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John Spacey, April 21, 2016 updated on September 08, 2017
The ludic fallacy is the misuse of games to model real life situations. It is associated with use of overly simple statistics and the mistaken belief that technology can predict the future.Games are far more constrained than real life and often have clearly defined probabilities. For example, artificial intelligence can examine all possible outcomes of a Chess game at any point by backwards induction to determine moves that are likely to win. Real life has unclear possibilities and probabilities. It is also common for real life situations to have far more possible moves than a complex game such as Chess or Go. Things can happen that have never happened before that are unpredictable. The following are examples of the ludic fallacy:
The Coin TossA coin is tossed 99 times with heads coming up each time. Two men are asked to predict the next toss: a mathematician and a gangster. The mathematician predicts that the next toss has approximately a 50% chance of being tails by pointing out that each toss in an independent random event. The gangster points out there is a 100% chance of heads coming up because the coin is obviously loaded.
The Unlikely InterviewA new graduate is offered a job interview for a job that she doubts she will get. She performs a risk-benefit analysis to see if her investment in studying and preparing for the interview is likely to pay a reasonable return. She considers factors such as the probability of getting the job, the salary of the job and the hours spend preparing and decides not to attend. She fails to consider the thousands of possibilities that exist in real life situations. For example, she may not get the job but the interview may be challenging enough to tune her interviewing skills resulting in greater future successes.
Algorithmic TradingAlgorithms that trade stocks based on historical patterns and derived attempts to predict future outcomes may neglect the true complexity of markets. For example, novel patterns can suddenly emerge and render the algorithm extremely risky or the algorithm may be vulnerable to manipulation by other algorithms.A/B TestingOver-optimization based on a simplistic factor such as Click Through Rate on a link by techniques such as A/B testing may not reflect the complex realities of a business. For example, a respected news organization begins A/B testing the titles of its articles. The result is a race to the bottom whereby titles become more and more sensational and less and less accurate. Click through rates improve but the organization suffers reputation declines that damage the business in greater ways.
Thinking
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