Current Issues Final Portfolio

“One definition of an economist is somebody who sees something happen in practice and wonders if it will work in theory.” – Ronald Reagan. Is there any better way to sum up the past four months in Econ 404W: Current Issues? Day after day we performed experiments to see first hand how certain economic theories and phenomenon work in practice, and afterwards try to understand how the theories relate. Throughout the semester, we participated in 27 games/experiments that covered a wide range of economic theories and principles. Of all the different concepts that have been discussed, two have stood out time after time. These are backward induction and the free riding problem.

The first experiment that used any sort of backward induction was Network Externalities. In this experiment my strategy was to think as a group of people would, rather than how I would personally, and act in the first round to maximize profits. Each decision, as simple as the two choices were, had one choice that I believed would appeal more to the rest of the class, regardless of what my personal preference was. One round, for example, had a choice between a blue and white paper and a red and white paper. As appealing as the red and white was to me, I knew that a class of Penn State students would clearly choose the blue and white as opposed to the aesthetically repulsive colors of Ohio State. Thinking of how others would act allowed me to get into the superior network each round, other than the last one which was based on the luck of a coin flip.

The Keynesian Beauty Contest further exemplified backward induction. In order to get acceptable results, one must use rationality assumptions. By understanding how others would rationalize and what they would think is important, you can try to estimate the correct timings or values to be one step ahead of the others. This is a very important concept in the stock market. When a stock is on the rise, you want to get sell or short the stock as close as possible to the peak before it starts to drop. You must think about how others will act on this situation and have to beat them to it in order to sell it at the highest possible price. If you wait too long the other people will sell it first causing the price to drop and you not getting the highest possible value. It gets complicated when you expect the other investors to use backward induction as well, so you have to go one step before that, and if you over rationalize this you can get too far ahead and not make as much as you could have. This is a prime example of asymmetric information. Stocks of course, although they sometimes tend to show some sort of trend, are volatile and you can never be sure when is the right time to act until after it happens. This can have a profound effect on the markets as if a little bad news was released, people may overcompensate and the stock could be affected more than it should have.

Perhaps the experiment that exemplified the concept of backward induction the most was A Puzzle for Pirates. With only three pirates per group this reasoning of backward induction is quite simple. Theoretically the first pirate could keep 99 and the last pirate accept only 1, because one is one more than if he had declined it, however humans tend to show some spite. The third pirate could decline out of spite because one gold piece isn’t really worth much at all. Therefore you have to work out how the other pirates would think and make an offer that you know they would accept. When more pirates are added, such as 4 or 5, the fiercest pirate has to work out multiple steps in order to figure out how to get a majority vote, not get kicked off, and keep enough to be satisfied. If backward induction was executed perfectly by all members of the group, a Nash Equilibrium would have been reached, where no one pirate can act differently and benefit. It essentially becomes a stand-still, unless two or more pirates team up to change the dynamics. The class results didn’t really show such results, although they did show signs of the correct train of thought. This shows how predictions are created using general concepts yet don’t take into account all of the other externalities and irrational human behavior. “The economy depends about as much on economists as the weather does on weather forecasters.” – Jean-Paul Kauffmann. This quote humorously displays how while the person is smart, people are irrational and can create completely different results than what is predicted.

The concept of free riding along with greed and competition is another common phenomenon which was displayed in many different experiments. In The Voluntary Provision of Public Goods, it was made clear that the more contributed to the public good would make everybody better off. Even with this knowledge people still held back hurting others as well as themselves. Those free-riders did better than the others comparatively yet they could have benefited more personally if everybody had just contributed. This same exact concept was displayed in Does Consumer Spending Matter? If everybody spent a large portion of their income, the economy would prosper, people would be making more money, and unemployment would drop or be non-existent. This game, however, displayed how people were saving too much for retirement and it hurt the economy and themselves as well as people were losing jobs and not having any income. Because of this self-interest, a Market Failure occurs, creating bad results for society as a whole. The Investment Game showed how if you gave your money to the other person it would be multiplied and the receiver would be more profitable. By giving the largest amount you showed that you are trustworthy and you, one would hope, would receive the most in return, making everybody richer as a whole. If one person tried to cheat the other, then the other person wont give anything either and the multiplier isn’t used and everybody is poorer than they could have been. These ideas of the public good versus free riding have been shown in many different experiments throughout the semester.

Based on the examples given about the free riding problem, it is evident that people, by human nature, are greedy and competitive. A non-cooperative Game Theory situation arises when these individuals interact with one another in an effort to achieve their own goals. There were a few times when the class as a whole has been close to achieving complete cooperation, yet those times were rare. Even though they are aware that the richer they are in the experiments the more extra credit they will receive (and that it is not real money!), they still tend to act competitively and hurt themselves.

The free-riding problem can be explained by the works of John Locke and Thomas Hobbes. Their writings concerning the state of nature and a general description of how capitalism works easily describes how these students, growing up in a capitalist economy, usually act in their own self interest. If the students acted in a more cooperative fashion, everybody would have been better off. This shows how cooperation, although a great concept, can not work in human society as it is against our nature. A utopian society run by the ideals of socialism promises many great things, including the greatest public good and eventually a perfect world. People, however, conceal their true nature by projecting a false image while acting in their own personal benefit. This, according to John Locke, is not their fault; it is simply human nature. It is the reason why a communal/socialist society has never prospered as it was ideally expected to.

These two economic concepts of backward induction and free-riding were a common theme among almost all of the experiments. These two ideas are intertwined with almost every other theory and concept that was discussed. All of the experiments as a whole paint a very thorough picture of the current issues that our economy experiences.

Favorite Experiment
My favorite experiment was Deal or No Deal. It made me appreciate the dynamics of the game-show by playing in it first hand. As one of the contestants, I had to constantly calculate my expected returns based on the remaining suitcases. Once the game approached the end with 4 more suitcases plus the one I chose, the tougher decisions arose. I had to decide how much of a risk-taker I really was; I found that I was slightly risk averse. The offer the banker gave me was below the highest remaining suitcase but above the other four. I figured I had a 4/5 chance that the next suitcase I chose would make the offer rise. As a regular poker player, I’m constantly calculating odds and how risky I want to be. An 80% chance to get more money is very acceptable for me. The next one was the same situation with only 4 suitcases left, so a 75% chance to gain more money was still acceptable. I had chosen correctly again, yet my odds were now only 66%. A 1/3 chance to lose it all was definitely not worth it. I decided to walk with around $250,000 and I was content, even though the suitcase which I had initially chosen was the $750,000 one. I can’t be disappointed, hindsight is 20/20. This game made me really take a look at myself and how risky I actually could be if this were the real game. It was a lot of fun; too bad no more than three students could participate.

Suggestions for Improvement
I must say that this course was exceptionally run. The games/experiments were a great and fun way to teach us the theories of economics first hand. It was the only class I looked forward to going to, even though it was at 8:00am! The only suggestion I have is an adjustment to the incentives. I don’t mean incentives to participate and come to class, because those incentives with extra credit as well as the necessary 20 journals seemed to work perfectly. I mean incentives so that the theories in certain experiments would work correctly. One example is the Puzzle for Pirates game. If an offer of one coin was given, it was usually declined because the difference in extra-credit between one coin and no coins was so marginal. A different system for extra-credit could be used for such an occurrence, perhaps extra credit based on rankings overall, not by the value of the coins.