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NeuroEconLab

Risk Behavioral Task & Utility Simulator
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Welcome to the Risk Choice Experiment

In this task, you will make a series of decisions between a Sure Money Payment and a Risky Lottery (50% chance of $100, 50% chance of $0).

Instructions:
  • There are no right or wrong answers; choose according to your personal preference.
  • You will complete 12 fast binary choices presented in randomized order.
  • Your economic profile (Utility Function & Risk Aversion) will be computed live!
Trial 1 / 12 Lottery: 50% $100 | 50% $0

Which option would you prefer to receive?

Option A

Guaranteed Payment

$50

100% Probability to get $50

Option B

50 / 50 Lottery

50% Chance: $100 50% Chance: $0

Expected Monetary Value (EV): $50.00

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Experiment Completed!

Your response choices have been registered and fitted against expected utility models.

Expected Value (EV)
$50.00
Mean value of 50/50 lottery
Certainty Equivalent (CE)
$0.00
Subjective cash equivalent
Risk Premium Paid
$0.00
EV - CE (Cost to avoid risk)
CRRA Exponent (α)
1.00
Utility curvature U(x) = xα

Risk Neutral

You treat money linearly ($50 sure == 50% chance of $100).

1. Psychometric Choice Curve
2. Fitted Utility U(x) & Expected Utility Chord

Export Submission File

Download your decision dataset and complete analytical summary for assignment submission.

Model Parameters

Averse (< 1) Neutral (1.0) Seeking (> 1)
Controls choice randomness via Softmax rule.
Preset Archetypes:
Simulated Utility Curve U(x) = xα
Simulated Softmax Probability P(Choose Sure)

Neuroeconomics Framework & Mathematical Models

1. Expected Utility Theory (EUT)

Expected Utility theory posits that decision makers evaluate risky alternatives based on expected subjective value rather than expected monetary value:

EU = p · U(x1) + (1 - p) · U(x2)

Where $p$ is probability, $x$ is outcome, and $U(\cdot)$ is the individual utility function.

2. Power Utility Function (CRRA)

We parameterize utility using the Constant Relative Risk Aversion (CRRA) family:

U(x) = xα
  • α < 1.0: Risk Averse (Concave curve, diminishing marginal utility)
  • α = 1.0: Risk Neutral (Linear utility)
  • α > 1.0: Risk Seeking (Convex curve)

3. Certainty Equivalent & Risk Premium

The Certainty Equivalent (CE) is the guaranteed payoff amount that yields equal utility to playing the risky lottery:

U(CE) = EU(Lottery) ⇒ CE = (0.5 · 100α)1/α

Risk Premium = Expected Value - Certainty Equivalent. A positive risk premium indicates compensation required to accept risk.

4. Softmax Decision Rule

To model decision noise in neural valuation networks, choice probabilities are specified via the Luce/Softmax logistic rule:

P(Sure) = 1 / [1 + e-(U(Sure) - EU(Lottery)) / τ]

Where $\tau$ (temperature) represents decision noise or bounded rationality.