Whenever I went to my mom’s office growing up, often they would collectively buy tickets for the mega millions jackpot in New York. Almost 22 years later and no one from the office has gotten the lucky numbers, how cruel! The chances of winning the lottery are extraordinarily low. In fact, your chances of winning are one in one-hundred seventy-five million! It is weird to think that people consistently put their hard earned money into something where their chances are so low to win. Maurice Allais was an economist who sought to answer the question of why this was the case.

Maurice Allais was a French economist who won the Nobel in 1988 “for his pioneering contributions to the theory of markets and efficient utilization of resources”. He did a lot of work in general equilibrium theory. This is a situation that shows under what conditions markets clear. He was originally inspired by economist Leon Walras, who was the creator of the theory. However, Allais believed that the theory, as it was, under Walras was not correct because it was a utopian market as opposed to the real markets we see on a day to day basis. He believed the answer to this solution was buried in the mathematics being used to find the general equilibrium. The economic dynamics are thus characterized by assuming there is a surplus (or oversupply of goods) general equilibrium is achieved when there is no longer any realizable surplus. We have seen other economists take on this section, so I dug into Allais and found one of his cooler contributions the Nobel committee did not mention, which was the development of the Allais paradox.

George Allais, 1988 Winner, photo taken from the Nobel website.

One of the cooler things that Allais was known for was developing the field of behavioral economics. Behavioral Economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making. A major contribution Allais made to this field was called, naturally, the Allais paradox. The Allais paradox basically sought to explain our mega millions question from earlier. Let us look at two situational experiments with the people from the office.

In situation one, the people at the office can choose option 1A getting 1 million dollars 100% guaranteed, or they can choose option 1B, which comes with an 89% chance of 1 million dollars, 10% chance of 5 million dollars, and 1% chance of nothing. Between the two options, most people should prefer option 1A to 1B.

In situation 2, the office can choose between option 2A, which has an 89% chance of getting nothing, and an 11% chance of getting a million dollars, and option 2B, which has a 90% chance of getting nothing and a 10% chance at 5 million dollars. Because of our results in experiment one, one would think that most would choose options 1A and 2A. However, when he ran the experiment, while in situation one, most people did, in fact, choose 1A, in the second experiment, they ended up choosing 2B over 2A.

This rationale is strange is it not? Photo taken from Reddit.

This led to the discovery of a couple of key contributions for Allais. One was the notion of framing in behavioral economics. This basically means how the question is asked. In the first scenario, the options are all relatively positive and choosing between high percentage opportunities. The second one, however, has that natural gamble to it — almost an all or nothing feel if you will. The way questions are framed will influence the way a respondent will answer questions.

Allais was also able to see people’s desire to avoid risk as a result of this experiment. The risk of going away from option 1A to 1B is too great for a person to want to give up the sure cash. However, in the second situation, since only one percent of probability separates the 1 million and 5 million dollar payouts, people don’t seem to really fret over that separation when choosing option 2B over 2A.

It’s all about that fine line between risk and reward

These basic insights offered up by Allais really played a big role in economics for the development of both behavioral economics as well as the world of finance. His contributions helped explained why the people at the office would break from the rational expectation of putting their money in places where they can definitely see a return as opposed to their mega millions tickets. It was a contribution that also helped the financial industry with understanding risk tolerance of investors.

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