In the blog before this, I wrote about “Cooperation” aka “Collaboration”. That subject cluster is closely related to the idea of the Commons, which is projected at the side of the Civil Society in the “Trias Internetica”, which I introduced years ago. The other two points are Business and State. The P2P Foundation in which Michel Bauwens plays a very important role discusses many aspects of how to organise groups of people to develop and grow a “Commons”. Recently Michel wrote the paper below, as an Editorial Statement for the P2P Foundation site, which I am glad to distribute as a guest blog here, with Michel’s permission.
I regard this paper as very fundamental and important for our future.
IMHO the “Cosmo Local Model”, see below, of working can be enhanced and made more succesful by concentrating on local Value Creation by combinations of people with different talents and skills, thus generating Synergy that all contributers can share.
This is a leading-edge research paper because P2P cooperation builds collective intelligence into society, supporting democratic processes that can help to innovate, evolve networks and cope with Complexity better than the outdated hierarchies can.
Warning: it is usually only AFTER people start to interconnect and try to cooperate that they discover that “the others” have different data definitions and different prejudices and ‘cognitive biases’. In other words: ” live on different planets/ bubbles. A recent example of this is the EU project “GAIA-X”, which even runs into problems explaining what their objectives are and what the benefits for participants are. Not only “the others” but also the participants themselves have to understand that they have to make changes 🙂
My friend Pierre Lévy of the University of Montreal has been working on a Semantic Meta-Language called IEML (Information Economy Metalanguage), to make cooperation possible. I wish him success !!
Jaap van Till, TheConnectivist
================start of guest blog===========
Towards a Global Open Ledger for Contributory and Ecological Economics
* Provisional title: Great Transition Initiative: Filling in the Commons Gap
Draft for the Great Transition Initiative.
With thanks to Jason Lee Lasky for editorial suggestions.
Michel Bauwens, Chiang Mai, 29/12/2021
Technological determinism or human agency
Whether we believe or not that technology itself has its own determinism, i.e. it has its own relative agency independent of collective human wishes; or whether we believe or not that technology is shaped by material interests, i.e. designed by the ruling classes, there is always a residue of human agency to transform technological tools in the service of social change, and even in particular the interests of the greater number. This is what we propose to do here with perhaps the most unlike candidate: the bitcoin-generated blockchain. Our contention will be that the blockchain can be used as an essential tool to construct the next cyber-physical infrastructure that will allow humanity to produce for its own needs, while respecting the material and ecological planetary boundaries, and its interdependency with the web of life.
Bitcoin blockchain & distributed ledgers: from an internet of communication to an internet of transactions
Bitcoin and its attached ledger is the recipient of many legitimate critiques by progressives. It was designed with Austrian economics and anarcho-capitalist values (1) in mind, is very energy-intensive in its production, and has a very unequal distribution in terms of income and property, a distribution that is not accidental in view of the ‘oligarchic protocols’ that is has chosen as incentives for its stakeholders. But is it important to distinguish the existence of a global distributed ledger, i.e. a open and interoperable accounting and logistical system that can be used to coordinate production on a global scale, from its first iteration as the ledger of Bitcoin. We now have different post-blockchain distributed ledgers that attest to this, and a number of these ledger projects are operated not according to libertarian values and rules, but by integrating the insights of Elinor Ostrom (2).
The question to answer is the following: what becomes possible once we have such technology at our disposal ? My contention is that an interoperable blockchain becomes the vehicle for global mutual coordination of human production within planetary boundaries.
A ledger is first and most a accounting tool, recording transactions. The peer production communities (i.e. open systems of production and distribution of value, based on the free association of participants who can enter and exit such ecosystems of collaboration). This is obviously not trivial: the first accounting systems in Sumer, with temple administrations recording the flow of grain and debts, stands for the origins of the state apparatuses; the double entry accounting system for private competitive units streamed by a Franciscan monk in the Italian city-states, stood for the emergence of private forms of capitalism which would eventually become dominant.
Accounting for cosmo-global emergence
Blockchain ledgers have current inaugurated various forms of post-capitalist accounting which seem to be just as non-trivial:
• Contributory accounting (3), which can record, value and recompense non-commodified forms of contributions to ecosystems and networks, already signify the recognition of value outside the commodity form; it is a crucial tool signifying a transition to contributory regimes of value.
• Flow accounting, such as the Resource-Events-Agents software, allows every transaction to be recorded as an event in a network, and has abandoned double entry; it is an accounting for externality-aware ecosystems, not externality-ignoring closed entities.
• Thermodynamic accounting directly record the flow of matter and energy into an accounting system, such as the systems pioneered by R30.org and the Global Commons Alliance, who use a ‘global thresholds and allocations’ approach to determine the maximum allowed flow in particular contexts.
These ledgers are linked to tokens and intelligent current-sees (4) which can allow for expanded and complex value regimes. We now have access to local currencies, which can protect and regulate local economies, domain-specific intelligent monies such as SolarCoin generated by renewable energy, or FishCoin which regulates the maximum volumes for the fishing industry. Local-geographic currencies, domain-specific virtual currencies, transformed nation-state currencies can co-exist in a regime of monetary biodiversity that is both socially aware (contribution and impact aware), ecologically aware (thermo-dynamically informed), linked to full-activity supporting national economic policies, and a global regulatory currency that insures the economy stays within agreed upon ecological limits.
Three layers: stigmergic coordination, generative market exchange and thermodynamically informed planning frameworks
With these tools at hand, integrated in a cosmo-global cooperative ledger, it becomes possible to transcend the violent competition between economic coordination systems that plagued the 20th cy, ie. The fight between centrally planned economies and private-capital run economic systems. What we propose is an integration of the best of markets, transformed into generative market mechanisms that work for human and extra-human commons; the best of planning, which incorporates vital protection mechanisms for the survival of the planet, without being top-down authoritarianism, but a system which allows the maximum of local choices; and the free coordination in open transparent systems, i.e. stigmergy, the gift of open source peer production, i.e. the practices of the contemporary productive commons communities.
Here is what is envisageable:
– The primary layer of human cooperation becomes ‘stigmergy’, the gift of the commons economy that has been operating successfully within the open-source economy for two dozen years by now: open and transparent systems allow participating producers to freely coordinate their work effort in view of the needs and possibilities of the ecosystem as a whole, without needing central command. Collective agreements then result from negotiated coordination. The agent of this are the contributive communities, and the for-benefit associations that maintain their infrastructure of cooperation. In this particular context, the blockchain economy is best seen as an extension of this movement: it enables the creation of open and global coordination systems, in which a substantial amount of the income is devoted to paying open source developers.
– The secondary layer consists of the generative market exchange mechanisms, post-capitalist market forms that regulate genuine exchange, within planetary limits; this is necessary for the flow of all the resources that need investment and need to be renewed. The agent for this are generative and cooperative market entities, that add value to the commons economy. Commons Stack focusing on commons regulations, and RadicalXchange focusing on the creation of generative market systems are amongs the initiatives dedicated to this.
– The third layer is the planning layer. This is where thermodynamic accounting systems come in, by rendering visible the flows of matter and energy in a economic system, and where the ruleset of ‘global thresholds and allocations’, allows for specific ‘maximums’ that are context-specific.
Three possible futures for the world system: neoliberal wokism, national protectionism, and cosmo-localism
Of course, to a sceptic, the description so far will sound like an utopian description of a unlikely future, given the current balance of forces. What could be the agent of sufficient change that would lead to the adoption of such a global infrastructure ?
Our answer is that the commoners are the agents of such a change, following cosmo-local models (5), which we see as the ‘third possible future’ for humankind.
The first future is the continuation of multicultural neoliberalism, from now on under the hegemony of the Successor Ideology (i.e wokism), as proposed by the World Economic Forum, it is a world run by public-private partnerships, with weak national governments, strong transnational capital, and instrumentalized global NGOs. It’s political preference is for alliances of the elite fractions of urban cognitive elites, organized under group allocation rules so that they get their piece of the unequal pie, which can be used to manage the unruly popular classes. This is a win-lose game in favour of the elite.
The second model is protectionist retreat, which aims for re-strengthening sovereign nation-states. creating solidaristic citizen-based class coalitions internally, and attempts to control global flows of capital and labor to benefit a competitive nation. This is based on alliances between the more business-oriented middle classes, with the national working classes, against ‘external (and internal) threats’. The danger of the second model is armed confrontation between states aiming for control of scarce resources. This is another version of a win-lose game.
The third model is the cosmo-local model: in this model, we aim for a subsidiarity of material production (i.e. intelligent relocalization ‘that makes sense’), based on distributed manufacturing models, producing on demand using the maximum amount of biodegradable materials, and preferable using cooperative models with distributed governance and property features. In such a model, local production units are linked to global open design communities, which we call ‘protocol cooperatives’, and that are the depository of global learning for that particular domain. The partner state concept stands for a ‘community state’ that enables and empowers individual and collective autonomy at the local scale, guarantees the equality of contributory capacity, using multi-stakeholder commons institutions, following the quintuple helix governance model pioneered for the Italian urban commons. Local alliances of public authorities, the commercial sector, the formal civil society and research organizations support commons-centric public initiatives; they are mirrored, in a fractal way, by similar transnational institutions that support a domain-specific commons institutions, which we call the ‘magisteria of the commons’.
Addressing the ‘commons gap’ at the global level: towards Magisteria of the Commons
This addresses the ‘commons gap’ that is the main feature of the current global world order, which confronts a strong globalized financial capital, to weak nation-status and a subordinated civil society. Trans-local and trans-national magisterial are the vehicles needed to legitimize the global limits necessary to protect planetary boundaries, the web of life and the needs of coming generations, all ill-served priorities today. The cyber-physical infrastructure is the means to the end of organizing such a new cosmo-local order, and the commoners, the networked working class, is its agent.
We see these networked workers, organized in common third spaces, i.e. revamped makerspaces, as both local agents, rooted in their communities, and as agents that are linked to the global open design communities which are the vehicle of their social and technical knowledge. The digital nomads, are the equivalent of the medieval guilds, with the travelling knowledge workers who create cultural unity across geographic space.
Barring or awaiting the emergence of political forces which can represent this cosmo-local order, the priority is to network the productive nodes, and to construct the necessary transnational layer which can represent the counterforce to transnational capital. Partner state organizations are a vital link to facilitate the connection of local producers to the global streams of shared knowledge, and the new domain-specific value streams (the token economies governing domains of production).
(3) The P2P Value project found that 75% of the 300 studied peer production projects were using, experimenting or researching such accounting conventions and tools, https://wiki.p2pfoundation.net/P2P_Value
(4) The concept is from Arthur Brock, founder of the Holochain post-blockchain ledger, a open and p2p-based interoperable ledger system that doesn’t require a world computer but is based on the free interconnection of autonomous ledgers.
(5) Cosmo-local production models are described in a new book by the P2P Foundation, which contains 40 case studies of initiatives combining local material production with globally shared open designs: The Cosmo-Local Reader. Ed. José Ramos, Sharon Ede, Michel Bauwens and Gien Wong. P2P Foundation, 2021, https://clreader.net/
The ideas expressed in this editorial are derived from the following in-depth report:
P2P Accounting for Planetary Survival: Towards a P2P Infrastructure for a Socially Just Circular Society. By Michel Bauwens and Alex Pazaitis. Foreword by Kate Raworth. P2P Foundation, June 2019.
Collaboration / Cooperation is urgently needed and succesful in many fields, and in Nature. My recommendation therefore is to take a good look at what Howard said in 2005 at his prophetic TED Talk. You will no doubt recognize many things he already did see coming.
A big advantage of sharing and collaboration is that it has an incentive: it gives SYNERGY to all participants: they get more out of it than they have to put in. In other words “all boats are lifted”. This still sounds counter intuitive to hard headed capitalist / neo-liberal economist enterpreneurs, who concentrate on Value Extraction in any way possible. But at the expense of their slaves/ 9 to 5 workers and often at the expense of Nature, which they still think is abundant enough. Collaboration allows for Value Creation. A lot of people find out that they can contribute to the community because they have an unique ability or skill. Everybody has one !!!!Appreciation is what drives them 🙂
It is vital for society to grasp and embrace these concepts since the next big waves are The Circular Economy and The Regenerative Economy both by re-connecting and even again be part of Nature , if we are lucky !!!!
IMHO, I have not studied this development well enough, so I can not judge this new fad. Best I can do is to let other guru’s tell you what they think.
This is a quote that Michel Bauwens, on his blog “P2P Research Clusters” on FaceBook, took out of the very relevant article quoted in 2. and posted below. : ” Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects.”
Very relevant Article in the “Scientific Reports” Journal (Nature) from European academics with Formal citation:
Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.
“WTF are NFTs? Why crypto is dominating the art market” is the title of the February 21, 2021 episode of The Art Newspaper podcast1, signalling both the impact of Non Fungible Tokens (NFTs) on the art world and the novelty they represent for most of the general public. The revolution is not confined to the art market. While NFT adoption in gaming has already reached a certain maturity, for example concerning the trade of in-game objects, different other industries, especially those involved with the production of digital content such as music or video, are experimenting with the technology. Overall, in the first four months of 2021, the NFT volume has exceeded 2 billion USD, ten times more than the entire NFT trading volume in 20202.
So, what’s an NFT? An NFT is a unit of data stored on a blockchain that certifies a digital asset to be unique and therefore not interchangeable, while offering a unique digital certificate of ownership for the NFT3. More broadly, an NFT allows to establish the “provenance” of the assigned digital object, offering indisputable answers to such questions as who owns, previously owned, and created the NFT, as well as which of the many copies is the original. Several types of digital objects can be associated to an NFT including photos, videos, and audio. NFTs are now being used to commodify digital objects in different contexts, such as art, gaming, and sports collectibles. Originally NFTs were part of the Ethereum blockchain but increasingly more blockchains have implemented their own versions of NFTs4.
The first popular example of NFTs is CryptoKitties, a collection of artistic images representing virtual cats that are used in a game on Ethereum that allows players to purchase, collect, breed, and sell them on Ethereum5. In December 2017, CryptoKitties congested the Ethereum network6. By many considered a chief example of the irrationality driving the cryptocurrency market in 20177, CryptoKitties remained the only popular example of NFTs for almost 2 years. In July 2020, the NFT market started to grow2 and attracted a huge attention in March 2021, when the artist known as Beeple sold an NFT of his work for $69.3 million at Christie’s8. The purchase resulted in the third-highest auction price achieved for a living artist, after Jeff Koons and David Hockney9. Several other record sales followed10,11: three Cryptopunks—a collection of 10,000 unique automatically generated digital characters—were sold at $11.8, $7.6, and $7.6 million dollars, respectively; the first tweet was sold at $2.9 million dollars; and the Auction Winner Picks Name, an NFT with music video and dance track, sold at $1.33 million dollars. The profitability of NFTs has motivated celebrities to create their own NFTs, with collectibles of NBA and famous football players getting sold for hundreds of thousands dollars12.
Research on NFTs is still limited, and focuses mostly on technical aspects, such as copyright regulations3; components, protocols, standards, and desired properties13; new blockchain-based protocols to trace physical goods14; and the implications that NFTs have on the art world15,16, in particular as they allow to share secondary sale royalties with the artist. Empirical studies aiming at characterizing properties of the market have focused on a limited number of NFT collections, such as, CryptoKitties17,18, Cryptopunks, and Axie19, or on a single NFT market, such as, Decentraland19,20 or SuperRare21,22. These analyses revealed that the digital abundance of NFTs in digital games has led to a substantial decrease of their value17, and that, even if NFT prices are driven by the prices of cryptocurrencies19, the NFT market could be prone to speculation18,20. Further, it was shown that NFTs valued by experts are more successful21, and that, based on 16,000 NFTs sold on the SuperRare market, the structure of the the NFT co-ownership network is highly centralized, and small-world-like22,23.
In this paper, we provide a first comprehensive quantitative overview of the NFT market. To this end, we analyse a large dataset including 6.1 million trades of 4.7 million NFTs in 160 cryptocurrencies, primarly Ethereum and WAX, and covering the period between June 23, 2017 and April 27, 2021. We start by analysing the overall statistical properties of the NFT market and its evolution over time. Then, we study the network of interactions between NFT traders, and the network of NFT assets. NFTs are further clustered based on their visual features. Finally, we present the results of regression and classification models predicting the occurrence of NFT secondary sales and their price.
We break down our analysis by NFT categories, which are classified by manual inspection, with references to the classification proposed by NonFungible Corporation24, a specialized company that track NFTs sales, and OpenSea25, one of the largest NFT marketplace. However, the exact classification of different categories in which NFTs are used is outside of the scope of the present paper. For example, Art objects can be in some cases classified as Collectibles, while some Game objects may present sophisticated aesthetic and cultural properties that may qualify them as Art.
The NFT market
Items exchanged on the NFT market are organized in collections, sets of NFTs that, in most cases, share some common features. Collections can be widely different in nature, from sets of collectible cards, to selections of art masterpieces, to virtual spaces in online games. Most collections can be categorised in six categories: Art, Collectible, Games, Metaverse, Other, and Utility (see also “SI”). We show the top 5 collections in terms of number of unique assets (n) for each category (see Fig. 1a).
an initial rapid growth in late 2017, when CryptoKitties collection gained worldwide popularity, the size of the NFT market has remained substantially stable until mid 2020, with an average of ∼60000∼60000 US dollars traded daily (see Fig. 1b). Starting from July 2020, the market has experienced a dramatic growth, with the total volume exchanged daily surpassing ∼10∼10 million US dollars in March 2021, thus becoming 150 times larger than it was 8 months earlier.
We measured to what extent different NFTs categories contribute to the size of the whole NFT market. Until the end of 2018, the market was fully dominated by the Art category, and in particular by the CryptoKitties collection. From January 2019, other categories started gaining popularity, both in terms of total volume exchanged (see Fig. 1b,c) and number of transactions (see Fig. 1d). Overall, in the period between January 2019 and July 2020, ∼90%∼90% of the total volume exchanged on NFT was shared by the Art, Games, and Metaverse categories, contributing 18%18%, 33%33%, and 39%39% respectively. Starting from mid July 2020, the market volume has been largely dominated by NFTs categorized as Art, which, since then, have contributed ∼71%∼71% of the total transaction volume, followed by Collectible assets accounting for 12%12%. Importantly, however, the market composition is quite different when considering the number of transactions. Since July 2020, the most exchanged NFTs belong to the categories Games and Collectible, which account for 44%44% and 38%38% of transactions. Instead, only 10%10% of transactions are related to NFTs categorized as Art. Overall, we observe that the share of volume spent in Art has been growing since 2020, while its share of transactions has been decreasing (Fig. 1d). The discrepancy between volume and transactions reveals that prices of items categorized as Art are higher, on average, compared to other categories.
We dig further into these differences by looking at the distribution of NFT prices across categories (see Fig. 2a), which we find to be broadly distributed. We observe that the average sale price of NFTs is lower than 15 dollars for 75%75% of the assets, and larger than 1594 dollars, for 1%1% of the assets. Considering individual categories, NFTs categorized as Art, Metaverse, and Utility reached higher prices compared to other categories, with the top 1%1% of assets having average sale price higher than 6290, 9485, and 12,756 dollars respectively. Note that these categories are different in sizes, so 1%1% of assets corresponds to 8593, 472, and 78 NFTs in the Art, Metaverse, and Utility categories, respectively. The highest prices so far were reached by assets categorized as Art, with 4 NFTs that were sold for more than 1 million dollars.
To assess the market activity, we measured how often individual assets are traded. Here, we refer to the first time an asset is sold as the asset’s primary sale, and to all other sales as secondary sales. All assets considered in this study had a primary sale, but only ∼20%∼20% of them had a secondary sale (see “SI”). We observe that the tail of the distribution of number of sales s per asset, for 𝑠≥10s≥10, is well characterized by a power-law function 𝑃(𝑠)∼𝑠𝛽P(s)∼sβ, with 𝛽=−1.4β=−1.4, estimated following26 (see Fig. 2b). When looking at different categories, the distribution of number of sales is affected by cut-off values. For example, the maximum number of sales for assets in the Utility category is 12, while an asset in the Games category is sold more than a thousand times, and an asset in the Art category more than five thousands times. Note that only 0.07%0.07% of all assets are sold more than 10 times. Also, the size of collections n is well described by a power-law function 𝑃(𝑛)∼𝑛𝛼P(n)∼nα, with 𝛼=−1.5α=−1.5 (see Fig. 2c), implying the distribution of sizes is broad. We find that ∼75%∼75% of collections comprise less than 37 unique assets, and ∼1%∼1% have more than 10,400 unique assets.
Temporal patterns of secondary sales are unique for each collection, as evidenced by considering the top collection in each category (see Fig. 3). For example, when Cryptokitties emerged in 2017, secondary sale prices were typically lower than the price of their first sale. More recently in 2021, instead, their secondary sale prices have gone up because of an increase in the number of potential customers. Other collections, like Alien, alternated periods when secondary sale prices went down and period when they went up. In the Unstoppable collection secondary sales are rare because NFTs correspond to web domains secured by blockchain technology. In 2017, secondary sale price were lower than primary in 66% of the cases, while in 2021 only 27% of secondary sales had lower prices than the primary one.
The networks of NFT trades
How do traders interact with each other? Are there central actors? We approach these questions adopting a network science approach23,27. We consider the network of trades, where nodes are traders, a directed link from a trader to another exists if the former (the buyer) purchases at least one NFT from the latter (the seller). Each link has a weight corresponding to the total number of items that the buyer bought from the seller.
First, we study the behaviour of individual NFT traders by focusing on properties of the nodes. We find that traders activity is highly heterogeneous: the strength of traders (nodes) s, defined as the total number of purchases and sales made by each trader, is distributed as a power law 𝑃(𝑠)∼𝑠𝜆1P(s)∼sλ1 with exponent 𝜆1=−1.85λ1=−1.85 (see Fig. 4a), such that the top 10% of traders alone perform 85% of all transactions and trade at least once 97% of all assets. Further, we find a superlinear relation between the strength of a trader and the total number of days of activity d, with 𝑠∼𝑑𝜆2s∼dλ2 and 𝜆2=1.28λ2=1.28 (see Fig. 4b). This result reveals that the average number of daily trades is larger for traders active over long periods of time. Traders are also specialized: measuring how individuals distribute their trades across collections, we find that traders perform at least 73% of their transactions in their top collection, while at least 82% in their top two collections combined. The relation between strength and specialization is not monotonic: the most specialized traders have either few (less than ten) or many (more than ten thousands) transactions (see Fig. 4c). A specialized trader is the one with Ethereum address “0xfc624f8f58db41bdb95aedee1de3c1cf047105f1”, that exchanges tens of thousands of CryptoKitties. Similar relationships hold when buying and selling behaviours are considered separately (see “SI”).
Secondly, we turn to properties of the network links, describing interactions between pairs of traders. We find that the distribution of link weights is well characterized by a power law distribution, with the top 10% of buyer–seller pairs contributing to the total number of transactions as much as the remaining 90% (see “SI”). An interesting question is whether traders connect preferentially to traders that have similar strength. We tackle this question by studying the assortativity coefficient r28, that measures the correlation between the sum of the weights of all outgoing links (the outgoing strength) of a given node with the average sum of the weights of incoming links (the incoming strength) of its neighbours. We find that the assortativity, which takes value 𝑟=−0.024r=−0.024, is close to the null value zero, implying that traders do not connect to other traders based on the similarity of their connection patterns.
Finally, we focus on the network structure. Building upon the result that traders are specialized, we assign each trader to their top collection, and we study the modularity29 of the network under this partition of nodes. The modularity is a metric bounded between −0.5−0.5 and 1, which is positive when the density of links among nodes assigned to the same partition is larger than it would be expected by chance. We find that the modularity Q of the collections partition is 𝑄=0.613Q=0.613, significantly higher than what expected from a random network 𝑄=0.0823±0.0001Q=0.0823±0.0001 (see “SI”). It reveals that the collections well represent the underlining network structure, where traders specialized in a collection tends to buy and sell NFTs with other traders specialized in the same collection.
We now turn to the exploration of how NFTs are connected to one another. To this end, we construct the network of NFTs, where nodes are NFTs and a directed link exists between two NFTs that are purchased “in sequence”, e.g. a link is created from an NFT to another when a buyer purchases the former and then the latter, with no purchases between the two (see “SI” for more details). Rather than linking all NFTs ever traded by the same trader, this choice allows to understand the relations between NFT that are semantically similar, because they are bought by the same trader in approximately the same period of time. Further, it ensures that the network structure is not dominated by large cliques.
The distribution of NFTs strength decays as a power law with exponent 𝜆3=−3.21λ3=−3.21 (see Fig. 4d). Note that the strength of NFTs is different to the total number of sales per NFT (previously shown in Fig. 2b), due to how the network is constructed. In fact, when two NFTs are purchased simultaneously, this creates two links for each of the two nodes (one ingoing and one outgoing). The next question we ask is: which NFTs are connected to one another? We find that NFTs in small collections tend to be bought in sequence with NFTs in other collections (see Fig. 4e). On the contrary, NFTs in large collections, like CryptoKitties or Gods-Unchained, tend to be bought in sequence with NFTs in the same collection.
What are the implications of this behaviour on the NFT network structure? We investigate the relation between the structure of the NFT network and NFTs collections, by studying the modularity29 of the network under the partition of NFTs (nodes) into NFT collections. We find that the modularity Q of the collections partition is 𝑄=0.80Q=0.80, significantly higher than what expected from a random network 𝑄=0.1110±0.0001Q=0.1110±0.0001. It reveals that (1) the network is clustered and (2) the collections well represent the underlining community structure. By further exploring the relationship between traders’ behaviour and NFT networks structure, we unveil that, while the NFT network is clustered, communities are not isolated. That is, some traders buy or sell assets belonging to multiple collections. The network of NFTs has two strongly connected components (SCC)30, defined as groups of nodes such that, starting from a given NFTs, it is possible to reach any other NFTs in the SCC following directed links. The largest SCC include NFTs traded in the WAX blockchain, consisting of 35% of all NFTs, while the second largest includes NFTs traded in the Ethereum blockchain, consisting of 20% of all NFTs (see Fig. 4f). While the high network modularity reveals that traders tend to purchase assets from the same collection in sequence, the presence of very large SCCs reveals that there are less frequent sequences of purchases in different collections.
representation of the trader network including the Art category on February 2021 shows the clusters formed by NFT traders specialized in the same collection (see Fig. 5a). Similarly, the same visualization for the NFT network shows a similar trend, where NFTs, albeit surrounded by other NFTs in the same collection, tend to form a sparser structure (see Fig. 5b).
We then study the networks consisting of assets in the same category and blockchain (see “SI”). We find that key results presented above, including the shape of the strength distributions, hold across categories. Also in this case, we find that traders, independently from the category considered, are specialized: the fraction of individual trades in the top collection is included between 59%, for the Other category, and 98%, for the Utility category. Similarly, the fraction of individual trades in the top collection is 70% for the WAX blockchain and 91% for the Ethereum blockchain category. Relative to the number of total NFTs in each category, the WAX component contains 55.0% of all NFTs labeled as Collectible, but only the 0.06% of all NFTs labeled as Utility. On the contrary, the Ethereum component has the 54.8% of all Art, but only the 10.6% of Games.
NFTs are linked to digital assets of different types, including videos, text, animated GIFs, and audio. Currently, the most popular NFTs are images10,11. We select NFTs associated with images and take a snapshot of animated GIFs, and analyse them with the pre-trained convolution neural network AlexNet. AlexNet extracts from an image a vector of 4096 values that is a dense representation of the image’s visual features. With this representation, vectors extracted from images that are visually similar are close in the vector space. To quantify the visual difference between pairs of pictures, we calculated the cosine distance (CD) between them, a value that goes from zero (for identical images) to one (for highly different images). We measured such distance between pictures within the same collection and across collections.
The average CD calculated between items which belong to the same collection is significantly lower (𝜇=0.59μ=0.59, 𝜎=0.20σ=0.20) compared to the one obtained for objects from two different collections (𝜇=0.87μ=0.87, 𝜎=0.06σ=0.06), confirming an intra-collection graphical homogeneity. Figure 6a shows the matrix of average CD values between all pairs of collections. Values on the diagonal represent the intra-collection CD values, and reveal that most collections have a high degree of homogeneity (e.g., Sorare (CD = 0.24) or Cryptopunks (CD = 0.33)) but some are more heterogeneous (Rarible (CD = 0.89)). In short, many collections have their own style, graphical hallmarks that distinguish them from others. There are also sub-groups of collections, usually within the same category (coloured band in Fig. 6a), which share some common visual features. This is the case for collections containing pieces of pixel-art, including Chubbie, Cryptopunks and Wrapped Punks, or the similarities observed between Cryptokitties and Axie.
To map the images into a lower-dimensional feature space that can be used in practice for prediction and visualization, we apply Principal Component Analysis (PCA) to the AlexNet vectors. PCA uses linear combinations of the 4096-dimensional vectors to project them into vectors with an arbitrarily lower number of dimensions and such that the variance of datapoints in the projected space is maximized. Considering the whole sample, which consists of about 1.25 million graphical objects, the first five principal components explain together about the 38.3% of the total variance, progressively distributed from PC1 to PC5 as follow: 20.3%, 7.3%, 4.0%, 3.8% and 2.7%. The PC1 to PC5 scores are used to test the capacity of visual features for predicting sales (see next subsection), while PC1, PC2, and PC3 for visually representing the data through a 3D scatter plot and showing intra-categories homogeneity (see Fig. 6b). This can be quantified by looking at the average Euclidean distance in the PC1, PC2, PC3 space between objects of the same category and comparing it to the one calculated among objects of different categories. Considering the whole sample and calculating the distance between all the points, the average value obtained between elements of different categories is 1.67 bigger than for elements of the same category. However, as we already described for the cosine distance in the AlexNet vector space, this is mainly due to the intra-collections homogeneity, as demonstrated calculating the average inter-collection distance which results more than three times (3.17) bigger than the intra-collection distance and secondarily to the presence of intra-categories clusters of similar looking collections. This is most likely caused by the market responsiveness to the success of a collection, which induces other creators to follow the trend and offer variations on the theme.
To identify the factors associated with an NFT’s market value, we fit a linear regression model to estimate the price of primary and secondary sales from different sets of features, calculated considering only the data preceding the day of the NFT’s primary sale. The features (whose detailed formulations are provided in “SI”) include the degree and PageRank centrality of the buyer and seller in the networks of NFT trades (𝑘𝑏𝑢𝑦𝑒𝑟|𝑠𝑒𝑙𝑙𝑒𝑟kbuyer|seller, 𝑃𝑅𝑏𝑢𝑦𝑒𝑟|𝑠𝑒𝑙𝑙𝑒𝑟PRbuyer|seller), the principal components of visual features of the object linked to the NFT (𝑣𝑖𝑠𝑃𝐶𝐴1…5visPCA1…5), a prior probability of sale within the collection (𝑝𝑟𝑒𝑠𝑎𝑙𝑒presale), and the past median price of primary and secondary sales within the collection (𝑚𝑒𝑑𝑖𝑎𝑛𝑝𝑟𝑖𝑐𝑒medianprice).
NFT’s price correlates strongly with the price of NFTs previously sold within the same collection (see “SI”). The median sale price of NFTs in the collection predicts more than half of the variance of price of future primary and secondary sales. The prediction is more accurate when the median of the past sale price is calculated over a recent time window preceding the primary sale, e.g., the prior time window of one week is better than considering the entire time window preceding the NFT’s primary sale. Similar results, albeit with generally lower correlations, are found when the secondary sale price is the object of the regression (see “SI”). As one would expect, the price of secondary sales is strongly correlated with the price of primary sale, and the predictive power of the variables declines as one attempts to cast a prediction over longer periods of time: 𝑅2𝑎𝑑𝑗=0.90Radj2=0.90 when predicting the median secondary sale price over the next week, and falls to 𝑅2𝑎𝑑𝑗=0.77Radj2=0.77 when extending the prediction over the next 2 years (see “SI”). A similar relation is found between the secondary sale price and the median price of the NFTs collection (see “SI”).
Other features than prior sale history are predictive of future primary sale price (see Fig. 7a) and median secondary sale price (see Fig. 7b). Centrality measures of the buyer and seller in the trader network (𝑅2𝑎𝑑𝑗∈[0.05,0.12]Radj2∈[0.05,0.12]) and visual features of the object linked to the NFT (𝑅2𝑎𝑑𝑗∈[0,0.08]Radj2∈[0,0.08]) explain roughly one-fifth to one-fourth of the variance when used in combination (𝑅2𝑎𝑑𝑗∈[0.18,0.25]Radj2∈[0.18,0.25]). When considered in combination with the median price of previous sales, they increase the predictive power by almost 10% for the secondary sale price (𝑅2𝑎𝑑𝑗Radj2 from 0.55 to 0.6). When fitting separate regressions for each category, it becomes apparent that the predictability of future prices and the predictive power of different sets of features varies depending on the NFT category. The collectible category is the easiest to predict, with centrality and visual features yielding 𝑅2𝑎𝑑𝑗∈[0.30,0.36]Radj2∈[0.30,0.36] and 𝑅2𝑎𝑑𝑗∈[0.40,0.50]Radj2∈[0.40,0.50], respectively. These two families of features have the largest compound effect in the Art category; in the secondary sale price prediction, centrality features boost the predictive power of visual features by more than 50%. Regression coefficients of individual features for the task of secondary sale price prediction one month after the primary sale are presented in Table 1.
Table 1 Secondary sale price prediction. Linear regressions to predict the NFTs’ median secondary sale price one month after their primary sale from three families of features: centrality on the trader network (k, PR), history of sales in the NFT’s collection (namely prior probability of secondary sale 𝑝𝑟𝑒𝑠𝑎𝑙𝑒presale and median sale price 1 week before the sale medianprice), and visual features (𝑣𝑖𝑠𝑃𝐶𝐴𝑖visPCAi). Regression models were fit to different categories of NFTs independently. For each category, the number of NFTs and collections it contains is reported. The 𝑅2𝑎𝑑𝑗Radj2 is a measure of goodness of fit, and it quantifies the proportion of the data variance explained by the model. The p-values of all 𝛽β coefficients are <0.01<0.01 except for those marked with ∙∙, which are all >0.05>0.05.Full size table
When predicting secondary sale prices, we consider only those NFTs that were sold in a secondary sale. These NFTs are the minority: less than 10% are sold at least once within one week after the primary sale, and only about 22% within 1 year (see “SI”). Using the same set of features that we selected for the price regression, we trained AdaBoost32, a binary classifier, to assess to what extent it is possible to predict whether an NFT will be sold after its primary sale (for more details see in “SI”). We find that this is possible to a certain extent. The prediction is most accurate when training and testing the classifier on Art NFTs only (𝐹1>0.8F1>0.8), whereas the prediction is less reliable for the other categories (𝐹1∈[0.14,0.33]F1∈[0.14,0.33], see “SI”). The median price of the collection is among the strongest predictors, but not always the strongest. The prior probability of sale in the collection is also a strong signal, and centrality and visual features combined can sometimes outperform other feature combinations (e.g., in the Metaverse category). Last, the prediction is most accurate when trying to predict the occurrence of a secondary sale over longer periods of time (see “SI”).
The NFT market is less than four years old and has boomed in 2021. This paper presented the first overview of some key aspects of it by looking at the market history of 6.1 million NFT trades across six main NFT categories including art, games and collectibles. In brief, (1) we analyzed the main properties of the market, (2) we built and studied the traders and NFTs networks and found that most traders are specialised, (3) we showed that NFT collections tend to be visually homogeneous, and (4) we explored the predictability of NFT prices revealing that, while past history is as expected the best predictor, also NFT specific properties, such as the visual features of the associated digital object, help increase predictability.
It is important to highlight the main limitations of our study, which represent also directions for future work. First, we gathered data from a variety of online NFT marketplaces and not directly from the Ethereum or WAX blockchains, so that we have likely missed a number of “independent” NFT producers. Second, we mostly adopted an accepted categorisation for the NFTs, which includes a number of arbitrary decisions and could however be further refined (as every categorization). Third, since our primary goal was to provide a general overview of the market, we did not extensively explore all the available methods e.g., for the features extraction from images33 and their clustering in a lower-dimensional space34, machine learning for price prediction35, or market modelling36. We also did not consider collective attention as measured e.g. from social media or Wikipedia, which can be a further source of information about market behaviour37,38,39. Fourth, we considered mostly the Ethereum and WAX blockchains, but several other platforms offer smart contracts and NFTs. Finally, our price prediction exercise did not include information about the creator of the (digital) object associated to the NFTs. While this is due mainly to the dataset, and in many cases the identity of the creator is not available or does not exist (e.g., for AI generated images), it is likely that in certain contexts, and specifically for art, this can be an important aspect to consider.
Overall, NFTs are a new tool that satisfies some of the needs of creators, users, and collectors of a large class of digital and non-digital objects. As such, they are probably here to stay or, at least, they represent a first step towards new tools to deal with property and provenance of such assets. We anticipate that our study will help accelerate new research on NFT in a broad array of disciplines, including economics, law, cultural evolution, art history, computational social science, and computer science. The results will also help practitioners make sense of a rapidly evolving landscape and inform the design of more efficient marketplaces as well as the associated regulation.
Data and methods
We summarize our data collection below and provide a detailed description of our data manipulations in “SI”.
Sales data collection
Our dataset includes only transactions representing purchases of NFTs, whose ownership change following that transaction. We exclude from our analysis any transactions representing the minting of NFTs or bids during an auction. We track different cryptocurrencies. Etherum blockchain data for the collections SuperRare, Makersplace, Knownorigin, Cryptopunks, and Asyncart were shared by NonFungible Corporation24, a company that tracks historical NFT sales data to build NFT valuations. Other Ethereum blockchain data were downloaded from four open-source APIs: CryptoKitties sales40, Gods-Unchained41, Decentraland42, and OpenSea43. With OpenSea that allows trading in multiple cryptocurrencies. We also monitored the WAX blockchain, through tracking transactions in the Atomic API44.
NFTs into six categories: Art consisting of digital artworks such as images, videos, or GIFs; Collectible representing items of interest to collectors; Games including digital object used in competitive games; Metaverse consisting of pieces of virtual worlds; Utility representing items having a specific function; and Other including the remaining collections. More details on the NFT categorization are explained in “SI”. The final, cleaned dataset includes 935 million USD traded in 6.1 million transactions involving 4.7 million NFTs grouped in 4624 collections. Our dataset includes transactions in 160 different cryptocurrencies with most of them made in WAX (52% of the total number of transactions), while the volume in USD is mostly ETH (81% of the total volume). We show general statistics of the categories of NFTs considered, involving a total of 359,561 buyers, 314,439 sellers, trading 4.7 millions NFTs involving 953 million USD in cryptocurrencies (see “SI”).
Image collection and visual feature extraction
For each NFT in our dataset (except for less than 3000 exceptions) we managed to collect at least one URL that points to a copy of the NFT’s digital object. We focused only on objects with image file formats (e.g. PNG, SVG, JPEG) and GIFs, for a total of about 1.2 million unique graphical objects associated with 4.7 million unique NFTs. Note that a single digital object can be related to multiple NFTs; this happens for example for identical playing cards that are minted in multiple copies, each associated with a different NFT. Since our algorithm for visual feature extraction works with static images, we converted the animated GIFs to PNGs by extracting central frame of each GIF. In order to succinctly represent the visual features that characterize an image, we encode it into a latent space using a neural network. Specifically, we pick the PyTorch45 implementation of AlexNet46, a deep convolutional neural network architecture designed for image classification. We initialize AlexNet with weights pre-trained on ImageNet47, a widely-used reference dataset of labeled images. Given an image in input, AlexNet passes it through multiple layers of transformation. The second to last layer (i.e., the layer before the classification layer) is a vector consisting of 4096 values that constitute a dense representation of the input image into a high-dimensional space. These vectors can be used for a variety of tasks such as similarity ranking, clustering, or classification. To reduce the dimensionality of AlexNet vectors, we extracted their principal components using Principal Component Analysis (PCA)48, and selected the five most relevant ones. PCA projects each point of the high-dimensional space into a space with a desired number of dimensions, while preserving the data variation as much as possible.
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The authors are grateful to NonFungible Corporation for helpful conversations and data sharing (see text). The research was partly supported by The Alan Turing Institute.
Department of Mathematics, City University of London, London, EC1V 0HB, UKMatthieu Nadini, Flavio Di Giacinto & Andrea Baronchelli
The Alan Turing Institute, British Library, 96 Euston Road, London, NW12DB, UKMatthieu Nadini & Andrea Baronchelli
Technical University of Denmark, DK-2800 Kgs., Lyngby, DenmarkLaura Alessandretti
Department of Neuroscience, Catholic University of the Sacred Heart, Rome, ItalyFlavio Di Giacinto
IBM Research, Cambridge, MA, USAMauro Martino
IT University of Copenhagen, Copenhagen, DenmarkLuca Maria Aiello
UCL Centre for Blockchain Technologies, University College London, London, UKAndrea Baronchelli
M.N., L.A., F.D.G., M.M., L.M.A., and A.B. designed the study. M.N. and F.D.G. carried out data collection. M.N., L.A., F.D.G., and L.M.A. performed the measurements. M.N., L.A., F.D.G., M.M., L.M.A., and A.B. analysed the data, discussed the results, and contributed to the final manuscript.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
===========end of re-blog from the “Scientific Reports” Journal (Nature)=========
I respect Kat’s views, but must add that at least BM has launched, instead of normal business value extraction, value creation as their “Turn Your Life into ART” slogan/ invitation sounds positive.
If you look more closely as Kat did, you can see that behind that BM is a Business. With a business model for the SHARKS to make money from the produced art by extracting it from the young creators DOLPHINS. It is the same what happened in Facebook. We do the work and the owners of FB get rich from our personal data.
Sure the dolphin-shark ecology is not illegal but it can be a reason for dolphins in the civil society to start fighting back and take back their P2P created value and own it.
Please address questions and remarks about her paper to Kat on Facebook(?)
jaap van till, TheConnectivist
Burning Man – a P2P nightmare? Questions for the movement
by Katarzyna Gajewska, PhD
Peer production is a concept still searching for the reality. Case studies will enrich the understanding of how to make this model work in all the messy structural, cultural, and psychological context of contemporary capitalism. Negative examples can pave the way towards a desirable future because it is easier to discern by seeing what you do not want. They can serve as a mirror to further initiatives of this kind. The advantage of writing and analyzing the future is that you can help shape it by asking questions to better understand what the visions are behind the actions shaping it. Burning Man (further abbreviated as BM), an annual event gathering up to 70 thousands people in Nevada, helps us to formulate these questions and anticipate some pitfalls.
Burning Man’s relevance to P2P model
Instead of deciding whether Burning Man is an example of peer production, let us depict it as a P2P nightmare. I propose to look at some of its dysfunctions to be more wary about possible dangers in setting up peer production spaces. BM experience helps us to define the details of a production system that corresponds to our moral standards.
Table: Comparison of Burning Man Principles and P2P Model
Burning Man Principles
Inclusion by organizing work in modular way
Voluntary contribution of granular tasks
Use value and not for profit principle
Leaving No Trace (mainly as Leaving No Trash in the venue)
Civic ethics of engagement
Not explicitly addressed but not in contradiction with this principle
Use value versus making a living
P2P literature distinguishes between extractive and commons based peer production (P2P Manifesto) or firm-hosted and community-hosted peer production (Mayo Fuster Morell). While BM is coordinated by a non-profit, it does not automatically mean that it meets standards of commons based or community-hosted peer production. This case raises further questions that the P2P typology may need to elaborate on. In the world that is largely commodified, an example of peer production without paid staff, especially in physical world, is difficult to find. Behind bigger peer production projects, there is a team of salaried coordinators. How to define whether the salaries of coordinators are not exploitative in relation to the free work provided by prosumers?
Problems with defining BM as for benefit production
It is difficult to define what BM produces. The participation is market-dependent. Participants have to pay for the ticket, buy food and equipment outside, so there is not much production happening. While there is gifting, it is a redistribution of bought products. Almost everything is bought so the event is just pooling of bought stuff. BM mainly produces experience. The works of art commissioned for the event are destroyed so there is no long-term contribution to the community. While the core ten principles include “Leaving No Trace,” this rather translates into leaving no trash on the site and deposing it to the neighboring garbage bins and offloading the waste to the local populations. You can read more about illegal trash dumping in 2018 article “Leave No Trace Has Become Hide the Evidence.”
A lot of waste is produced during the event and wood is burnt. Therefore, it is questionable whether there is any benefit of this event to the society at large. For P2P movement, this raises concerns of what means are justified by what ends. How can the benefit be calculated against the potential environmental costs?
The relations to the state and local community
Burners produce pollution and garbage that directly affects tax-payers in the locality. The local authorities have imposed a tax on the festival. BM evokes interesting questions regarding the relations between P2P and the state. For example, benefits produced may be globally dispersed and the negative externalities locally concentrated. How these discrepancy can be mediated by peer production projects?
BM culture and governance regarding sexual violence and consent
Sexual assault is the subject of criminal law and is treated by the state authorities. The limits of state protection, especially in such a setting, are so obvious that abstaining from the intervention is a statement of organization’s values. On one of the stories of sexual violence reported to the media, Cate Edelstein, a nineteen-year-old woman accepted a glass of water in a bar at BM in 2012. She was found unconscious with visible signs of sexual abuse on her body.
We do not know how many other acts of violence have happened there. Official data gathered by the organizers may not be representative seeing that sexual violence is under-reported in the society, there are probably much more violations happening there.
Limited cultural work to prevent sexual assault
Culture of gifting may incite potential victims to be less cautious and prone to the intake of hidden drugs. There are no preventive measures that address this fragility. Recent initiative to promote awareness around sexual consent has attracted 350 attendees to workshops by 2018. They were organized by Bureau of Erotic Discourse – a group of activists at BM. However, for the population of about seventy thousand, this is a drop in the sea.
Silencing victims of sexual assault
In 2019 report “Exclusive: Burning Man calls itself a safe space. Assault survivors say it’s got a sex crime problem” Karlis points to the lack of appropriate response to sexual misconduct: “An inadequate self-policing system has the effect, intended or otherwise, of silencing and dismissing victims of sexual assault and other forms of abuse before they have an opportunity to report the crime to law enforcement.” The article cites testimony of two victims who have been ignored by BM organization and a former volunteer of several years at the festival. The latter pointed out how safety team skewed the process of reporting sexual misconduct. After having raised these concerns to the organization, she ended up being uninvited in 2018. Another person has been uninvited after reporting a sexual assault they have witnessed.
BM’s ambiguity about employment and labor protection
High rate of suicide among seasonal workers working on the site raises questions about labor conditions there. A seasonal worker who raised issues of labor treatment and the lack of transparency about pay was uninvited in 2017. An accident involving lasers has caused blindness. The affected woman has not received 250,000 dollars, which are usually due in case of such a disability being inflicted at a workplace. CEO’s salary for 2018 was 268,000 dollars.
Spencer mentions that wealthy participants hire labor to set up their camps and sherpas to assist them in their comfort during the festival. A venture capitalist paid 180 dollars for fifteen- to twenty-hour days during the festival.
The blurred definition of what is work and what is not serves exploitation because potential contributors may not be able to distinguish for themselves whether they are exploited or being part of fun and community. What a better way to have people contribute labor than by using this touchy-feely discourse?
People enchanted with the atmosphere may forget that there is profit making behind the work that they contribute. By definition, it goes not to the participants that are bound by the principle of gifting but to the core BM management, whose comfortable annual salaries. CEO Marian Goodell earned 268,000 dollars of annual salary in 2018.
This business model resembles Facebook. While BM talks about de-commodification and gifting, Facebook is free and makes money out of “selling” us an illusion of online community in exchange for our clicks. BM sets a stage to produce and consume a lofty “community infatuation” and other experiences, which are actually commodities working for core management’s salaries.
Authority and responsibility
BM core team exercises authority. Does the power they have accorded to themselves imply taking responsibility? The principle of self-reliance may be an excuse to avoid taking responsibility or seeing the need for measures that will help prevention.
BM raises important questions for P2P movement, namely how responsibility and prevention can be best assured in a decentralized spontaneous organization. What if there is not enough centralized capacity to deal with safety issues and there is not enough spontaneous contribution? Safety requires specific skills and is difficult to assure in a decentralized way. Who takes responsibility and how to enforce it in such settings beyond the state regulation?
The perils of transiency
BM illustrates very well the tyranny of short-term perspective and atomization among participants, which is the constitutive part of the festival. The atmosphere of living-in-the-moment is generated, which may lead to suppressing any confrontational behavior. The perspective of being with certain people only for a short period of time may lead to two types of pitfalls in inter-personal relations: 1) refraining from addressing when boundaries have been crossed; and 2) being more “adventurous” or intrusive because of the lack of the usual social control.
Another consequence of transiency is non-commitment to collective processes – the lack of real community – and the unusual proximity of participants, which may be confused with being part of a community. The result is a co-existence of two opposites: an extreme atomization and an extreme proximity. Such a mix is potentially dangerous: at best it produces confusion, at worst it may lead to an abuse of the situation.
The dangers of cultural void and fun doctrine
A superficial consideration of what work is delivered in the context of such an event would foreground building installations and mechanics of the site but all this does not matter if the culture is lacking. Coincidently, the structures are built in the desert. Both building physical structures in the desert and creating a culture in a void is a humanly challenging and heroic work. The difficulty of the latter is that you do not know what has been achieved as the cultural poverty is often more visible in cases where there is a failure.
Stating principles does not automatically translate into a culture. Especially in a situation of following them (or not) in an atomized way without a collective deliberation process, they are meaningless.
A person with a nickname Caveat Magister, who is part of BM team, published a comment on Spencer’s article entitled “Why the Rich Love Burning Man.” This blog post on BM website reveals organization’s attitude is to a deeper reflection. Certainly, one person does not speak for the entire group. They (the gender of the author is not revealed) defined BM as people having fun and “an experience of life outside of politics.” Ideology is a collection of ideas regarding goals and methods. You want it or not, BM is implementing an ideology.
They suggest that BM transcends politics, which misses the point that everything has political impact and that practices and behaviors allowed in the space carry values and doctrines (whether the actors are aware of them or not). Dismissing a deeper reflection on what this event is producing prevents an analysis of the consequences and setting moral standards. By not making the ideology and precepts explicit and knocking democratic process back, the life energy put into the event is delivered to the hegemonic agenda. The lack of intentionality generates a fertile ground for letting the dominant culture overtake the space.
Unaware people are potentially dangerous. Pursuing an implicit ideology – that underpins actions without being spelled out – they may be deprived of the possibility and wherewithal to evaluate their actions. History has shown us on many occasions the cruelty brought about by people who refuse to think. In the spirit of BM mentality, “having fun” dogma may silence critical voices and reinforce self-censorship against “heavy” topics and moral considerations because of stigma of being uncool. A Japanese proverb says that vision/dream without action is daydreaming, and action without vision is a nightmare. BM has had many victims.
How much intervention is morally enough?
The principle of self-reliance echoes P2P principles of self-selection, spontaneity, or limited hierarchy. Therefore, the example of BM can be relevant in detecting possible consequences of such an ideology.
Self-organized spaces face a dilemma of how much regulation is appropriate. Too much of it may lead to rigidity and deter some people from participation. Hence a space becomes exclusive to some. However, not enough rules may lead to disruptive behaviors, which may cause a real damage to vulnerable individuals. The psychological and physical repercussions may stay with victims for their whole lives. The BM illustrates the dangers of the principle of self-reliance, which seems to go hand-in-hand with refraining from responsibility.
Making self-reliance a norm may lead to self-blaming instead of seeing inequality in the capacity to defend self-interests. The principle may motivate neglecting the fact that we differ in our vulnerability and capacity to be in charge. For example, a person that has not been exposed to the knowledge about rape drugs or the consequences of taking drugs in general cannot make informed decisions. Also there are discrepancies in the probability to fall prey to an assault. For example, past trauma may affect the ability to prevent and react to any kind of abuse. Furthermore, the culture and socialization may inhibit some people in expressing their boundaries. The literature on problematic or dubious consent documents the complexity of consent. A first person account by Lux Alptraum bears out such dynamics in sexual encounters.
P2P movement would benefit from reflecting on these inequalities and the problems of consent. What measures can be undertaken to limit oppression in these spaces and raise awareness about our conditionings that may play out in different forms of interactions? The issues of potential domination dynamics within the P2P movement are raised in the interview with Elena Martinez and Silvia Diaz. I have carried out research in two projects resembling P2P model, although they do not call themselves this way. In People’s Potato, the anti-oppression awareness is seen as a requirement for the coordinator job. In Park Slope Food Coop, which self-organizes 17,000 members, there are committees dealing with discrimination and educating about it.
Forking and dealing with mismanagement
On the surface, the organization pretends to be very open with seemingly non-existent hierarchy but control and top-down approach to power shows up when management’s comfort is challenged. BM’s ways of dealing with conflict situation shows that there is no space for feedback and processing concerns. The organization applies top down thinking and simply uninvites a person that has raised difficult issues. This is benevolent dictatorship at its worst.
Forking is part of peer production system of governance. There is a lot of evidence that BM organization is not only unable to induce cultural change organically but also does not behave in a way that would model democratic process at the top. In the logic of peer production governance, forking is a way to deal with a benevolent dictator.
There have been many initiatives around the world to set regional festivals drawing upon BM principles. Some participants in these events are critical of the festival happening in Nevada and boycott it. Several public announcements of personal boycott – such as Daniel Pinchbeck’s in 2015 – are examples of voting with feet.
BM is not just some people having fun. Dressed with a “feel-good” ideology and a promise of transformation, the organization raises hopes. It is a venue where people may have had their first experience of what they were sold as a community. For some, it may turn out to be a bad experience, which may bring them on the path of cynicism and resignation. It is important to take responsibility for what you are selling and how you are appropriating the words and concepts that may be debased by your actions. For the P2P movement, the lesson offered by BM experience is a mirror to see potential shadows that may come alive on the way. It is important to be aware of the hegemonic culture and its temptations.
Conflict of interest disclaimer
The author has never been to a Burning Man festival. She is not planning to participate in the event in Black Rock City for moral and environmental reasons. This may speak both in favor and against her objectivity.
About the author
Katarzyna Gajewska, PhD, is in health activism, currently focusing on clean water and pesticide-free farming. You can read her book “A School from a Saner Future” (free of charge) here.
Interesting initiative from Australia, where in some local areas renewable Solar and Wind turbine energy use is already exceeding fossil fuel energy use. Obstacle: these local sources yield fluctuating energy streams, because sunshine and windforce varies; and also sharing & consumption varies so it must be distributed computer controled and accounted for. Redgrid.io and its operating company IOEN.tech are building and rolling out systems to do that succesfully. Money is exchanged by way of tokens that are now swappable with bitcoin systems.
To understand where this comes from think about the wonderful combination many people are discovering of solar panels on your house with the Ecar (batteries on wheels) you have. But then you find out it is more clever to share the solar energy and the batteries of your friends in the whole neighborhood. But then you want a low cost way to collect usage data (for this internet of things) and do accounting and billing, so costs and revenues are distributed fairly. That system is what Redgrid and ioen.tech have built.
IOEN is an interconnected system of virtual microgrids that facilitates transactions within & between local energy ecosystems based on @Holochain (transaction system on distributed and interconnected PC’s or server boxes).
IOEN – The Internet Of Energy Network Unlocking Global Minigrids • The Internet of Energy Network is an interconnected system of minigrids that facilitate transactions within and between local energy ecosystems: from the appliance level, to energy generation, storage, and consumption.
This is a message from them last week:
Hey Everyone, just want to jump in and share something pretty exciting. Our Internet of Energy Network (IOEN) token launches have been going very successfully so far.
This is a massive milestone in our 3 year journey to build the protocol that enables intelligent IoT microgrids. ️️
Look for yourself if this will fly or not !! My guess is that it will grow like a bushfire, bottom up. Reason: these guys and girls need no permission. Neighborhoods will benefit and the climate of our Nature. Aim is climate neutrality in 2029 ?.
A bit more on the “carrier” of these systems #Holo its HoloChain and its ambitions:
Recently, on September 28 2021, I gave a brief presentation at the famous Wireless Optic Communication Conference (#WOCC), Eindhoven, but presented online. The conference lectures can be seen at https://www.owcconference.com/previous-editions/
3. My view on what drives the ongoing huge growth in Bandwidth Demand is as follows.
In my paper I have shown a drawing with on the right side a Computer Bus with Chips (like cpu’s) connected that do CONTENTION on the Bus/backplane, consisting of multiple parallel wires. Only one chip at the time can do a call or answer on this Bus. Further to the left there are a number of other “places of contention for connection resources”: The I/O interface, the USB (universal serial Bus) cable, the hub/router, Ethernet cables, the outside optic fiber link with Ethernet like traffic (VLAN), a level 2 Switch, wide area links, level 3 routers, etc. all the way to server farms of Cloud Services. At each of these “places of contention” digital traffic (streams of IP packets) are filtered (Bus calls are not sent all over the network) and handled localy, so only data that is destined for longer distant servers is passed through. This also means that the long distance link speeds do not have to be designed for the addition of the device (like Laptop) speeds. The architecture i propose can handle a number of local indoor 1 Gb/s devices on one 1 Gb/s link from the network provider/ ISP.
My most important message is that Telcos & cableco’s & network access providers should notice who their CLIENTS are: the new superpowerful CPU’s and GPU’s !!! Like from Intel and Apple Mx. These millions of IC’s will drive bandwidth demand, and ……will need optical Ti-Fi. It is not only the people who experience occasional slow network speed but their computers, and even more specific the CPU chips inside them who get restless.
Let me explain this in more understandable terms. If you open an online webpage on your screen then, DEPENDING ON THE NETWORK ACCESS SPEED, the App working on the CPU tries to get connection to a number*) of internet addesses to get blocks of info and or pieces of film to put on the screen. The App and the CPU want reactions to their request and one of them is delayed it repeats the request. etc. etc. The cpu is pushing and pushing very unforgiving until the answered pieces are on the screen. If the speed of the connection is better the number*) of outstanding requests is increased, with adds or suggestions to the page. In that way a slow line does not seem very bad. It just gives the impression of slowly filling screens, not showing its incompleteness. In other words, the billions of recent Faster CPU’s from Apple computers and Intel are driving bandwidth demand.
This project fits in the long time trend of changing networks from Electronics to Photonics (#Fotonica). In other words from moving Electrons to moving and switching Photons.
This Ti-Fi project is an example a rare “Moonshot Project”(vital breakthrough), as defined by powerlady Mariana Mazzocato. So is RedGrid.io and IOEN.org in Australia and #Holo in Turkey. During WWII in the UK that was Radar.
Just out: the book by Parag Khanna (see other posts on this Blog) called “MOVE: How Mass Migration will reshape the World”
This important because it goes far beyond the stream of migrants on rubber boats from North Africa barely reaching Spain and Italy. And beyond the streams from Syria and Afganistan. Decades ago the CIA already warned that climate change would force many in the World from their farms to the cities and then abroad to survive with their families. As Ai Wei Wei explained these families do not start to migrate unless they have no other choice to leave everything behind. It is not just fortune-seekers. Or criminals who often populate the first waves, as we have experienced in our country. And it is rather sinister that some countries keep them out, for instance Saudi Arabia, who shoots every bordercrosser through the feet so they can not even walk any further. Hungary is also a ruthless stopper, although they should realize the Hungarians themselves are migrants from long ago forced out of Ugria, now in Siberia, during the Big Westward Migration.
Migration of large numbers of people is of all ages, and sometimes creating opportunities and wealth like the commercialy skilled Jews who where forced out of Spain, who could settle in Antwerp and Amsterdam. and like the very hard working and reliable Protestants who where forced out of France (Hugenots) and could settle in Amsterdam and London.
Here is the review of the book:————————–
A compelling look at the powerful global forces that will cause billions of us to move geographically over the next decades, ushering in an era of radical change.
In the 60,000 years since people began colonizing the continents, a recurring feature of human civilization has been mobility–the ever-constant search for resources and stability. Seismic global events–wars and genocides, revolutions and pandemics–have only accelerated the process. The map of humanity isn’t settled–not now, not ever.
As climate change tips toward full-blown crisis, economies collapse, governments destabilize, and technology disrupts, we’re entering a new age of mass migrations–one that will scatter both the dispossessed and the well-off. Which areas will people abandon and where will they resettle? Which countries will accept or reject them? As today’s world population, which includes four billion restless youth, votes with their feet, what map of human geography will emerge?
In Move, celebrated futurist Parag Khanna provides an illuminating and authoritative vision of the next phase of human civilization–one that is both mobile and sustainable. As the book explores, in the years ahead people will move people to where the resources are and technologies will flow to the people who need them, returning us to our nomadic roots while building more secure habitats.
Move is a fascinating look at the deep trends that are shaping the most likely scenarios for the future. Most important, it guides each of us as we determine our optimal location on humanity’s ever-changing map.
I ordered it today so I can not yet give my own impression, but it is obvious that this “migration” will be one of the most important issues, related to Climate Change, we have will have to deal with.
It contains a number of very telling maps of EurAsia changing.
To just close the borders and pretend it does not exist will be useless. If we look more closely, and not only at Football Teams, we should realise: “WE ARE ALL MIGRANTS”.