June 19, 2022

Dcentral vs. Consensus: Are institutions “frens” or enemies of crypto?

As a part of an ethnographic study on blockchain organizations, I recently attended two major conferences – Dcentral Con and Consensus – held back-to-back in Austin, Texas during a blistering heatwave. My collaborator, Johannes Lenhard, and I had conducted a handful of interviews with angel investors, founders, and venture capitalists, but we’d yet to conduct any fieldwork to observe these types of operators in the wild. Dcentral, held at Austin’s Long Center for the Performing Arts, and Consensus, held at the Austin Convention Center and other venues throughout downtown, provided the perfect opportunity. The speaker and panel topics at both conferences varied widely–from non-fungible tokens (NFTs), to the metaverse, to decentralized finance (DeFi). At both conferences an underlying debate regarding the role of established institutions repeatedly bubbled to the surface. The differences between the two conferences themselves offered a stark contrast between those who envision a new frontier of crypto cowboys dismantling existing social and economic hierarchies and those who envision that same industry gaining traction and legitimacy through collaboration with regulators and the traditional financial (aka “TradFi”) sector. 

Dcentral was populated by scrappy developers of emerging protocols, avid gamers, and advocates for edgy decentralized autonomous organizations (DAOs), such as Treat DAO, which allows adult content creators to sell “NSFW” (i.e., not safe for work) NFTs. Attendees at Dcentral sported promotional t-shirts and sneakers, and a few even showed up in Comic Con style garb, flaunting flowing white togas and head-to-toe blue body paint. Over the course of Dcentral, many speakers and attendees crafted passionate arguments around common libertarian talking points–self sovereignty, individualism, opposition to the Federal Reserve, and skepticism about government oversight more broadly. Yet governments were not the only institutions drawing the ire of the Dcentral crowd. Speakers and attendees alike took aim at corporate actors from traditional finance systems as well as venture capital (VC) firms and accredited investors.

Perhaps the most acerbic critique of institutionalization in the crypto sector was issued by Stefan Rust, founder and CEO of Laguna. Wearing a white cowboy hat, he opened his presentation [see 3:19] with a criticism of protocols that impose undesirable “middlemen” between the user and their intended transactions:

“This is what we want to avoid. We invited these institutions into our ecosystem and we now have layers, on layers, on layers that have been created in order to take a decentralized peer-to-peer electronic cash ecosystem to fit a traditional, TradFi world, the system that we’ve been fighting so hard since 2008 to combat […]. Do we want this? I don’t know. I didn’t sign up to get into crypto and Bitcoin and a peer-to-peer electronic cash system for multiple layers of multiple middlemen and multiple fees”

Stefan Rust, Laguna

In his view, increasing involvement of institutional actors could lead to “SSDD.” That is, same shit, different day, which according to Rust, is exactly what the ecosystem should be dismantling.

Consensus, held directly after Dcentral, had an entirely different feel. In contrast to the casual dress of Dcentral, many attendees at Consensus wore conservative silk dresses, high heel pumps, or well-tailored suits, despite temperatures that topped 100 degrees just outside the conference center doors. In a panel aptly entitled, “Wall Street Suits Meet Hoodies,” Ryan VanGrack, a former advisor at the Securities and Exchange Commission (SEC), opened with a comment about how he felt uncomfortably informal in his crisp button-down shirt, slacks, and pristine gray sneakers. According to one marketer at a well-known technology company, the cost of hosting a booth on the exhibit floor was in the neighborhood of 75K. This was not the ragtag gang of artists and emerging protocols from Dcentral; these people were established crypto players who saw the pathway to revolution as running straight through the front door of institutions rather than by burning them to the ground.

Like Dcentral, speakers and panelists at Consensus called for the reform of the financial industry, often similarly drawing from libertarian values and arguments; however, unlike Dcentral, many at Consensus emphasized that regulation of the crypto industry is not only warranted, but necessary to expand its scope and market adoption. According to them, the lack of regulation has imposed an artificial ceiling on what the crypto sector can achieve because retail investors, would-be protocol founders, and institutional players are still “waiting on the sidelines” for regulatory clarity. This position was not merely abstract rhetoric. Current and former government actors such as Rostin Behnam, Chairman of the  Commodity Futures Trading Commission (CFTC) as well as Senators Kirsten Gillibrand, Cynthia Lummis, and Pat Toomey, participated in panels. These panels focused on the role of regulation in the crypto ecosystem, such as measures that preserve innovation while also preventing catastrophic failures such as the recent collapse of Terra, which financially decimated many retail investors. 

At Consensus, advocates of institutionalization were no less enthusiastic in their endorsement of the mission of crypto and web3 than the anti-institutionalists at Dcentral. In other words, they too were true believers, just with a different theory of change. On Friday night I was invited to attend an event hosted by Pantera Capital, a top-tier crypto VC fund. I mentioned to one of the other attendees that I had attended Dcentral. His face pulled into a grimace. “Why the look of disgust?” I asked. He clarified that while “disgust” was too strong of a word, he felt that events like Dcentral delegitimize what the industry seeks to accomplish. Rather than being the true embodiment of the web3 ethos, he felt these crypto cowboys and their antagonistic rhetoric risked undermining the very efforts that were likely to have the biggest impact.

At the conference, panelists and attendees referred to Terra as the “elephant in the room.” But it struck me that personal wealth and its tension with the crypto vision was a much bigger and far less acknowledged elephant. Possibly the only speaker to directly and unambiguously call attention to this was Assistant Professor of Law Rohan Grey. In a panel entitled “Who Should be Allowed to Issue Digital Dollars,” Grey noted that as the “resident pet skeptic” he would act as a rare detractor to the “self-congratulatory industry love-fest” or “circle jerk” that would unfold at Consensus. Establishing common ground with the crypto community, he noted that he too supported efforts to resist “Big Brother as well as Wall Street and Silicon Valley.” But then he offered a withering critique of crypto industry actors, especially those with ties to the established financial sector:

“We should be very clear about the difference between private, for-profit actors providing public goods for their own material benefit and actual public goods. So, who are the people who want to issue digital dollars if not the government? We’re talking about licensed limited liability companies backed by venture capitalists, many of whom are standard Wall Street actors. We’re talking about people with a fiduciary responsibility to a particular group of shareholders. We’re talking about decisions being made on behalf of the public by private individuals who are there only because of their capacity to hold wealth initially, and those actors will then be lobbying for laws favorable to themselves in government and creating the same revolving door that we’ve seen with Wall Street for decades.” 

Rohan Grey, Assistant Professor at Willamette University College of Law

The idea that private sector actors who made their fortunes in the traditional financial sector could serve as the vanguard of a financial revolution certainly merits scrutiny. Yet, even if somewhat dubious, it is at least possible that these actors, having seen from the inside the corruption and ill-effects of existing financial institutions, could leverage their insight to import better, more democratic values into an emerging crypto financial system. Along these lines, one man I chatted with at an after party said it was his experience witnessing what he felt were morally reprehensible, exploitative lending policies while working at a bank that ultimately pushed him to adopt the crypto vision. Still, more than a little skepticism is warranted given that institutional or even anti-institutional actors stand to materially benefit from greater adoption of crypto and its associated technologies, a point that Grey himself underscored.

Following such skepticism, a cynical take is that people will always behave in alignment with their own incentives, even when doing so causes harm to others. I have heard people espouse exactly this sentiment when excoriating scams, NFT “rug pulls,” or even failed DeFi applications. Yet such a bleak view of humanity is overly simplistic given the body of empirical data about human prosocial behavior (e.g., Fehr, Fischbacher & Kosfeld, 2005). People can and often do behave in ways that are altruistic or in the service of others, even at a cost to themselves. Many advocates both for and against institutionalization of the web3 and cryptocurrency sector are likely motivated by a sincere desire to benefit their fellow man. But intentions aren’t the only thing that matters. The positive and negative real-world impacts of blockchain applications both direct and indirect are critical. Whether this increasingly institutionalized sector will spark a real revolution or further entrench SSDD remains to be seen.

Improving Your Relationship with Social Media May Call for a Targeted Approach

By Max Fineman and Matthew Salganik 

Chances are, you’re on social media every day. If you have teens, they are too. And everyone seems worried about just how much social media they’re consuming; even many teens.  Beyond these individual worries, some researchers have linked social media use to increases in political tribalism, mental health problems, and suicide.  Yet at the same time, many people seem to really love using social media.  This combination was puzzling to us, as social scientists.  So, as part of our recent undergraduate course in social networks at Princeton University, we decided to explore it further.  In particular, we decided to ask: How can people use scientific ideas to create a healthier relationship with social media?

You might think the best approach is to change your behavior based on previous research, but that runs into two problems.  First, prior research doesn’t necessarily shed light on how individual users are affected by social media, and second, as best as researchers can tell, different social media platforms seem to impact people differently.  Therefore, if you want to understand and improve your own relationship with social media, a promising approach is self-experimentation, where you basically run experiments on yourself.

The students in our social networks class did just that – designing, conducting, and reflecting on a self-experiment involving social media. For example, one student who was interested in improving their sleep decided to stop using TikTok after 10 p.m. Another student interested in being less lonely posted more Instagram Stories.

About 60 students did the activity, and there were some interesting patterns in what they found. We expected that students who limited their use—as opposed to increasing it—would benefit more in terms of personal well-being, loneliness, productivity, and sleep quality. But it turns out that the students who saw the most positive outcomes were those who designed their social media intervention in a targeted way – like avoiding Instagram while in the library. These students benefited more than students that tried something blunt, like quitting TikTok altogether. In other words, changes that should be the easiest to try — small interventions students could stick to long-term — had the most positive effects. 

Below we describe what our students did and learned. We’ve also included all of our materials so that you can try it yourself.

What students did:

Our class had about 60 students, from a variety of years and majors, and like most Princeton students, many of them were heavy users of several social media platforms.  Each of them designed their own treatment and selected an outcome of interest.  For example, some students were interested in improving their sleep and others were interested in wasting less time.  In addition to these student-specific outcomes, we also had all students track two common outcomes that have been studied by other researchers: subjective well-being and time use changes.  

This process of self-experimentation is a bit different from what social scientists normally do. Typically, researchers standardize the treatment, randomly assign treatments to participants, and collect data so that we can compare across participants and treatment groups. In our class, however, each participant was a researcher and designed a unique treatment for themself. Even though this is not standard for research, self-experimentation can be a good way to learn.  The treatments students developed fell into three main groups:

  • Targeted limitation (about 45%). Students in this group restricted – but did not eliminate – their social media use. For example, students in this group did things like stopping TikTok use after 8pm and avoiding the Instagram feed (but still using Instagram for messaging).
  • Targeted increase (about 15%).  In class, we learned about some research that suggests people who use social media actively—rather than passively scrolling—see an improvement in their well-being.  So some students committed to increasing their active engagement with social media.  For example, students in this group did things like posting 3 times per day on Instagram or direct-messaging at least 3 friends. 
  • Elimination (about 40%). Students in this group eliminated their social media use altogether on one or more apps. Students who designed these treatments did things like delete Instagram or TikTok from their phone, and some actively replaced their social media use with another activity they valued such as reading the news or spending time with friends.

What students found:

Students who designed a targeted intervention—either a decrease or an increase in use—experienced the greatest benefits to their overall well-being. Students that made untargeted changes, such as deleting apps, tended not to experience as much benefit. This difference is probably because many students already had strong intuitions about which parts of their social media use were harming them. 

In the following sections, we provide a bit more detail on the effects of the different types of treatments.

Targeted limitation

The most popular type of treatment was to restrict just a part of their social media use.  These treatments fell into roughly 3 groups: 

  1. limiting time (e.g., only using social media 30 minutes a day, no using Instagram after 8pm);
  2. adding friction (e.g., moving the social media apps from their phone’s home screen); and,
  3. avoiding specific features (e.g., not using the NewsFeed but continuing to use other parts of Facebook).  

Here’s what happened to students who restricted –  but did not eliminate – their social media use:

  • Well-being improved: About half reported increased daily happiness and more positive emotions throughout the day.
  • Loneliness decreased: More than a third reported feeling less lonely, while fewer than 15% experienced an increase in loneliness.
  • Productivity increased: Almost every student told us they were more productive.
  • Sleep quality improved: Half slept better, and the majority of the rest experienced no change. The effect on sleep quality was especially strong for students who added friction or avoided specific features.
  • More in-person social interaction: Most reported engaging in more social interaction during the treatment period, usually hanging out with friends in person.
  • Overall phone usage decreased: A majority spent less overall time on their phones during the experiment. For the most part, these were students that added friction or limited time. On the other hand, students who avoided specific features were more likely to spend the same amount of time on their phones as they did before their experiment.
  • Many students adopted their limitation after the treatment period ended: About half stuck to their intervention, even after the class activity was over. 

Targeted increase

In contrast to students that limited use, about 15% of students in the class increased their usage for the experiment.  Students might have designed these kinds of treatments because in class we discussed a few studies suggesting that some kinds of social media usage can have a positive effect on well-being.  Overall, students that increased their usage in a targeted way saw some positive effects, but they weren’t as strong as the students who did targeted limitations. 

  • Well-being improved. More than 60% said they experienced an increase in their well-being, happiness, and other positive emotions. 
  • Less stressed, anxious, and lonely. About a third reported feeling less stressed, anxious, and lonely.  
  • Changes in phone usage were mixed. A third said they used their phones less, a third said they used their phones more, and the rest said they used their phones the same amount as before the treatment period.
  • Many students adopted their intervention after the treatment period ended. Interestingly, more than 40% also continued their increased use long-term. Most of these students had increased some type of active engagement, such as direct messaging with friends or regularly posting photos and videos on Instagram or TikTok.

Elimination

Among students who completely eliminated usage of at least one social media app, we didn’t see as much of an overall pattern:

  • Well-being was mixed: Half said their well-being didn’t change, and the other half was split between students who reported their well-being getting better and worse. 
  • Stress and anxiety decreased for some, worsened for others: Around 70% said they experienced less stress and anxiety, but the other 30% actually felt more stress and anxiety during the treatment period than before.
  • Loneliness worsened or did not change: More than half did not report a change in how lonely they felt, and almost a third felt more lonely during the treatment period.
  • Productivity changes were mixed: These students were roughly equally split between those who said they were more productive and those who said their productivity didn’t really change.
  • Sleep quality improved for some: Half said they slept better but about a third said they got less sleep when they eliminated their social media use.
  • In-person social interaction increased: The vast majority spent more time with their friends in-person than before.
  • Overall phone use decreased: Most said their overall phone screen time went down during the treatment period.
  • Most returned to their old behavior: Most returned to their pretreatment use patterns after the experiment ended, but about 40% reduced or eliminated after the activity ended. Some said that during the activity they discovered benefits of reducing their usage and introduced limitations in their usage after the treatment period was over. 

Why did targeted interventions show more positive effects than elimination interventions?

When we compared the three groups, we saw a general pattern indicating that targeted interventions—either limitations or increases in social media use—worked better than elimination.  This pattern surprised some of us who thought that the most important difference would be between increases and decreases in usage.  The students’ self-reflections after the experiment offer some clues about the pattern.

Students using targeted interventions frequently wrote that they targeted only the parts of social media that their past experiences suggested were especially influential for them, whether positive or negative. By designing a strategic, specific intervention, they still maintained their use of other parts of social media that they liked and believed were beneficial to them. 

For example, many students already suspected that social media was distracting them from their coursework. They limited the time they spent on social media during the hours of the day when they did their coursework and saw an improvement in their productivity. But by focusing their treatment only on their coursework hours, they were able to keep using social media in other ways that benefited them.

By contrast, students who eliminated every part of their usage were more likely to tell us that they missed certain aspects of social media during their intervention.  For example, many students who deleted their apps altogether expressed frustration at not being able to do something they liked to do. Many also reported that they worried they were missing out on online social interactions and opportunities.  They may have thrown out the good with the bad, leading to less overall improvement in well-being. 

Our main takeaway is that, if you want to reduce social media’s harmful effects on you and increase its benefits, the most effective approach may be to try a targeted intervention.

We want to point out several caveats. First, this targeted invention will vary from person to person.   All our students were doing this as part of a class activity, and the treatment period was only a few days.  Also, because students designed their own treatment—rather than having it assigned to them—it is hard to rule out the possibility that certain kinds of students might have self-selected into targeted interventions.  Further, many of the measurements were not as precise and our analysis was more informal than we would use in other settings.  Finally, not that many students did a targeted increase so it is hard to say very much about this group.

Try it yourself.

Although our findings are limited in important ways, one of the great things about this activity is that anyone can do it, even outside of a classroom setting.  If you want to try it yourself, we’ve included a slightly modified version of the materials that we used in our class at Princeton.  In just three weeks, you can potentially improve your relationship with social media and learn about the joys and struggles of doing real social science research.

If you are interested in trying this out, here are the materials we used for this activity and the class more generally.

Please note that for some people, social media has very significant impacts on their mental health, both positive and negative. We urge you to exercise caution when experimenting with something that affects your mental health, and you may want to consult a mental health professional before trying any experimentation.  If you are struggling with your mental health and need help, the National Alliance on Mental Illness provides numerous resources.

Also, if you are considering using this activity in a class that you teach, here are three things to consider:

  1. The activity tries to provide a mix of structure and flexibility.  Based on our conversations with students, we think that the freedom to choose their own treatment, outcomes, and hypothesis is key to making this successful.  We also think the chance to discuss the activity with peers was valuable. It helped the students see themselves differently and learn more about the variety of ways that people interact with social media.  That said, this flexibility often makes the results less scientifically rigorous.  Whenever there was a tension between making this a good learning activity and a good research project, we tried to lean into that tension and remind students that all research designs involve trade-offs.
  2. A major design decision you’ll need to make is the length of the treatment period.  For our class, the treatment periods typically lasted between 3 days and a week.  After the experiment many students reported wishing that the treatment period was longer. However, if your treatment period is longer, it may be harder to sustain.
  3. In our evaluation, the students reported finding the activity valuable, interesting, and not too time consuming.  Although we didn’t assess it formally, we think that many students would also say that this activity helped improve their well-being and relationship with social media.

Thanks to the teaching staff from this year and last year for helping us shape this activity: Emily Cantrell, Kyle Chen, and Katie Donnelly-Moran.  We also want to thank Janet Vertesi who has used a related activity in some of her classes. 

A PDF File Is Not Paper, So PDF Ballots Cannot Be Verified

new paper by Henry Herrington, a computer science undergraduate at Princeton University, demonstrates that a hacked PDF ballot can display one set of votes to the voter, but different votes after it’s emailed – or uploaded – to election officials doing the counting.

For overseas voters or voters with disabilities, many states provide “Remote Accessible Vote By Mail,” or RAVBM, a system that allows voters the ability to download and print an absentee ballot, fill it out by hand on paper, and physically mail it back.  Some states use commercial products, while others have developed their own solutions.  In general, this form of RAVBM can be made adequately secure, mainly because the voters make their own marks on the paper.  

In some forms of RAVBM, the voter can fill out the ballot using an app on their computer before printing and mailing it.  This is less secure: if malware on the voter’s computer has “hacked” the voting app, what’s printed out may differ from what the voter indicated on the screen, and voters are not very good at reviewing the printouts and noticing such changes.

The most dangerous form of RAVBM is one that allows electronic ballot return, in which the voter uploads or emails a PDF file. Thirty states allow overseas voters to do electronic ballot return, either by email, fax, or web-portal upload, as shown in Table 5 (pages 34-35) of Herrington’s longer paper, Ballot Acrobatics: Altering Electronic Ballots using Internal PDF Scripting

The danger is that malware on the voter’s computer could send a different PDF file than the one that the voter has viewed and verified.  A hacker who wanted to steal an election could propagate such malware to thousands of voters’ computers.  The malware could alter the operation of the voting app, the PDF viewer, the browser, or the email/upload software.  There is a clear scientific consensus on this: According to “Securing the Vote, Protecting American Democracy,” a 2018 report released by the National Academies of Sciences, Engineering, and Medicine: the internet “should not be used for the return of marked ballots . . . as no known technology guarantees the secrecy, security, and verifiability of a marked ballot transmitted over the Internet.” 

Electronic ballot return is promoted by technology vendors, Democracy Live and  Voatz; and by Nevada, with its own EASE, system, which gives voters “the option of saving the ballot materials as a PDF file and emailing the document as an attachment to the respective county clerk or registrar’s office.” Democracy Live uses OmniBallot, an electronic method of delivering and returning ballots.

In all of these cases, the “final” ballot that the voter reviews is a PDF file.* The election-app vendors are implicitly relying on your intuition that “it’s a document” and we humans think we can read a document. At 8:32 in this Democracy Live promotional video, “this ballot happens to be a document.” Clearly, in the video, it’s a PDF, viewed in a PDF viewer, and from Specter and Halderman (2021) we know it’s a PDF.

It’s dangerous enough that the PDF you view may not be the PDF that’s transmitted to the election administrator.  But even if it were the same PDF file, what you see now is not necessarily what you get later.

A recent article by Herrington, “Altering Electronic Ballots Using PDF Scripting,” contains a live demonstration (on page 2) of a PDF ballot that changes what votes are marked from one minute to the next.  Of course, a real election hacker wouldn’t produce a PDF whose votes change every minute; the voter might notice that. The real threat model is between verification time and vote counting time.  Herrington demonstrates a minute-by-minute change for the convenience of his readers.

A voter might mark a ballot using the EASEVoatz, or Democracy Live app provided by their county election office, then inspect it using a browser or PDF viewer:

Ballot with vote for Emily Stone

By inspecting the ballot, the voter might think they have verified their selection of candidates.  Then they email or upload this PDF ballot, as instructed.

But when the election administrator processes that very same PDF file to count the votes, the filled-in oval has moved from one name to another:

Ballot with vote for Jenny Wagoner

The vote has been hacked!

PDF files are not static; they contain active program software.  If a hacker has infected thousands of voters’ home computers with vote-stealing malware, that malware can corrupt the operation of the official ballot-marking app to produce dynamic PDF files.  

You might think, “my computer probably isn’t hacked, so I’ll take that risk.”  But the real risk is not only your computer.  A hacker can spread the same malware to the computers of thousands of your fellow citizens, and steal their votes in that same election—and the election result can be altered.  That’s not democracy, that’s hackocracy.

In conclusion:  Mark your ballots on physical paper.   And tell your state and local election officials not to adopt electronic ballot return. For example, you can refer them to this 2020 report of the U.S. Cybersecurity and Infrastructure Security Agency (CISA), which says,  “Electronic ballot return is high risk. Electronic ballot return, the digital delivery of a voted ballot back to the election authority, faces significant security risks to voted ballot integrity, voter privacy, and system availability.   There are no compensating controls to manage electronic ballot return risk using current technologies. While many risks associated with electronic ballot return have a physical analog with the risk associated with the mailing of ballots, the comparison can miss that electronic systems provide the opportunity to rapidly affect voting at scale.”

*The use of PDF for this purpose in Democracy Live and Voatz is confirmed by independent peer-reviewed analysis: (1) Specter, Michael, and J. Alex Halderman. “Security analysis of the Democracy Live online voting system.” 30th USENIX Security Symposium (USENIX Security 21), 2021; and (2) Specter, Michael A., James Koppel, and Daniel Weitzner. “The Ballot is Busted Before the Blockchain: A Security Analysis of Voatz, the First Internet Voting Application Used in US Federal Elections.” 29th USENIX Security Symposium (USENIX Security 20), 2020;  and, (3) the use of PDF in EASE is stated in plain language on Nevada’s web site.