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The Free-Rider Problem: Why Group Projects Drive Us Mad

  • Writer: Vicenta Wheatley
    Vicenta Wheatley
  • Aug 19
  • 4 min read

Have you ever experienced a group project where a few people do all the heavy lifting while others seem to disappear into thin air? Me too. What you’re seeing is a classic case of the free rider problem and it is perhaps one of the most infuriating economic realities that’s also a perfect game theory case study. Whether it’s a uni group project, a roommate chore schedule, or running a country, people often have an incentive to do less if they can still enjoy the benefits of the collective effort. Game theory shows us why this happens, and why it’s so common in group settings. 


Modelling the Group Project with Incentives and Outcomes

Think of a group assignment as a mini version of what experimental economists call a ‘public goods game’. Everyone’s supposed to chip in, but the final grade is shared no matter who actually puts in the effort. The “rational” strategy for a self-interested player? Do as little as possible and hope the rest of the group carries you across the finish line. 


In game theory terms, we can model it with a simple game tree. 


Figure 1: Game tree model of a group project involving three students
Figure 1: Game tree model of a group project involving three students

To someone who hasn’t studied economic game theory, this graph may look a little confusing at first - but it’s really just a map of a familiar nightmare: the group project. You know the one. Everyone’s supposed to pitch in, but someone always ghosts the group chat, does nothing, and still gets the grade. In this simplified model, we have three students - Tom, Simon, and Kate - each deciding whether to work hard (W) or slack off (S). The catch is that they all make their choices without knowing what the others have done. That’s why the graph has dotted lines connecting some of the decision points, it means players are acting under uncertainty, choosing at the same time or without full information. This setup is what’s known as a simultaneous-move game in game theory.


Now, to be fair, most real-life group projects aren’t purely simultaneous or purely sequential. They usually fall somewhere in between. Maybe someone starts the document early and others jump in after. Or maybe everyone waits until the last minute, watching to see who’ll make the first move. But for this model, we’re assuming a simultaneous structure - because in many group situations, especially ones with poor coordination or time pressure, you don’t actually know what others are doing until the end. The uncertainty is exactly what makes slacking such an attractive gamble.


Back to the tree: at the bottom of each branch, you’ll see a triple of numbers like (8,8,8) or (10,5,10). These are the payoffs, the benefits each student gets depending on who worked and who slacked off. The first number is Tom’s payoff, the second is Simon’s, and the third is Kate’s. Higher numbers mean better outcomes. In this case, if everyone works hard, they each get a payoff of 8. But if one person slacks off while the others carry the weight, the slacker scores a 10 while the workers get less. For example, in the outcome (S,W,S), where Tom and Kate both slack off, but Simon works, the payoffs are (10,5,10). So Tom and Kate benefit more than Simon, the one who actually did the work.


This is the free rider problem in action. Everyone wants the collective benefit, but no one wants to put in the individual effort - especially if they think someone else will do it for them.


So what does game theory predict? When game theorists look at a game, they usually search for what is called a ‘Nash Equilibrium’ - an outcome where no one can improve their situation by changing their strategy alone. This concept is important because it often represents what actually happens in real-life situations: people tend to stick with strategies that work best for them given what others are doing, even if the overall outcome isn’t ideal. I won’t make you go through all the formal analysis here so I’ll just share the results. In analysing this particular game tree we find that there are actually multiple nash equilibriums, and none of them are exactly fair:


  • (S,S,W) → Tom and Simon slack off, Kate works. Payoffs: (10,10,5)

  • (S,W,S) → Tom and Kate slack off, Simon works. Payoffs: (10,5,10)

  • (W,S,S) → Tom works, Simon and Kate slack off. Payoffs: (5,10,10)


In each of these cases, two people benefit while one person does the work and gets the short end of the stick. No one wants to switch strategies because doing so would make their own outcome worse. Even though the best overall results - everyone working - gives each player a decent (8,8,8), that outcome is not stable (meaning no player would want to unilaterally change their choice because it wouldn’t improve their payoff). The moment someone thinks others might work, they have an incentive to slack off.


There’s the harsh truth: group projects are basically rigged. Even when everyone’s best off working hard, the way incentives line up rewards those who coast and let others do the heavy lifting. This also applies to many other circumstances where these same dynamics show up, like in governments and in global efforts to tackle climate change - where every country benefits from a stable planet but may hesitate to do their fair share. Anywhere cooperation depends on everyone doing their part, the free rider problem is lurking. So next time you’re stuck carrying the group project, remember: it’s not personal. It’s the game.


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