tests/Test_Birnbaum_2007.R
In gary-au/pt: Computational models for prospect theory and other theories of risky decision making
library("pt")
########################
# Birnbaum, M. H. (2007). Tests of branch splitting and branch-splitting independence in Allais paradoxes with positive and mixed consequences. Organizational Behavior and Human Decision Processes, 102(2), 154-173.
########################
########################
#
# Table 2. Dissection of Allais paradox with large consequences (series A)
#
########################
# Choice problem 6, Table 2, p.161
# S = (1000000, 0.11; 2, 0.89)
# ~ 125k TAX
# ~ 132k PT
# R = (2000000, 0.10; 2, 0.90)
# ~ 236k TAX
# ~ 248k PT
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(1000000, 2, 2000000, 2)
probability_strings <-
c("0.11", "0.89", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 110002 125288 125288 -15286
# 2 1 2 200002 235759 235759 -35757
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 110002 32055 131926 -21924
# 2 1 2 200002 55872 248052 -48050
########################
# Choice problem 9, Table 2, p.161
# S = (1000000, 0.1; 1000000, 0.01; 2, 0.89)
# ~ 155k TAX
# ~ 132k PT
# R = (2000000, 0.10; 2, 0.01; 2, 0.89)
# ~ 172k TAX
# ~ 248k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(1000000, 1000000, 2, 2000000, 2, 2)
probability_strings <-
c("0.1", "0.01", "0.89", "0.1", "0.01", "0.89")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 110002 154612 154612 -44610
# 2 1 2 200002 171859 171859 28143
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 110002 32055 131926 -21924
# 2 1 2 200002 55872 248052 -48050
########################
# Choice problem 12, Table 2, p.161
# S = (1000000, 1.0)
# ~ 1000k TAX
# ~ 1000k PT
# R = (1000000, 0.10; 2000000, 0.89; 2, 0.01)
# ~ 810k TAX
# ~ 1065k PT
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 2, 2, 2)
outcome_ids <- c(1, 1, 2, 3)
objective_consequences <- c(1000000, 2000000, 1000000, 2)
probability_strings <-
c("1.0", "0.1", "0.89", "0.01")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 1000000 1000000 1000000 0
# 2 1 2 1090000 810212 810212 279788
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 1000000 190546 1000000 -0.000000001397
# 2 1 2 1090000 201336 1064590 25410
########################
# Choice problem 16, Table 2, p.161
# S = (2000000, 0.89; 1000000, 0.1; 1000000, 0.01)
# ~ 1400k TAX
# ~ 1714k PT
# R = (2000000, 0.89; 2000000, 0.1; 2, 0.01)
# ~ 1449k TAX
# ~ 1825k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(2000000, 1000000, 1000000, 2000000, 2000000, 2)
probability_strings <-
c("0.89", "0.1", "0.01", "0.89", "0.1", "0.01")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 1890000 1396927 1396927 493073
# 2 1 2 1980000 1448566 1448566 531434
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 1890000 306095 1713673 176327
# 2 1 2 1980000 323580 1825338 154662
########################
# Choice problem 19, Table 2, p.161
# S = (2000000, 0.89; 1000000, 0.11)
# ~ 1541k TAX
# ~ 1714k PT
# R = (2000000, 0.99; 2, 0.01)
# ~ 1282k TAX
# ~ 1825k PT
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(2000000, 1000000, 2000000, 2)
probability_strings <-
c("0.89", "0.11", "0.99", "0.01")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 1890000 1541380 1541380 348620
# 2 1 2 1980000 1281939 1281939 698061
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 1890000 306095 1713673 176327
# 2 1 2 1980000 323580 1825338 154662
########################
#
# Table 3. Dissection of Allais paradox into branch independence and coalescing (series B)
#
########################
# Choice problem 10, Table 3, p.161
# S = (500000, 0.15; 11, 0.85)
# ~ 76k TAX
# ~ 81k PT
# R = (1000000, 0.10; 11, 0.90)
# ~ 118k TAX
# ~ 124k PT
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(500000, 11, 1000000, 11)
probability_strings <-
c("0.15", "0.85", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 75009 76328 76328 -1318
# 2 1 2 100010 117888 117888 -17878
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 75009 20801 80706 -5696
# 2 1 2 100010 30365 124054 -24045
########################
# Choice problem 17, Table 3, p.161
# S = (500000, 0.1; 500000, 0.05; 11, 0.85)
# ~ 100k TAX
# ~ 81k PT
# R = (1000000, 0.10; 11, 0.05; 11, 0.85)
# ~ 82k TAX
# ~ 124k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(500000, 500000, 11, 1000000, 11, 11)
probability_strings <-
c("0.1", "0.05", "0.85", "0.1", "0.05", "0.85")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 75009 99514 99514 -24505
# 2 1 2 100010 82132 82132 17878
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 75009 20801 80706 -5696
# 2 1 2 100010 30365 124054 -24045
########################
# Choice problem 20, Table 3, p.161
# S = (500000, 0.1; 500000, 0.85; 500000, 0.05)
# ~ 500k TAX
# ~ 500k PT
# R = (1000000, 0.10; 500000, 0.85; 11, 0.05)
# ~ 378k TAX
# ~ 470k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(500000, 500000, 500000, 1000000, 500000, 11)
probability_strings <-
c("0.1", "0.85", "0.05", "0.1", "0.85", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 500000 500000 500000 0
# 2 1 2 525001 378151 378151 146850
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 500000 103537 500000 -0.0000000006403
# 2 1 2 525001 98095 470246 54755
########################
# Choice problem 14, Table 3, p.161
# S = (1000000, 0.85; 500000, 0.1; 500000, 0.05)
# ~ 684k TAX
# ~ 834k PT
# R = (1000000, 0.85; 1000000, 0.1; 11, 0.05)
# ~ 674k TAX
# ~ 791k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(1000000, 500000, 500000, 1000000, 1000000, 11)
probability_strings <-
c("0.85", "0.1", "0.05", "0.85", "0.1", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 925000 683663 683663 241337
# 2 1 2 950001 674176 674176 275825
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 925000 162349 833616 91384
# 2 1 2 950001 155017 790969 159031
########################
# Choice problem 8, Table 3, p.161
# S = (500000, 0.15; 11, 0.85)
# ~ 757k TAX
# ~ 834k PT
# R = (1000000, 0.10; 11, 0.90)
# ~ 591k TAX
# ~ 791k PT
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(1000000, 500000, 1000000, 11)
probability_strings <-
c("0.85", "0.15", "0.95", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 925000 757015 757015 167985
# 2 1 2 950001 591381 591381 358619
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 925000 162349 833616 91384
# 2 1 2 950001 155017 790969 159031
########################
#
# Table 7. Dissection of Allais paradox (series A)
#
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 6, Table 7, p.165
# S = (40, 0.2; 2, 0.8)
# ~ 9.0 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.90)
# ~ 13.3 TAX
# ~ 12.9 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(40, 2, 98, 2)
probability_strings <-
c("0.2", "0.8", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 8.962 8.962 0.6384
# 2 1 2 11.6 13.32 13.32 -1.716
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 12.94 12.94 -1.341
########################
# Choice problem 11, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 0.1; 40, 0.1; 2, 0.8)
# ~ 11.1 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.90)
# ~ 13.3 TAX
# ~ 12.9 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(40, 40, 2, 98, 2)
probability_strings <-
c("0.1", "0.1", "0.8", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 11.07 11.07 -1.466
# 2 1 2 11.6 13.32 13.32 -1.716
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 12.94 12.94 -1.341
########################
# Choice problem 21, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 0.2; 2, 0.8)
# ~ 9.0 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.10; 2, 0.80)
# ~ 9.6 TAX
# ~ 7.2 GDU ? an error, should be 9.681 ?
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(40, 2, 98, 2, 2)
probability_strings <-
c("0.2", "0.8", "0.1", "0.1", "0.8")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 8.962 8.962 0.6384
# 2 1 2 11.6 9.635 9.635 1.965
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 9.681 9.681 1.919
########################
# Choice problem 9, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 0.1; 40, 0.1; 2, 0.8)
# ~ 11.1 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.10; 2, 0.80)
# ~ 9.6 TAX
# ~ 7.2 GDU ? an error, should be 9.681 ?
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(40, 40, 2, 98, 2, 2)
probability_strings <-
c("0.1", "0.1", "0.8", "0.1", "0.1", "0.8")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 11.07 11.07 -1.466
# 2 1 2 11.6 9.635 9.635 1.965
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 9.681 9.681 1.919
########################
# Choice problem 12, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 1.0)
# ~ 40.0 TAX
# ~ 40.0 GDU
# R = (98, 0.10; 40, 0.80; 2, 0.10)
# ~ 30.6 TAX
# ~ 31.9 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 2, 2, 2)
outcome_ids <- c(1, 1, 2, 3)
objective_consequences <- c(40, 98, 40, 2)
probability_strings <-
c("1.0", "0.1", "0.8", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 40 40 40 0
# 2 1 2 42 30.58 30.58 11.42
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 40 40 40 0
# 2 1 2 42 31.91 31.91 10.09
########################
# Choice problem 16, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.1; 40, 0.1)
# ~ 59.8 TAX
# ~ 65.0 GDU
# R = (98, 0.8; 98, 0.1; 2, 0.1)
# ~ 62.6 TAX
# ~ 65.8 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(98, 40, 40, 98, 98, 2)
probability_strings <-
c("0.8", "0.1", "0.1", "0.8", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 59.77 59.77 26.63
# 2 1 2 88.4 62.55 62.55 25.85
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 65 65 21.4
# 2 1 2 88.4 65.83 65.83 22.57
########################
# Choice problem 7, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.1; 40, 0.1)
# ~ 59.8 TAX
# ~ 65.0 GDU
# R = (98, 0.9; 2, 0.1)
# ~ 54.7 TAX
# ~ 65.8 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(98, 40, 40, 98, 2)
probability_strings <-
c("0.8", "0.1", "0.1", "0.9", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 59.77 59.77 26.63
# 2 1 2 88.4 54.68 54.68 33.72
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 65 65 21.4
# 2 1 2 88.4 65.83 65.83 22.57
########################
# Choice problem 13, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.2)
# ~ 68.0 TAX
# ~ 71.4 GDU
# R = (98, 0.8; 98, 0.1; 2, 0.1)
# ~ 62.6 TAX
# ~ 65.8 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(98, 40, 98, 98, 2)
probability_strings <-
c("0.8", "0.2", "0.8", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 68.04 68.04 18.36
# 2 1 2 88.4 62.55 62.55 25.85
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 71.42 71.42 14.98
# 2 1 2 88.4 65.83 65.83 22.57
########################
# Choice problem 19, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.2)
# ~ 68.0 TAX
# ~ 71.4 GDU
# R = (98, 0.9; 2, 0.1)
# ~ 54.7 TAX
# ~ 65.8 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(98, 40, 98, 98, 2)
probability_strings <-
c("0.8", "0.2", "0.8", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 68.04 68.04 18.36
# 2 1 2 88.4 62.55 62.55 25.85
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 71.42 71.42 14.98
# 2 1 2 88.4 65.83 65.83 22.57
########################
#
# Table 8. Dissection of Allais paradox (series BA)
#
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 10, Table 8, p.166
# S = (50, 0.15; 7, 0.85)
# ~ 13.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.90)
# ~ 18.0 TAX
# ~ 17.6 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(50, 7, 100, 7)
probability_strings <-
c("0.15", "0.85", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 13.56 13.56 -0.1134
# 2 1 2 16.3 17.96 17.96 -1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 17.6 17.6 -1.299
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 15, Table 8, p.166
# S = (50, 0.1; 50, 0.05; 7, 0.85)
# ~ 15.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.90)
# ~ 18.0 TAX
# ~ 17.6 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(50, 50, 7, 100, 7)
probability_strings <-
c("0.1", "0.05", "0.85", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 15.56 15.56 -2.107
# 2 1 2 16.3 17.96 17.96 -1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 17.6 17.6 -1.299
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 22, Table 8, p.166
# S = (50, 0.15; 7, 0.85)
# ~ 13.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.05; 7, 0.85)
# ~ 14.6 TAX
# ~ 12.6 GDU ? an error, should be 15.26?
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(50, 7, 100, 7, 7)
probability_strings <-
c("0.15", "0.85", "0.1", "0.05", "0.85")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 13.56 13.56 -0.1134
# 2 1 2 16.3 14.64 14.64 1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 15.26 15.26 1.045
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 17, Table 8, p.166
# S = (50, 0.1; 50, 0.05; 7, 0.85)
# ~ 15.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.05; 7, 0.85)
# ~ 14.6 TAX
# ~ 12.6 GDU ? an error, should be 15.26?
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(50, 50, 7, 100, 7, 7)
probability_strings <-
c("0.1", "0.05", "0.85", "0.1", "0.05", "0.85")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 15.56 15.56 -2.107
# 2 1 2 16.3 14.64 14.64 1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 15.26 15.26 1.045
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 20, Table 8, p.166
# S = (50, 1.0)
# ~ 50.0 TAX
# ~ 50.0 GDU
# R = (100, 0.10; 50, 0.85; 7, 0.05)
# ~ 40.1 TAX
# ~ 44.0 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 2, 2, 2)
outcome_ids <- c(1, 1, 2, 3)
objective_consequences <- c(50, 100, 50, 7)
probability_strings <-
c("0.1", "0.1", "0.85", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 5 50 50 -45
# 2 1 2 52.85 40.1 40.1 12.75
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 5 50 50 -45
# 2 1 2 52.85 44.06 44.06 8.792
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 14, Table 8, p.166
# S = (100, 0.85; 50, 0.1; 50, 0.05)
# ~ 68.4 TAX
# ~ 74.9 GDU
# R = (100, 0.85; 100, 0.10; 7, 0.05)
# ~ 69.7 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(100, 50, 50, 100, 100, 7)
probability_strings <-
c("0.85", "0.1", "0.05", "0.85", "0.1", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 68.37 68.37 24.13
# 2 1 2 95.35 69.7 69.7 25.65
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 74.91 74.91 17.59
# 2 1 2 95.35 77.55 77.55 17.8
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 5, Table 8, p.166
# S = (100, 0.85; 50, 0.1; 50, 0.05)
# ~ 68.4 TAX
# ~ 74.9 GDU
# R = (100, 0.95; 7, 0.05)
# ~ 62.0 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(100, 50, 50, 100, 7)
probability_strings <-
c("0.85", "0.1", "0.05", "0.95", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 68.37 68.37 24.13
# 2 1 2 95.35 62 62 33.35
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 74.91 74.91 17.59
# 2 1 2 95.35 77.55 77.55 17.8
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 18, Table 8, p.166
# S = (100, 0.85; 50, 0.15)
# ~ 75.7 TAX
# ~ 79.8 GDU
# R = (100, 0.85; 100, 0.1; 7, 0.05)
# ~ 69.7 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(100, 50, 100, 100, 7)
probability_strings <-
c("0.85", "0.15", "0.85", "0.1", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 75.7 75.7 16.8
# 2 1 2 95.35 69.7 69.7 25.65
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 79.84 79.84 12.66
# 2 1 2 95.35 77.55 77.55 17.8
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 8, Table 8, p.166
# S = (100, 0.85; 50, 0.15)
# ~ 75.7 TAX
# ~ 79.8 GDU
# R = (100, 0.95; 7, 0.05)
# ~ 62.0 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(100, 50, 100, 7)
probability_strings <-
c("0.85", "0.15", "0.95", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 75.7 75.7 16.8
# 2 1 2 95.35 62 62 33.35
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 79.84 79.84 12.66
# 2 1 2 95.35 77.55 77.55 17.8
########################
#
# Table 10. Test of EU plus entropy model
#
########################
# Choice problem 9, Table 10, p.170
# S = (47, 0.5; 43, 0.15; 43, 0.15; 43, 0.1; 43, 0.1)
# ~ 43.5 TAX
# R = (89, 0.5; 11, 0.15; 11, 0.15; 11, 0.1; 11, 0.1)
# ~ 21.4 TAX
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
objective_consequences <- c(47, 43, 43, 43, 43, 89, 11, 11, 11, 11)
probability_strings <-
c("0.5", "0.15", "0.15", "0.1", "0.1", "0.5", "0.15", "0.15", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 43.53 43.53 1.469
# 2 1 2 50 21.36 21.36 28.64
########################
# Choice problem 25, Table 10, p.170
# S = (47, 0.5; 43, 0.25; 43, 0.25)
# ~ 43.9 TAX
# R = (89, 0.5; 11, 0.15; 11, 0.15; 11, 0.1; 11, 0.1)
# ~ 28.5 TAX
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(47, 43, 43, 89, 11, 11)
probability_strings <-
c("0.5", "0.25", "0.25", "0.5", "0.25", "0.25")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 43.9 43.9 1.104
# 2 1 2 50 28.48 28.48 21.52
########################
# Choice problem 7, Table 10, p.170
# S = (47, 0.5; 43, 0.5)
# ~ 44.3 TAX
# R = (89, 0.5; 11, 0.5)
# ~ 37.0 TAX
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(47, 43, 89, 11)
probability_strings <-
c("0.5", "0.5", "0.5", "0.5")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 44.33 44.33 0.6667
# 2 1 2 50 37 37 13
########################
# Choice problem 23, Table 10, p.170
# S = (47, 0.25; 47, 0.25; 43, 0.5)
# ~ 44.7 TAX
# R = (89, 0.25; 89, 0.25; 11, 0.5)
# ~ 43.3 TAX
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(47, 47, 43, 89, 89, 11)
probability_strings <-
c("0.25", "0.25", "0.5", "0.25", "0.25", "0.5")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 44.66 44.66 0.3446
# 2 1 2 50 43.28 43.28 6.72
########################
# Choice problem 13, Table 10, p.170
# S = (47, 0.1; 47, 0.1; 47, 0.15; 47, 0.15; 43, 0.5)
# ~ 45.0 TAX
# R = (89, 0.1; 89, 0.1; 89, 0.15; 89, 0.15; 11, 0.5)
# ~ 50.1 TAX
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
objective_consequences <- c(47, 47, 47, 47, 43, 89, 89, 89, 89, 11)
probability_strings <-
c("0.1", "0.1", "0.15", "0.15", "0.5", "0.1", "0.1", "0.15", "0.15", "0.5")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 45 45 -0.004934
# 2 1 2 50 50.1 50.1 -0.09622
gary-au/pt documentation built on May 16, 2019, 5:41 p.m.
R Package Documentation
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library("pt")
########################
# Birnbaum, M. H. (2007). Tests of branch splitting and branch-splitting independence in Allais paradoxes with positive and mixed consequences. Organizational Behavior and Human Decision Processes, 102(2), 154-173.
########################
########################
#
# Table 2. Dissection of Allais paradox with large consequences (series A)
#
########################
# Choice problem 6, Table 2, p.161
# S = (1000000, 0.11; 2, 0.89)
# ~ 125k TAX
# ~ 132k PT
# R = (2000000, 0.10; 2, 0.90)
# ~ 236k TAX
# ~ 248k PT
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(1000000, 2, 2000000, 2)
probability_strings <-
c("0.11", "0.89", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 110002 125288 125288 -15286
# 2 1 2 200002 235759 235759 -35757
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 110002 32055 131926 -21924
# 2 1 2 200002 55872 248052 -48050
########################
# Choice problem 9, Table 2, p.161
# S = (1000000, 0.1; 1000000, 0.01; 2, 0.89)
# ~ 155k TAX
# ~ 132k PT
# R = (2000000, 0.10; 2, 0.01; 2, 0.89)
# ~ 172k TAX
# ~ 248k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(1000000, 1000000, 2, 2000000, 2, 2)
probability_strings <-
c("0.1", "0.01", "0.89", "0.1", "0.01", "0.89")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 110002 154612 154612 -44610
# 2 1 2 200002 171859 171859 28143
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 110002 32055 131926 -21924
# 2 1 2 200002 55872 248052 -48050
########################
# Choice problem 12, Table 2, p.161
# S = (1000000, 1.0)
# ~ 1000k TAX
# ~ 1000k PT
# R = (1000000, 0.10; 2000000, 0.89; 2, 0.01)
# ~ 810k TAX
# ~ 1065k PT
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 2, 2, 2)
outcome_ids <- c(1, 1, 2, 3)
objective_consequences <- c(1000000, 2000000, 1000000, 2)
probability_strings <-
c("1.0", "0.1", "0.89", "0.01")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 1000000 1000000 1000000 0
# 2 1 2 1090000 810212 810212 279788
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 1000000 190546 1000000 -0.000000001397
# 2 1 2 1090000 201336 1064590 25410
########################
# Choice problem 16, Table 2, p.161
# S = (2000000, 0.89; 1000000, 0.1; 1000000, 0.01)
# ~ 1400k TAX
# ~ 1714k PT
# R = (2000000, 0.89; 2000000, 0.1; 2, 0.01)
# ~ 1449k TAX
# ~ 1825k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(2000000, 1000000, 1000000, 2000000, 2000000, 2)
probability_strings <-
c("0.89", "0.1", "0.01", "0.89", "0.1", "0.01")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 1890000 1396927 1396927 493073
# 2 1 2 1980000 1448566 1448566 531434
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 1890000 306095 1713673 176327
# 2 1 2 1980000 323580 1825338 154662
########################
# Choice problem 19, Table 2, p.161
# S = (2000000, 0.89; 1000000, 0.11)
# ~ 1541k TAX
# ~ 1714k PT
# R = (2000000, 0.99; 2, 0.01)
# ~ 1282k TAX
# ~ 1825k PT
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(2000000, 1000000, 2000000, 2)
probability_strings <-
c("0.89", "0.11", "0.99", "0.01")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 1890000 1541380 1541380 348620
# 2 1 2 1980000 1281939 1281939 698061
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 1890000 306095 1713673 176327
# 2 1 2 1980000 323580 1825338 154662
########################
#
# Table 3. Dissection of Allais paradox into branch independence and coalescing (series B)
#
########################
# Choice problem 10, Table 3, p.161
# S = (500000, 0.15; 11, 0.85)
# ~ 76k TAX
# ~ 81k PT
# R = (1000000, 0.10; 11, 0.90)
# ~ 118k TAX
# ~ 124k PT
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(500000, 11, 1000000, 11)
probability_strings <-
c("0.15", "0.85", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 75009 76328 76328 -1318
# 2 1 2 100010 117888 117888 -17878
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 75009 20801 80706 -5696
# 2 1 2 100010 30365 124054 -24045
########################
# Choice problem 17, Table 3, p.161
# S = (500000, 0.1; 500000, 0.05; 11, 0.85)
# ~ 100k TAX
# ~ 81k PT
# R = (1000000, 0.10; 11, 0.05; 11, 0.85)
# ~ 82k TAX
# ~ 124k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(500000, 500000, 11, 1000000, 11, 11)
probability_strings <-
c("0.1", "0.05", "0.85", "0.1", "0.05", "0.85")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 75009 99514 99514 -24505
# 2 1 2 100010 82132 82132 17878
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 75009 20801 80706 -5696
# 2 1 2 100010 30365 124054 -24045
########################
# Choice problem 20, Table 3, p.161
# S = (500000, 0.1; 500000, 0.85; 500000, 0.05)
# ~ 500k TAX
# ~ 500k PT
# R = (1000000, 0.10; 500000, 0.85; 11, 0.05)
# ~ 378k TAX
# ~ 470k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(500000, 500000, 500000, 1000000, 500000, 11)
probability_strings <-
c("0.1", "0.85", "0.05", "0.1", "0.85", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 500000 500000 500000 0
# 2 1 2 525001 378151 378151 146850
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 500000 103537 500000 -0.0000000006403
# 2 1 2 525001 98095 470246 54755
########################
# Choice problem 14, Table 3, p.161
# S = (1000000, 0.85; 500000, 0.1; 500000, 0.05)
# ~ 684k TAX
# ~ 834k PT
# R = (1000000, 0.85; 1000000, 0.1; 11, 0.05)
# ~ 674k TAX
# ~ 791k PT
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(1000000, 500000, 500000, 1000000, 1000000, 11)
probability_strings <-
c("0.85", "0.1", "0.05", "0.85", "0.1", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 925000 683663 683663 241337
# 2 1 2 950001 674176 674176 275825
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 925000 162349 833616 91384
# 2 1 2 950001 155017 790969 159031
########################
# Choice problem 8, Table 3, p.161
# S = (500000, 0.15; 11, 0.85)
# ~ 757k TAX
# ~ 834k PT
# R = (1000000, 0.10; 11, 0.90)
# ~ 591k TAX
# ~ 791k PT
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(1000000, 500000, 1000000, 11)
probability_strings <-
c("0.85", "0.15", "0.95", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 925000 757015 757015 167985
# 2 1 2 950001 591381 591381 358619
tk_1992_utility <- Utility(fun="power",
par=c(alpha=0.88, beta=0.88, lambda=2.25))
linear_in_log_odds_probability_weighting <-
ProbWeight(fun="linear_in_log_odds",
par=c(alpha=0.61, beta=0.724))
comparePT(my_choices,
prob_weight_for_positive_outcomes=linear_in_log_odds_probability_weighting,
prob_weight_for_negative_outcomes=linear_in_log_odds_probability_weighting,
utility=tk_1992_utility, digits=4)
# cid gid ev pt ce rp
# 1 1 1 925000 162349 833616 91384
# 2 1 2 950001 155017 790969 159031
########################
#
# Table 7. Dissection of Allais paradox (series A)
#
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 6, Table 7, p.165
# S = (40, 0.2; 2, 0.8)
# ~ 9.0 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.90)
# ~ 13.3 TAX
# ~ 12.9 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(40, 2, 98, 2)
probability_strings <-
c("0.2", "0.8", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 8.962 8.962 0.6384
# 2 1 2 11.6 13.32 13.32 -1.716
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 12.94 12.94 -1.341
########################
# Choice problem 11, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 0.1; 40, 0.1; 2, 0.8)
# ~ 11.1 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.90)
# ~ 13.3 TAX
# ~ 12.9 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(40, 40, 2, 98, 2)
probability_strings <-
c("0.1", "0.1", "0.8", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 11.07 11.07 -1.466
# 2 1 2 11.6 13.32 13.32 -1.716
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 12.94 12.94 -1.341
########################
# Choice problem 21, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 0.2; 2, 0.8)
# ~ 9.0 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.10; 2, 0.80)
# ~ 9.6 TAX
# ~ 7.2 GDU ? an error, should be 9.681 ?
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(40, 2, 98, 2, 2)
probability_strings <-
c("0.2", "0.8", "0.1", "0.1", "0.8")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 8.962 8.962 0.6384
# 2 1 2 11.6 9.635 9.635 1.965
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 9.681 9.681 1.919
########################
# Choice problem 9, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 0.1; 40, 0.1; 2, 0.8)
# ~ 11.1 TAX
# ~ 8.4 GDU
# R = (98, 0.10; 2, 0.10; 2, 0.80)
# ~ 9.6 TAX
# ~ 7.2 GDU ? an error, should be 9.681 ?
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(40, 40, 2, 98, 2, 2)
probability_strings <-
c("0.1", "0.1", "0.8", "0.1", "0.1", "0.8")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 9.6 11.07 11.07 -1.466
# 2 1 2 11.6 9.635 9.635 1.965
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 9.6 8.353 8.353 1.247
# 2 1 2 11.6 9.681 9.681 1.919
########################
# Choice problem 12, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (40, 1.0)
# ~ 40.0 TAX
# ~ 40.0 GDU
# R = (98, 0.10; 40, 0.80; 2, 0.10)
# ~ 30.6 TAX
# ~ 31.9 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 2, 2, 2)
outcome_ids <- c(1, 1, 2, 3)
objective_consequences <- c(40, 98, 40, 2)
probability_strings <-
c("1.0", "0.1", "0.8", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 40 40 40 0
# 2 1 2 42 30.58 30.58 11.42
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 40 40 40 0
# 2 1 2 42 31.91 31.91 10.09
########################
# Choice problem 16, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.1; 40, 0.1)
# ~ 59.8 TAX
# ~ 65.0 GDU
# R = (98, 0.8; 98, 0.1; 2, 0.1)
# ~ 62.6 TAX
# ~ 65.8 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(98, 40, 40, 98, 98, 2)
probability_strings <-
c("0.8", "0.1", "0.1", "0.8", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 59.77 59.77 26.63
# 2 1 2 88.4 62.55 62.55 25.85
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 65 65 21.4
# 2 1 2 88.4 65.83 65.83 22.57
########################
# Choice problem 7, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.1; 40, 0.1)
# ~ 59.8 TAX
# ~ 65.0 GDU
# R = (98, 0.9; 2, 0.1)
# ~ 54.7 TAX
# ~ 65.8 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(98, 40, 40, 98, 2)
probability_strings <-
c("0.8", "0.1", "0.1", "0.9", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 59.77 59.77 26.63
# 2 1 2 88.4 54.68 54.68 33.72
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 65 65 21.4
# 2 1 2 88.4 65.83 65.83 22.57
########################
# Choice problem 13, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.2)
# ~ 68.0 TAX
# ~ 71.4 GDU
# R = (98, 0.8; 98, 0.1; 2, 0.1)
# ~ 62.6 TAX
# ~ 65.8 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(98, 40, 98, 98, 2)
probability_strings <-
c("0.8", "0.2", "0.8", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 68.04 68.04 18.36
# 2 1 2 88.4 62.55 62.55 25.85
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 71.42 71.42 14.98
# 2 1 2 88.4 65.83 65.83 22.57
########################
# Choice problem 19, Table 7, p.165
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# S = (98, 0.8; 40, 0.2)
# ~ 68.0 TAX
# ~ 71.4 GDU
# R = (98, 0.9; 2, 0.1)
# ~ 54.7 TAX
# ~ 65.8 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(98, 40, 98, 98, 2)
probability_strings <-
c("0.8", "0.2", "0.8", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 86.4 68.04 68.04 18.36
# 2 1 2 88.4 62.55 62.55 25.85
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 86.4 71.42 71.42 14.98
# 2 1 2 88.4 65.83 65.83 22.57
########################
#
# Table 8. Dissection of Allais paradox (series BA)
#
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 10, Table 8, p.166
# S = (50, 0.15; 7, 0.85)
# ~ 13.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.90)
# ~ 18.0 TAX
# ~ 17.6 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(50, 7, 100, 7)
probability_strings <-
c("0.15", "0.85", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 13.56 13.56 -0.1134
# 2 1 2 16.3 17.96 17.96 -1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 17.6 17.6 -1.299
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 15, Table 8, p.166
# S = (50, 0.1; 50, 0.05; 7, 0.85)
# ~ 15.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.90)
# ~ 18.0 TAX
# ~ 17.6 GDU
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(50, 50, 7, 100, 7)
probability_strings <-
c("0.1", "0.05", "0.85", "0.1", "0.9")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 15.56 15.56 -2.107
# 2 1 2 16.3 17.96 17.96 -1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 17.6 17.6 -1.299
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 22, Table 8, p.166
# S = (50, 0.15; 7, 0.85)
# ~ 13.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.05; 7, 0.85)
# ~ 14.6 TAX
# ~ 12.6 GDU ? an error, should be 15.26?
# R > S
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(50, 7, 100, 7, 7)
probability_strings <-
c("0.15", "0.85", "0.1", "0.05", "0.85")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 13.56 13.56 -0.1134
# 2 1 2 16.3 14.64 14.64 1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 15.26 15.26 1.045
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 17, Table 8, p.166
# S = (50, 0.1; 50, 0.05; 7, 0.85)
# ~ 15.6 TAX
# ~ 13.1 GDU
# R = (100, 0.10; 7, 0.05; 7, 0.85)
# ~ 14.6 TAX
# ~ 12.6 GDU ? an error, should be 15.26?
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(50, 50, 7, 100, 7, 7)
probability_strings <-
c("0.1", "0.05", "0.85", "0.1", "0.05", "0.85")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 13.45 15.56 15.56 -2.107
# 2 1 2 16.3 14.64 14.64 1.663
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 13.45 13.08 13.08 0.3652
# 2 1 2 16.3 15.26 15.26 1.045
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 20, Table 8, p.166
# S = (50, 1.0)
# ~ 50.0 TAX
# ~ 50.0 GDU
# R = (100, 0.10; 50, 0.85; 7, 0.05)
# ~ 40.1 TAX
# ~ 44.0 GDU
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 2, 2, 2)
outcome_ids <- c(1, 1, 2, 3)
objective_consequences <- c(50, 100, 50, 7)
probability_strings <-
c("0.1", "0.1", "0.85", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 5 50 50 -45
# 2 1 2 52.85 40.1 40.1 12.75
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 5 50 50 -45
# 2 1 2 52.85 44.06 44.06 8.792
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 14, Table 8, p.166
# S = (100, 0.85; 50, 0.1; 50, 0.05)
# ~ 68.4 TAX
# ~ 74.9 GDU
# R = (100, 0.85; 100, 0.10; 7, 0.05)
# ~ 69.7 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(100, 50, 50, 100, 100, 7)
probability_strings <-
c("0.85", "0.1", "0.05", "0.85", "0.1", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 68.37 68.37 24.13
# 2 1 2 95.35 69.7 69.7 25.65
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 74.91 74.91 17.59
# 2 1 2 95.35 77.55 77.55 17.8
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 5, Table 8, p.166
# S = (100, 0.85; 50, 0.1; 50, 0.05)
# ~ 68.4 TAX
# ~ 74.9 GDU
# R = (100, 0.95; 7, 0.05)
# ~ 62.0 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2)
objective_consequences <- c(100, 50, 50, 100, 7)
probability_strings <-
c("0.85", "0.1", "0.05", "0.95", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 68.37 68.37 24.13
# 2 1 2 95.35 62 62 33.35
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 74.91 74.91 17.59
# 2 1 2 95.35 77.55 77.55 17.8
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 18, Table 8, p.166
# S = (100, 0.85; 50, 0.15)
# ~ 75.7 TAX
# ~ 79.8 GDU
# R = (100, 0.85; 100, 0.1; 7, 0.05)
# ~ 69.7 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 1, 2, 3)
objective_consequences <- c(100, 50, 100, 100, 7)
probability_strings <-
c("0.85", "0.15", "0.85", "0.1", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 75.7 75.7 16.8
# 2 1 2 95.35 69.7 69.7 25.65
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 79.84 79.84 12.66
# 2 1 2 95.35 77.55 77.55 17.8
########################
# GDU uses parameterisations from Birnbaum, M. H. (2005). A comparison of five models that predict violations of first-order stochastic dominance in risky decision making. Journal of Risk and Uncertainty, 31(3), 263-287.
# p.269
# Choice problem 8, Table 8, p.166
# S = (100, 0.85; 50, 0.15)
# ~ 75.7 TAX
# ~ 79.8 GDU
# R = (100, 0.95; 7, 0.05)
# ~ 62.0 TAX
# ~ 77.5 GDU
# S > R
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(100, 50, 100, 7)
probability_strings <-
c("0.85", "0.15", "0.95", "0.05")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 92.5 75.7 75.7 16.8
# 2 1 2 95.35 62 62 33.35
my_pwf <-
ProbWeight(fun="compound_invariance",
par=c(alpha=0.542, beta=1.382))
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareGDU(my_choices,
prob_weight=my_pwf,
utility=my_utility,
digits=4)
# cid gid ev gdu ce rp
# 1 1 1 92.5 79.84 79.84 12.66
# 2 1 2 95.35 77.55 77.55 17.8
########################
#
# Table 10. Test of EU plus entropy model
#
########################
# Choice problem 9, Table 10, p.170
# S = (47, 0.5; 43, 0.15; 43, 0.15; 43, 0.1; 43, 0.1)
# ~ 43.5 TAX
# R = (89, 0.5; 11, 0.15; 11, 0.15; 11, 0.1; 11, 0.1)
# ~ 21.4 TAX
# S > R
choice_ids <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
objective_consequences <- c(47, 43, 43, 43, 43, 89, 11, 11, 11, 11)
probability_strings <-
c("0.5", "0.15", "0.15", "0.1", "0.1", "0.5", "0.15", "0.15", "0.1", "0.1")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 43.53 43.53 1.469
# 2 1 2 50 21.36 21.36 28.64
########################
# Choice problem 25, Table 10, p.170
# S = (47, 0.5; 43, 0.25; 43, 0.25)
# ~ 43.9 TAX
# R = (89, 0.5; 11, 0.15; 11, 0.15; 11, 0.1; 11, 0.1)
# ~ 28.5 TAX
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(47, 43, 43, 89, 11, 11)
probability_strings <-
c("0.5", "0.25", "0.25", "0.5", "0.25", "0.25")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 43.9 43.9 1.104
# 2 1 2 50 28.48 28.48 21.52
########################
# Choice problem 7, Table 10, p.170
# S = (47, 0.5; 43, 0.5)
# ~ 44.3 TAX
# R = (89, 0.5; 11, 0.5)
# ~ 37.0 TAX
# R > S
choice_ids <- c(1, 1, 1, 1)
gamble_ids <- c(1, 1, 2, 2)
outcome_ids <- c(1, 2, 1, 2)
objective_consequences <- c(47, 43, 89, 11)
probability_strings <-
c("0.5", "0.5", "0.5", "0.5")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 44.33 44.33 0.6667
# 2 1 2 50 37 37 13
########################
# Choice problem 23, Table 10, p.170
# S = (47, 0.25; 47, 0.25; 43, 0.5)
# ~ 44.7 TAX
# R = (89, 0.25; 89, 0.25; 11, 0.5)
# ~ 43.3 TAX
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 1, 2, 3)
objective_consequences <- c(47, 47, 43, 89, 89, 11)
probability_strings <-
c("0.25", "0.25", "0.5", "0.25", "0.25", "0.5")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 44.66 44.66 0.3446
# 2 1 2 50 43.28 43.28 6.72
########################
# Choice problem 13, Table 10, p.170
# S = (47, 0.1; 47, 0.1; 47, 0.15; 47, 0.15; 43, 0.5)
# ~ 45.0 TAX
# R = (89, 0.1; 89, 0.1; 89, 0.15; 89, 0.15; 11, 0.5)
# ~ 50.1 TAX
# R > S
choice_ids <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
gamble_ids <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
outcome_ids <- c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
objective_consequences <- c(47, 47, 47, 47, 43, 89, 89, 89, 89, 11)
probability_strings <-
c("0.1", "0.1", "0.15", "0.15", "0.5", "0.1", "0.1", "0.15", "0.15", "0.5")
my_choices <- Choices(choice_ids=choice_ids,
gamble_ids=gamble_ids,
outcome_ids=outcome_ids,
objective_consequences=objective_consequences,
probability_strings=probability_strings)
my_choices
my_pwf <-
ProbWeight(fun="power",
par=c(alpha=0.7, beta=1))
delta <- -1
my_utility <- Utility(fun="power",
par=c(alpha=1, beta=1, lambda=1))
compareTAX(my_choices,
prob_weight=my_pwf,
utility=my_utility,
delta=delta,
digits=4)
# cid gid ev tax ce rp
# 1 1 1 45 45 45 -0.004934
# 2 1 2 50 50.1 50.1 -0.09622
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