Why We Work

Barry Schwartz

Psychologist Barry Schwartz on what motivates us to work, why incentives fail, and how our ideas about human nature shape who we become – immensely insightful read:

The Mathematics of Love: Patterns, Proofs, and the Search for the Ultimate Equation (TED Books)

Hannah Fry

What math reveals about the secret of lasting love and the relationship myth of compromise

The Art of Stillness: Adventures in Going Nowhere

Pico Iyer

“Going nowhere … isn’t about turning your back on the world; it’s about stepping away now and then so that you can see the world more clearly and love it more deeply.”

Pico Iyer on what Leonard Cohen teaches us about presence and the art of stillness:

Cheating The Impossible: Ideas and Recipes from a Rebellious High-Wire Artist

Philippe Petit

On August 7th, 1974, shortly after the World Trade Center was erected, French high-wire artist Philippe Petit stood in front of the impossible and took it full stride as he walked 200 feet between the Twin Towers, 1,368 feet above ground, on a 55-pound balancing rope. Dubbed “the artistic crime of the century,” the feat took six years of planning. Petit — who never finished formal education — had to acquaint himself with the most esoteric details of engineering, architecture, and the physics of wind, among other preemptive intricacies. At long last, Petit tells his story of autodidactic learning and creative ingenuity in a broader context of how to live life with “patience and tenacity” in an age of silver bullets and shortcuts.

Why We Love: The Nature and Chemistry of Romantic Love

Helen Fisher

Biological anthropologist Helen Fisher studies the evolution of human emotions and the intricacies of the brain in — and on — love, exploring the science of love without losing a sense of romance and shedding light on some of the complex ways in which the brain and the heart diverge.

The Filter Bubble

Eli Pariser

A compelling deep-dive into the invisible algorithmic editing on the web, a world where we’re being shown more of what algorithms think we want to see and less of what we should see.