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How do we make decisions when the outcomes are uncertain?
One possible way would be to calculate the expected value of each option by multiplying each possible outcome amount by its probability and then choosing the option with the highest expected value.
While this strategy would maximize the payoff in expectation, this is not what we tend to do. In particular, we seem to be irrationally influenced by past outcomes of our decisions when making subsequent choices.
Research in the multidisciplinary field of neuroeconomics has mainly been driven by two influential theories regarding human economic choice:
Researchers from the University of Tsukuba, Japan have developed and validated a model (“dynamic prospect theory”) that integrates the most popular model in behavioral economics to describe decision-making under uncertainty—prospect theory, and a well-established model of learning from neuroscience—reinforcement learning theory.
This model more accurately described the decisions that people (and monkeys) made while facing risk than prospect theory or reinforcement learning theory alone, and revealed a systematic violation of prospect theory’s assumption that probability weighting is static.
Specifically, the researchers asked 70 people to repeatedly choose between two lotteries in which they could gain some reward with some probability. The lotteries varied in the size of the reward, the probability of receiving it, and the amount of risk involved.
The results showed that immediately after experiencing an outcome that was bigger than the expected value of the selected option, participants behaved as if the probability of winning in the next lottery increased.
Senior author of the study Assistant Professor Hiroshi Yamada says “This behavior is surprising because winning probabilities were clearly described to the participants (participants did not have to learn them from experience) and these probabilities were also completely independent of previous outcomes.”
Using their dynamic prospect theory model, the researchers were able to determine that the change in behavior is driven by a change in the perception of probabilities rather than by a change in valuation of rewards.
Professor Yamada also says: “Such learning from unexpected events underlies reinforcement learning theory and is a well-known algorithm that occurs when people need to learn the rewards from experience.
It is interesting that it occurs even if learning is not necessary.”
This intriguing findin expands our understanding of decision-making mechanisms.
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