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How do humans 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 quantity by its probability and then choosing the option with the highest expected value. While this strategy would maximize the expected reward, this is not what people tend to do. In particular, people seem to be irrationally influenced by the past results of their decisions when making subsequent decisions.
Researchers at the University of Tsukuba have developed and validated a model (“dynamic perspective theory”) that integrates the most popular model in behavioral economics to describe decision-making under uncertainty: perspective theory and a well-known model. established learning neuroscience. — Reinforcement learning theory. This model more accurately described the decisions that people and monkeys made while facing risks than perspective theory or reinforcement learning theory alone.
Specifically, the researchers asked 70 people to repeatedly choose between two lotteries in which they could win some reward with a certain probability. 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 a result greater than the expected value of the selected option, the participants behaved as if the probability of winning in the next lottery increased. The study’s lead author, Assistant Professor Hiroshi Yamada, says “This behavior is surprising because the probabilities of winning were clearly described to the participants (participants did not have to learn them from experience) and these probabilities were also completely independent of past results.Using their dynamic perspective theory model, the researchers were able to determine that change in behavior is driven by a change in the perception of probabilities rather than a change in the valuation of rewards.
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 of experience. It is interesting that it occurs even if learning is not necessary.”
In similar experiments with macaque monkeys, whose brains closely resemble those of humans, essentially the same results were observed. The researchers commented that the similarity in human and monkey behavior was remarkable in this study.
Based on the results of this research, it is hoped that monkey brain research will lead to an understanding of the brain mechanisms involved in perception of rewards and the probability that we all use when making risky decisions, as well as joy. We feel when we are successful.
This study was supported by JSPS KAKENHI Grant Numbers JP: 15H05374 and 21H02797, Takeda Science Foundation, Council for Addiction Behavior Studies, Narishige Neuroscience Research Foundation, Moonshot R&D JPMJMS2294 (HY), and ARC DP190100489 (AT).
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