Lecture 1 — Course Introduction (2026)

Lecture Outline

• What this course is about: how our digital technologies impact society, positively or negatively, and in particular what this means for self-organization and democracy.

  • Computers are powerful tools enabling people to communicate, cooperate, deliberate, make decisions, and innovate in a multitude of ways. Some uses:

    • Communication: Alice sends Bob an e-mail or makes a conference call.
    • Association: a set of users form an online discussion group or forum.
    • Cooperation: users work together to write or design something together.
    • Curation: users finding, filtering, vetting, organizing relevant content.
    • Deliberation: users employ formal processes to debate issues or policy.
    • Decision: users employ social choice (e.g., voting) to decide collectively.
    • Organization: users form organizations/societies, possibly decentralized.
    • Governance: users employ processes to govern membership, behavior.
    • Incentives: digital payments, cryptocurrencies, finance and investment.
    • Evolution: collectively finding and introducing better ways to self-organize.

  • Promises of computing and networking technology as a “democratizing” force:

    • Inclusiveness:
      • Borderless communication with anyone anywhere on Earth.
      • Access to information for “everyone.”
      • Ability to collaborate productively with anyone globally.
      • Ability to associate with like-minded people anywhere.
    • Support for individual freedoms:
      • Speech: privacy, anonymity supports expression of true sentiment.
      • Information: access to [good?] information from anywhere.
      • Association: find, form groups with like-minded people anywhere.
      • Press: anyone can publish blogs, social media, etc.
    • Resistance to control, censorship by entrenched authorities:
      • Internet as a tool to “route around” failures, censorship.
      • Cryptocurrencies as tools for bottom-up economic empowerment.

  • These tools sometimes succeed, but often fail to serve people in a multitude of ways, either due to flaws in the abstraction or flaws in the implementation. This promise-vs-reality pattern is the course’s throughline — we’ll come back to it, technology by technology, all semester, starting next week with UseNet.

    • Abstraction flaws and challenge examples:

      • Unscalable communication or human attention costs: e.g., broadcast communication, or “pure” direct democracy.
        • Naive cooperation designs O(n²), fail to scale.
      • Unreliable or misbehaving human participants: e.g., spam, trolling, sock puppetry, harassment, fake news, fake reviews.
        • The design of the platform and process greatly affects the behavior of its users — so how to design properly?
        • Who should “police” a community, and how? Government, platform operator employees, community self-policing?
      • Unintended effects of algorithms: e.g., polarization, radicalization.
        • Social recommendation/newsfeed systems, echo chambers.
        • Interaction-maximizing and click-maximizing algorithms and anger-exploiting media (e.g., YouTube).
      • Bias, both real and perceived, from self-selecting online groups.
        • Tribalism, mistrust of information from other ideologies.
        • Information relativism: lack of objective notion of “truth.”
      • Overcompression of noisy, unreliable, manipulable information.
        • Large-scale campaigns, “beauty contest” elections.
        • Marketing-driven vs “efficient” markets, lemon markets.
        • Accountability of large [government] bureaucracies to electorate via the bottleneck of representation.
      • The competence- and expertise-identification problem.
        • Populace choosing experts: may be poor judges.
        • Experts choosing experts: unaccountable, may gradually become insular and fragmentation-prone.
      • Funding/incentivizing the production, curation of information.
        • Free information vs monetization, copyright, DRM.
        • Social media undermining traditional business models.
        • Who bears the cost of information review and policing? Users? Private platform operators? Governments?
      • Use and misuse of AI in democratic societies.
        • Potential tool to assist democratic self-organization: e.g. AI-assisted deliberation summarization, sampling, moderation at scale.
        • Risks: how much should we let (powerful) algorithms do our thinking for us? Synthetic/AI-generated content and coordinated inauthentic behavior.

    • Implementation flaws and challenges:

      • Network security, availability, resilience; “routing around” failures.
        • The upsides (resilience) and downsides (spam).
      • Identity and personhood problem: weak or strong digital identities; security, privacy, and coercion, Sybil and sock-puppet attacks.
        • The tension between anonymity (for freedom) versus accountability (for civility).
        • Individual misbehavior versus Sybil amplification.
      • Software bugs, exploitable flaws.
        • Bitcoin wallets as a universal bug bounty.
        • Smart contract bugs: e.g., DAO, Fomo3D attacks.
      • Transparency issues: should people trust it? Will they?
      • Fairness challenges:
        • Investment-centric versus democratic stake models, the problem of “rich get richer” and power centralization.
          • Corporations versus governments, foundations.
          • Proof-of-work/stake/etc versus PoP.
      • Inclusion challenges:
        • Digital divide: access to/expense of devices, connectivity.
        • Deliberate exclusion: disenfranchisement, guardianship.
        • Effective exclusion via trolling, harassment.
        • Identity attributes, personal data, coercion, black market.

  • Goals: What would we like to achieve? What might our long-term end goals be?

    • Technology enabling and incentivizing civil bottom-up self-organization.
    • Ensure freedoms of expression and association but with accountability.
    • Enable attention-limited users to obtain the (good) information they need to participate fully as time and interests permit, avoiding polarization.
    • Give communities not just voting but effective and scalable deliberation, agenda-setting, and long-term governance evolution processes.

  • Particular social/technical tools and techniques this course will explore:

    • Basic fault analysis techniques applying to failures and compromises.
    • Network/graph algorithms for communication, and social/trust networks.
    • Content filtering, recommendation, curation, and peer review processes.
    • Social choice: election methods, sampling, direct and liquid democracy.
    • Deliberation processes: peer review, online deliberation, agenda-setting.
    • Techniques to achieve privacy and anonymity with accountability: e.g., verifiable shuffles, anonymous credentials, zero-knowledge proofs, PoP.
    • AI/ML as both an analytical lens (recommenders, ranking, sampling) and a subject of its own (benefits/risks to democratic society).

  • Deployed technologies we will look at, exploring their promise and flaws:

    • UseNet (historical) and other public discussion forums.
    • News media: both traditional and social media platforms.
    • Deliberation: applications like Loomio and Liquid Feedback.
    • Peer review: Slashdot, Reddit, GitHub, HotCRP.
    • Cryptocurrencies and smart contracts: Bitcoin, Ethereum.
    • Organizations and decentralized autonomous organizations (DAOs).
    • Modern AI systems and their role in/impact on democratic processes.

  • Tentative 13-week roadmap:

    1. Introduction
    2. UseNet and democratization
    3. Social influence and choice
    4. Trust and online reputation
    5. Election methods and social choice
    6. Liquid democracy
    7. Smart contracts, DeFi, DAOs
    8. Market-based mechanisms
    9. Deliberative representation by lottery
    10. AI and democratic society
    11. Social bots/Sybils + identity/anonymity
    12. Polarization and radicalization
    13. E-voting and e-democracy

• Course logistics:

  • Content: readings, lectures, quizzes, occasional hands-on exercises.

    • Some may involve light programming in various languages, but not programming-intensive.

  • Discussion-driven lecture format: oral quizzes and panels, structured small groups, short presentations.

    • Note: regular lecture attendance is expected. Considerable graded work will happen during lectures.

  • Reading structure:

    • Read all Required readings before lecture.
    • Read at least one secondary reading of your choice.

  • Sessions: approx 5 hours per week total expected time commitment.

    • Lecture: attendance normally expected.
      • Missing one or two won’t seriously hurt your grade, provided you catch up via colleagues, but missing many lectures will impact your grade.
      • Discussion-oriented, moving further this year toward discussion and away from lecture-delivered content.
      • Conflicting lecture? Make sure you can normally attend this one (with few exceptions).
    • Exercises: need to attend only as directed in assigned exercises.
      • TAs will present to help with assigned exercises.
    • Practical work: purely-optional time/space for reading, discussion.
      • Instructors will not be present unless announced.

  • Grading:

    • 50% — work done during semester, in-lecture and homework exercises.
    • 50% — written final exam during exam session.

  • Workload expectations:

    • Lecture attendance and participation expected.
    • Significant time required for readings, exercises.

  • Grading approach:

    • Grading is mostly based on whether it’s clear that you “did the work” or not, and not on the basis of whether you arrived at a prescribed answer — so just do the work, and you’ll do fine.
    • The same instructor grades all students for a given part of a given exercise, ensuring that the subjective grading is consistent and fair across the class.
    • Not graded on a curve: no one needs to fail the course, and everyone can and will get at least a solid passing grade if they do the required work consistently.

  • All students are expected to do their own work! Plagiarism is not tolerated.

    • AI policy: You may use AI tools to help you study and learn outside of lectures, provided you are actually reading and thinking about assigned material (not just reading summaries of it) and provided you are not outsourcing your thinking to AI.