Do you make decisions based on probability, or take the chance? Maybe you have a gambler’s fallacy thought process.
If you flip a coin and do this three times, and it lands on heads, can you say the next toss will also land on heads? Your answer may well determine whether you have gambler’s fallacy or not.
What exactly is the gambler’s fallacy?
Researchers Amos Tversky and Daniel Kahneman rationalized thought processes related to the fallacy of gambling on their research paper “Judgement under uncertainty: Heuristics and Biases” 1.
They said: “Many decisions are based on beliefs concerning the outcome of an election, the guilt of a defendant, or the future value of a dollar. These beliefs express themselves in statements such as “I think that…” or “chances are….” Or even “It is unlikely that…” and so forth.”
These statements use heuristic principles. People rely on these to reduce the complex tasks of assessing probabilities. They can also predict values to simpler judgmental operations.
Going back to the coin-flipping example. A decision maker, using rational thinking, knows the chance of another coin flip landing on heads is 50-50. However, according to Tversky and Kahneman’s definition, it’s never by chance that winning streaks happen, so we shouldn’t adopt this belief.
It’s a misconception many call the gambler’s fallacy. Research has shown this fallacy is alive and well in a multitude of everyday scenarios. In fact, there is evidence that it can cause bias in decision making.
How can it impact your decision making?
Tversky and Kahneman’s research describes three heuristics that are used to make judgments under uncertainty. These include representativeness, availability of instances or scenarios and adjustment of an anchor.
These heuristics summarizes that Tversky and Kahneman, are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and biases could improve judgments and making decisions in situations of uncertainty.
This “better understanding” is present in other recent research. This research shows how individuals might, in subjective cases, be biased against decisions.
“Decision-Making under the Gambler’s Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires” by Daniel Chen, Tobias J. Moskowitz and Kelly Shue 2 is one example.
Their research found that with all else equal the judge approves the case before an asylum seeker has higher points of 3.3 % within fair situations. This was true with several settings that were different.
They noted that it is likely that a judge influences decisions related to a previous event and that with both negative or positive decisions and previous cases of similarity, The sequence lengths will be increased.
Similar happenings occurred in India. Completed research came from loan officers who were also students. Reviews of processed files were completed by the same officers. Recommendations were considered on the subject of loan approval.
Pressure was placed on true assessment at various levels. This was because of schemes faced for different reasons. The previous review of the files helped authors study how well and fair the officers made decisions. Plus, they were able to explore whether loans on recommendation were, on average, performing better.
Can circumstances affect the gambler’s fallacy?
Looking at the same research of the loan officers, a basic plan rewards loans despite the quality of these loans. Loan officers who previously rejected loans, even though incentives were good, had a small decrease in chance of approval in the review when the loan before was approved. There was little concern about bias when accuracy with strong incentives were present.
There is also evidence of this in the sporting world. The researchers looked at baseball and analyzed umpires in the major leagues. After 1.5 million pitches were analyzed, between 2008 and 2012, batters did not swing when going to bat.
The researchers then controlled many situations of the game including the speed of the pitch, count of pitches, what happens in the game, the winner, and if the home team had the batter. Whatever data that was collected was their reliance. To track speed and understand trajectories in the major league, they had to use the PITCH/system.
When a pitch was a strike, umpires rarely called the next one a strike. In fact, this was 1.5 % true. There was a bias even more if two calls were the same. The next call has a higher percentage in this bias.
When it comes to regretful calls do umpires make subsequent calls? Is it fair? Is there a gambler’s fallacy? Umpires were reluctant to make an opposing call after an incorrect call but felt comfortable with this same act after a correct decision. This was noticed by researchers.
Here are the facts as we know them
“Fairness concerns and a desire to be equally nice to two opposing teams are unlikely to explain our results.”
The gambler’s fallacy can be seen in studies by various researchers where identical situations and numbers of decisions are going in the same direction and happening close together.
Experienced decision-makers took less notice of this at all. And this could be a reason for concern.
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