Loss Aversion Lambda Estimate
Restricted linear prospect utility gain/loss indifferenceλ=G/L, zero reference/endowment and independently actual indifference. Curved utilities require separate exponents; not universal loss aversion. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
A linear gain–loss indifference model expresses loss aversion as a ratio of magnitudes.
A linear gain–loss indifference model expresses loss aversion as a ratio of magnitudes.1
Curved utility branches need additional parameters beyond this restricted ratio. The calculation and input requirements are explained below.
When to use Loss Aversion Lambda Estimate
Enter positive indifference gain and loss magnitudes in compatible units.
How to use Loss Aversion Lambda Estimate and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run Loss Aversion Lambda Estimate.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualIndifferenceGainMagnitude | Actual Indifference Gain Magnitude. Use the units and matching context stated in the calculation below. | number | Required; strictly positive; finite; other constraints below |
| actualIndifferenceLossMagnitude | Actual Indifference Loss Magnitude. Use the units and matching context stated in the calculation below. | number | Required; strictly positive; finite; other constraints below |
Enter positive indifference gain and loss magnitudes in compatible units. Divide gain by loss for the restricted linear utility model. Curvature parameters and behavioral model validity are not fitted.
lambda=gainMagnitude/lossMagnitude Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"actualIndifferenceGainMagnitude": 4,
"actualIndifferenceLossMagnitude": 2
},
"output": {
"result": {
"value": 2
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Thurstone Case V Scale Value Calculator for a complementary operation: Thurstone Case V scaling converts pairwise preference probabilities to normal deviates.
Use Prospect Value Function Alpha/Beta Estimator for a complementary operation: A power-value model becomes a straight line after logarithmic transformation.
References
-
Loss Aversion Lambda Estimate — tool.io
Similar or alternative tools
- Thurstone Case V Scale Value Calculator
Thurstone Case V scaling converts pairwise preference probabilities to normal deviates.
- Prospect Value Function Alpha/Beta Estimator
A power-value model becomes a straight line after logarithmic transformation.