The Kahneman Chronicles: Lessons from a Fly Lab

The Kahneman Chronicles #1: When a Nobel Laureate Fixed Our Lab’s Scheduling Disasters

Daniel Kahneman (1934-2024) was a legendary psychologist who revolutionized our understanding of human decision-making and became known as the “grandfather of behavioral economics.” Awarded the 2002 Nobel Prize in Economics, Kahneman’s groundbreaking research with Amos Tversky revealed systematic biases and mental shortcuts leading people to make irrational choices. 

This article series imagines what would transpire when Daniel Kahneman took a sabbatical and worked in a fly lab. 
Part of “The Kahneman Chronicles: Lessons from a Fly Lab” – A report from our imaginary interdisciplinary fellowship program

On the day Nobel Laureate Prof. Daniel Kahneman arrived for his sabbatical, our Drosophila lab buzzed with nervous excitement. Here was the legend himself—extraordinary psychologist who’d won economics’ highest prize, revolutionizing our understanding of errors in decision-making.

The ghost of Thomas Morgan urged us to do our best. We’d prepared our most impressive experiments, polished our presentations, and practiced our pitch for explaining fly development.

What we hadn’t prepared for was Kahneman spending his entire first morning silently observing us work. Often he scribbled notes in a small black notebook with the focused intensity of Jane Goodall studying chimps.

Why do we spend so much time in the lab?

“I’ll just quickly mount these embryos—twenty minutes, tops,” announced postdoc Shweta. This became a two-hour odyssey involving broken coverslips and dried glue. Followed by an existential crisis, wondering if the fluorescent blob she saw was signal or autofluorescence from a properly developing embryo.

“Quick PCR setup, maybe thirty minutes,” declared grad student Fillip, before vanishing into an afternoon-long quest. Missing primers. Buffer math. Finding the thermal cycler waited on “infinite hold” since previous Tuesday. You know the drill.

“Fascinating,” Kahneman murmured after each wildly inaccurate prediction.

By day three, a pattern was undeniable. Every time estimate in our lab was spectacularly yet consistently wrong. “Simple” tasks morphed into epic quests.

The Intervention

Kahneman approached the whiteboard where we’d sketched our weekly schedule – optimistically planning seventeen different experiments into forty work hours.

“Let’s implement realistic time budgeting,” he announced with the calm authority of someone who’d spent decades studying how humans delude themselves. Our simple thirty-minute embryo injections were now allocated one-hour blocks.

The room erupted in protests. “But we’ve done these injections hundreds of times!” “We know exactly how long they take!”

Kahneman smiled. “You’re all victims of the planning fallacy. Your System 1 is wildly optimistic about everything. Your mind accounts only for quick needle preparations while forgetting inevitable moments someone drops the cover slip itself”

“Your intuitive mind,” he explained, “only remembers the core task—actual injection. It conveniently forgets the setup, troubleshooting, inevitable equipment malfunction, and time spent staring at embryos wondering if they are worth injecting at all.”

The Planning Fallacy: The tendency to underestimate time, costs, and risks of future actions while overestimating their benefits. Even when people know similar tasks have taken longer than expected in past, they still predict future tasks will take less time.

System 1 vs. System 2 : Kahneman’s framework for two modes of thought. System 1 is fast, automatic, and intuitive (like quickly estimating “this should take twenty minutes”). System 2 is slow, deliberate, and logical (like carefully calculating each step: needle prep, embryo collection, injection setup, actual injection, cleanup, and imaging).

The Kahneman Method in Action

His solution was deceptively simple: multiply every time estimate by two, then add buffer time for “unknown unknowns.” “There are things you know will probably go wrong—known unknowns, like occasional broken needle or contaminated sample,” he explained.

“But then there are unknown unknowns—the completely unexpected problems you can’t even anticipate. The incubator that dies on a weekend, the new batch of reagent that behaves differently, or the day your hands just won’t stop shaking. You can’t plan for specific unknown unknowns, but you can acknowledge they exist.”

He made us track everything for two weeks: actual injection times, PCR setup duration…and the data was humbling. Our “standard” twenty-minute procedure had a median time of 40 minutes, with some taking over 1.5 hours when equipment misbehaved.

We tried his interventions skeptically. To our disbelief, the results were miraculous and maddening in equal measure.

For the first time in lab history, experiments actually finished when scheduled. Postdocs stopped working until midnight to complete “quick afternoon experiments.” Stress level plummeted as people stopped running late for their next commitment.

“Your emotional attachment to each experiment makes you treat it as special,” Kahneman explained. “You think ‘this time will be different’ or ‘I’m more prepared now.’ But from a statistical perspective, today’s PCR is just another data point in the distribution of ‘times PCR has taken in this lab.’
The planning fallacy tricks you into believing you can beat the historical average through wishful thinking.”

The lesson was profound: scientists are ultimately human and prone to same cognitive biases that affect everyone else. We bring these same mental shortcuts to our labs, our experimental designs, and our data interpretations. The first step toward better science maybe a more nuanced use of an important equipment—our own minds.

Have you experienced similar planning fallacies and overcome them? Do share in the comments.

What else did the Prof. Kahneman advise us on? Stay tuned for the next article in the series.

Practical Applications: The Kahneman Time Revolution

1. Track Reality First: Record actual times for routine procedures for couple of weeks.

2. Use the 1.5x Rule: Multiply routine task estimates by 1.5.

3. Use the 3x Rule: Triple your estimate for novel experiments.

4. Build Break Points: Schedule natural stopping points in long experiments, to allow buffers for unknown unknowns.

5. Try the Three-Point Method: For familiar tasks, estimate your best-case time (everything goes perfectly) and worst-case time (multiple things go wrong). Then calculate the geometric mean (root of the product) for a realistic schedule estimate.

Example: Embryo injection times (Best case 20 minutes, worst case 1 hours), geometric mean√(20 × 60) = √1200 ≈ 34 minutes.

Sameer Thukral is a post doc in the lab of Yu-Chiun Wang at RIKEN-BDR, Kobe, Japan, where he loves discussing science in the healthy and respectful lab environment. He is a developmental biologist with a focus on mechanics of yolk-blastoderm interactions. He is also the co-founder of BDR-Launchpad, a post-doc network for supporting ECRs with the hidden curriculum of science.

The observations made here are his own and do not reflect the opinions of the employer. This article was written by Sameer Thukral, with formatting, structuring and framing support of Claude AI.

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