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Lottery Sure Equivalent Gap Tool

Compare an explicitly elicited sure equivalent with expected payoff; no risk preference is inferred.

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Description

Return expected lottery payoff and sure-equivalent gap.

Decision models distinguish monetary payoffs, probabilities, model values and subjective equivalents. Discounting, probability weighting and reference-dependent utility terms depend on supplied parameters and cannot by themselves establish actual preferences or an appropriate allocation.

Return expected lottery payoff and sure-equivalent gap.

When to use this tool

Use Lottery Sure Equivalent Gap when evaluating the stated decision-model, lottery or discount contribution.

How to use the tool and how it works

  1. Enter sureEquivalent, lotteryPayoffs, probabilities. Follow any array-alignment requirements below. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read expectedPayoff, gap. Use the output convention below.

Return expected lottery payoff and sure-equivalent gap. Require aligned payoffs/probabilities, each probability in [0,1] and sum within 1e-12 of one; expectedPayoff is the dot product, gap = sureEquivalent-expected.1

Input fieldMeaning and unitsAccepted input
sureEquivalentSure EquivalentRequired. Finite number.
lotteryPayoffsLottery PayoffsRequired. Array of finite number; maximum items 256.
probabilitiesProbabilitiesRequired. Array of finite number; maximum items 256.
Output fieldInterpretationReturned shape
expectedPayoffExpected PayoffRequired. Finite number.
gapSigned difference under the stated comparison convention, with matching units.Required. Finite number.
json
{
  "input": {
    "sureEquivalent": 4,
    "lotteryPayoffs": [
      0,
      10
    ],
    "probabilities": [
      0.5,
      0.5
    ]
  },
  "output": {
    "expectedPayoff": 5,
    "gap": -1
  }
}

Limitations and assumptions

  • Probabilities and equivalent are supplied; the result does not elicit utility, model probability weighting or recommend a real financial choice.
  • Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
  • Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.

Alternative or Complementary methods

Fit and validate an appropriate complete decision model against relevant observations; distinguish model scores from money and assess actual financial choices separately.

References

  1. Calculation and input contract: behavioral-economics/lottery-sure-equivalent-gap — tool.io

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