The answers to these questions – and so many other perplexities of human behaviour – are the domain of behavioural science.
“Losses loom larger than corresponding gains.”
– Amos Tversky & Daniel Kahneman
The Surprising Ways Behavioral Science Boosts Businesses
For tuberculosis patients, complying with a full course of treatment can be daunting and difficult. But a new experiment conducted by MIT researchers in Kenya, in collaboration with the digital health company Keheala, shows that a digital program used on mobile phones helps patients successfully finish their treatments.
When buying a new car, a new phone, a new mattress, most of us can’t claim to be experts. Navigating countless features and benefits tests our patience and analytical prowess; we’re lay people and choosing is tough. What’s the process to compare the best battery life, the most comfortable or the safest? Companies muddy the water further with advertising: if every phone is the best, how do I decide what to buy? On many occasions consumers don’t know what their genuine motivations are. They’re not lying; they’re confabulating.
The findings from a groundbreaking report released earlier this month are clear: Curbing climate change will require revolutionizing the way the world produces food. WRI’s own research shows that a widespread reduction in beef consumption is essential for keeping global temperature rise to 1.5 degrees C (2.7 degrees F) and preventing the most dangerous climate impacts.
There’s a lot of press around algorithms being promoters of inequity or of bias. But we know from the behavioral-science literature that human beings are quite biased. We don’t just look at objective data; we also add our own internal biases. Study after study has demonstrated that when viewing a man and a woman doing a task at the same level of performance, people will make inferences about the woman they don’t make about the man. The mind just adds its own bias. The algorithms, while they may have other problems, tend not to add their own biases. They tend to reflect whatever is in the data.