1000 1 1 2 0 1 -1 Dynamic simulation periods are specified in Time's definition. This is usually a list of numbers or labels, typically in some unit of time (days, weeks, months, etc.). Use the ÒDynamic()Ó function in your variables to perform dynamic simulation. [0,1,2] jtue 11. Julta 2005 14:25 jtue 11. Julta 2005 15:23 48,24 1,44,32,451,300,17 0,Model Voi_2,2,2,0,1,N:\Huippuyksikko\Tutkimus\Mallit\Functions\VOI_2.ANA The transport mode: either personal car or composite traffic. ['Buy car','Lease car'] 192,168,1 48,12 2,220,199,476,224 11.4.2005 Jouni Tuomisto Nimi vaihdettu Travel -> Mode (Mode1) A list of uncertain variables used in the model. ['Car price','Car lifetime','Blank'] 304,256,1 48,12 1,1,1,1,1,1,0,0,0,0 2,102,90,476,469 2,351,356,688,342,0,MIDM 2,168,178,582,361,0,MIDM [Self,Self] A list of uncertain variables used in the model. This list is used to analyse the role of each variable by e.g. value-of-information analysis or importance analysis. The variables with 'V:' are not uncertain but describe variability within the population. Note that the last variable 'Blank' is NOT included in the model and therefore whatever significance is attached to this variable, is just a random effect. Table(Uncertain_var)( Car_price,Car_lifetime,Uniform(0,1)) ['Pollutant levels in fish feed after lower limits (S+P)','Salmon consumption after feed limits (S+P)','Does omega-3 help CHD patients or everyone? (S)','Dose-response of health benefit (S)','Highest omega-3 dose with health benefit (S)','Current average consumption of salmon (S)','Fraction of farmed from total salmon use (S)','Omega3 content in salmon (S)','Consider pollutant or net health effect? (P)','Dieldrin concentration in farmed salmon (S)','Toxaphene concentration in farmed salmon (S)','PCB concentration in farmed salmon (S)','Farmed salmon use after recommendation (S)','Lower limits for pollutants in fish feed? (P)','Recommend restricted farmed salmon consumption? (P)'] 304,224,1 48,24 1,1,1,1,1,1,0,0,0,0 2,541,193,476,275 2,30,112,465,479,0,MIDM 2,148,242,582,361,1,PDFP 52425,39321,65535 Graphtool:0 Distresol:10 Diststeps:0 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:-20K Xmaximum:140K Yminimum:0 Ymaximum:30u Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [0,0,0,0] euro/month var car:= car_price/car_lifetime/12; array(mode1,[car,leasing]) 192,136,1 48,24 2,122,153,476,567 2,351,85,680,471,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:4 Baroverlap:0 Linestyle:4 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1000 Yminimum:-3 Ymaximum:3 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 8 [Run,Mode1] Variation [0,0,0,0] 10.5.2004 Jouni Tuomisto Keksin mielestŠni paremman tavan laskea tŠmŠ. Uudessa systeemissŠ on etuna, ettŠ vaihtelu lasketaan joka iteraatiossa erikseen ja siis vŠhennetŠŠn tŠsmŠllisesti pois. Vanha syntaksi: var a:= cost_variation[Mode1='Composite']-cost_variation[Mode1='Car']; a:= rank(a,run)/samplesize; var b:= expected_total_varia; a:= Linearinterp(b.i,b, a,b.i); cost-a (out:prob;deci:indextype;input:prob;input_ind:indextype;classes) Value of information (VOI) Version 2. This function calculates the total VOI (expected value of perfect information, EVPI) for a given decision, and VOI (expected value of partial perfect information, EVPPI) for certain variables. The outcome to be optimised is out; the decision to be made must be indexed by deci; the variables for EVPPI calculation must be listed in input, which is indexed by input_ind. The solution is numerical, and for this purpose, the outcome is classified into a number of bins (the number is defined by classes, which may be a number or an array of numbers). The VOI function assumes that costs are calculated and that the correct optimising function is MIN. Procedure First, a new index is generated. It contains 'Total VOI' in the first row and the EVPPI variables in the subsequent rows. Then, net cost under uncertainty (ncuu) and evpi are calculated. The rest of the procedure is calculated separetely for each value of classes. Varia is a temporary index that has classes number of bins. Each iteration is located in one of the bins depending on the value of input. After this classification, the value of out is located into the bin for that iteration. When the mean is taken, the result is the average of outcome multiplied by the probability that the true value of input is in the same bin. The best decision is made given the bin, and then the expected outcomes of each bin are summed up. When ncuu is subtracted from this value, we get EVPPI. Finally, the EVPI and EVPPI are concatenated into a single index. It may be a good idea to include a row 'Blank' in the input_ind and use it for a random variable that is NOT part of the model. This gives a rough estimate on how much random noise may produce VOI in the system. It might also be good to use different values for Classes, because there may be numerical instability with low iteration numbers, and it is not obvious what low is in each case. Developed by Jouni Tuomisto and Marko Tainio, National Public Health Institute (KTL), Finland, 2005. index a:= ['Total VOI']; index variable:= concat(a,input_ind); var ncuu:= min(mean(sample(out)),deci); var evpi:= (if a='Total VOI' then mean(min(sample(out),deci))-ncuu else 0); for x[]:= classes do ( index varia:= sequence(1/x,1,1/x); var evppi:= ceil(rank(input,run)*x/samplesize)/x; evppi:= if evppi=Varia then out else 0; evppi:= sum(min(mean(evppi),deci),varia)-ncuu; concat(evpi,evppi,a,input_ind,variable) ) 64,40,1 48,24 2,620,19,476,676 out,deci,input,input_ind,classes ?.?Ennen maaliskuuta.2005 Jouni Tuomisto Keksin juuri uuden konseptin: Value of consensus (VOC) joka lasketaan niistŠ muuttujista, jotka eivŠt ole epŠvarmuutta vaan vaihtelua vŠestšssŠ. Se lasketaan siis samalla tavalla kuin VOI mutta tulkinta on eri: mikŠ on arvo sille, jos saataisiin kaikki konsensukseen jonkin muuttujan arvon taakse (eri arvoja painotetaan sen suhteessa, paljonko niiden alkuperŠinen kannatus on). 11.7.2005 Jouni Tuomisto PŠivitettiin VOI versio 1 versioksi 2. Laskentaa ei muutettu muuta kuin rivijŠrjestystŠ siistimŠllŠ. LisŠksi kirjoitettiin ensimmŠinen kunnon (?) Description-osa. The number of classes in the value-of-information analysis. This is a technical parametre only, and it should be large enough. However, the samplesize should be at least 100 times larger than this to avoid random noise. [17,29] 72,224,1 48,12 2,102,90,476,405 52425,39321,65535 Huom! Kaikki alajuoksun solmut eivŠt toimi, jos tŠmŠ on indeksi. Kuitenkin uusi VOI-solmu toimii. euro/month Value of information analysis for the input variables with the passenger decision between composite and car traffic. The analysis calculates the expected benefit for the passenger when the uncertainty of a variable is resolved. Voi1(Cost,Mode1,uncertain_inputs,uncertain_var,Classes) 192,224,1 48,24 2,172,98,324,480,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] 1,D,4,3,0,0 10.5.2005 Jouni Tuomisto Vanha syntaksi siltŠ ajalta, kun cost__variation laskettiin Costista: var a:= array(Mode1,[sample(Cost__variation),0]); a:= sum(a[stakeholder='Passenger']*sum(trip_fraction,mode1),length); Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes) euro lognormal(19k,1.5) 192,72,1 48,24 65535,52427,65534 years triangular(8,10,15) 304,72,1 48,24 65535,52427,65534 euro/month Uniform( 100, 300 ) 304,136,1 48,24 65535,52427,65534