Collective Action Problems Part 1

Main ideas & lessons

• Why governments exist: The lecture poses a “provocative question” for political theory—why do we need government?—and links it to collective action problems: situations where rational, self-interested individuals struggle to make shared decisions or resolve conflicts.
• Starting theoretical assumptions (rational/strategic actors):
  • Everyone is assumed to be strategic (often called rational actor theory).
  • People pursue goals, trying to maximize benefits of a choice while minimizing costs.
• Politics as games (game theory lens): Political interactions can be analyzed as games involving:
  • Players (individuals, politicians, etc.)
  • Rules (constitutional procedures, senate procedure, informal norms)
  • Actions
  • Payoffs
  • Preferences (modeled as a utility function)
• Government helps, but can also create problems:
  • Institutions, norms, and rules can reduce collective action problems by structuring incentives and coordinating action.
  • But the same incentives and rules can also cause predictable collective action failures.

Key questions the lecture will address

• What are collective action dilemmas, and why are they essential?
• How does government help overcome coordination and collective action problems?
• How can collective action theory explain challenges in contemporary American politics?

Method: how the course uses the collective action framework

Core analytical approach (implicit methodology in the lecture)

• Assume:
  • actors are strategic rational maximizers,
  • there are identifiable players, rules, and preferences.
• Model political settings as a game:
  • determine what actions are allowed,
  • identify each actor’s preferences and incentives,
  • identify the rules/norms that constrain behavior,
  • analyze outcomes based on strategic interaction.
• Interpret political outcomes by asking:
  • Why did cooperation/coordination succeed or fail?
  • Were preferences aligned, ambiguous, or conflicting?
  • Did the rules/institutions create incentives that pushed toward cooperation or non-cooperation?

Collective action problems (core concepts)

• Individual action vs. group action:
  • Acting alone is straightforward (e.g., personal activities like surfing or playing Minecraft).
  • Group decisions become difficult as the number of participants rises.
• Why coordination breaks down:
  • More people → harder to coordinate.
  • Divergent preferences → harder to choose a collective action.
  • Too few rules or procedures → unclear how to aggregate choices.
  • Overly limited choices or too many options → systems break down.
  • Unequal resources/ability/time/energy → those with more influence may dominate.
• Preference aggregation challenges:
  • A major problem is not only “what should we do?” but how to convert many individuals’ preferences into a collective choice.
  • The lecture highlights voting and rule choices as tools for aggregation, including:
    • Plurality voting: elect the candidate with the most votes (not necessarily a majority)
    • Weighted voting: some people count more due to assigned importance
    • Majority rule: choose the option preferred by the most people

Examples used to illustrate collective action logic

• Coronavirus stimulus reauthorization (senate negotiation game):
  • The government needs new stimulus legislation to avoid severe recession.
  • Actors (described as players) have different preferences and constraints:
    • Trump faces immediate re-election incentives
    • Schumer (minority party senator) seeks benefits favorable to Democratic voters
    • McConnell is described as more business-deferential vs. labor-oriented interests
  • Cooperation requires constructing legislation—failure would be severe.
• COVID press conferences / Fauci (strategic public messaging):
  • Even public-health messaging is treated as strategic:
    • when to cooperate publicly,
    • when to separate or contradict each other.
• Voting during a pandemic (individual risk/strategic calculation):
  • Individuals decide whether to vote based on:
    • expected behavior of others,
    • perceived probability their vote matters,
    • personal cost/risk (e.g., whether voting is “worth it” during a pandemic).
  • Worry: if many people make the same calculation, it could lead to undesirable outcomes (consistent with collective action failure).
• Protests (e.g., Black Lives Matter) during COVID:
  • People weigh:
    • whether to attend and participate (turnout),
    • mask use and personal/public health concerns,
    • whether staying home undermines collective expression/impact.
• Irvine Unified School District survey (preference aggregation failure):
  • The survey offered three choices:
    • Hybrid (some in-person + some at home)
    • Traditional (in-person only)
    • Virtual/online academy (no in-person)
  • There was no clear majority for any single option.
  • The lecture argues this can show that even when the public wants outcomes like “safe schools,” there may be:
    • no clear consensus on the specific actions required,
    • disagreements driven by strong beliefs, rights concerns, or information asymmetries,
    • agreement on goals but conflict on implementation details.

Conclusion framing

• Many political issues arise from preference aggregation and coordination problems.
• Institutions and norms are studied because they are intended to resolve collective action problems—but they can also produce new problems through incentives and constraints.

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Speakers / sources featured

• Thomas Hobbes (cited via Leviathan)
• Chuck Schumer
• Mitch McConnell
• Donald Trump
• Anthony Fauci
• Uno (the card game used as an example)
• Black Lives Matter
• Irvine Unified School District
• The course instructor / “I” narrator (the lecturer speaking, including personal anecdotes like playing UNO)