Prospect Theory Value Function

Evaluate Kahneman-Tversky Prospect Theory value function with loss aversion and diminishing sensitivity. Value function: v(x) = x^alpha for gains (x ≥ 0), v(x) = -lambda*|x|^beta for losses (x < 0). Captures behavioral phenomena: loss aversion (losses hurt more than gains feel good), reference dependence, and diminishing sensitivity. Returns value, marginal value, and loss aversion ratio. Essential for behavioral economics and decision theory. [Tier: STANDARD, Credits: 2]

POST /quantlib/economics/utility/prospect-theory

Prospect Theory Value Function

cURL

curl --request POST \
  --url https://api.fincept.in/quantlib/economics/utility/prospect-theory \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "alpha": 0.88,
  "beta": 0.88,
  "lambda_param": 2.25,
  "wealth": 0,
  "outcome": -100
}
'

200

{
  "success": true,
  "data": {
    "value": -106.54,
    "marginal": 1.06,
    "loss_aversion_ratio": 2.25
  }
}

Authorizations

X-API-Key · string · header · required

API key for authentication. Get your key at https://api.fincept.in/auth/register

Body

application/json

outcome · number · required

Outcome to evaluate (positive = gain, negative = loss relative to reference)

Example:

-100

alpha · number · default: 0.88

Diminishing sensitivity exponent for gains (typically 0.88)

Required range: 0 <= x <= 1

Example:

0.88

beta · number · default: 0.88

Diminishing sensitivity exponent for losses (typically 0.88)

Required range: 0 <= x <= 1

Example:

0.88

lambda_param · number · default: 2.25

Loss aversion parameter (typically 2.25, meaning losses hurt 2.25x more than gains)

Required range: x >= 1

Example:

2.25

wealth · number · default: 0

Reference point (typically 0 for gains/losses relative to status quo)

Example:

0

Response

200

application/json

Successful Response

success · boolean

Example:

true

data · object

Show child attributes