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Why it is hard to be happy with what we have: A reinforcement learning perspective

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Dayan,  P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Dubey, R., Griffiths, T., & Dayan, P. (submitted). Why it is hard to be happy with what we have: A reinforcement learning perspective.


Abstract
The pursuit of happiness is not easy. Habituation to positive changes in lifestyle and constant comparisons leave us unhappy even in the best of conditions. Given their disruptive impact, it remains a puzzle why habituation and comparisons have come to be a part of cognition in the first place. Here, we present computational evidence that suggests that these features might play an important role in promoting adaptive behavior. Using the framework of reinforcement learning, we explore the benefit of employing a reward function that, in addition to the reward provided by the underlying task, also depends on prior expectations and relative comparisons. We find that while agents equipped with this reward function are less "happy", they learn faster and significantly outperform standard reward-based agents in a wide range of environments. The fact that these features provide considerable adaptive benefits might explain why we have the propensity to keep wanting more, even if it contributes to depression, materialism, and overconsumption.