420 lives in the dataset

Discover how you really make decisions.

Live one simulated life, from a kitchen table at 18 to a crossroads at 40. Money, careers, love, leaps of faith. While you play, DecisionLab quietly maps the hidden forces steering your choices — then shows you the profile you didn't know you had.

Enter the simulation~12 minutes · anonymous · no sign-up
Sample behavioral profile
RiskAppetiteLossAversionFramingSensitivityAnchoringSocialProofConfidenceCalibrationPresentBiasSunkCost Attachment
youeveryone else
27
life decisions
8
behavioral dimensions
22 yrs
of simulated life
100%
anonymous
How it works

A game on the surface. An instrument underneath.

01
Live a life

Four chapters, ages 18 to 40. Job offers, windfalls, market frenzies, health scares — every choice moves your money, happiness, health, and relationships.

02
We watch the patterns

Every scenario is a calibrated behavioral probe. Which risks you take, which numbers sway you, when you follow the crowd — the instrument reads between your choices.

03
Meet your decision mind

Your behavioral profile: eight measured dimensions, your decision archetype, where you sit against every other participant — and what to do about it.

The science

Eight forces that decide before you do

Each dimension comes from decades of behavioral economics research — Kahneman, Tversky, Thaler and beyond — and each is measured by multiple in-game probes, including randomized A/B framings you'll never notice.

Risk Appetite
Your comfort with uncertain outcomes

Risk preference is one of the most stable individual differences in decision research, though it varies by domain (financial vs. health vs. social) — Weber, Blais & Betz (2002).

Loss Aversion
How much losses loom larger than gains

First quantified by Kahneman & Tversky in Prospect Theory (1979). Typical people demand about $2 of potential gain to accept $1 of potential loss.

Framing Sensitivity
Same facts, different words, different choice

The Asian Disease Problem (Tversky & Kahneman, 1981): people prefer certainty under gain frames and gambles under loss frames — for mathematically identical options.

Anchoring
How much first numbers steer you

Tversky & Kahneman (1974) showed even random numbers shift estimates. In negotiations, first offers explain much of the final price (Galinsky & Mussweiler, 2001).

Social Proof
How much the crowd moves you

Information cascades (Banerjee, 1992) show rational individuals can pile into wrong choices. Asch (1951) demonstrated conformity even against clear evidence.

Confidence Calibration
Your certainty versus reality

Roughly 90% of drivers rate themselves above average (Svenson, 1981). Overconfidence is called "the mother of all biases" for how many failures trace back to it (Moore & Healy, 2008).

Present Bias
Now versus later

Modeled as hyperbolic discounting (Laibson, 1997). People routinely reverse their own long-term plans when a reward becomes immediate.

Sunk Cost Attachment
Throwing good money after bad

Arkes & Blumer (1985) showed people attend more events they overpaid for. Escalation of commitment (Staw, 1976) traps organizations in failing projects.

For researchers

An open behavioral dataset, growing with every life

Every session contributes anonymized decision data: choices, response latencies, randomized variant assignments, and derived scores. The research console tracks live framing and anchoring experiments across the whole population, with one-click CSV export for R or Python.

Framing experiment
Do gain vs. loss wordings flip real choices?
Anchoring experiment
Randomized anchors: $350 vs $900, $58k vs $84k
Herding experiment
Same decision, with and without social proof
About the founder
Pranav Sai Adusumilli

DecisionLab AI was designed and built by Pranav Sai Adusumilli, a high school student passionate about behavioral economics and decision science. Fascinated by why smart people make predictably irrational choices, he built DecisionLab to turn the classic experiments of Kahneman, Tversky, and Thaler into something anyone can experience firsthand — and to grow an open dataset for studying how we all decide.

More about the project