# PJM_example_DSC

#### 2024-05-13

Now we code the PJM (using ACP here) example in DS-ECP.

On $$SSM_{W_1}:\{w_1\text{ is T},w_1\text{ is F}\}$$, we define $$DSM_{W_1}:\mathcal{P}(SSM_{W_1})\rightarrow[0,1]$$ where $$DSM_{W_1}(\{w_1\text{ is T}\})=0.4$$ and $$DSM_{W_1}(\{w_1\text{ is F}\})=0.6$$ and $$DSM_{W_2}(X)=0$$ for all other $$X=\emptyset,\{w_2\text{ is T},w_2\text{ is F}\}$$.

tt_SSMw1 <- matrix(c(1,0,0,1,1,1), nrow = 3, ncol = 2, byrow = TRUE)
m_DSMw1 <- matrix(c(0.4,0.6,0), nrow = 3, ncol = 1)
cnames_SSMw1 <- c("w1y", "w1n")
varnames_SSMw1 <- "w1"
idvar_SSMw1 <- 1
DSMw1 <- bca(tt_SSMw1, m_DSMw1, cnames = cnames_SSMw1, idvar = idvar_SSMw1, varnames = varnames_SSMw1)
bcaPrint(DSMw1)
##   DSMw1 specnb mass
## 1   w1y      1  0.4
## 2   w1n      2  0.6

PJM_example_DSC.R

Similarly, on $$SSM_{W_2}:\{w_2\text{ is T},w_2\text{ is F}\}$$, we define $$DSM_{W_2}(\mathcal{P})SSM_{W_2}\rightarrow[0,1]$$ where $$DSM_{W_2}(\{w_2\text{ is T}\})=0.3$$ and $$DSM_{W_2}(\{w_2\text{ is F}\})=0.7$$ and $$DSM_{W_2}(X)=0$$ for all other $$X=\emptyset,\{w_2\text{ is T},w_2\text{ is F}\}$$.

tt_SSMw2 <- matrix(c(1,0,0,1,1,1), nrow = 3, ncol = 2, byrow = TRUE)
m_DSMw2 <- matrix(c(0.3,0.7,0), nrow = 3, ncol = 1)
cnames_SSMw2 <- c("w2y", "w2n")
varnames_SSMw2 <- "w2"
idvar_SSMw2 <- 2
DSMw2 <- bca(tt_SSMw2, m_DSMw2, cnames = cnames_SSMw2, idvar = idvar_SSMw2, varnames = varnames_SSMw2)
bcaPrint(DSMw2)
##   DSMw2 specnb mass
## 1   w2y      1  0.3
## 2   w2n      2  0.7

PJM_example_DSC.R

We also need three placeholder SSMs, DSMs. On $$SSM_A:\{A\text{ is T},A\text{ is F}\}$$, we define vacuous $$DSM_A:\mathcal{P}(SSM_A)\rightarrow[0,1]$$ where $$DSM_A(\{A\text{ is T},A\text{ is F}\})=1$$ and $$DSM_A(X)=0$$ for all other $$X=\emptyset,\{A\text{ is T}\},\{A\text{ is F}\}$$.

tt_SSMA <- matrix(c(1,1), nrow = 1, ncol = 2, byrow = TRUE)
m_DSMA <- matrix(c(1), nrow = 1, ncol = 1)
cnames_SSMA <- c("Ay", "An")
varnames_SSMA <- "A"
idvar_SSMA <- 3
DSMA <- bca(tt_SSMA, m_DSMA, cnames = cnames_SSMA, idvar = idvar_SSMA, varnames = varnames_SSMA)
bcaPrint(DSMA)
##    DSMA specnb mass
## 1 frame      1    1

PJM_example_DSC.R

Similarly, on $$SSM_C:\{C\text{ is T},C\text{ is F}\}$$, we define vacuous $$DSM_C:\mathcal{P}(SSM_C)\rightarrow[0,1]$$ where $$DSM_C(\{C\text{ is T},C\text{ is F}\})=1$$ and $$DSM_C(X)=0$$ for all other $$X=\emptyset,\{C\text{ is T}\},\{C\text{ is F}\}$$.

tt_SSMC <- matrix(c(1,1), nrow = 1, ncol = 2, byrow = TRUE)
m_DSMC <- matrix(c(1), nrow = 1, ncol = 1)
cnames_SSMC <- c("Cy", "Cn")
varnames_SSMC <- "C"
idvar_SSMC <- 4
DSMC <- bca(tt_SSMC, m_DSMC, cnames = cnames_SSMC, idvar = idvar_SSMC, varnames = varnames_SSMC)
bcaPrint(DSMC)
##    DSMC specnb mass
## 1 frame      1    1

PJM_example_DSC.R

Similarly, on $$SSM_P:\{P\text{ is T},P\text{ is F}\}$$, we define vacuous $$DSM_P:\mathcal{P}(SSM_P)\rightarrow[0,1]$$ where $$DSM_P(\{P\text{ is T},P\text{ is F}\})=1$$ and $$DSM_P(X)=0$$ for all other $$X=\emptyset,\{P\text{ is T}\},\{P\text{ is F}\}$$.

tt_SSMP <- matrix(c(1,1), nrow = 1, ncol = 2, byrow = TRUE)
m_DSMP <- matrix(c(1), nrow = 1, ncol = 1)
cnames_SSMP <- c("Py", "Pn")
varnames_SSMP <- "P"
idvar_SSMP <- 5
DSMP <- bca(tt_SSMP, m_DSMP, cnames = cnames_SSMP, idvar = idvar_SSMP, varnames = varnames_SSMP)
bcaPrint(DSMP)
##    DSMP specnb mass
## 1 frame      1    1

PJM_example_DSC.R

$$SSM_{R_1}$$ is on the product space of $$W_1 \times A \times C \times P$$. $$DSM_{R_1}: \mathcal{P}(SSM_{R_2}) \rightarrow [0,1]$$. When w1 is true, one of A, C are true, which has + = 1 + 2 = 3 cases; when w1 is false, everything can be true, which has $$\binom{3}{3} + \binom{3}{2} + \binom{3}{1} = 1 + 3 + 3 = 7$$ cases. So $$DSM_{R_1}(X)=1$$ if $$X$$ is the subset of all these cases and $$0$$ otherwise.

tt_SSMR_1 <- matrix(c(1,0,1,0,1,0,0,1,
1,0,0,1,1,0,0,1,
1,0,1,0,0,1,0,1,

0,1,1,0,0,1,0,1,
0,1,0,1,1,0,0,1,
0,1,0,1,0,1,1,0,
0,1,1,0,1,0,0,1,
0,1,0,1,1,0,1,0,
0,1,1,0,0,1,1,0,
0,1,1,0,1,0,1,0,

1,1,1,1,1,1,1,1), nrow = 3 + 7 + 1, ncol = 8, byrow = TRUE, dimnames = list(NULL, c("w1y","w1n","Ay","An","Cy","Cn","Py","Pn")))
spec_DSMR_1 <- matrix(c(1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,0), nrow = 3 + 7 + 1, ncol = 2)
infovar_SSMR_1 <- matrix(c(1,3,4,5,2,2,2,2), nrow = 4, ncol = 2)
varnames_SSMR_1 <- c("w1", "A", "C", "P")
relnb_SSMR_1 <- 1
DSMR_1 <- bcaRel(tt_SSMR_1, spec_DSMR_1, infovar_SSMR_1, varnames_SSMR_1, relnb_SSMR_1)
bcaPrint(DSMR_1)
##                                                                                                                                                DSMR_1
## 1 w1y Ay Cy Pn + w1y Ay Cn Pn + w1y An Cy Pn + w1n Ay Cy Py + w1n Ay Cy Pn + w1n Ay Cn Py + w1n Ay Cn Pn + w1n An Cy Py + w1n An Cy Pn + w1n An Cn Py
##   specnb mass
## 1      1    1

PJM_example_DSC.R

$$SSM_{R_2}$$ is on the product space of $$W_2 \times A \times C \times P$$. $$DSM_{R_2}: \mathcal{P}(SSM_{R_2}) \rightarrow [0,1]$$. When w2 is true, one of C, P are true, which has 2 + 1 = 3 cases; when w1 is false, everything can be true, which has $$\binom{3}{3} + \binom{3}{2} + \binom{3}{1} = 1 + 3 + 3 = 7$$ cases. So $$DSM_{R_2}(X)=1$$ if $$X$$ is the subset of all these cases and $$0$$ otherwise.

tt_SSMR_2 <- matrix(c(1,0,0,1,1,0,0,1,
1,0,0,1,0,1,1,0,
1,0,0,1,1,0,1,0,

0,1,1,0,0,1,0,1,
0,1,0,1,1,0,0,1,
0,1,0,1,0,1,1,0,
0,1,1,0,1,0,0,1,
0,1,0,1,1,0,1,0,
0,1,1,0,0,1,1,0,
0,1,1,0,1,0,1,0,

1,1,1,1,1,1,1,1), nrow = 3 + 7 + 1, ncol = 8, byrow = TRUE, dimnames = list(NULL, c("w2y","w2n","Ay","An","Cy","Cn","Py","Pn")))
spec_DSMR_2 <- matrix(c(1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,0), nrow = 3 + 7 + 1, ncol = 2)
infovar_SSMR_2 <- matrix(c(2,3,4,5,2,2,2,2), nrow = 4, ncol = 2)
varnames_SSMR_2 <- c("w2", "A", "C", "P")
relnb_SSMR_2 <- 2
DSMR_2 <- bcaRel(tt_SSMR_2, spec_DSMR_2, infovar_SSMR_2, varnames_SSMR_2, relnb_SSMR_2)
bcaPrint(DSMR_2)
##                                                                                                                                                DSMR_2
## 1 w2y An Cy Py + w2y An Cy Pn + w2y An Cn Py + w2n Ay Cy Py + w2n Ay Cy Pn + w2n Ay Cn Py + w2n Ay Cn Pn + w2n An Cy Py + w2n An Cy Pn + w2n An Cn Py
##   specnb mass
## 1      1    1

PJM_example_DSC.R

Now we apply Dempster-Shafer calculus. First, we up-project $$DSM_{W_1}$$ onto $$SSM_{R_1}$$ to get $$DSM1_{uproj_{SSM_{R_1}}}=(\{w_1\text{ is T}\}\times SSM_A\times SSM_C\times SSM_P)=0.4$$ and $$DSM1_{uproj_{SSM_{R_2}}}(\{w_1\text{ is F}\}\times SSM_A\times SSM_C\times SSM_P)=0.6$$ and $$DSM1_{uproj_{SSM_{R_1}}}(X)=0$$ for all other $$X$$.

DSMw1_uproj <- extmin(DSMw1,DSMR_1)
bcaPrint(DSMw1_uproj)
##                                                                                                             DSMw1_uproj
## 1 w1y Ay Cy Py + w1y Ay Cy Pn + w1y Ay Cn Py + w1y Ay Cn Pn + w1y An Cy Py + w1y An Cy Pn + w1y An Cn Py + w1y An Cn Pn
## 2 w1n Ay Cy Py + w1n Ay Cy Pn + w1n Ay Cn Py + w1n Ay Cn Pn + w1n An Cy Py + w1n An Cy Pn + w1n An Cn Py + w1n An Cn Pn
##   specnb mass
## 1      1  0.4
## 2      2  0.6

PJM_example_DSC.R

Combining $$DSM_{W_1}$$ with $$DSM_{R_1}$$ to get $$DSM1$$ where $$DSM1(\{w_1\text{ is T}\}\times\{\text{one of A,C is T}\})=0.4$$ and $$DSM1(\{w_1\text{ is F}\}\times(SSM_A\times SSM_C\times SSM_P\backslash\{\text{all of }A,C,P\text{ are F}\}))=0.6$$ and $$DSM1(X)=0$$ for all other $$X$$.

DSM1 <- dsrwon(DSMw1_uproj,DSMR_1)
bcaPrint(DSM1)
##                                                                                                     DSM1
## 1                                                             w1y Ay Cy Pn + w1y Ay Cn Pn + w1y An Cy Pn
## 2 w1n Ay Cy Py + w1n Ay Cy Pn + w1n Ay Cn Py + w1n Ay Cn Pn + w1n An Cy Py + w1n An Cy Pn + w1n An Cn Py
##   specnb mass
## 1      1  0.4
## 2      2  0.6

PJM_example_DSC.R

Then, down-project $$DSM1$$ to $$SSM_A\times SSM_C\times SSM_P$$ to get $$DSM1_{dproj_{SSM_A\times SSM_C\times SSM_P}}$$ where $$DSM1_{dproj_{SSM_A\times SSM_C\times SSM_P}}(\{\text{one of A,C is T}\})=\sum_{X|_{SSM_{W_1}} \in SSM_{W_1}}DSM1(X)=0.4$$ and $$DSM1_{dproj_{SSM_A\times SSM_C\times SSM_P}}(SSM_A\times SSM_C\times SSM_P\backslash\{\text{all of }A,C,P\text{ are F}\})=\sum_{X|_{SSM_{W_1}} \in SSM_{W_1}}DSM1(X)=0.6$$ and $$DSM1_{dproj_{SSM_A\times SSM_C\times SSM_P}}(X)=0$$ for all other $$X$$.

DSM1_dproj <- elim(DSM1,1)
bcaPrint(DSM1_dproj)
##                                                                   DSM1_dproj
## 1                                             Ay Cy Pn + Ay Cn Pn + An Cy Pn
## 2 Ay Cy Py + Ay Cy Pn + Ay Cn Py + Ay Cn Pn + An Cy Py + An Cy Pn + An Cn Py
##   specnb mass
## 1      1  0.4
## 2      2  0.6

PJM_example_DSC.R

Similarly, we up-project $$DSM_{W_2}$$ onto $$SSM_{R_2}$$ to get $$DSM2_{uproj_{SSM_{R_2}}}$$. Combining $$DSM_{W_2}$$ with $$DSM_{R_2}$$ to get $$DSM2$$. Then, down-project $$DSM2$$ to $$SSM_A\times SSM_C\times SSM_P$$ to get $$DSM2_{dproj_{SSM_A\times SSM_C\times SSM_P}}$$.

DSMw2_uproj <- extmin(DSMw2,DSMR_2)
DSM2 <- dsrwon(DSMw2_uproj,DSMR_2)
DSM2_dproj <- elim(DSM2,2)
bcaPrint(DSM2_dproj)
##                                                                   DSM2_dproj
## 1                                             An Cy Py + An Cy Pn + An Cn Py
## 2 Ay Cy Py + Ay Cy Pn + Ay Cn Py + Ay Cn Pn + An Cy Py + An Cy Pn + An Cn Py
##   specnb mass
## 1      1  0.3
## 2      2  0.7

PJM_example_DSC.R

Now we can combine $$DSM1_{dproj_{SSM_A\times SSM_C\times SSM_P}}$$ and $$DSM2_{dproj_{SSM_A\times SSM_C\times SSM_P}}$$ on $$SSM_A\times SSM_C\times SSM_P$$ to get $$DSM3$$ where $$DSM3(\{\text{A is F and C is T and P is F}\})=0.12$$ and $$DSM3(\{\text{(A is T or C is T) and P is F}\})=0.12$$ and $$DSM3(\{\text{A is F and (C is T or P is T)}\})=0.28$$ and $$DSM3(\{\text{One of A,C,P is T}\})=0.42$$.

DSM3 <- dsrwon(DSM1_dproj,DSM2_dproj)
bcaPrint(DSM3)
##                                                                         DSM3
## 1                                                                   An Cy Pn
## 2                                             Ay Cy Pn + Ay Cn Pn + An Cy Pn
## 3                                             An Cy Py + An Cy Pn + An Cn Py
## 4 Ay Cy Py + Ay Cy Pn + Ay Cn Py + Ay Cn Pn + An Cy Py + An Cy Pn + An Cn Py
##   specnb mass
## 1      1 0.12
## 2      2 0.28
## 3      3 0.18
## 4      4 0.42

PJM_example_DSC.R

Now, we can marginalize $$DSM3$$ to $$C$$ to get $$DSM3_{dproj_{SSM_C}}$$ where $$DSM3_{dproj_{SSM_C}}(\{\text{C is T}\})=\sum_{X|_{SSM_A\times SSM_P}\in SSM_A\times SSM_P}DSM3(X)=0.12$$ and $$DSM3_{dproj_{SSM_C}}(\{\text{C is F}\})=\sum_{X|_{SSM_A\times SSM_P}\in SSM_A\times SSM_P}DSM3(X)=0$$ and $$DSM3_{dproj_{SSM_C}}(X)=0$$ for all others. The (p,q,r) triplet on $$SSM_C$$ is then $$(0.12,0,0.88)$$.

DSM3_dprojSSMC <- elim(elim(DSM3, 3), 5)
bcaPrint(DSM3_dprojSSMC)
##   DSM3_dprojSSMC specnb mass
## 1             Cy      1 0.12
## 2          frame      2 0.88

PJM_example_DSC.R