensus of an open blockchain network while preventing the operation of multiple identities by the same entity (Sybil attack) through an admission method called proof-of-work. In Bitcoin, this method requires the participants to use tremendous computation power. Clearly, an economic cost is required to secure a distributed computing network, and new blockchains use the alternative proof-of-stake mechanism for validator admission, which is simply locking capital deposit to become a participant. This deposit must be costly enough, so that malicious actions or being offline are sufficiently disincentivized. Any attempt to own the majority of a crypto asset would compromise the security of the underlying decentralized blockchain network, thereby causing its value to depreciate even before such monopolization could be fully realized.

This inherent resistance to monopolization in decentralized crypto networks underscores the robustness of “money outside” against centralized control. However, centralizing tendencies persist through mining or validation pools. Nevertheless, the journey towards truly liberating “money inside” requires moving beyond blockchain-based replicas of traditional banking and credit mechanisms.

Credit money on blockchains, as demonstrated in protocols like MakerDAO, AAVE, and Compound, is currently employed by users who deposit and lock their assets into a pool and borrow other assets, often stablecoins with interest, against their collateral. More automated and generalized central bank-like mechanisms, such as Rico and Reflexer, provide stablecoin credit based on crypto commodity reserves like Bitcoin or Ethereum. [42] [43] Furthermore, under-collateralized lend-borrow protocols such as Centrifuge, Goldfinch, and Maple offer a kind of banking platform for intermediary actors to be underwriters. These intermediaries manage the funding of lending pools and facilitate a know-your-customer process, along with credit scoring for borrowers.[44] These approaches mirror the business loan practices in traditional fractional reserve banking systems, where banks extend loans by creating new deposits with interest, lending to borrowers who provide assets as collateral for the loan. Beyond merely emulating traditional banking models that provide intermediated trust for credit, there exists a potential for direct peer-to-peer or mutual credit systems facilitated by crypto protocols. Particularly, the concept of deferring payment for actual transactions based on trust within communities or trader networks, remains largely unexplored and ripe for innovation.

Credit money based on trust between the members of a community has been criticized for its inability to scale. Traditionally, this challenge stems from the difficulty of having a consistent unit of account across various communities, each with their own forms of credit money, and the limited transferability of peer credit between different communities.[45] However, blockchains greatly simplify the creation of credit systems like bills of exchange and promissory notes and can enable them to reach much greater scale.

The Cycles Protocol is developing a novel approach to crypto credit.[46] It aims to integrate the functions of clearing houses, akin to the role central banks fulfill for commercial banks, with existing networks of trade obligations through bills.[47] Given that companies must share their confidential bill information to allow a solver to identify cycles of liabilities and settle them, this process will be facilitated through a privacy-preserving blockchain mechanism.[48] This system could enable participants to benefit from credit clearing mechanisms in their trades at a global scale.

Another approach to crypto credit could involve using on-chain promissory notes for payments involving non-fungible tokens (NFTs), where the involved parties trust each other. In such a mechanism, the recording of debt repayments to peers would inform a relative reputation score. Similar to negotiable bills of exchange, transitive notes could facilitate the extension of credit and wealth across different communities. The primary challenge is not necessarily the lack of traditional identity verification, rather underdeveloped webs of trust, immature reputation records, and liquidity within social networks. Circles UBI is an early pioneer of social graph-based credit money, yet it needed ways to increase liquidity for actual participation.[49] Ancient credit money facilitated exchanges among individuals in the same town or those who had developed trust relationships through frequent trading. Similarly, for peer-to-peer crypto credit to function effectively within communities, internet-scale webs of trust has to emerge on blockchain networks.

The magic of instantly transferable digital commodities may have created blind spots for the crypto community. Crypto assets aiming to be the dominant store of value miss the essence of money, which is to facilitate exchange through trust-based credit. People have accepted notes as payment from those they trusted for centuries in Asia, Mesopotamia, and Medieval Europe. Why not leverage latent trust in our community to enhance liquidity and local economic stability using peer crypto credit protocols?

The medium of money encompasses more than the combination of its traditionally well-known functions as a unit of account, medium of exchange, and store of value. It constitutes societal relations across different trust contexts, whether inside or outside communities, and in the presence or absence of authorities. Crypto monies, leveraging blockchain technology, offer capabilities beyond mere monetary transactions or digital commodities. They serve as a communal and global record for verifiable data, managed not by any single company or state, but by a network of independent entities. In doing so, blockchains present innovative solutions to scaling social trust systems and enhancing the accountability of vital social institutions.

References

1. Schmandt-Besserat, D. (1996). How writing came about. University of Texas Press.

2. Schmandt-Besserat, D. (n.d.). Tokens: Their significance for the origin of counting and writing. https://sites.utexas.edu/dsb/tokens/tokens/

3. Graeber, D. (2011). Debt: The first 5000 years. Melville House.

4. Szabo, N. (2016). Artifacts of wealth: Patterns in the evolution of collectibles and money. https://unenumerated.blogspot.com/2016/07/artifacts-of-wealth-patterns-in_15.html

5. Buchman, E. (2023). The properties of money - Origin account. https://ebuchman.github.io/posts/origin-account

6. Penn Museum. (2005). Hanging in the balance. https://www.penn.museum/sites/expedition/hanging-in-the-balance-2/

7. Kroll, J. H. (2010). The coins of Sardis. In The Lydians and their world. https://sardisexpedition.org/en/essays/latw-kroll-coins-of-sardis

8. Schmandt-Besserat, D. (2018). Prehistoric administrative technologies and the ancient Near Eastern redistribution economy – The case of greater Susiana. https://sites.utexas.edu/dsb/tokens/prehistoric-administrative-technologies-and-the-ancient-near-eastern-redistribution-economy-the-case-of-greater-susiana/

9. Bernholz, P. (2003). Monetary regimes and inflation: History, economic and political relationships. Edward Elgar Publishing.

10. Szabo, N. (2002). Shelling out: The origins of money. https://nakamotoinstitute.org/shelling-out/

11. Graeber, D. (2011). Debt: The first 5000 years. [Referenced as Axial age discussion].

12. Visual Capitalist. (2016). Currency and the collapse of the Roman Empire. https://money.visualcapitalist.com/currency-and-the-collapse-of-the-roman-empire/

13. Buchman, E. (2023). History of European coins - 500 to 1500. https://ebuchman.github.io/posts/coins-history/

14. Bank of England. (n.d.). Collection of early banknotes. https://www.bankofengland.co.uk/museum/online-collections/banknotes/early-banknotes

15. American Numismatic Association. (n.d.). Chinese paper currency. https://www.money.org/money-museum/virtual-exhibits-hom-case16

16. Graeber, D. (2011). Debt: The first 5000 years. Melville House.

17. Hawala. https://en.wikipedia.org/wiki/Hawala

18. Boyer-Xambeu, M.-T., Deleplace, G., & Gillard, L. (1994). Private money and public currencies: The sixteenth century challenge. Routledge.

19. The Hundred Years War. (2021). How did Edward III finance the conflict with France in its initial stages? https://thehundredyearswar.co.uk/how-did-edward-iii-finance-the-conflict-with-france-in-its-initial-stages/

20. Postrel, V. (2020). The fabric of civilization. Basic Books.

21. Faure, A. P. (2013). Money creation: Genesis 2: Goldsmith-Bankers and bank notes. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2244977

22. North, D. C., & Weingast, B. R. (1989). Constitutions and commitment: The evolution of institutions governing public choice in seventeenth-century England. https://www.jstor.org/stable/2122739

23. Stasavage, D. (2003). Public debt and the birth of the democratic state. Cambridge University Press.

24. Ohno, K. (n.d.). History of international monetary systems, Part 2. https://www.grips.ac.jp/teacher/oono/hp/lecture_F/lec02.htm

25. International Monetary Fund. (n.d.). The dollar glut. https://www.imf.org/external/np/exr/center/mm/eng/mm_sc_03.htm

26. Restrepo-Echavarría, P., Grittayaphong, Praew. (2022). Bretton Woods and the growth of the Eurodollar market. Federal Reserve Bank of St. Louis. https://www.stlouisfed.org/on-the-economy/2022/january/bretton-woods-growth-eurodollar-market

27. Wen, Y., & Reinbold, B. (2020). The changing relationship between trade and America’s gold reserves. Federal Reserve Bank of St. Louis. https://www.stlouisfed.org/publications/regional-economist/first-quarter-2020/changing-relationship-trade-americas-gold-reserves

28. Wong, A. (2016). The untold story behind Saudi Arabia’s 41-year secret debt. https://www.independent.co.uk/news/business/news/the-untold-story-behind-saudi-arabia-s-41year-us-debt-secret-a7059041.html

29. Mitchell, T. (2013). Carbon democracy: Political power in the age of oil. Verso Books.

30. Ohno, K. (n.d.). History of international monetary systems, Part 3. https://www.grips.ac.jp/teacher/oono/hp/lecture_F/lec03.htm

31. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.

32. WikiLeaks now accepts anonymous Bitcoin donations. https://twitter.com/wikileaks/status/80774521350668288

33. Narayanan, A., & Clark, J. (2017). Bitcoin's academic pedigree. https://queue.acm.org/detail.cfm?id=3136559

34. 3Blue1Brown. (2017). But how does bitcoin actually work? https://www.youtube.com/watch?v=bBC-nXj3Ng4

35. Arikan, B. (2022). A comparison of heterogeneous blockchain networks. https://medium.com/@arikan/a-comparison-of-heterogeneous-blockchain-networks-4bf7ff2fe279

36. Arikan, B. (2022). Cosmos Ecosystem. https://graphcommons.com/graphs/570556cb-de40-4bc7-ad3d-f4d110e96f35

37. McHenry, P., & Thompson, G. (2023). Digital asset market structure discussion draft. https://financialservices.house.gov/news/documentsingle.aspx?DocumentID=408838

38. Legal Nodes. (2024). The EU markets in crypto-assets (MiCA) regulation explained. https://legalnodes.com/article/mica-regulation-explained

39. Digital renminbi. https://en.wikipedia.org/wiki/Digital_renminbi

40. Tether. (2023). Tether holds $72.5bn in US treasuries. https://tether.to/en/tether-usdt-and-us-treasury-dynamics/

41. Reflexer (RAI). (2023). A generic decentralized central bank technology. https://docs.reflexer.finance/ More on RAI. https://dankradfeist.de/ethereum/2023/01/31/rai-crypto-experiment.html

42. Rico. (n.d.). https://bank.dev/rico0_lite

43. Reflexer. (n.d.). https://docs.reflexer.finance/

44. Real World Assets. (n.d.). Website lists crypto credit loans. https://app.rwa.xyz

45. Alden, L. (2023). Broken money. Timestamp Press.

46. Cycles Protocol. (2024). https://twitter.com/cyclesmoney/status/1758492634304118928

47. Collaborative Finance. (n.d.). https://cofi.informal.systems/about

48. Buchman, E. (2021). Local money and liquidity saving. https://ebuchman.github.io/posts/local-money-liquidity-saving

49. Circles UBI. (n.d.). https://handbook.joincircles.net/docs/developers/whitepaper/

Topologies of inter-chain economic security in Cosmos, Polkadot, Avalanche.

It is now a common sense that Bitcoin opened Pandora’s Box and became “digital gold” over time. Ethereum introduced programmable internet money and became the platform for crypto-economic innovation. And yet Bitcoin, Ethereum, and their variants have critical issues preventing masses from adopting crypto-networks. We will first look at these issues, then use these points to compare new generation blockchain platforms.

1. 🌍 Energy efficiency: For an open decentralized network of computers to function properly, its independent participants need to come to agreement over a shared state. And while doing this, the network should remain fault tolerant with valid consensus despite imperfect information or malicious actors (Byzantine Fault Tolerance). Allowing participation in the consensus of an open network while preventing operation of multiple identities by the same entity (sybil attack) is handled by an admission method called proof-of-work (first introduced by Cynthia Dwork in 1992 for combating junk mail). This method requires the participants to use tremendous computation power heating the planet and leaking value to power companies. Clearly, an economic cost is required to secure a distributed computing network [1], and new projects use the alternative proof-of-stake mechanism for validator admission, which is simply locking a token deposit to become a participant. This deposit must be costly enough, so that malicious actions or being offline are sufficiently disincentivized. In fact, similar economies of scale apply to proof-of-stake and proof-of-work: the cost of running a validator node moves from OPEX (operational expense of computer farms) to CAPEX (opportunity cost of capital).

2. ⏳ Transaction latency: Bitcoin, Ethereum, and their variants use Satoshi Nakamoto’s consensus, which requires waiting for the creation of several blocks to ensure transactions cannot be reverted. As a result, Nakamoto chains have high availability, but low transaction speed due to their probabilistic finalization guarantee, which requires waiting for the chain to be long enough. To achieve faster finality, many blockchain projects use the classical Practical Byzantine Fault Tolerance (PBFT) consensus, which comes with its own weaknesses, including how large the validator set may be without slowing down the network and favoring safety over uptime or liveness.

3. 🌊 Computational throughput: The amount of computational work that can be done per second in a distributed computer network is throughput that defines how much a network can scale. A commonly used metric “transactions per second’’ is misleading, because a transaction can refer to a simple transfer or a complex financial calculation; they require different amounts of computing capacity. The realistic throughput, as a function of network participants, is the amount of computational work per second a network can handle. To achieve total high throughput, projects employ either vertical scaling strategy, that is requiring high performance computing for nodes and optimizing the node software, or horizontal scaling strategy, that is doing parallel processing by splitting the network into multiple parts.

4. 💰 Transaction cost: Blockchains must find a way to limit their executions, or else the network of nodes that run the blockchain are vulnerable to denial-of-service attacks. To get around this constraint, Bitcoin allows a rather limited scripting language, Ethereum charges transaction fees based on gas-metering for smart contract executions. The problem is whether you do a simple transfer or a complex calculation within a transaction, they are all handled on the same network. As a result, when the network traffic increases, transaction fees increase for even simple actions, so using the chain becomes exclusive to those who have big wallets. Fees are paid to miners as incentive to prioritize transactions. While Bitcoin transaction fees will function as the only incentive after the issuance reaches the 21 million cap, in Ethereum their sole purpose is to prioritize transactions. Burning transaction fees is a mechanism gaining traction in new projects, and most recently Ethereum too started burning fees partially, so as the network activity grows all token holders benefit from increased scarcity.

5. 🕸️ Level of decentralization: Contrary to popular belief, the level of decentralization Bitcoin and Ethereum actually achieve is low because of the miner pool concentration (as of November 2021 Bitcoin’s 90% hashrate is controlled by 11 mining pools and Ethereum’s 90% hashrate is controlled by 16 mining pools). As the cost of mining increases in Nakamoto consensus, it becomes harder to successfully produce a block and the power to run the network is pooled, thereby concentrating on a few aggregate miners. New generation blockchains address this problem with varying solutions that we will explore below.

6. 👫 Equitable distribution: How do blockchain projects distribute ownership shares (tokens) as the network grows over time? Bitcoin’s token distribution created an interlocking interdependency between the security of the blockchain, the mining ecosystem, and the exchange rate. This became the model for many projects: as miners joined the network to get token rewards, the network became more decentralized and more secure, which attracted more people to use it. As demand increases, price increases, attracting more miners to secure the network closing the circle. However, as the cost of mining increases, it becomes harder to successfully mine a block; as a result the distribution of tokens or power to run the network is pooled, thereby concentrating on a few aggregate entities to run miners. Ethereum followed a different strategy: they pre-mined tokens, removed the total supply cap, sold a portion of tokens to early investors and public sale participants, allocated a portion to its foundation for running grants and bounty programs, and started rewarding miners over time like the Bitcoin model. Soon, Ethereum’s token issuance was concentrated on a few mining pools and the largest token holders became exchanges. Ultimately, fair distribution over time defines who has power within the network: power to produce blocks (order, accept or censor transactions), power to fork the network, power to decide on the protocol upgrades, and power to invest and stake in the applications running on the network.

7. 🏛️ Governance: Changes to the network protocol have significant effects on all existing and future users, whether they are aware or not. In Bitcoin and Ethereum, improvement proposals lead to protocol upgrades and parameter changes, which are discussed, decided, implemented, and applied by a core community of experts. If a group of miners are interested in pursuing a different direction than the majority, they can fork the protocol and start a new network, painfully leaving the majority of network effects behind. Also, fund allocations to research and development are commonly managed by a central foundation, while alternatives are emerging as communities gather around fund coordination DAOs (Decentralized Autonomous Organization). Larger groups of token holders or users do not really have a say in governance decisions, because they may not have expertise, interest, or awareness in the topics of the decisions. Even if they do, they may have a little effect compared to large token holders, because voting is commonly token-weighted. This is changing as new projects employ a mix of more fair on-chain governance (i.e. quadratic voting, time-lock voting, adaptive quorum biasing, vote delegation, decentralized identity schemes to enable one-person-one-vote) and off-chain signaling (signed votes in forums) mechanisms that are accessible to a larger number of token holders.

These issues do not only constrain mass adoption of decentralized networks, but push existing users to remain relying on centralized exchanges and hosted wallets. It is too hard for non-technical folks to regularly use a truly decentralized application. On the other hand, existing users continue using Ethereum and Bitcoin because they are not aware of these problems; companies and investors continue to use them because they want to be where liquidity is; and early entrants or “original gangsters” defend these networks because they have large stakes. But another world is possible.

Daily active Ethereum addresses. Source: Etherscan.io

Today, Ethereum accommodates 500,000 daily active users in average, whereas a popular web application like Twitter is used by 200 million daily active users (400x of Ethereum) and Facebook has almost 2 billion daily active users (4000x of Ethereum). Layer 2 and Bitcoin users would add up, but still that is long way away from web scale. Scaling is a highly critical challenge for an open decentralized internet, it is not a problem of tomorrow, but a priority be tackled here and now.

While Ethereum’s new version aims to remedy scaling problems and its interim Layer 2 solutions are currently trying to accommodate the ever increasing demand, a new generation of platforms Cosmos, Polkadot, Avalanche — with mainnets launched in 2019 and 2020 — have reanimated the promise of a true decentralized internet. We will first look into the promises of Ethereum’s new version.

Since its inception, Ethereum’s new version has been changing by adopting mechanisms from new scientific research as well as from inventions of the new blockchain platforms. Ethereum’s new version will use proof-of-stake, split the network into synchronized shards aiming to increase the total computational throughput. Validators running the same Ethereum Virtual Machine (EVM) will be assigned to different network shards, produce blocks, accrue different user activity data, and synchronize with one another from a relay chain called Beacon. However, trying to synchronize all the sharded parts means trying to achieve full replication, that is having consistent copy of the database in all the nodes. This is problematic because the point of sharding in distributed computing is to achieve scaling by not replicating all data in the overall network. In a synchronous model, or a homogeneous network topology, when a shard -for example a popular DeFi shard- accrues way more usage than others, it will start having the same speed, cost, and scaling problems. Plus, there is a new problem of efficiently synchronizing data among shards.

While Ethereum’s transition to new version is said to be fully completed in a year or so, what are known as Layer 2 solutions — rollups (Optimistic, zkSync), plasma, and state channels — have launched to provide efficiency and speed for the increasing demand to use Ethereum. The dilemma is that Layer 2 trust models have either intermediary central operators defeating the purpose of decentralization and censorship-resistance, or multiple incentivized operators (i.e. Polygon is built with Tendermint and running on multiple validators, Matter Labs is aiming for a validator network with zkSync), which is akin to being another decentralized blockchain with its own token (e.g., MATIC), and eventually competing with its Layer 1. Hence, these single chain architectures will hit the same transaction cost issue as more users join them.

Most recently Ethereum adopted a new strategy called rollup-centric roadmap, which positions Ethereum Layer 1 for data availability and Layer 2 projects for computation. In other words, Ethereum wants to become the base layer to guarantee data availability and share security with rollups. As a result, Ethereum is embracing an ecosystem of EVM blockchains for computation, whether a single rollup dominates or multiple rollups co-exist (see Vitalik Buterin’s article Endgame). In fact, this strategy fits into the emerging modular blockchain design, where a blockchain can outsource data availability or execution to other blockchains. A generalized model for this strategy is developed by Celestia and EigenLayr. Moreover, Ethereum’s new strategy is resembling the shared security models already used in Polkadot and Avalanche.

On the other hand, because Cosmos, Polkadot, Avalanche have bridges to Ethereum on at least one of their EVM compatible chains, they are sometimes put in the same “Layer 2” bucket, whereas these projects often call themselves Layer 0, as they provide infrastructure for building interconnected Layer 1 blockchains.

Cosmos, Polkadot, Avalanche aim to scale horizontally with an asynchronous heterogeneous network model, where application-specific blockchains have different virtual machines and can interoperate with other chains when needed. These infrastructure platforms provide capacity to build your own custom blockchains, which enables a much larger design space for decentralized applications and assets. Running your project as a sovereign chain instead of a set of smart contracts has three fundamental advantages:

1. Performance isolation: Isolating your chain from other chains ensures your users’ experience is not affected by unrelated high activity on the network, so it provides better performance, and you can bridge to other chains when needed.

2. Predictable and customizable fees: Fees on a shared permissionless network are not under your control. Some application’s high activity on the network can increase fees that are arbitrary for your application. Having custom fee structure allows you to have predictable fees, and removes the infrastructure between the applications and their users. You don’t need ATOM, DOT, or AVAX, to use the application-specific chains. Not forcing users to use the infrastructure token for fees is crucial for mainstream adoption.

3. Customizable validators: Custom validator rules and requirements focus your chain to its domain specific needs. Your chain’s validators can be compliant to certain jurisdictions (e.g., GDPR in EU), can have high performance hardware requirements, or have certain proofs to become a validator.

These new generation networks also have bridges to Ethereum and soon to Bitcoin, as well as bridges to one another are under development to fully realize the internet of blockchains vision.

Cosmos, Polkadot, Avalanche have critical differences at the protocol level (e.g., consensus mechanism, economic security topology) that impact platform features (e.g., inter-chain communications, token economics, types of possible applications) and how they scale their networks (e.g., validator participation, staking attributions). The comparison below aims to help developers, entrepreneurs, investors, researchers, and those who consider building on these new generation infrastructures understand the differences between these architectures and their trade-offs.

Comparison table of Cosmos, Polkadot, Avalanche

Secure and consistent replication of an application state on an open network of machines is achieved by a consensus mechanism. While doing this, the network should remain fault tolerant with valid consensus despite imperfect information or malicious actors (Byzantine Fault Tolerance). The Practical Byzantine Fault Tolerance (PBFT), used in Cosmos and and Polkadot, requires all participating nodes to talk to each another, so the network agrees on a decision with absolute certainty. It has low latency and quick finality, but it cannot scale to many participants in a global open network because load on each validator node increases exponentially as the validation work increases. Bitcoin introduced the longest chain consensus mechanism (Nakamoto Consensus) that allows probabilistic finality and astronomically low error rate. It allows a robust and scalable network over time, but it is very slow.

• Cosmos, mainnet live as of March 2019, uses Tendermint PBFT consensus, which provides fast finality. However, because each node has to communicate with one another, it has quadratic messaging complexity and can finalize one block at a time.

• Polkadot, mainnet live as of March 2020, separates block production and finalization in consensus: BABE (variant of Ouroboros Praos) authors candidate blocks and GRANDPA (variant of PBFT) finalizes them in batches. This hybrid consensus optimizes quadratic messaging complexity to a certain degree.

• Avalanche, mainnet live as of September 2020, uses Avalanche Consensus, a unique mechanism that combines repeated sub-sampling of votes among validator nodes (Snowball) and transitive voting in Directed Acyclic Graph (DAG), instead of a linear chain. Because Avalanche consensus has constant messaging complexity, it allows low latency and large participation in the network. It has probabilistic finality like Nakamoto Consensus, yet it is configurable and has an astronomically low failure rate.

Allowing participation in the consensus of an open network while preventing operation of multiple identities by the same entity (sybil attack) is handled by a proof-of-work or a proof-of-stake mechanism. Like all new projects, Cosmos, Polkadot, Avalanche use proof-of-stake due to its energy efficiency and its capacity to provide larger design space. There are also projects on these networks that implement a lighter proof-of-work mechanism for fair coin distribution mechanism.

• Cosmos achieves finality in 6–7 seconds.

• Polkadot achieves finality in 12–60 seconds in total, block creation and finalization are separated.

• Avalanche achieves finality in a second. It is a probabilistic finality like Bitcoin, and has an astronomically low failure rate.

The total amount of computational work per second a network can handle depends on the complexity of the virtual machine and runtime functions used on the network. Cosmos, Polkadot, and Avalanche are building specialized asynchronous blockchain networks, so eventually their network as a whole is unbounded in terms of throughput. What really matters is how much these networks can grow, and their choices of inter-chain economic security matter.

As the activity grows on the overall network, transaction fees increase. Cosmos, Polkadot, Avalanche build specialized networks where each chain has its own custom fee mechanism based on their own state growth.

• Cosmos has a customizable fee mechanism per chain.

• Polkadot has a customizable fee mechanism per chain. Fees are pre-calculated with the Weight system. Fee burning is optional per chain.

• Avalanche has a customizable fee mechanism per chain. The primary network fees are fixed or zero for different types of functions, and all fees are burned, so token holders benefit from usage over time.

The numbers below are from March 17, 2022.

• Cosmos has quadratic messaging between nodes, hence a limited number of participants. Number of active validators is 150 in Cosmos, 115 in IRIS, 100 in Osmosis. Currently, you need minimum 147,231 ATOM (~$1.3M) to be in the active validator set of the Cosmos Hub, and minimum 1 ATOM for delegating. Total staked value is ~$5B.

• Polkadot has optimized quadratic messaging between nodes, and a limited number of participants. Number of active validators is 297 in Polkadot and 1000 in Kusama. Currently, you need minimum 1.75 Million DOT (~$33M) to be in the active validator set of the Polkadot relay chain, and minimum 120 DOT for nominating. Total staked value is ~$12B.

• Avalanche has a constant number of messaging between nodes, so unbounded number of participants. Number of active validators is 1311 in the Primary Network. Currently, you need minimum 2000 AVAX (~$160K) to be in the active validator set for the Primary Network, and minimum 25 AVAX for delegating. Total staked value is ~$16B.

Decentralization is also a function of validator stake and reward concentration (rewards weighted based on stake), which typically follows a long-tail distribution –few validators have the most stake while many validators have few. Fair stake distribution is still an open problem for blockchain platforms and each project is trying to achieve fairness differently. For example, Polkadot can have a limited active validator set due to its PBFT-based consensus at its core, but those active validators are rewarded equally through the Phragmén election method. Avalanche can have unbounded number of active validators due its novel consensus mechanism, and average validator weight is dropping progressively, increasing its level of decentralization.

The numbers below are from March 17, 2022.

• Cosmos allows a distributed network of chains with their own validator sets. Interoperability between these chains is achieved via Inter-Blockchain Communication (IBC) bridging protocol. Each chain has to implement IBC in order to bridge to other chains. Currently, 28 IBC enabled chains are live with specializations such as DeFi, EVM smart contracts, social media, privacy, regenerative agriculture, and games. Bridges to Ethereum, Bitcoin and others are in development.

• Polkadot allows hierarchically inherited security from a central relay chain to the connected chains (parachains). Parachains do not have their own validators, they have collator nodes which collect transactions and produce state transition proofs for relay chain validators. Interoperability between parachains is achieved via Cross-Chain Message (XCM) format and arbitrary data passing is possible due to inherited security. Currently, 10 parachains are live with different specializations such as DeFi, EVM smart contracts, social media, privacy, and games. Bridges to Ethereum, Bitcoin and others are in development.

• Avalanche allows an overlapping network of validators organized as subnets running multiple chains while also validating the primary network. Different chains in the same subnet can transfer (export-import) assets to one another almost instantly. Whereas subnet-to-subnet communication, meaning one chain in its subnet talking to another chain in its own subnet, is currently handled via bridges (using ChainBridge-Solidity contracts for EVM chains). In fact, the more subnets have overlapping validators with other subnets, the higher security guarantees they can have for communicating with one another. This is because those intersecting validators will have shared interests in both subnets. If a group of validators act maliciously in one subnet, they will also risk their validation stake in the Primary Network as well as in other subnets. Although a subnet-to-subnet direct interoperability method has not yet been announced, it would not be surprising to see Avalanche Primary Network itself act as a mediator between all subnets. Currently, 3 mainnet chains are live: X-Chain for transfers, P-Chain for staking, C-Chain for EVM smart contracts. Other chains and subnets are being built in the ecosystem. Furthermore, like other platforms, there is the Avalanche-Ethereum bridge which works via a trusted federation and and one of the most used among the 60 Ethereum bridges today.

Bridging between blockchains with separate security levels without some sort of a security sharing mechanism, as in current Cosmos architecture, is not that different than bridging any chain in general. So without common finality guarantees, inter-chain communication has varying risk levels. Polkadot’s inherited security model allows unified finality guarantee, and under that umbrella parachains can pass arbitrary data to one another safely. Avalanche’s overlapping network of validators model currently enables security sharing between chains in the primary network, and soon between the chains in different subnets directly without a need for a bridge. So, the more subnets have overlapping validators (who have shared interests in both subnets), the higher security guarantees their communication can have. In general, overlapping validators between different chains (like merged mining in proof-of-work) can provide more secure inter-chain communication.

• Cosmos has an on-chain mechanism for changing consensus parameters and coordinating funds.

• Polkadot’s entire runtime logic is stored on-chain as a Web Assembly (WASM) binary, allowing forkless runtime upgrades, meaning decisions are enacted autonomously following the result of a referendum, without relying on developers or validators. Governance modules include token-weighted voting, rotating councils, time-lock token voting, adaptive quorum biasing mechanisms.

• Avalanche has certain parameters upgradeable by on-chain voting. An extended governance mechanism stemming from its unique consensus is under development.

All blockchains in their core have the following components: a database, a p2p network, a consensus mechanism, a transaction handling mechanism, and state transition functions (runtime or virtual machine). Cosmos, Polkadot, Avalanche provide these core components and let developers build their custom state transition functions.

• Cosmos provides the Cosmos SDK, and Tendermint middleware that enables transactions to be programmed by any language. You can build your own virtual machine and grow your own validator community. In order for your chain to go live, you need to build a validator community from scratch and also attract the ones from existing chains. You can also deploy smart contracts on EVM compatible chains (Ethermint or CosmWasm).

• Polkadot provides a Wasm based meta-protocol and Substrate development kit in Rust. You can develop your own virtual machine using the provided modules such as accounts, assets, governance, EVM and building custom ones. Also you can benefit from Substrate’s free-execution model for on-chain scheduling, off-chain workers, and feeless transactions. Your chain goes live after you win a slot in the parachain auction, which provides inherited security from the relay chain. Alternatively, you can grow your own validator community. You can also deploy smart contracts on EVM compatible chains (Moonbeam, Acala) or use Ink smart contracts.

• Avalanche provides Avalanche Virtual Machine (AVM) where you can clone and customize an instance or build an entirely new one as your own virtual machine (a modular SDK for VM development has not yet been released). In order for your chain to go live, you need start a subnet and attract validators — who already validate the primary network — to run your chain. There is a subnet-evm code available for starting a custom EVM chain. You can deploy smart contracts on the EVM-compatible C-Chain.

Hosting web-scale user activity has a better chance via asynchronous network of specialized blockchains than a network of blockchains running instances of the same virtual machine (i.e., Ethereum’s new version). In this section, we discuss how blockchain networks and inter-chain communications are composed in more detail for Cosmos, Polkadot, and Avalanche.

The Cosmos ecosystem has a distributed network topology, where different blockchains with different purposes have their own validator set, and these chains communicate to one another via bridges when needed. This topology is criticized to be as secure as the least secure chain (when the most secured chain accepts assets from the least secured chain, it becomes less secure). However, it also makes the overall network resilient that there is no one individual chain whose security is critical to the entire ecosystem’s survival. But then, how is the Cosmos ecosystem different than pretty much any blockchain bridging to other chains? Cosmos has the “no strings attached” policy, which allows projects such as Binance DEX, Oasis, Terra, Nym, and many others to use Tendermint to develop and launch their own application specific blockchains.

The Inter-Blockchain Communication (IBC) protocol connects the blockchains in the Cosmos ecosystem (see the 28 interconnected chains on Map of Zones). As chains implement the IBC protocol they bridge to one another and the overall Cosmos ecosystem liquidity increases. IBC follows pretty much how blockchain bridges work. When you send an asset from one chain to another, i) you lock them in the source chain, ii) then a third party -possibly federated- relayer, who is monitoring the chains, picks up the receipt and delivers to destination chain, iii) the receiving chain verifies the receipt and gives you a representation of assets in the source chain. Within the Cosmos ecosystem, chains that implemented IBC have the Tendermint light client verifiers so that they can consume and verify those receipts in their communication. Also, IBC is a general protocol that can be implemented in different blockchain architectures (there is an IBC implementation for Substrate). Furthermore, the new IBC versions will have shared security schemes (see Billy Rennekamps’s talk for more).

Polkadot has a hierarchically inherited security topology, which is efficient for arbitrary data communication between its parallel chains (parachains), yet those parachains are dependent on leasing security from a central relay chain. Polkadot parachains do not need to build a validator community, instead they lease security from the relay chain. They do this by winning a slot (total ~100 slots) in an auction and locking Polkadot’s DOT tokens (they raise DOT funds via crowdloans). When these domain-specific parachains get connected and synchronized to the relay chain via their collator nodes, their functionalities become immediately available. One criticism of this mechanism is that different chains may not need the same level of security, and furthermore, that there shouldn’t be any single chain whose security is critical to the ecosystem’s survival. Although Polkadot’s narrative promotes the idea of parachains without validators today, one can just start a blockchain using Substrate and grow a validator community without relying on the central relay chain (see Compound Gateway). Furthermore, a parachain can grow its own validator community, unlock its DOT funds at the end of the lease period, and use bridges when cross-chain communication is needed. Moreover, there can be multiple relay chains which benefits the overall Polkadot ecosystem. The hierarchical topology will most likely remain because inherited-security-enabled cross-chain communication is more efficient than using bridges between parachains.

Polkadot developed Cross-Consensus Message Format (XCM), a generalized format for communication not only between parachains, but also between different smart contracts, bridges, and Substrate pallets. XCM works with Vertical Message Passing (VMP), which enables message exchange from the relay chain to parachains and back, and Cross-Chain Message Passing (XCMP), which allows the parachains to exchange messages with other parachains on the same relay chain. A message in XCM is a program that runs on Cross-Consensus Virtual Machine (XCVM) (see Gavin Wood’s article series). This abstraction for programing networks and building composable inter-chain applications can be also used in other heterogeneous blockchain networks.

As the parachains grow their community, they may also want to have their own validator sets (see Acala’s presentations), so that they can become a relay chain leasing security to other chains. Although a nested security sharing mechanism can get complex, all sub-parachains would share common finality guarantees, and the total number of state transitions per second would increase, expanding the aggregate computational throughput of the entire Polkadot network.

Avalanche has an overlapping networks topology. Every Avalanche validator node has to secure the Primary Network while securing other subnetworks. A set of validators form a subnet. A subnet can validate multiple blockchains, whereas each blockchain is validated by exactly one subnet. In other words, a validator node may be a member of many subnets. When you start a new chain you have to provide incentives to attract a subnet of validators, who already run the Primary Network and may possibly be running other chains. If your chain is attracting new validators, then they have to be able to run the Primary Network as well as the subnet that runs your chain. Overall, the subnet architecture enables overlapping network of validators (see the diagram above), which stems from the novel Avalanche consensus mechanism. Because Avalanche consensus does repeated sub-sampling among its validator nodes, it needs not all nodes but a small set of nodes to communicate to one another, which results in low messaging complexity in the network. So, the bandwidth and processing power requirements per node stays constant even as the network grows to many thousands of validators. As a result, in terms of validator participation, chains built on Avalanche are more inclusive than Polkadot and Cosmos because of its unbounded participation per chain. How many chains a validator can run depends on the chain runtime / virtual machine design complexity and currently remains an open question.

Interoperability between Avalanche chains is efficient not only because of the fast finality but also being in the same Primary Network enables sharing common finality guarantees (currently asset transfer between X-Chain, P-Chain, and C-Chain are near instant). Security sharing model is different than Polkadot or how it is envisioned in new Ethereum rollup-centric ecosystem. Avalanche’s novel subnet architecture enables higher density networks. This is because security sharing takes place not only between the chains in the Primary Network, but among chains in all overlapping subnets. This allows composability and programmability of networks, opening a new design space, while enabling a type of group-forming network (see Reed’s law) that can scale exponentially to many millions of daily active users to materialize the Web 3 vision.

Heterogeneous blockchain networks Cosmos, Polkadot, Avalanche offer a vast design space with their core infrastructure innovations. To this day, Ethereum has been the place where crypto-economy innovation has taken place. In fact, teams building on these new networks initially created glorified versions of what exists on Ethereum (decentralized exchanges, automated market makers (AMMs), lending, stablecoins, aggregators, insurance, NFT platforms etc.), but there are also projects discovering novel use cases by taking advantage of these new infrastructures.

On the Cosmos network, Osmosis combines transaction privacy (using threshold decrypted transactions to prevent front-running) with cross-chain AMM functionality and has IBC implemented for bridging with other chains. Celestia encodes block data to improve security of light clients, which is a key component for chain interoperability between self-sovereign chains and their varying security levels in a distributed chain ecosystem. Regen enables a crypto-economic platform to incentivize regenerative agriculture and utilizes data from sensors and satellites with an auditor ecosystem. Nym enables mixnet which prevents network traffic analysis from an adversary capable of watching the entire network. Nym uses Tendermint and Cosmwasm smart contract controlling the directory service, node bonding, and delegated mixnet staking. Penumbra enables privacy-preserving cross-chain network transactions. Tendermint is also used by large projects such as Binance DEX and Terra. Even greater value will be unlocked when these separate blockchain networks start to interoperate via IBC.

On the Polkadot network, Acala parachain is a DeFi hub that provides functionality from AMM to lending to stablecoins. Moonbeam is an EVM-compatible smart contract chain. Subsocial is building a decentralized social network platform. Robonomics is building autonomous robot services. Bit Country is a platform for launching your virtual world / metaverse for your community. Integritee and Phala use Trusted Execution Environments (TEEs) to enable decentralized confidential computing and encrypted data storage. Polkadot’s development framework Substrate is also used independently (not as parachain) for running blockchains such as Compound Gateway. While all parachains are compatible with Polkadot’s inter-chain ecosystem by design, they should really be harnessing the incredible composability, memory-efficiency, and auto-upgrading meta-protocol governance capabilities of the Substrate framework to enable novel use cases.

Avalanche’s EVM compatible C-Chain initially attracted developers building efficient versions of Ethereum projects. Pangolin is a fast AMM cloned from Uniswap. Sherpa Cash enables private transactions, cloned from Tornado. TraderJoe started as an AMM and added lending on its way to becoming a DeFi hub. Benqi lend-borrow application is a version of Compound, but also started liquid staking for AVAX. Platypus is a better version of Curve stable-swap in that it has asset liability management. The largest Ethereum projects such as Aave, Curve, Sushiswap who employ a multi-chain strategy also launched on C-Chain and attracted massive liquidity, which are being fueled by the thriving Avalanche-Ethereum Bridge. Avalanche ecosystem also has new type of assets, one of which is for litigation financing, when combined with DAOs, it may have a huge impact in bridging existing legal systems with crypto-networks. In fact, the ingenious Avalanche consensus and the overlapping subnets topology together provide a tremendous innovation space for new projects to come.

Heterogeneous blockchain networks Cosmos, Polkadot, Avalanche provide extraordinary infrastructures to enable internet of blockchains, which demonstrates that asynchronous heterogeneous network model works efficiently and it is an improvement over Bitcoin and Ethereum as they are now. They will eventually accommodate millions of daily active users, and achieve the user owned and controlled Web 3 vision.

Co-existence of these major architectures is healthy for a true decentralized internet, as they have their own design choices and tradeoffs in their own right. Understanding the differences and similarities of these new infrastructures today would help building future-proof systems. Projects using these infrastructures will go beyond smart contract applications, become scalable production quality systems with their own specialized chains and communities, and demonstrate previously unimaginable use cases. As this might happen in a vacuum, we are still left with open questions: How might we make sure liquidity efficiently flows between inter-chains instead of being siloed within particular chains? How will those open organizations running across chains prevent emergence of multi-chain whales and ensure fair distribution of wealth and power?

[1] See Bitcoin’s Academic Pedigree paper by Arvind Narayanan and Jeremy Clark for several decades’ worth of cryptography research Bitcoin is built on.

Special thanks to Sam Hart, İstem D. Akalp, Engin Erdogan, Joe Petrowski for their feedback and review.

Disclosure: The author may hold positions in the assets of the projects discussed in this article.

The level of participation in collective decision making is a dynamic metric that affects the security of plutocratic governance systems, where voting is not “one person, one vote”, but weighted by the token holdings of each account. When voting turnout is low, the powerful accounts — token holders with an outsized proportion of total supply — can significantly swing the numbers. This article examines this risk, particularly in MolochDAO, and suggests potential remedies.

A decentralized autonomous organization (DAO) is an organization whose decision-making processes are transparent and enforced programmatically by a blockchain rather than a central authority.

After the tragic failure of the first DAO on Ethereum in 2016, several new DAO projects emerged. DAOs are typically used for collective fund coordination to support projects within the blockchain ecosystem, but the potential for DAOs to serve as new social coordination tools goes far beyond finance (Kei Kreutler’s talk “Autonomous ecologies at a distance” presents a good summary). Today, to start a DAO for your community you can design and implement the smart contract logic directly using best practices on the blockchain of choice, fork and redeploy an existing one such as MolochDAO, which is considered a minimum viable DAO because of its simplicity, or you can use a service such as Aragon or DAO Stack.

MolochDAO works because it has off-chain reputation within Ethereum community. — Ameen Soleimani

The MolochDAO smart contract on Ethereum was deployed on Feb 14th 2019 and 22 founding members each donated 100 ETH for a total of 2,200 ETH (330K USD at the time). As of August 12th 2019, MolochDAO has 71 members. To become a member you have to know an existing member who can submit a membership application on your behalf, tribute some amount of ETH to the MolochDAO funding pool, and then have the members vote you in.

MolochDAO Beneficiary Graph presents the “Proposer -> Proposal -> Beneficiary” relationships. Data captured on Aug 12th.

While these are early data points, we have analyzed the MolochDAO proposal activity. To join MolochDAO, an existing member (Proposer) creates a proposal to add an Ethereum account as a member (Beneficiary). The above graph is built based on all the passed proposals using the Proposer -> Proposal -> Beneficiary relationship. So far we observe that the membership growth is centralized, that is, a single account has proposed most new members. Yet from the periphery of the beneficiary graph above we see new member proposals are emerging. In the long run, we may observe a preferential attachment pattern in the growth of the membership network, where most new members are proposed by the most connected nodes, which further reinforces their influence.

The distribution of share ownership among MolochDAO members.

From the plot of membership shares above we see Vitalik Buterin and Joseph Lubin each have 1000 shares, ten times the voting power of most members. One account (Jez San from FunFair) has 250 shares, 52 accounts have 100 shares, and few accounts have around 10 shares.

The voting activity in MolochDAO is concentrated around the MolochDAO founders, Ethereum Foundation, Consensys, and Fun Fair, meaning they are the most active participants in proposal voting so far.

MolochDAO Voting Graph presents the active clusters in proposal voting. Data captured on Aug 12th, 2019.

The voter turnout rate affects the relative power distribution and security risk. In theory, if Buterin and Lubin both vote NO on a proposal, a minimum of 20 accounts, each with 100 shares voting YES, will be required to counter it. However, given the current turnout rates (max 30%), it is unlikely the remaining members will be able to gather support to oppose a Buterin + Lubin voting block.

The distribution of voter turnout across all MolochDAO proposals.

Like MolochDAO, Polkadot uses a token-weighted voting system. However, to counter the low voter turnout problem, Polkadot proposes the Adaptive Quorum Biasing scheme, in which the supermajority required for a referendum to pass changes as a function of percent voter turnout.

Polkadot’s Adaptive Quorum Biasing

A positive turnout bias requires a heavy supermajority of aye votes to carry at low turnouts, but as turnout increases towards 100%, it becomes a simple majority-carries as above. We call this a “positive” turnout bias because the required margin of ayes increases as turnout increases.

A negative turnout bias requires a heavy supermajority of nay votes to reject at low turnouts, but as turnout increases towards 100%, it becomes a simple majority-carries as above. We call this a “negative” turnout bias because the required margin of nays increases as turnout increases.

If MolochDAO had an adaptive quorum schema in its voting system, then the low turnout rates around 30% would require higher majority to pass a proposal.

To limit the problem of large holders having outsized influence, a common problem in all share-based governance systems, Polkadot introduces the notion of time-lock voting, an approach that allows token holders to increase the weight of their vote by locking their shares for a certain amount of time after voting has ended, trading opportunity cost for increased voting power.

With this mechanism, a voter with five tokens willing to lock them up only for the minimum allowed duration would have the same number of votes as a voter with only one token happy to have it locked for the maximum amount of time.

If MolochDAO had time-lock voting, then, even with a low voter turnout, opposing a Vitalik + Lubin voting block (assuming they lock their tokens for the minimum allowed duration) would take the remaining members to lock their shares for the maximum amount of time.

Although these solutions to the problems of low voter turnout and weighted voting are game-theoretically sound, there are no guarantees in reality. As the number of participants grow and the system increases in complexity, information asymmetry can impede members making sense of, and coordinating around proposals.

Reducing information asymmetry for members can be achieved by tracking critical metrics, including explanations and visualizations of voting data. Furthermore, network topology measurements of on-chain activity could be natively supported in blockchains and integrated into governance mechanisms. In the end, learning from our experience and continuing to improve our social coordination tools in ways that enfranchise and enable a broad community is how we progress toward more equitable governance in decentralized networks.

Data modeling, visualization, and analysis are done using Graph Commons. Data sourced from TheGraph.

Thanks to Sam Hart for conversations and editing which contributed to this post.

Interactive map “Towards Digital Constitutionalism?”

With the revelations of mass government surveillance, the intensity of internet governance debates has heightened in the past years. At the core of such debates is the future of fundamental rights, such as freedom of expression, privacy, access to knowledge, and access to the affordable internet, but also how to decision-making process can become more transparent and democratic.

A recent research by Lex Gill, Dennis Redeker and Urs Gasser, “Towards Digital Constitutionalism? Mapping Attempts to Craft and Internet Bill of Rights”, conducts an analysis of 30 initiatives, from advocacy statements to official positions of intergovernmental organizations to proposed legislation, spanning from 1999 to 2015. They each seek to advance a relatively comprehensive set of rights, principles, and governance norms for the Internet. Here is an excerpt from the paper:

The idea of an “Internet Bill of Rights” is by no means a new one: in fact, serious efforts to draft such a document can be traced at least as far back as the mid-1990s. Though the form, function, and scope of such initiatives has evolved, the concept has had remarkable staying power, and now — two full decades later — principles which were once radically aspirational have begun to crystallize into law.

We turned the dataset provided in the article into an interactive network map on Graph Commons.

Interactive map “Towards Digital Constitutionalism?”

The graph Towards Digital Constitutionalism? contains two types relations between three node types:

Actor -ACTS IN-> Document

Document -ADDRESSES-> Topic

There are many ways to read this landscape of internet policies. It was surprising to discover how central the “African Declaration on Internet Rights and Freedoms” on the map, while quite obvious to see “Right to be forgotten” topic to be peripheral, which is only supported by few European political parties.

We suggest you explore the network map yourself as an accompany to the paper. Simply, you can start reading the network map with the following questions:

• Which documents are similar to each other based on shared topics? Hint: The nodes that are close by have more shared connections, thus similar.

• Which topics are at the center, which ones are on the periphery? What do their positions on the graph tell us?

• What would those separately clustered topics mean?

• How do certain organizations aggregate together to support one document?

• Are there interesting paths between organizations supporting similar topics via the documents?

We suggest you try the graph interface to explore these questions.

Click on the search and type first few letter of your interest; see the suggested list of topics, documents, or actors; select one and it will be located on the graph with immediate relations.

You can filter the graph by checking and unchecking options as well setting ranges of dates or number of connections. Filter proposed documents and topics just for the year 2015. Also, see the documents proposed only by the governments from 2013 to 2015. That would reveal the shared interests of various governments on the internet policy.

The source code to used to generate this graph can be found here.

We hope that you will spend some time browsing this graph. Feel free to share anything you create or find.

If you want to try Graph Commons, sign up for free and start creating and publishing your own graphs.

We welcome feedback on your experience. You can send all problems, suggestions, questions and comments to contact@graphcommons.com, we’d love to hear from you.

Follow @graphcommons on Twitter for a daily dose of network arts and sciences, read our publication on Medium for guides and in depth articles, 
 join our Slack chat channel for discussions.

This post was co-authored with ahmet kizilay and originally published on the Graph Commons blog, January 26, 2016.

The map shows which organizations support (green) or oppose (red) “Renewable Energy Target” policy in Europe. Excerpt from EU Climate and Energy Targets 2030 Consultations. Data mapping by SIMPOL Project.

Civic data initiatives are quite different in their data work than international non-governmental organizations such as UN, OECD, World Bank and other similar bodies. Such organizations track social, economical, political conditions of countries and concentrate upon producing general statistical data, ​whereas civic data initiatives aim to produce actionable data on issues that impact individuals directly. The change in the GDP value of a country is useless for people struggling for free transportation in their city. Incarceration rate of a country does not help the struggle of the imprisoned journalists. Corruption indicators may serve as a parameter in a country’s credit score, but does not help to resolve monopolization created with public procurement. Carbon emission statistics do not prevent the energy deals between corrupt governments that destroy the nature in their region.

Green House Gas Emissions stats are quite limited for addressing the cause of the problem. Chart by OECD.

Partnership / financing network of oil refinery and thermal power plants around SOCAR (The State Oil Company of Azerbaijan). Data mapping by Network of Dispossession.

Needless to say, civic data initiatives also differ from governmental institutions, which are reluctant to share any more that they are legally obligated to. Many governments in the world simply dump scanned hard copies of documents on official websites instead of releasing machine readable data, which prevents systematic auditing of government activities. Civic data initiatives, on the other hand, make it a priority to structure and release their data in formats that are both accessible and queryable.

Civic data initiatives also deviate from general purpose information commons such as Wikipedia. Because they consistently engage with problems, closely watch a particular societal issue, make frequent updates, even record from the field to generate and organize highly granular data about the matter.

In fact, the purpose of civic data initiatives is not necessarily to inform public about what is happening, but to provide dependable data based on specific facts and evidences. Civic data initiatives proactively conduct research and converge data from their own field records (interviews and examinations), existing empirical research (other studies), public information (government records to media reports), and private sources (leaks and what not). They organize data into structures, connect the dots, employ data standards, form databases, and provide APIs (Application Programming Interfaces). They systematically publish data supported with analysis and stories.

Once generated, civic data moves. It supports advocacy campaigns, mediates focusing attention on the perpetrator of a cause, fuels investigative journalism, helps build resilient civil positions, becomes a stepping stone for another NGO’s maneuver against status quo, contributes the development of solidarity among struggling communities. The work of civic data initiatives becomes a useful reference for anyone who care about societal issues including journalists, activists, advocates, lawyers, artists, designers, technologists, academics and other civil society organizations.

As a civic database evolves, a distinctive vocabulary emerges, a vocabulary that prioritizes the civil society and freedoms as opposed to the status quo. When applications and interfaces use such data, the vocabulary would circulate with protocological interventions, which would allow the public explore the issues from the perspective of civil society instead of the government and corporations. Thus, it would help gain positions of influence that can develop counter-hegemony for the socialist movement, as Gramsci puts it in his writings the War of Position / War of Manoeuvre.

In fact, systematic abuse of power, bluntly oppressive or subtly hypocrite, has to be confronted with systematic struggle. Civic data work emerges as one particular mode of contributing to such struggle.

Several civic data initiatives generate data on variety of issues at different geographies, scopes, and scales. The non-exhaustive list below have information on founders, data sources, and financial support. It is sorted according to each initiative’s founding year. Please send your suggestions to contact at graphcommons.com. See more detailed information and updates on the spreadsheet of civic data initiatives.

Open Secrets tracks data about the money flow in the US government, so it becomes more accessible for journalists, researchers, and advocates. Founded as a non-profit in 1983 by Center for Responsive Politics, gets support from variety of institutions.

PolitiFact is a fact-checking website that rates the accuracy of claims by elected officials and others who speak up in American politics. Uses on-the-record interviews as its data source. Founded in 2007 as a non-profit organization by Tampa Bay Times. Supported by Democracy Fund, Bill & Melinda Gates Foundation, John S. and James L. Knight Foundation, Ford Foundation, Knight Foundation, Craigslist Charitable Fund, and the Collins Center for Public Policy.

Littlesis is a database of who-knows-who at the heights of business and government. Their data derives from government filings, news articles, and other reputable sources. It also provides an API for retrieving the data. Founded in 2009 as a project of Project of Public Accountability Initiative. Financially supported by institutions including Sunlight Foundation, Chorus Foundation, and Arca Foundation.

OpenCorporates aims to have a URL for every company in the world. Founded as a for-profit company by Chrinon Ltd (Registered in UK) in 2010. Data source from official company registers, company website scrapes, recently from Word Bank Institute and Oknf. They generate income from paid plans for using their API.

OpenSpending maps the money worldwide — that is, to track and analyse public financial information globally. It is meant to be a resource for individuals and groups who wish to discuss and investigate public financial information, including journalists, academics, campaigners, and more. Founded as a project in 2011 by Open Knowledge Foundation, it gets financial support from Open Knowledge Foundation, 4iP, Open Society Foundation, Knight Foundation, Omidyar Network, Hewlett Foundation

OpenOil is a consultancy, publishing house and training provider, specialised on open data products and services around natural resources. Provides API access to the database. Founded as a company in 2011 by a Reuters correspondent. Supported by Shuttleworth Foundation and also provides consulting to international organizations.

EJOLT is a global research project bringing science and society together to catalogue and analyze ecological distribution conflicts and confront environmental injustice. Founded as a platform in 2011 with many constituents. Financially supported by the European Union 7th Framework Programme.

Istanbul Worker’s Health and Safety Labor Watch reports on the workplace homicides, accidents, occupational diseases and safety conditions across Turkey. Founded in 2011 as a platform by members from Istanbul Medical Chamber, Chamber of Turkish Engineers and Architects, Scholars from Istanbul, Unions. Financially supported by individual donations, Istanbul Medical Chamber, Petrol-İş Union, TEK-GIDA İş Union, voluntary work.

Open Duka provides a freely accessible database of information on Kenyan entities. Founded by Open Institute in 2012, the project scrapes data from various sources that range from shareholder information, procurement information, legal cases and company information. It is built in partnership with the National Council of Law Reporting and funded by A.T.T.I.

InfoAmazonia provides timely news and reports of the endangered Amazon region. Founded by oEco, Internews in 2012, the project combines data from government databases, Open Street Map, and other NGO databases. Supported by ICFJ, Avina, CDKN, Skoll Foundation.

Networks of Dispossession maps the relations of capital and power in Turkey. Founded as a working group in June 2013 during the Gezi Park resistance, the project compiles data from government databases, media reports, and company websites. Publishes interactive maps and provides a search engine and an API via the Graph Commons platform. It is a voluntary work and does not have any financial support.

Poderopedia Mapping issues of local-regional socio-economic development, public investments, and ecology in Chile, Venezuela, and Colombia. Founded in 2013, the project is funded by Knight Foundation and International Center for Journalists.

Open Interests is a catalogue of political and commercial actors related to the European Union. Founded by the LobbyFacts project team in 2014, the project compiles data from European Lobby Register, Register of Expert Groups membership, Financial Transparency System, Tenders Electronic Daily (TED) and provides a search engine, which can be used to quickly retrieve information about the activities of companies, people and institutions in a European context. The project is supported by Corporate Europe Observatory (CEO), Friends of the Earth Europe (FoEE) and LobbyControl, OKFN Labs and Knight-Mozilla OpenNews.

La Fabrique de La loi (The Law Factory) maps issues of local-regional socio-economic development, public investments, and ecology in France. Started in 2014, the project builds a database by tracking bills from government sources, provides a search engine as well as an API. The partners of the project are CEE Sciences Po, médialab Sciences Po, Regards Citoyens, and Density Design.

Mapping Media Freedom identifies threats, violations and limitations faced by members of the press throughout European Union member states, candidates for entry and neighbouring countries. Initiated by Index on Censorship and European Commission in 2004, the project identifies threats, violations and limitations faced by members of the press throughout European Union member states, candidates for entry and neighbouring countries.

Regional Governance and Local Democarcy maps issues of local-regional socio-economic development, public investments, and ecology in Turkey. Commissions researchers and participants from the region, maps the generated data and publishes along with video discussions . Founded in 2015 by Helsinki Citizens’ Assembly and financially supported by European Commission, Turkey’s Ministry of European Union.

Open Contracting Partnership open up public contracting through disclosure, data and engagement so that the huge sums of money involved are spent honestly, fairly, and effectively. Data source is the participating governments in their program. Spun out of the World Bank in 2015 as a non-profit, and financially supported William and Flora Hewlett Foundation, Omidyar Network, Open Society Foundation, Laura and John Arnold Foundation, GIZ, Hivos.

This is a non-exhaustive list, please send your suggestions to contact at graphcommons.com. See more detailed information and updates on the spreadsheet of civic data initiatives.

This article was originally published in Turkish (11.06.2016) and translated to Kurdish (15.06.2016), both published on Bianet.org. Thank you Ahmet Kizilay and Zeyno Ustun for proofreading and suggestions.

Design and understanding of complex networks through mapping & analysis

This is the introduction of a 3-part guide on mapping, understanding, and analyzing complex networks. For the other parts, view “Mapping Networks” and “Analyzing Data-Networks“.

Complexity is characterized as something with many independent parts interacting with each other in multiple ways. Signaling of neurons in our body, messaging of devices in the telecommunication infrastructure, trading activity between agents in markets, social formations among people are some generic examples of complex systems, where small interactions together constitute a larger whole.

If we want to understand a complex system, we first need a map of its relationship diagram that is composed of nodes and links and make a network form, which is by its very nature the fabric of most complex systems.

While the nature of the nodes and the links differs widely, each network has the same graph representation, consisting of 4 nodes 4 links.

A network diagram offers a “common language” that is both visual and mathematical. So, from a network map, we can get qualitative information by reading its actors and relations, as well as do quantitative analysis by computing its connection structure. In fact, we can use this common language to study systems that may differ widely in their nature, appearance, or scope. For example, rather three different systems 1) devices sending messages to each other, 2) film actors connected through taking roles in the same movies, 3) organizations connected through partnerships may have exactly the same network structure. While the nature of the nodes and the links differs widely, each network has the same graph representation, consisting of 4 nodes and 4 links. We can use this simple method to begin studying a variety of complex systems.

Let us look at why do networks matter today, although they existed in every society in the history. It is known that ~3000 years ago the ancient Phoenician and Greek colonies formed their trade routes and built network of harbours in the Mediterranean Sea. Whereas today, in the same geography, submarine cables carry messages and enable a global communication system. What we have today, is not just networks as analog or bare life happenings, but networks that are digitized, like the way many aspect of life is digitized. Thus, networks matter today, because electronic and software based communication systems made networks measurable. Only in this day and age, networks are able to reach a global scale and infiltrate into every part of our life. With today’s advanced information technologies the metrics of network effect have become trackable and measurable even in one’s daily course of life, at the same structuring the social world as such.

~3000 years ago Phoenician and Greek colonies formed the trade routes in the Mediterranean Sea. Map Source

Today, in the same geography, submarine cables are enabling a global communication system. Map Source

Only in this day and age, networks are able to reach a global scale and infiltrate into every part of our life. Map Source

In fact, we all experience the network effect, from email to e-commerce, from social networking to banking, from telecommunication to transportation. We all acknowledged the fact that the world is complex more than ever. It feels both flat –one can email anyone any time– and chaotic –our inbox is inundated with information coming from all directions. Sometimes we are opportunistic about the internet, talking about it as a global good, other times we are pessimistic knowing that we are all under surveillance all the time. In such an antagonistic world that is at once flat and chaotic, the question arises again: Where does power reside and circulate?

Photograph of Obama and NYPD experts investigating the links of the suspect of a bombing attempt in Times Square. Image from an New York Times article “Police Find Car Bomb in Times Square”, May 1st 2010.

Additionally, the state’s tactics in partnership with certain corporations to monitor its own citizens through what has been called “the big data”, the NSA leaks and so on and so forth have also attracted our attention towards an inaccessible but quite magical tool of knowing and predicting of what people want. This indeed was possible through understanding the interrelated or linkable structure of the information generated by many, but as said only open to the reach of a handful of institutions. In other words, network logic have been mystified in the eyes of the public. Only experts in certain fields have been aggregating large amount of data and using scientific tools for mapping and analyzing it on relational basis. Neither the relational data nor the mapping and analysis tools are accessible to the average user.

Photograph from a Graph Commons workshop with NGOs. Representatives from two organizations in France and Turkey present the shared links between their organizations.

However, it is a myth that common people has no access to data. We are the data for the governments and corporations who continuously sense our activity. In fact today, a wide array of people pursue projects containing complex data. All such people would benefit from structuring and interlinking their information. Connecting our discrete data sets would generate new knowledge, render complex issues visible and generate crucial discussions that impact us and our communities. The Graph Commons platform is a step to allow you to do so.

Conceptual drawing for the idea of Graph Commons.

Graph Commons is the first collaborative online platform for making and publishing interactive network maps publicly. It is dedicated to investigative journalism, civic data research, archive exploration, creative research, and organizational analysis.

On Graph Commons, you can collectively compile data about the topics you are interested, define and categorize relations, and transform your data into interactive network maps, discover new patterns, and share your insight about complex issues using a simple interface.

The platform serves both producers and consumers of graphs by linking entities together in useful ways and thereby creating a whole that is greater than the sum of its parts. Graph Commons members collectively experiment in the act of network mapping as an ongoing practice: Search across variety of graphs, invite collaborators to their work and ask to contribute to theirs.

We believe everybody will find a unique way to use Graph Commons in their own connected world. We invite you to join us, and join one another in connecting partial information to explore complex relations that impact us and our communities.

We’d love to hear your feedback at contact@graphcommons.com. Follow @graphcommons on Twitter, subscribe to Graph Commons Journal on Medium, join our Slack chat channel for discussions.

This is the introduction of a 3-part guide on mapping, understanding, and analyzing complex networks. For the other parts, view “Mapping Networks” and “Analyzing Data-Networks“.