Skip to main content

Leadership in the minimum effort game

The minimum effort (or weakest link) game is fascinating - simple, yet capable of yielding profound insight. The basic idea is that there is a group of people who individually have to decide how much effort to put into a group task. Effort is costly for the individual but beneficial for the group. Crucially, group output is determined by the minimum effort that any one group member puts in to the task. A classic example is an airline flight: If any person involved in the flight - pilot, fuel attendant, mechanic, luggage handler etc. - gets delayed, then the flight is delayed, no matter how hard others try.
           In experiments the minimum effort game is usually reduced to the matrix in Figure 1. Here subjects are asked to choose a number between 1 and 7 with the interpretation that a higher number is higher effort. Someone choosing effort 1 is guaranteed a payoff of 70. Someone who chooses 2 gets 80 if everyone else chooses 2 or more, but only gets 60 if someone in the group chooses 1. Someone who chooses 7 can get from 10 to 130 depending on the choice of others.
 
     
       Suppose the minimum choice of others is 5. What should you choose? You do best to choose 5 and get a payoff of 110. What if the minimum of others is 6? You do best to choose 6 and get payoff 120. Following this logic we can see that 'everyone choose the same number' is a Nash equilibrium. Now here are the key points: Everyone choose 1 is the worst equilibrium while everyone choose 7 is the best equilibrium; but choosing 7 is very risky because others might let you down. To return to the airline example. It is no use the pilot racing around to get the flight ready to go if the fuel attendant is having an after-lunch snooze.
         In experiments we typically observe that effort converges over time to 1 - the worst equilibrium. Most people start by choosing high effort. But it takes only one bad egg to ruin the team and that drags average effort down. This is a bad outcome! The airline is not going to be on time. So how to fix things?
          An obvious answer seemed to be leadership. So, together with Mark van Vugt and Joris Gillet we ran some experiments on leadership. The basic idea was that one person chooses first and then others follow. We reasoned that if the leader chose 7 this would signal to the others in the group to also choose 7. Problem solved!
          Things did not work out quite as well as expected. The figure below summarizes average effort over the 10 rounds subjects played the game. In a 4 player simultaneous choice version (Sim4) effort fell over time. In the leadership treatments, with either an endogenously or exogenously chosen leader (End and Exo), effort was higher but not by much. Moreover, it was no higher than we got if we just took one person out of the group (Sim3). This is not a ringing endorsement of leadership. We found that leadership failed to increase efficiency as much as expected because leaders were not bold enough. If a leader chose high effort followers responded but leaders were reluctant to choose high effort.


 
        A recent study by Selhan Garip Sahin, Catherine Eckel and Mana Komai adds a slightly different twist. The figure below shows the average contributions they observed in a 6 player version (where effort could go up to 9). They looked at leadership by example (Exemplar) and leadership by 'communication' (manager). There overall results are very similar to ours. Again, leadership merely seems to stabilize effort and stop it falling. Their results, though, suggested more blame should be placed on followers. Specifically, leader effort increased over the rounds while follower effort fell.    



        There is still a lot we can learn about leadership in the minimum effort game. The failure of leadership to push effort up to the efficient level does though clearly illustrate the difficulty of getting groups to coordinate. And note that this is not because of some social dilemma like incentive to free-ride. There is no way to free-ride in this game. The problem is one of group members trusting that others will put in high effort. Trust, it seems, does not come easy.

Comments

Popular posts from this blog

Honesty around the world

In my last post I looked at dishonesty in the banking industry. Sticking with a similar theme, this time I will at dishonesty across different countries.        Let us start with a study by David Pascual-Ezama and a long list of co-authors on 'Context dependent cheating: Experimental evidence from 16 countries'. They asked 90 students in 16 different countries to perform a very simple task: toss a black and white coin and record the outcome. If the coin came up white the student obtained a red Lindt Lindor Truffle. If it came up black they got nothing. Crucially, the coin toss took place in private and so the student could report whatever outcome they wanted. If they wanted a chocolate then they simply had to report white. (The study contrasted three different methods of reporting - form put in a box, form given to the experimenter or verbally telling the experimenter - but I will skip those details here.)           The chart below summa...

Prisoners dilemma or stag hunt

Over Christmas I had chance to read The Stag Hunt and the Evolution of Social Structure by Brian Skyrms. A nice read, very interesting and thought provoking. There’s a couple of things in the book that prompt further discussion. The one I want to focus on in this post is the distinction between the stag hunt game and the prisoners dilemma game.    To be sure what we are talking about, here is a specific version of both type of game. Adam and Eve independently need to decide whether to cooperate or defect. The payoff matrix details their payoff for any combination of choices, where the first number is the payoff of Adam and the second number the payoff of Eve. For example, in the Prisoners Dilemma, if Adam cooperates and Eve defects then Adam gets 65 and Eve gets 165. Prisoners Dilemma Eve Cooperate Defect Adam Cooperate 140, 140 65, 165 Defect 165,...

Measuring risk aversion the Holt and Laury way

Attitudes to risk are a key ingredient in most economic decision making. It is vital, therefore, that we have some understanding of the distribution of risk preferences in the population. And ideally we need a simple way of eliciting risk preferences that can be used in the lab or field. Charles Holt and Susan Laury set out one way of doing in this in their 2002 paper ' Risk aversion and incentive effects '. While plenty of other ways of measuring risk aversion have been devised over the years I think it is safe to say that the Holt and Laury approach is the most commonly used (as the near 4000 citations to their paper testifies).           The basic approach taken by Holt and Laury is to offer an individual 10 choices like those in the table below. For each of the 10 choices the individual has to go for option A or option B. Most people go for option A in choice 1. And everyone should go for option B in choice 10. At some point, therefore, we expect the...