Showing posts with label decision making. Show all posts
Showing posts with label decision making. Show all posts

Sunday, November 21, 2010

Good Decisions. Bad Outcomes by Dan ariely



If you practice kicking a soccer ball with your eyes closed, it takes only a few tries to become quite good at predicting where the ball will end up. But when “random noise” is added to the situation—a dog chases the ball, a stiff breeze blows through, a neighbor passes by and kicks the ball—the results become quite unpredictable.

If you had to evaluate the kicker’s performance, would you punish him for not predicting that Fluffy would run off with the ball? Would you switch kickers in an attempt to find someone better able to predict Fluffy’s involvement?

That would be absurd. And yet it’s exactly how we reward and punish managers. Managers attempt to make sense of the environment and predict what will result from their decisions.

The problem is that there’s plenty of random noise in competitive strategic decisions. Predicting where the ball will go is equivalent to deciding whether to open a chain of seafood restaurants on the Gulf Coast. The dog running off with the ball is the BP oil spill. When the board reviews the manager’s performance, they’ll focus on the failed restaurants. The stock is down. The chain lost money. Since the manager’s compensation is tied to results, he’ll incur financial penalties. To save face and appear to be taking action, the board may even fire him—thus giving up on someone who may be a good manager but had bad luck.

The oil spill example is an extreme case. In the real world, the random noise is often more subtle and various—a hundred little things rather than one big thing. But the effect is the same. Rewarding and penalizing leaders based on outcomes overestimates how much variance people actually control. (This works both ways: Just as good managers can suffer from bad outcomes not of their own making, bad managers can be rewarded for good outcomes that occur in spite of their ineptitude.) In fact, the more unpredictable an environment becomes, the more an outcomes-based approach ends up rewarding or penalizing noise.

In the last year I’ve asked many board members how much of a company’s stock value they think should be attributed to the CEO’s strength, and the answer is surprising. They estimate that you’ll get about 10% more stock value, on average, from a good CEO than from a mediocre one. Implicit in that estimate is the understanding that many outcomes are outside a leader’s control.

We can’t entirely avoid outcome-based decisions. Still, we can reduce our reliance on stochastic outcomes. Here are four ways companies can create more-sound reward systems.

1. Change the mind-set. Publicly recognize that rewarding outcomes is a bad idea, particularly for companies that deal in complex and unpredictable environments.

2. Document crucial assumptions. Analyze a manager’s assumptions at the time when the decision takes place. If they are valid but circumstances change, don’t punish her, but don’t reward her, either.

3. Create a standard for good decision making. Making sound assumptions and being explicit about them should be the basic condition for getting a reward. Good decisions are forward-looking, take available information into account, consider all available options, and do not create conflicts of interests.

4. Reward good decisions at the time they’re made.Reinforce smart habits by breaking the link between rewards and outcomes.

Our focus on outcomes is understandable. When a company loses money, people demand that heads roll, even if the changes are more about assuaging shareholders than sound management. Moreover, measuring outcomes is relatively easy to do; decision-making–based reward systems will be more complex. But as I’ve I said before, “It’s hard” is a terrible reason not to do something. Especially when that something can help reward and retain the people best able to help you grow your business.

Monday, August 23, 2010

A classic example of the prisoner's dilemma...


The prisoner's dilemma is a fundamental problem in game theory that demonstrates why two people might not cooperate even if it is in both their best interests to do so. It was originally framed by Merrill Flood and Melvin Dresher working at RAND in 1950. Albert W. Tucker formalized the game with prison sentence payoffs and gave it the "prisoner's dilemma" name (Poundstone, 1992).

A classic example of the prisoner's dilemma (PD) is presented as follows:

Two suspects are arrested by the police. The police have insufficient evidence for a conviction, and, having separated the prisoners, visit each of them to offer the same deal. If one testifies for the prosecution against the other (defects) and the other remains silent (cooperates), the defector goes free and the silent accomplice receives the full 10-year sentence. If both remain silent, both prisoners are sentenced to only six months in jail for a minor charge. If each betrays the other, each receives a five-year sentence. Each prisoner must choose to betray the other or to remain silent. Each one is assured that the other would not know about the betrayal before the end of the investigation. How should the prisoners act?

If we assume that each player cares only about minimizing his or her own time in jail, then the prisoner's dilemma forms a non-zero-sum game in which two players may each either cooperate with or defect from (betray) the other player. In this game, as in most game theory, the only concern of each individual player (prisoner) is maximizing his or her own payoff, without any concern for the other player's payoff. The unique equilibrium for this game is a Pareto-suboptimal solution, that is, rational choice leads the two players to both play defect, even though each player's individual reward would be greater if they both played cooperatively.

In the classic form of this game, cooperating is strictly dominated by defecting, so that the only possible equilibrium for the game is for all players to defect. No matter what the other player does, one player will always gain a greater payoff by playing defect. Since in any situation playing defect is more beneficial than cooperating, all rational players will play defect, all things being equal.

In the iterated prisoner's dilemma, the game is played repeatedly. Thus each player has an opportunity to punish the other player for previous non-cooperative play. If the number of steps is known by both players in advance, economic theory says that the two players should defect again and again, no matter how many times the game is played. However, this analysis fails to predict the behavior of human players in a real iterated prisoners dilemma situation, and it also fails to predict the optimum algorithm when computer programs play in a tournament. Only when the players play an indefinite or random number of times can cooperation be an equilibrium, technically a subgame perfect equilibrium meaning that both players defecting always remains an equilibrium and there are many other equilibrium outcomes. In this case, the incentive to defect can be overcome by the threat of punishment.

In casual usage, the label "prisoner's dilemma" may be applied to situations not strictly matching the formal criteria of the classic or iterative games, for instance, those in which two entities could gain important benefits from cooperating or suffer from the failure to do so, but find it merely difficult or expensive, not necessarily impossible, to coordinate their activities to achieve cooperation.

Strategy for the classical prisoner's dilemma

The classical prisoner's dilemma can be summarized thus:

Prisoner B Stays Silent Prisoner B Betrays
Prisoner A Stays Silent Each serves 6 months Prisoner A: 10 years
Prisoner B: goes free
Prisoner A Betrays Prisoner A: goes free
Prisoner B: 10 years Each serves 5 years