Training assessment: Difference between revisions

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(→‎Calculations: under construction, testing a few things)
Line 158: Line 158:
# Build multiple condition vectors that correspond to a unique decision - option combination
# Build multiple condition vectors that correspond to a unique decision - option combination
var <- list()
for (j in 1:nrow(dectable)) {  
for (j in 1:nrow(dectable)) {  
Line 174: Line 174:
print(sel2)
print(sel2)
selection <- list()  
selection <- list()  
print(as.character(dectable[j, "Variable"]))
var[[j]] <- get(as.character(dectable[j, "Variable"]))@output
for (k in 1:length(sel1)) { # For each condition separated by ";"
for (k in 1:length(sel1)) { # For each condition separated by ";"
if (length(sel2[[k]]) > 1) { # If ":" has been used for condition k
if (length(sel2[[k]]) > 1) { # If ":" has been used for condition k
locs <- strsplit(sel2[[k]][2], split = ",")[[1]] # Split by "," for multiple locs per given index
locs <- strsplit(sel2[[k]][2], split = ",")[[1]] # Split by "," for multiple locs per given index
selection[[k]] <- out[, sel2[[k]][1]] %in% locs # Match our data.frame to the condition
var[[j]] <- var[[j]][var[[j]][, sel2[[k]][1]] %in% locs , ] # Match our data.frame to the condition
#print(selection)
cat(j, k, "\n")
print(head(var[[j]]))
}  
}  
}
}
#Pseudocode for the rest:
# Take each Stakeholder, one at a time.
# Take each row of Endpoints table that belongs to that stakeholder.
# Make an ovariable of the data.frame that is on the jth row, renaming the Result column as jvariableResult so that it can be used in computing.
# Multiply each ovariable with Result on row j.
# Sum each ovariable that belongs to that Stakeholder.
# Give outputs as a list that has one ovariable for each Stakeholder.
# Match all conditions given for this decision - option combination.
selection <- as.data.frame(selection)
selection$optslice <- out[[as.character(dectable[["Decision"]][j])]] == dectable[["Option"]][j] # We only want rows where the relevant option is in use to be affected
selection <- as.matrix(selection)
cond[[j]] <- apply(
selection,
1,
all
)
}
} else { # Otherwise use given condition functions.
for (j in 1:length(dec@condition)) {
cond[[j]] <- dec@condition[[j]](variable@output)
}
}
# Effects
if (length(dec@effect) == 1 & dec@effect[[1]](variable@output) == 0) {
eff <- list()
for (j in 1:nrow(dectable)) {
eff[[j]] <- EffectPreset(dectable[["Change"]][j]) # Returns a standard function from presets defined below.
}
} else {
eff <- dec@effect
}
# Applying effects
for (j in 1:nrow(dectable)) {
out[
cond[[j]],
paste(variable@name, "Result", sep = "")
] <- eff[[j]](
out[
cond[[j]],
paste(variable@name, "Result", sep = "")
],
dectable[["Result"]][j]
)
}
variable@marginal <- colnames(out) %in% c(colnames(variable@output)[variable@marginal], colnames(temp2))
variable@output <- out
}
# return(variable)
#}


########################
########################

Revision as of 17:35, 1 January 2013



This is a training assessment about an imaginary, simple case. The purpose is to illustrate assessment functionalities.

Scope

Question

What decisions are worth implementing in the training assessment?

Boundaries

  • Time: Year 2012 - 2020

Scenarios

  • Factory can reduce emissions, or continue business as usual.
  • School can increase health education, decrease it to save money, or continue business as usual.

Intended users

  • Anyone who wants to learn to make open assessments.

Participants

  • Main participants:
    • YMAL,
    • Summer workers of YMAL in 2012,
    • Participants of [[Decision analysis and risk management 2013}}

Answer

Conclusions

Results

Not yet available.

Rationale

The causal diagram for the training assessment.

Assessment-specific data

Decisions
Decisions(-)
ObsDecisionmakerDecisionOptionVariableCellChangeUnitAmountDescription
1FactoryCleaning.policyReduce emissionsexposureYear:2020Multiply-0.5
2SchoolHealth.promotionIncrease health educationhealth.impactYear:2020Multiply-0.9
3SchoolHealth.promotionPromotion budget reducedhealth.impactYear:2020Multiply-1.1
Probabilities
Probabilities(P)
ObsStakeholderVariableCellProbabilityDescription
1City of KuopioexposureCleaning.policy: Reduce emissions0.8
2City of KuopioexposureCleaning.policy: BAU0.2
3Factoyhealth.impactHealth.promotion: Increase health education0.1
4Factoyhealth.impactHealth.promotion: Promotion budget reduced0.4
5Factoyhealth.impactHealth.promotion: BAU0.5
Endpoints
Endpoints(-)
ObsStakeholderVariableCellModelResultDescription
1City of Kuopiohealth.impactYear:2012Weighted sum1000
2City of Kuopiohealth.impactYear:2020Weighted sum1000
3Citizenshealth.impactYear:2012Weighted sum1000
4Citizenshealth.impactYear:2020Weighted sum2000Future years are twice as important.
Variables
Analyses
  • Decision analysis on each policy: Which option minimises the health risks?
  • Value of information (VOI) analysis for each policy about the major variables in the model and the total VOI.

Calculations

+ Show code

See also

Materials and examples for training in Opasnet and open assessment
Help pages Wiki editing • How to edit wikipages • Quick reference for wiki editing • Drawing graphs • Opasnet policies • Watching pages • Writing formulae • Word to Wiki • Wiki editing Advanced skills
Training assessment (examples of different objects) Training assessment • Training exposure • Training health impact • Training costs • Climate change policies and health in Kuopio • Climate change policies in Kuopio
Methods and concepts Assessment • Variable • Method • Question • Answer • Rationale • Attribute • Decision • Result • Object-oriented programming in Opasnet • Universal object • Study • Formula • OpasnetBaseUtils • Open assessment • PSSP
Terms with changed use Scope • Definition • Result • Tool


  • Descriptions of a previous structure
  • ----#: . Päätöksenteon sokea piste: se mitä ihmiset eivät näe mutta eivät myöskään huomaa etteivät näe. Kuitenkin tutkimalla sitä mitä mitä ihmiset eivät näe saadaan selville asioita sokeasta pisteesta. Ymmärtämällä sokeaa pistettä voidaan keksiä asioita jotka järjestelmällisesti jäävät huomaamatta ja asioita, joilla voidaan korjata järjestelmällisiä puutteita. Avoin arviointi on tämmöinen päätöksenteon järjestelmällisten puutteiden korjausmekanismi. --Jouni 08:55, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

References


Related files

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