Humans’ desire to want more may serve an important purpose


The human mind may have a bias to constantly want more. Al Barry/Getty Images

Happiness is one of the most sought-after human emotions. Achieving it over the long term, however, is unattainable for many.
This is because happiness depends on changing expectations that make people quickly habituate to ‘new reasons to be happy’. It also depends on whether people compare what they have with others or what they wish they could have.
Both habituation and comparison can lead to a cycle of never-ending wants and desires, which negatively affect mental health and well-being.

Understanding the costs and benefits of habituation and comparison could help researchers develop policies and large-scale interventions to tackle these mental biases.

Recently, researchers used a computational framework known as reinforcement learning to model the effects of different levels of habituation and comparison-making.
They found that while making comparisons reduces happiness, it speeds up learning.
Dr. Nathaniel Daw, professor in computational and theoretical neuroscience at Princeton University, who was not involved in the study, told Medical News Today:

“You might think that building a robot that can choose among different options is easy: you just give everything a score and choose the best. But actually figuring out how to set up that score to get your robot to make good choices is surprisingly tricky. This paper looks at human happiness from this perspective.”

“[In particular, the researchers answer the question:] why is the same outcome delightful today but boring tomorrow? They show it has advantages—if we are never satisfied, we are constantly driven to find better outcomes—but also disadvantages, as this comes at the expense of constantly devaluing what we already have achieved, which the authors suggest might, taken to extremes, relate to depression.”
— Dr. Nathaniel Daw

Comparing and exceeding expectations
The researchers used a reinforcement learning framework. Rachit Dubey, a fifth-year Ph.D. student at Princeton University and lead author of the study, told MNT:

“Reinforcement learning methods focus on training an agent- for example, a robot—so that the agent learns how to map situations to actions—such as learning how to play chess. The guiding principle of these methods is that they train agents using rewards —they provide positive rewards to desired behaviors and/or negative rewards to undesired ones.”

“This is similar to the way we learn from rewards—we are more likely to take those actions which give us positive rewards like money, praise, etc., and we avoid actions that give us negative rewards like pain, sadness, etc,” he added.

For the study, the researchers trained an agent by giving it a ‘reward’ each time it exceeded its previous expectation and the performance of other agents. They then conducted various experiments in different environments with the agents.

In doing so, they found that agents rewarded for habituation and comparison learned significantly faster than standard reward-based agents, although they were less happy.

This means that habituation and comparison might promote adaptive behavior by serving as a powerful learning signal.

They also found that making comparisons sped up learning as it provided an exploration incentive to agents, and that proper expectations served as useful aids for comparison, especially in environments with sparse rewards.

They further noted, however, that agents were unhappy and performed sub-optimally when comparisons were left unchecked and when there were too many similar options to choose from.

This, noted Dubey, means that when faced with many choices, we should try to make decisions without relying on comparisons