UtilityModels
UtilityModels.jl is a collection of utility based decision models. Currently, expected utlity theory, transfer of attention exchange, and prospect theory are implemented. More models soon to follow.
Installation
In the REPL, enter ]
to activate package mode, then type
add UtilityModels
Help
In the REPL, enter ?
to activate help mode, then type the name of the function or object, such as:
TAX
Examples
Expected Utility Theory
using UtilityModels
α = .8
model = ExpectedUtility(α)
p = [.3,.2,.3,.2]
v = [10.0,3.0,2.0,1.0]
gamble = Gamble(;p, v)
Expected Utility
mean(model, gamble)
1.65219
Standard Deviation of Utility
std(model, gamble)
3.38863
Transfer of Attention Exchange
using UtilityModels
# TAX with default values
model = TAX()
p = [.25,.25,.50]
v = [100.0,0.0,50.0]
gamble = Gamble(;p, v)
Expected Utility
mean(model, gamble)
15.51253
References
 Birnbaum, M. H., & Chavez, A. (1997). Tests of theories of decision making: Violations of branch independence and distribution independence. Organizational Behavior and human decision Processes, 71(2), 161194.
 Birnbaum, M. H. (2008). New paradoxes of risky decision making. Psychological review, 115(2), 463.
Prospect Theory
using UtilityModels
α = .8; γg = .6; λ = 2.25
# By default, α=β and γg = γl
model = ProspectTheory(;α, γg, λ)
p = [.3,.2,.3,.2]
v = [10.0,3.0,2.0,1.0]
gamble = Gamble(;p, v)
Expected Utility
mean(model, gamble)
0.77268
Standard Deviation of Utility
std(model, gamble)
3.7516
References

Fennema, H., & Wakker, P. (1997). Original and cumulative prospect theory: A discussion of empirical differences. Journal of Behavioral Decision Making, 10(1), 5364.

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297323.