Class BehavioralEconomics

java.lang.Object

org.episteme.social.economics.BehavioralEconomics

public final class BehavioralEconomics
extends Object

Models the value function from Prospect Theory (Kahneman invalid input: '&' Tversky). Provides models for loss aversion, probability weighting, and hyperbolic discounting.

Version:

1.2

Author:

Silvere Martin-Michiellot, Gemini AI (Google DeepMind)

Method Summary

Modifier and Type	Method	Description
static Real	decisionValue(Real amount, Real probability, Real gamma)	Calculates the "Decision Weight" for a prospect.
static Real	probabilityWeight(Real p, Real gamma)	Probability Weighting Function (Prelec). w(p) = exp(-(-ln p)^gamma)
static Real	prospectValue(Real gainLoss, Real alpha, Real beta, Real lambda)	Tversky invalid input: '&' Kahneman Value Function. v(x) = x^alpha if x >= 0 v(x) = -lambda * (-x)^beta if x invalid input: '<' 0

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

prospectValue

public static Real prospectValue(Real gainLoss,
 Real alpha,
 Real beta,
 Real lambda)

Tversky invalid input: '&' Kahneman Value Function. v(x) = x^alpha if x >= 0 v(x) = -lambda * (-x)^beta if x invalid input: '<' 0

Typically: alpha=0.88, beta=0.88, lambda=2.25 (Loss Aversion)

Parameters:

gainLoss - the magnitude of the gain (positive) or loss (negative)

alpha - the exponent for gains

beta - the exponent for losses

lambda - the loss aversion coefficient

Returns:

the subjective value

probabilityWeight

public static Real probabilityWeight(Real p,
 Real gamma)

Probability Weighting Function (Prelec). w(p) = exp(-(-ln p)^gamma)

Parameters:

p - the objective probability (0 to 1)

gamma - the curvature parameter

Returns:

the weighted probability

decisionValue

public static Real decisionValue(Real amount,
 Real probability,
 Real gamma)

Calculates the "Decision Weight" for a prospect. Combines value function and probability weighting.

Parameters:

amount - the potential outcome amount

probability - the probability of the outcome

gamma - the weighting parameter

Returns:

the decision weight