{ From user Wynship Hillier, Model Donor_presenter_dash at Sun, May 27, 2007 2:53 PM~~
}
Softwareversion 4.0.0
{ System Variables with non-default values: }
Time := [0,1,2,3,4,5,6,7,8,9,10]
Description Time: Dynamic simulation periods are specified in Time's d~~
efinition. This is usually a list of numbers or labels, typically in ~~
some unit of time (days, weeks, months, etc.). Use the “Dynamic()” f~~
unction in your variables to perform dynamic simulation.
Samplesize := 4000
{!40000|Att_previndexvalue Run: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16~~
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263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280~~
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298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315~~
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350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367~~
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385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402~~
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437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454~~
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472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489~~
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559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576~~
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611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628~~
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681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698~~
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733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750~~
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768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785~~
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925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942~~
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960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977~~
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,1472,1473,1474,1475,1476,1477,1478,1479,1480,1481,1482,1483,1484,1485~~
,1486,1487,1488,1489,1490,1491,1492,1493,1494,1495,1496,1497,1498,1499~~
,1500,1501,1502,1503,1504,1505,1506,1507,1508,1509,1510,1511,1512,1513~~
,1514,1515,1516,1517,1518,1519,1520,1521,1522,1523,1524,1525,1526,1527~~
,1528,1529,1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541~~
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,1556,1557,1558,1559,1560,1561,1562,1563,1564,1565,1566,1567,1568,1569~~
,1570,1571,1572,1573,1574,1575,1576,1577,1578,1579,1580,1581,1582,1583~~
,1584,1585,1586,1587,1588,1589,1590,1591,1592,1593,1594,1595,1596,1597~~
,1598,1599,1600,1601,1602,1603,1604,1605,1606,1607,1608,1609,1610,1611~~
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,1626,1627,1628,1629,1630,1631,1632,1633,1634,1635,1636,1637,1638,1639~~
,1640,1641,1642,1643,1644,1645,1646,1647,1648,1649,1650,1651,1652,1653~~
,1654,1655,1656,1657,1658,1659,1660,1661,1662,1663,1664,1665,1666,1667~~
,1668,1669,1670,1671,1672,1673,1674,1675,1676,1677,1678,1679,1680,1681~~
,1682,1683,1684,1685,1686,1687,1688,1689,1690,1691,1692,1693,1694,1695~~
,1696,1697,1698,1699,1700,1701,1702,1703,1704,1705,1706,1707,1708,1709~~
,1710,1711,1712,1713,1714,1715,1716,1717,1718,1719,1720,1721,1722,1723~~
,1724,1725,1726,1727,1728,1729,1730,1731,1732,1733,1734,1735,1736,1737~~
,1738,1739,1740,1741,1742,1743,1744,1745,1746,1747,1748,1749,1750,1751~~
,1752,1753,1754,1755,1756,1757,1758,1759,1760,1761,1762,1763,1764,1765~~
,1766,1767,1768,1769,1770,1771,1772,1773,1774,1775,1776,1777,1778,1779~~
,1780,1781,1782,1783,1784,1785,1786,1787,1788,1789,1790,1791,1792,1793~~
,1794,1795,1796,1797,1798,1799,1800,1801,1802,1803,1804,1805,1806,1807~~
,1808,1809,1810,1811,1812,1813,1814,1815,1816,1817,1818,1819,1820,1821~~
,1822,1823,1824,1825,1826,1827,1828,1829,1830,1831,1832,1833,1834,1835~~
,1836,1837,1838,1839,1840,1841,1842,1843,1844,1845,1846,1847,1848,1849~~
,1850,1851,1852,1853,1854,1855,1856,1857,1858,1859,1860,1861,1862,1863~~
,1864,1865,1866,1867,1868,1869,1870,1871,1872,1873,1874,1875,1876,1877~~
,1878,1879,1880,1881,1882,1883,1884,1885,1886,1887,1888,1889,1890,1891~~
,1892,1893,1894,1895,1896,1897,1898,1899,1900,1901,1902,1903,1904,1905~~
,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919~~
,1920,1921,1922,1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933~~
,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947~~
,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961~~
,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975~~
,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989~~
,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000]}
Typechecking := 1
Checking := 1
Saveoptions := 2
Savevalues := 0
Allwarnings := 0
{!40000|Att_catlinestyle Graph_cumprob_valdim: 1}
Attribute Reference
Attribute Date_bough
Askattribute Recursive,Function,Yes
Model Donor_presenter_dash
Title: Donor/Presenter Dashboard
Description: This model represents steady-state and dynamic models for~~
donors and presenters. Donors and presenters are assumed to enter t~~
he system either directly (from outside the system) or by advancing f~~
rom the previous state in the system. Once in the system, they are a~~
ssumed to eventually either drop back out of it or advance to the nex~~
t state in the system. "Arrival rate" applies to the new entrants fr~~
om outside the system. "Average Time Until Advance" applies to peopl~~
e who advance from previous states.~
~
Please note that defining the states (e.g. who counts as someone who ~~
is "regular audience" and who does not?) is up to you. Define them h~~
owever you want, but you must supply the inputs in terms of your defi~~
nition, in order for the outputs to be meaningful in terms of that de~~
finition.~
~
In priciple, you can "shorten" either pipeline by simply entering a "~~
0" (zero) for all three parameters of the arrival rate of the first s~~
tate ("Friends of Friends or Presenters Invited") and any subsequent ~~
states that you want to exclude. Because there will be no people flo~~
wing in at these states, these portions of the pipe will remain "dry.~~
" (This is a one-way pipeline.) However, this feature is not availa~~
ble yet.
Author: Wynship Hillier
Date: Sat, Apr 14, 2007 4:39 PM
Saveauthor: Wynship Hillier
Savedate: Sun, May 27, 2007 2:53 PM
Defaultsize: 48,24
Diagstate: 1,20,7,1238,747,17
Windstate: 2,541,327,476,224
Fontstyle: Arial, 18
Fileinfo: 0,Model Donor_presenter_dash,2,2,0,0,C:\Documents and Settin~~
gs\Wynship Hillier\My Documents\CounterPULSE\Donor_Presenter Dashboar~~
d II.ANA
Form Details
Title: Details
Description: This contains the guts of how the system works.~
~
Author: Wynship Hillier
Date: Sun, Apr 15, 2007 1:41 PM
Defaultsize: 48,24
Nodelocation: 152,1744,1
Nodesize: 40,16
Diagstate: 1,87,34,624,343,17
Module Arrival_rates
Title: Arrival Rates
Description: These are the rates at which people arrive at various sta~~
tes from OUTSIDE the system.~
~
These are represented as triangular distributions on parameters given~~
by the user interface form. Triangular distributions are commonly u~~
sed in engineering applications when the actual distribution is unkno~~
wn.
Author: Wynship Hillier
Date: Sun, Apr 15, 2007 1:41 PM
Defaultsize: 48,24
Nodelocation: 488,56,1
Nodesize: 80,28
Diagstate: 1,157,141,719,575,17
Variable Fofarhi
Title: highest possible value
Description: This applies to the "arrival rate," in people per unit ti~~
me, for the first population: friend-of-friends, or presenters who h~~
ave only been identified. This specific box is for the highest possi~~
ble value that you think this number could be. It assumes that you a~~
re uncertain about the value. If you are certain, put the number tha~~
t you are certain it is.
Nodelocation: 48,328,1
Nodesize: 44,40
Aliases: Formnode Highest_possible_va2
Variable Olarhi
Title: highest possible value
Nodelocation: 168,328,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_va5
Variable Suarhi
Title: highest possible value
Nodelocation: 288,328,1
Nodesize: 44,40
Aliases: Formnode Show_up_arrival_rate
Variable Voarhi
Title: highest possible value
Nodelocation: 408,328,1
Nodesize: 44,40
Aliases: Formnode Volunteer_arrival_ra
Variable Doarhi
Title: highest possible value
Nodelocation: 528,328,1
Nodesize: 48,40
Aliases: Formnode Donor_arrival_rate__
Variable Boarhi
Title: highest possible value
Nodelocation: 648,328,1
Nodesize: 48,40
Aliases: Formnode On_board_arrival_rat
Variable Fofarml
Title: most likely value
Description: This is where you should put what you think is the correc~~
t value for the arrival rate (in people per unit time), given that yo~~
u don't really know what it is.~
~
If you do know what it is, then put that number.
Nodelocation: 48,424,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value7
Variable Olarml
Title: most likely value
Nodelocation: 168,424,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value10
Variable Suarml
Title: most likely value
Nodelocation: 288,424,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value13
Variable Voarml
Title: most likely value
Nodelocation: 408,424,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value18
Variable Doarml
Title: most likely value
Nodelocation: 528,424,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value23
Variable Boarml
Title: most likely value
Nodelocation: 648,424,1
Nodesize: 48,40
Aliases: Formnode Obarmi1
Variable Fofarlo
Title: lowest possible value
Description: This is where you put a "hard floor" for the arrival rat~~
e (in people per unit of time), given that you don't know the real an~~
swer. The hard floor can be zero.~
~
If you know what the actual arrival rate is, put that number here.
Nodelocation: 48,520,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_val6
Variable Olarlo
Title: lowest possible value
Nodelocation: 168,520,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_val9
Variable Suarlo
Title: lowest possible value
Nodelocation: 288,520,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va12
Variable Voarlo
Title: lowest possible value
Nodelocation: 408,520,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va17
Variable Doarlo
Title: lowest possible value
Nodelocation: 528,520,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va22
Variable Boarlo
Title: lowest possible value
Nodelocation: 648,520,1
Nodesize: 48,40
Aliases: Formnode Obarlo1
Chance Fofar
Title: Friends of Friends Arrival Rate
Description: This is the rate at which people arrive in the friends of~~
friends state
Definition: Triangular( Fofarlo,Fofarml,Fofarhi)
Nodelocation: 48,200,1
Nodesize: 48,64
Chance Olar
Title: Mailing List Arrival Rate
Description: This is the rate at which people arrive in the mailing l~~
ist only state.
Definition: triangular(Olarlo,Olarml,Olarhi)
Nodelocation: 168,200,1
Nodesize: 48,64
Chance Suar
Title: Show Up Arrival Rate
Description: This is the rate at which people arrive at the regular au~~
dience state from outside the system.
Definition: triangular(Suarlo,Suarml,Suarhi)
Nodelocation: 288,200,1
Nodesize: 48,64
Chance Voar
Title: Volunteer Arrival Rate
Description: This is the rate at which people arrive in the volunteer-~~
only state from outside the system.
Definition: triangular(Voarlo,Voarml,Voarhi)
Nodelocation: 408,200,1
Nodesize: 48,64
Chance Doar
Title: Donor Arrival Rate
Description: This is the rate at which people arrive at the donor-only~~
(not board member...yet) state from outside the system.
Definition: triangular(Doarlo,Doarml,Doarhi)
Nodelocation: 528,200,1
Nodesize: 48,64
Chance Boar
Title: Board Arrival Rate
Description: This is the rate at which people mysteriously arrive on t~~
he board, not having even been on the mailing list or knowing someone~~
who knows the artist, etc....
Definition: triangular(Boarlo,Boarml,Boarhi)
Nodelocation: 648,200,1
Nodesize: 48,64
Variable Fof_interarrival_tim
Title: FoF Interarrival Time
Definition: 1/Fofar
Nodelocation: 48,56,1
Nodesize: 56,48
Variable Mailing_list_interar
Title: Mailing List Interarrival Time
Definition: 1/Olar
Nodelocation: 168,56,1
Nodesize: 56,48
Variable Show_up_interarrival
Title: Show Up Interarrival Time
Definition: 1/Suar
Nodelocation: 288,56,1
Nodesize: 56,48
Variable Volunteer_interarriv
Title: Volunteer Interarrival Time
Definition: 1/Voar
Nodelocation: 408,56,1
Nodesize: 56,48
Variable Donor_interarrival_t
Title: Donor Interarrival Time
Definition: 1/Doar
Nodelocation: 528,56,1
Nodesize: 56,48
Variable Board_interarrival_t
Title: Board Interarrival Time
Definition: 1/Boar
Nodelocation: 648,56,1
Nodesize: 56,48
Close Arrival_rates
Module Dropout_rates
Title: Dropout Rates
Description: These are the rates at which people drop OUT of the syste~~
m, from the various states.~
~
These are represented as reciprocals of triangular distributions on p~~
arameters given by the user interface form. Triangular distributions~~
are commonly used in engineering applications when the actual distri~~
bution is unknown. Taking the reciprocal gives the rate associated w~~
ith the average wait time.
Author: Wynship Hillier
Date: Sun, Apr 15, 2007 1:41 PM
Defaultsize: 48,24
Nodelocation: 296,56,1
Nodesize: 80,28
Diagstate: 1,94,122,714,479,17
Variable Fofdohi
Title: highest possible value
Description: In this space, put a "hard ceiling" on the average amount~~
of time that someone will spend in this state before they drop out o~~
f it, starting at the time of arrival -- the highest number that you ~~
think this average could possibly be. Exaggerate. Studies show that~~
people have a strong tendency not to put a high enough number here, ~~
much stronger than putting a number that is too high. Furthermore, p~~
utting a number that is not high enough is a lot worse than putting a~~
number that is too high.~
~
Nodelocation: 64,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_va3
Variable Fofdoml
Title: most likely value
Description: In this box belongs your best estimate of what the averag~~
e amount of time is before someone drops out of this state.
Nodelocation: 64,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value8
Variable Fofdolo
Title: lowest possible value
Description: In this box, you should put a "hard floor," or lowest pos~~
sible value, for the average amount of time that someone spends in th~~
e friends-of-friends/presenters identified state, before they disappe~~
ar from this state without moving on to the next, "invited" state.
Nodelocation: 64,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_val7
Variable Oldohi
Title: highest possible value
Nodelocation: 176,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_va6
Variable Oldoml
Title: most likely value
Nodelocation: 176,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value11
Variable Oldolo
Title: lowest possible value
Nodelocation: 176,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va10
Chance Bodo
Title: Board Member Dropout Rate
Definition: 1/triangular(Bodolo,Bodoml,Bodohi)
Nodelocation: 624,80,1
Nodesize: 48,64
Variable Sudohi
Title: highest possible value
Nodelocation: 288,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v10
Variable Sudoml
Title: most likely value
Nodelocation: 288,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value15
Variable Sudolo
Title: lowest possible value
Nodelocation: 288,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va14
Variable Vodohi
Title: highest possible value
Nodelocation: 400,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v15
Variable Vodoml
Title: most likely value
Nodelocation: 400,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value20
Variable Vodolo
Title: lowest possible value
Nodelocation: 400,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va19
Variable Dodohi
Title: highest possible value
Nodelocation: 512,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v20
Variable Dodoml
Title: most likely value
Nodelocation: 512,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value25
Variable Dodolo
Title: lowest possible value
Nodelocation: 512,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va24
Variable Bodohi
Title: highest possible value
Nodelocation: 624,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v25
Variable Bodoml
Title: most likely value
Nodelocation: 624,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value30
Variable Bodolo
Title: lowest possible value
Nodelocation: 624,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va29
Chance Fofdo
Title: Friends of Friends Dropout Rate
Definition: 1/triangular(Fofdolo,Fofdoml,Fofdohi)
Nodelocation: 64,80,1
Nodesize: 48,64
Chance Oldo
Title: On List Dropout Rate
Definition: 1/triangular(Oldolo,Oldoml,Oldohi)
Nodelocation: 176,80,1
Nodesize: 48,64
Valuestate: 2,40,50,416,303,1,CDFP
Chance Sudo
Title: Show Up Dropout Rate
Definition: 1/triangular(Sudolo,Sudoml,Sudohi)
Nodelocation: 288,80,1
Nodesize: 48,64
Chance Vodo
Title: Volunteer Dropout Rate
Definition: 1/triangular(Vodolo,Vodoml,Vodohi)
Nodelocation: 400,80,1
Nodesize: 48,64
Chance Dodo
Title: Donor Dropout Rate
Definition: 1/triangular(Dodolo,Dodoml,Dodohi)
Nodelocation: 512,80,1
Nodesize: 48,64
Close Dropout_rates
Module Advancement_rates
Title: Advancement Rates
Description: These are the rates at which people advance WITHIN the sy~~
stem, from one node to the subsequent node.~
~
These are represented as reciprocals of triangular distributions on p~~
arameters given by the user interface form. Triangular distributions~~
are commonly used in engineering applications when the actual distri~~
bution is unknown. Taking the reciprocal derives the rate from the a~~
verage wait time.
Author: Wynship Hillier
Date: Sun, Apr 15, 2007 1:41 PM
Defaultsize: 48,24
Nodelocation: 104,56,1
Nodesize: 80,28
Diagstate: 1,15,102,597,470,17
Variable Fofadlo
Title: lowest possible value
Nodelocation: 64,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_ave2
Variable Fofadml
Title: most likely value
Nodelocation: 64,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_average2
Variable Fofadhi
Title: highest possible value
Nodelocation: 64,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_av2
Variable Oladlo
Title: lowest possible value
Nodelocation: 176,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_aver
Variable Oladml
Title: most likely value
Nodelocation: 176,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_average_
Variable Oladhi
Title: highest possible value
Nodelocation: 176,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_ave
Variable Suadhi
Title: highest possible value
Nodelocation: 288,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v12
Variable Suadml
Title: most likely value
Nodelocation: 288,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value17
Variable Suadlo
Title: lowest possible value
Nodelocation: 288,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va16
Variable Voadhi
Title: highest possible value
Nodelocation: 400,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v17
Variable Voadml
Title: most likely value
Nodelocation: 400,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value22
Variable Voadlo
Title: lowest possible value
Nodelocation: 400,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va21
Variable Doadhi
Title: highest possible value
Nodelocation: 512,200,1
Nodesize: 48,40
Aliases: Formnode Highest_possible_v22
Variable Doadml
Title: most likely value
Nodelocation: 512,288,1
Nodesize: 48,40
Aliases: Formnode Most_likely_value27
Variable Doadlo
Title: lowest possible value
Nodelocation: 512,376,1
Nodesize: 48,40
Aliases: Formnode Lowest_possible_va26
Chance Fofad
Title: Friends of Friends Advancement Rate
Definition: 1/triangular(Fofadlo,Fofadml,Fofadhi)
Nodelocation: 64,72,1
Nodesize: 56,68
Chance Olad
Title: On List Advancement Rate
Definition: 1/triangular(Oladlo,Oladml,Oladhi)
Nodelocation: 176,72,1
Nodesize: 56,68
Chance Suad
Title: Show Up Advancement Rate
Definition: 1/triangular(Suadlo,Suadml,Suadhi)
Nodelocation: 288,72,1
Nodesize: 56,68
Chance Voad
Title: Volunteer Advancement Rate
Definition: 1/triangular(Voadlo,Voadml,Voadhi)
Nodelocation: 400,72,1
Nodesize: 56,68
Chance Doad
Title: Donor Advancement Rate
Definition: 1/triangular(Doadlo,Doadml,Doadhi)
Nodelocation: 512,72,1
Nodesize: 56,68
Close Advancement_rates
Module Results
Title: Steady State Results
Description: These are the populations of the various states on the ba~~
sis of the above rates, at equilibrium.
Author: Wynship Hillier
Date: Sun, Apr 15, 2007 1:41 PM
Defaultsize: 48,24
Nodelocation: 488,168,1
Nodesize: 80,28
Diagstate: 1,222,79,831,397,17
Variable Steady_state_fof_pre
Title: Average Pop.
Description: This button creates a graph that shows only the number of~~
friends-of-friends or of presenters that you have identified. This ~~
button has been provided so that you can see your results after only ~~
filling out the values for this population.~
~
For more on graphs in general, see "Population Size" at the top.
Definition: Fofar/(Fofdo+Fofad)
Nodelocation: 56,128,1
Nodesize: 56,40
Valuestate: 2,82,105,768,493,1,CDFP
Aliases: Formnode Friends_of_friends_1
Graphsetup: Graphtool:0~
Distresol:10~
Diststeps:0~
Cdfresol:5~
Cdfsteps:1~
Symbolsize:6~
Baroverlap:0~
Linestyle:10~
Frame:1~
Grid:1~
Ticks:1~
Mesh:1~
Scales:1~
Rotation:45~
Tilt:0~
Depth:70~
Frameauto:1~
Showkey:1~
Xminimum:20~
Xmaximum:110~
Yminimum:0~
Ymaximum:0.03~
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]~
Fontstyle: Arial, 6
Variable Ol
Title: Average Pop.
Definition: (Olar+Steady_state_fof_pre*Fofad)/(Olad+Oldo)
Nodelocation: 192,128,1
Nodesize: 56,40
Valuestate: 2,60,114,804,591,1,CDFP
Aliases: Formnode Steady_state2
Variable Su
Title: Average Pop.
Definition: (Suar+Ol*Olad)/(Suad+Sudo)
Nodelocation: 328,128,1
Nodesize: 56,40
Valuestate: 2,40,50,829,567,1,CDFP
Aliases: Formnode Steady_state4
Variable Vo
Title: Average Pop.
Definition: (Voar+Su*Suad)/(Vodo+Voad)
Nodelocation: 464,128,1
Nodesize: 56,40
Valuestate: 2,40,50,762,544,1,CDFP
Aliases: Formnode Steady_state6
Variable Don
Title: Average Pop.
Definition: (Doar+Vo*Voad)/(Doad+Dodo)
Nodelocation: 600,128,1
Nodesize: 56,40
Valuestate: 2,69,117,889,608,1,CDFP
Aliases: Formnode Steady_state8
Variable Bo
Title: Average Pop.
Definition: (Boar+Don*Doad)/Bodo
Nodelocation: 744,128,1
Nodesize: 56,40
Valuestate: 2,47,121,937,645,1,CDFP
Aliases: Formnode Steady_state10
Variable Steady_state_populat
Title: Average All Populations
Description: This is a graph that shows the size of each of the popula~~
tions (each group of cells with a common color on the input screen re~~
fers to a population).~
~
Because parameters are uncertain, in this model, so are population si~~
zes. Therefore, they are represented as probability distributions. ~~
The way to read the probability distributions is that the X axis deno~~
tes numbers of people, and the Y axis denotes the probability that th~~
e actual number of people will be less than the corresponding number ~~
on the X axis.~
~
For example, if one of the lines crosses the point X=50, Y=20, that m~~
eans that there is a 20% chance that the actual size of that populati~~
on will be less than 50, in its steady state.~
~
For this reason, all of the lines slope up and to the right. At some~~
small value, the chance of being less than that is 0%. As you move ~~
to the right, there is an increasing chance that the real value will ~~
be smaller. Eventually, you reach some value at which the chance of ~~
the actual value being less than it is 100%.~
~
Some people find this representation less natural. In fact, it is th~~
e least misleading representation. However, if you prefer the "hill ~~
of sand" representation, you can change it to that. Go to the tiny p~~
ull-down menu in the upper-lefthand corner of the graph and change it~~
from "Cumulative Probability" to "Probability Density." Results may~~
appear jagged.
Definition: Table( Self )( Steady_state_fof_pre, Ol, Su, Vo, Don, Bo )~~
Indexvals: ['FoF/Presenters IDd','On List/Presenters Invited','Show Up~~
','Volunteer/Offer to Present','Donors/Presenters','Board Members']
Nodelocation: 400,32,1
Nodesize: 60,28
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
Valuestate: 2,75,55,1119,710,1,CDFP
Aliases: Formnode Population_size1, Formnode Population_size2
Reformval: [Undefined,Self,Undefined,Undefined,1]
{!40000|Att_previndexvalue: ['FoF/Presenters IDd','On List/Presenters ~~
Invited','Show Up','Volunteer/Offer to Present','Donors/Presenters','~~
Board Members']}
Variable A2nd_degree
Title: 2nd Degree
Definition: Steady_state_fof_pre+Ol+Su+Vo+Don+Bo
Nodelocation: 56,272,1
Nodesize: 56,40
Variable Mailing_list
Title: Mailing List
Definition: Ol+Su+Vo+Don+Bo
Nodelocation: 192,272,1
Nodesize: 56,40
Variable Audience
Title: Audience
Definition: Su+Vo+Don+Bo
Nodelocation: 328,272,1
Nodesize: 56,40
Variable Volunteers
Title: Volunteers
Definition: Vo+Don+Bo
Nodelocation: 464,272,1
Nodesize: 56,40
Variable Donors
Title: Donors
Definition: Don+Bo
Nodelocation: 600,272,1
Nodesize: 60,40
Variable Cumulative_populatio
Title: Average All Cumulative Populations
Description: This graph measures cumulative populations -- that is, ea~~
ch of the populations characterized on the input screen by a group of~~
cells of the same color *PLUS* all of the populations below it. So,~~
"friends" includes all of the populations added together, because th~~
ey are all your friends, right?~
~
For more on graphs in general, see the comments under "Population Siz~~
e."
Definition: Table( Self )( Bo, A2nd_degree, Mailing_list, Audience, Vo~~
lunteers, Donors )
Indexvals: ['Board Members','2nd Degree','Mailing List','Audience','Vo~~
lunteers','Donors']
Nodelocation: 400,376,0
Nodesize: 60,28
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
Valuestate: 2,40,50,1163,677,1,CDFP
Aliases: Formnode Cumulative_populati1, Formnode Cumulative_populati2
Reformval: [Undefined,Self,Undefined,Undefined,1]
{!40000|Att_previndexvalue: ['Board Members','2nd Degree','Mailing Lis~~
t','Audience','Volunteers','Donors']}
Close Results
Module Voas
Title: Dynamic Results
Description: This module shows results changing over a short period of~~
time, starting with specified conditions.
Author: Wynship Hillier
Date: Sun, Apr 15, 2007 9:45 PM
Defaultsize: 48,24
Nodelocation: 488,272,1
Nodesize: 80,28
Diagstate: 1,255,83,628,671,17
Variable Friends_of_friends_i
Title: Initial Pop.
Description: This is where you enter the current population of friends~~
-of-friends (I assume that you know what this is).
Nodelocation: 112,72,1
Nodesize: 48,52
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
Aliases: Formnode Friends_of_friends_2
Variable On_list_initial
Title: Initial Pop.
Nodelocation: 112,184,1
Nodesize: 48,52
Valuestate: 2,40,50,416,303,0,MIDM
Aliases: Formnode On_list_initial1
Variable Volunteer_initial
Title: Initial Pop.
Nodelocation: 112,408,1
Nodesize: 48,52
Aliases: Formnode Volunteer_initial1
Variable Donor_initial
Title: Initial Pop.
Nodelocation: 112,520,1
Nodesize: 48,52
Aliases: Formnode Donor_initial1
Variable Show_up_initial
Title: Initial Pop.
Nodelocation: 112,296,1
Nodesize: 48,52
Aliases: Formnode Show_up_initial1
Variable On_board_initial
Title: Initial Pop.
Nodelocation: 112,632,1
Nodesize: 48,52
Aliases: Formnode On_board_initial1
Variable Fofas
Title: Short Term
Definition: Dynamic(Friends_of_friends_i,Fofae[Time-1])
Nodelocation: 256,72,1
Nodesize: 48,52
Valuestate: 2,171,101,945,671,1,CDFP
Aliases: Formnode Friends_of_friends_3
Graphsetup: {!40000|Att_catlinestyle Graph_cumprob_valdim:1}~
Probindex:[0.05, 0.25, 0.5, 0.75, 0.95 ]~
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0 ]~
Includezzero:0~
Includeyzero:0~
Includexzero:0~
Yintervals:0~
Xintervals:0~
Zmaximum:1~
Zminimum:1~
Ymaximum:100~
Yminimum:14~
Xmaximum:6~
Xminimum:0~
Showkey:1~
Frameauto:1~
Depth:70~
Tilt:0~
Rotation:45~
Scales:1~
Mesh:1~
Ticks:1~
Grid:1~
Frame:1~
Linestyle:10~
Baroverlap:0~
Symbolsize:6~
Cdfsteps:1~
Cdfresol:5~
Diststeps:0~
Distresol:10~
Graphtool:0~
{!40000|Att_areafill Graph_cumprob_valdim:0}
Fontstyle: Arial, 2
Reformval: [Undefined,Time,Undefined,Undefined,1]
Variable Olas
Title: Short Term
Definition: Dynamic(On_list_initial,Olae[Time-1])
Nodelocation: 256,184,1
Nodesize: 48,52
Valuestate: 2,193,130,903,673,1,CDFP
Aliases: Formnode On_list_start1
Graphsetup: {!40000|Att_catlinestyle Graph_cumprob_valdim:1}
Reformval: [Undefined,Time,Undefined,Undefined,1]
Variable Suas
Title: Short Term
Definition: Dynamic(Show_up_initial,suae[Time-1])
Nodelocation: 256,296,1
Nodesize: 48,52
Valuestate: 2,204,120,870,632,1,CDFP
Aliases: Formnode Show_up_start1
Graphsetup: {!40000|Att_catlinestyle Graph_cumprob_valdim:1}
Reformval: [Undefined,Time,Undefined,Undefined,1]
Variable Volunteer
Title: Short Term
Definition: Dynamic(Volunteer_initial,voae[Time-1])
Nodelocation: 256,408,1
Nodesize: 48,52
Valuestate: 2,216,141,933,650,1,CDFP
Aliases: Formnode Volunteer_start1
Graphsetup: {!40000|Att_catlinestyle Graph_cumprob_valdim:1}
Reformval: [Undefined,Time,Undefined,Undefined,1]
Variable Doas
Title: Short Term
Definition: Dynamic(Donor_initial,doae[Time-1])
Nodelocation: 256,520,1
Nodesize: 48,52
Valuestate: 2,188,132,980,704,1,CDFP
Aliases: Formnode Donor_start1
Reformval: [Undefined,Time,Undefined,Undefined,1]
Variable Obas
Title: Short Term
Definition: Dynamic(On_board_initial,obae[Time-1])
Nodelocation: 256,632,1
Nodesize: 48,52
Windstate: 2,102,90,476,224
Valuestate: 2,163,21,916,716,1,CDFP
Aliases: Formnode On_board_start1
Reformval: [Time,Time,Undefined,Undefined,1]
Variable Fofae
Title: FoF at End
Definition: begin~
/* msgbox ("Advances[FoF]=" + advances[dest__='FoF'] + ", Arrivals[Fo~~
F]=" + arrivals[dest__1='FoF']); */~
Advances[dest__='FoF']+Arrivals[dest__1='FoF']~
end~
Nodelocation: 496,72,1
Nodesize: 48,28
Variable Olae
Title: On List at End
Definition: Advances[dest__='On List']+Arrivals[dest__1='On List']
Nodelocation: 496,184,1
Nodesize: 48,28
Variable Suae
Title: Show Up at End
Definition: Advances[dest__='Show Up']+Arrivals[dest__1='Show Up']
Nodelocation: 496,296,1
Nodesize: 48,28
Variable Voae
Title: Volunteer at End
Definition: Advances[dest__='Volunteer']+Arrivals[dest__1='Volunteer']~~
Nodelocation: 496,408,1
Nodesize: 48,28
Variable Doae
Title: Donor at End
Definition: Advances[dest__='Donor']+Arrivals[dest__1='Donor']
Nodelocation: 496,520,1
Nodesize: 48,28
Variable Obae
Title: On Board at End
Definition: begin~
/* msgbox ("Advances[Board]=" + advances[dest__='Board'] + ".");~
msgbox ("Arrivals[Board]=" + arrivals[dest__1='Board'] + "."); */~
Advances[dest__='Board']+Arrivals[dest__1='On Board']~
end
Nodelocation: 496,632,1
Nodesize: 48,40
Close Voas
Module Dp
Title: Dynamic Advancement
Description: This module calculates the rates of advancement between s~~
tates, which are used in the dynamic model. For each state, the prob~~
ability of remaining in that state or advancing to each downstream st~~
ate are calculated here. A multinomial sample is then taken of these~~
probabilities, and the results are summed over sources. The result ~~
is a scenario of populants distributed over nodes denoting populants ~~
in the system at the start of the period that remain in it at the end~~
of the period.~
~
Transition probabilities are calculated according to a cumulative hyp~~
oexponential distribution adapted for emigration. The distribution f~~
unction for the hypoexponential is taken from Sheldon Ross, "Introduc~~
tion to Probability Models," 6th ed., p. 245-6. The transition proba~~
bilities, differently adjusted for emigration, are then subtracted fr~~
om one another to determine probabilities of landing in a given state~~
. This form is suitable for multinomial sampling to determine actual~~
migration from each node to its downstream nodes.
Author: Wynship Hillier
Date: Tue, May 01, 2007 10:02 PM
Defaultsize: 48,24
Nodelocation: 296,168,1
Nodesize: 80,28
Diagstate: 1,433,17,624,737,17
Index Dest
Title: dest
Description: This is the index denoting the destination node of a popu~~
lant.
Definition: ['FoF','On List','Show Up','Volunteer','Donor','On Board']~~
Nodelocation: 248,72,1
Nodesize: 68,36
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
{!40000|Att_previndexvalue: ['FoF','On List','Show Up','Volunteer','Do~~
nor','On Board']}
Index Term
Title: term
Description: This indexes the terms in the hypoexponential formula, gi~~
ven a source and destination.
Definition: ['FoF','On List','Show Up','Volunteer','Donor','Board']
Nodelocation: 400,72,1
Nodesize: 68,36
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
{!40000|Att_previndexvalue: ['FoF','On List','Show Up','Volunteer','Do~~
nor','Board']}
Variable Rr
Title: Ratios of Rates
Description: This table contains the ratios of rates terms used in the~~
calculation of the C constant in the hypoexponential distribution (s~~
ee module-level description). Diagonal entries contain the cumulativ~~
e exponential probabilities (probability of a populant exiting the no~~
de by the end of the period).~
~
Rates are slightly more complicated than in Ross' formula, because ea~~
ch node has two exit paths, rather than one. to use the hypoexponent~~
ial distribution, each node is considered only to transition to the d~~
ownstream node (i.e. no emigration allowed). Resulting hypoexponenti~~
al probabilities are then conditioned to reflect emigration.~
~
The hypoexponential is calculated several times, once on the basis of~~
each node as the starting node.
Definition: Table(Dest,Term,Source)(~
1-exp(-fofad-fofdo),0,0,0,0,0,~
(fofad+fofdo)/(fofad+fofdo-olad-oldo),1,0,0,0,0,~
(fofad+fofdo)/(fofad+fofdo-suad-sudo),1,1,0,0,0,~
(fofad+fofdo)/(fofad+fofdo-voad-vodo),1,1,1,0,0,~
(fofad+fofdo)/(fofad+fofdo-doad-dodo),1,1,1,1,0,~
(fofad+fofdo)/(fofad+fofdo-bodo),1,1,1,1,1,~
(olad+oldo)/(olad+oldo-fofad-fofdo),0,0,0,0,0,~
1-exp(-olad-oldo),1-exp(-olad-oldo),0,0,0,0,~
(olad+oldo)/(olad+oldo-suad-sudo),(olad+oldo)/(olad+oldo-suad-sudo),1~~
,0,0,0,~
(olad+oldo)/(olad+oldo-voad-vodo),(olad+oldo)/(olad+oldo-voad-vodo),1~~
,1,0,0,~
(olad+oldo)/(olad+oldo-doad-dodo),(olad+oldo)/(olad+oldo-doad-dodo),1~~
,1,1,0,~
(olad+oldo)/(olad+oldo-bodo),(olad+oldo)/(olad+oldo-bodo),1,1,1,1,~
(suad+sudo)/(sudo+suad-fofad-fofdo),0,0,0,0,0,~
(suad+sudo)/(sudo+suad-olad-oldo),(suad+sudo)/(sudo+suad-olad-oldo),0~~
,0,0,0,~
1-exp(-suad-sudo),1-exp(-suad-sudo),1-exp(-suad-sudo),0,0,0,~
(suad+sudo)/(sudo+suad-voad-vodo),(suad+sudo)/(sudo+suad-voad-vodo),(~~
suad+sudo)/(sudo+suad-voad-vodo),1,0,0,~
(suad+sudo)/(sudo+suad-doad-dodo),(suad+sudo)/(sudo+suad-doad-dodo),(~~
suad+sudo)/(sudo+suad-doad-dodo),1,1,0,~
(suad+sudo)/(sudo+suad-bodo),(suad+sudo)/(sudo+suad-bodo),(suad+sudo)~~
/(sudo+suad-bodo),1,1,1,~
(voad+vodo)/(vodo+voad-fofad-fofdo),0,0,0,0,0,~
(voad+vodo)/(vodo+voad-olad-oldo),(voad+vodo)/(vodo+voad-olad-oldo),0~~
,0,0,0,~
(voad+vodo)/(vodo+voad-suad-sudo),(voad+vodo)/(vodo+voad-suad-sudo),(~~
voad+vodo)/(vodo+voad-suad-sudo),0,0,0,~
1-exp(-voad-vodo),1-exp(-voad-vodo),1-exp(-voad-vodo),1-exp(-voad-vod~~
o),0,0,~
(voad+vodo)/(vodo+voad-doad-dodo),(voad+vodo)/(vodo+voad-doad-dodo),(~~
voad+vodo)/(vodo+voad-doad-dodo),(voad+vodo)/(vodo+voad-doad-dodo),1,~~
0,~
(voad+vodo)/(vodo+voad-bodo),(voad+vodo)/(vodo+voad-bodo),(voad+vodo)~~
/(vodo+voad-bodo),(voad+vodo)/(vodo+voad-bodo),1,1,~
(doad+dodo)/(doad+dodo-fofad-fofdo),0,0,0,0,0,~
(doad+dodo)/(doad+dodo-olad-oldo),(doad+dodo)/(doad+dodo-olad-oldo),0~~
,0,0,0,~
(doad+dodo)/(doad+dodo-suad-sudo),(doad+dodo)/(doad+dodo-suad-sudo),(~~
doad+dodo)/(doad+dodo-suad-sudo),0,0,0,~
(doad+dodo)/(doad+dodo-voad-vodo),(doad+dodo)/(doad+dodo-voad-vodo),(~~
doad+dodo)/(doad+dodo-voad-vodo),(doad+dodo)/(doad+dodo-voad-vodo),0,~~
0,~
1-exp(-doad-dodo),1-exp(-doad-dodo),1-exp(-doad-dodo),1-exp(-doad-dod~~
o),1-exp(-doad-dodo),0,~
(doad+dodo)/(doad+dodo-bodo),(doad+dodo)/(doad+dodo-bodo),(doad+dodo)~~
/(doad+dodo-bodo),(doad+dodo)/(doad+dodo-bodo),(doad+dodo)/(doad+dodo~~
-bodo),1,~
bodo/(bodo-fofad-fofdo),0,0,0,0,0,~
bodo/(bodo-olad-oldo),bodo/(bodo-olad-oldo),0,0,0,0,~
bodo/(bodo-suad-sudo),bodo/(bodo-suad-sudo),bodo/(bodo-suad-sudo),0,0~~
,0,~
bodo/(bodo-voad-vodo),bodo/(bodo-voad-vodo),bodo/(bodo-voad-vodo),bod~~
o/(bodo-voad-vodo),0,0,~
bodo/(bodo-doad-dodo),bodo/(bodo-doad-dodo),bodo/(bodo-doad-dodo),bod~~
o/(bodo-doad-dodo),bodo/(bodo-doad-dodo),0,~
1-exp(-bodo),1-exp(-bodo),1-exp(-bodo),1-exp(-bodo),1-exp(-bodo),1-ex~~
p(-bodo)~
)
Nodelocation: 96,176,1
Nodesize: 68,36
Defnstate: 2,246,201,1118,410,0,MIDM
Valuestate: 2,66,164,487,233,0,MIDM
Reformdef: [Term,Dest]
Reformval: [Term,Dest]
{!40000|Att_resultslicestate: [Source,1,Dest,1,Term,1]}
{!40000|Att_editslicestate: [Source,1,Dest,1,Term,1]}
Variable C_s
Title: C and exp terms
Description: This table contains the cumulative products of each of th~~
e terms in the "Ratios of Rates" table. Each of these products is on~~
e of the "C" terms in Ross' formula (see module-level description).
Definition: cumProduct(Rr,dest)
Nodelocation: 96,264,1
Nodesize: 68,36
Valuestate: 2,48,190,492,228,0,MIDM
Reformval: [Term,Dest]
{!40000|Att_resultslicestate: [Source,1,Dest,1,Term,1]}
Index Source
Title: source
Description: This indexes the source node of a hypoexponential distrib~~
ution.
Definition: ['FoF','On List','Show Up','Volunteer','Donor','Board']
Nodelocation: 96,72,1
Nodesize: 68,36
{!40000|Att_previndexvalue: ['FoF','On List','Show Up','Volunteer','D~~
onor','Board']}
Variable Af
Title: Incoming Advancement Factors
Description: This table is the basis for modifying the hypoexponential~~
to account for emigration from nodes. Populants advancing to the ne~~
xt node in the chain occur in ratio proportional to the ratio of the ~~
advancement rate to the total exit rate from the node (sum of the adv~~
ancement rate and emigration rate).~
~
"Incoming" denotes that the advancement factor applies to populants a~~
rriving in the destination node from the previous node in the chain.
Definition: Table(Dest,Source)(~
1,1,1,1,1,1,~
fofad/(fofad+fofdo),1,1,1,1,1,~
olad/(olad+oldo),olad/(olad+oldo),1,1,1,1,~
suad/(suad+sudo),suad/(suad+sudo),suad/(suad+sudo),1,1,1,~
voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),1~~
,1,~
doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),d~~
oad/(doad+dodo),1~
)
Nodelocation: 248,264,1
Nodesize: 68,36
Defnstate: 2,216,340,784,297,0,MIDM
Valuestate: 2,50,1,416,303,0,MIDM
Reformdef: [Source,Dest]
Reformval: [Source,Dest]
Variable Ap
Title: Incoming Advancement Product
Description: This table contains the products of successive advancemen~~
t factors. These products are the probabilities that a populant begi~~
nning in a source node and advancing to a destination node actually m~~
ake it to that node, rather than emigrating out of the system along t~~
he way.
Definition: cumProduct(Af,dest)
Nodelocation: 248,352,1
Nodesize: 68,36
Valuestate: 2,21,372,484,203,0,MIDM
Reformval: [Source,Dest]
Variable Soc
Title: Sums of C and exp terms
Description: This is the sum of the "C" terms in Ross' formula (see mo~~
dule-level description).
Definition: cumulate(C_s,term)
Nodelocation: 96,352,1
Nodesize: 68,36
Valuestate: 2,62,514,502,245,0,MIDM
Reformval: [Term,Dest]
{!40000|Att_resultslicestate: [Source,1,Dest,1,Term,1]}
Variable He
Title: Hypoexponential
Description: This is the cumulative hypoexponential distribution calcu~~
lated over sources and destinations. In other words, cells in the ta~~
ble contain probabilities that a populant at the source node will hav~~
e escaped the destination node (which could be the same as the source~~
node) by the end of the period.
Definition: Table(Dest,Source)(~
Soc[Term = 'FoF' ],0,0,0,0,0,~
Soc[Term = 'On List' ],Soc[Term = 'On List' ],0,0,0,0,~
Soc[Term = 'Show Up' ],Soc[Term = 'Show Up' ],Soc[Term = 'Show Up' ],~~
0,0,0,~
Soc[Term = 'Volunteer' ],Soc[Term = 'Volunteer' ],Soc[Term = 'Volunte~~
er' ],Soc[Term = 'Volunteer' ],0,0,~
Soc[Term = 'Donor' ],Soc[Term = 'Donor' ],Soc[Term = 'Donor' ],Soc[Te~~
rm = 'Donor' ],Soc[Term = 'Donor' ],0,~
Soc[Term = 'Board' ],Soc[Term = 'Board' ],Soc[Term = 'Board' ],Soc[Te~~
rm = 'Board' ],Soc[Term = 'Board' ],Soc[Term = 'Board' ]~
)
Nodelocation: 96,440,1
Nodesize: 68,36
Defnstate: 2,169,445,1140,288,0,MIDM
Valuestate: 2,133,517,518,231,0,MIDM
Reformdef: [Source,Dest]
Reformval: [Source,Dest]
Variable We
Title: With Emigration
Description: This is the hypoexponential distribution after conditioni~~
ng to account for emigration.
Definition: Ap*He
Nodelocation: 248,440,1
Nodesize: 68,36
Valuestate: 2,435,169,611,242,0,MIDM
Reformval: [Source,Dest]
Variable Oaf
Title: Outgoing Advancement Factors
Description: These are the probabilities that a populant exits a node ~~
in the direction of advancing to the next node in the chain, rather t~~
han emigrating from the system.
Definition: Table(Dest,Source)(~
fofad/(fofad+fofdo),1,1,1,1,1,~
olad/(olad+oldo),olad/(olad+oldo),1,1,1,1,~
suad/(suad+sudo),suad/(suad+sudo),suad/(suad+sudo),1,1,1,~
voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),1~~
,1,~
doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),d~~
oad/(doad+dodo),1,~
1,1,1,1,1,1~
)
Nodelocation: 400,352,1
Nodesize: 68,36
Defnstate: 2,218,70,765,227,0,MIDM
Valuestate: 2,829,539,520,224,0,MIDM
Reformdef: [Source,Dest]
Reformval: [Source,Dest]
Variable Oap
Title: Outgoing Advancement Probability
Description: These are the probabilities that populants will traverse ~~
from the source node to the destination node, with emigration, and th~~
en exit in the direction of further advancement, rather than emigrati~~
on.
Definition: We*Oaf
Nodelocation: 400,440,1
Nodesize: 68,36
Valuestate: 2,520,428,590,249,0,MIDM
Reformval: [Source,Dest]
Variable Depro
Title: Deposition Probabilities
Description: These are the probabilities that populants will, after ad~~
vancing to the destination node from the source node, remain at the d~~
estination node at the end of the period.~
~
They are calculated by simply subtracting the probability that a popu~~
lant will traverse to the destination node and then exit it from the ~~
probability that a populant will traverse to the destination node.
Definition: Table(Dest,Source)(~
1-We,0,0,0,0,0,~
Oap[Dest='FoF']-We,1-We,0,0,0,0,~
Oap[Dest='On List']-We,Oap[Dest='On List']-We,1-We,0,0,0,~
Oap[Dest='Show Up']-We,Oap[Dest='Show Up']-We,Oap[Dest='Show Up']-We,~~
1-We,0,0,~
Oap[Dest='Volunteer']-We,Oap[Dest='Volunteer']-We,Oap[Dest='Volunteer~~
']-We,Oap[Dest='Volunteer']-We,1-We,0,~
Oap[Dest='Donor']-We,Oap[Dest='Donor']-We,Oap[Dest='Donor']-We,Oap[De~~
st='Donor']-We,Oap[Dest='Donor']-We,1-We~
)
Nodelocation: 400,528,1
Nodesize: 68,36
Defnstate: 2,141,618,940,264,0,MIDM
Valuestate: 2,204,373,1090,235,0,SAMP
Reformdef: [Source,Dest]
Reformval: [Source,Dest,Undefined,Undefined,Undefined,0]
Att__totalsindex: Index Dest
{!40000|Att_resultslicestate: [Run,45,Dest,1,Source,1]}
Index Dest__
Title: dest +
Description: Same as dest, but an additional entry for the complementa~~
ry probability (1 - sum), needed to use multinomial function.
Definition: ['FoF','On List','Show Up','Volunteer','Donor','Board','Ou~~
t']
Nodelocation: 544,72,1
Nodesize: 64,36
{!40000|Att_previndexvalue: ['FoF','On List','Show Up','Volunteer','Do~~
nor','Board','Out']}
Variable Dpc
Title: Deposition Plus Complement
Description: This array simply adds the complementary probability (whi~~
ch, in this case, is the probability of exiting the system entirely) ~~
to the end of the destinations of the deposition probabilities.~
~
This is done in order that the multinomial function expel the correct~~
number of populants from the system.
Definition: Table(Source,Dest__)(~
Depro[dest='FoF'],Depro[dest='On List'],Depro[dest='Show Up'],Depro[d~~
est='Volunteer'],Depro[dest='Donor'],Depro[dest='On Board'],1-sum(Dep~~
ro,dest),~
0,Depro[dest='On List'],Depro[dest='Show Up'],Depro[dest='Volunteer']~~
,Depro[dest='Donor'],Depro[dest='On Board'],1-sum(Depro,dest),~
0,0,Depro[dest='Show Up'],Depro[dest='Volunteer'],Depro[dest='Donor']~~
,Depro[dest='On Board'],1-sum(Depro,dest),~
0,0,0,Depro[dest='Volunteer'],Depro[dest='Donor'],Depro[dest='On Boar~~
d'],1-sum(Depro,dest),~
0,0,0,0,Depro[dest='Donor'],Depro[dest='On Board'],1-sum(Depro,dest),~~
~
0,0,0,0,0,Depro[dest='On Board'],1-sum(Depro,dest)~
)
Nodelocation: 400,616,1
Nodesize: 68,36
Defnstate: 2,83,408,1071,307,0,MIDM
Valuestate: 2,-14,366,1364,278,0,SAMP
Reformdef: [Source,Dest__]
Reformval: [Source,Dest__,Undefined,Undefined,Undefined,0]
{!40000|Att_resultslicestate: [Run,6,Dest__,1,Source,1]}
Chance Multinomial_sample
Title: Multinomial Sample
Description: For each source node, this takes a multinomial sample ove~~
r the set of possible destination nodes, using the starting populatio~~
n as N. In other words, each run of this node contains one scenario ~~
of the distribution of populants over itself, all downstream nodes, a~~
nd having left the system (not currently used).
Definition: multinomial(sp,makenn,dest__)
Nodelocation: 400,792,1
Nodesize: 68,36
Defnstate: 2,54,165,1260,116,0,MIDM
Valuestate: 2,280,290,416,303,0,SAMP
Reformdef: [Source,Undefined]
Reformval: [Time,Dest__]
{!40000|Att_resultslicestate: [Time,6,Source,6,Dest__,1,Run,1]}
Variable Advances
Title: Sum by Destination
Description: This node sums the multinomial sample over source nodes. ~~
This gives a population of each node, at the end of the period, of p~~
opulants that began the period in a node within the system.
Definition: sum(Multinomial_sample,source)
Nodelocation: 400,880,1
Nodesize: 68,36
Variable Sp
Title: Starting Populations
Description: These are simply the various populations at the beginning~~
of the period, put in tabular form for use with the other tables.
Definition: Table(Source)(~
fofas,olas,suas,Volunteer,Doas,Obas)
Nodelocation: 248,704,1
Nodesize: 68,36
Variable Makenn
Title: Make NonNegative
Description: Because we are subtracting nearly equal numbers calculate~~
d on the basis of complicated formulas, one case in a thousand will e~~
nd with a negative result for the deposition probability. As this wo~~
uld cause an error, this node repairs these negatives back to zero be~~
fore they are used in multinomial sampling.
Definition: makeNonNeg (dpc)
Nodelocation: 400,704,1
Nodesize: 72,36
Close Dp
Module Dp1
Title: Dynamic Arrival
Description: This module calculates the distribution of new immigrants~~
to the system. A multinomial sample is then taken to determine the ~~
actual distribution of immigrants in a particular scenario.~
~
A Poisson distribution is used to determine the number of new entrant~~
s to each node. A cumulative hypoexponential distribution, modified ~~
for emigration, is then used to distribute these new entrants among t~~
he entry node and all nodes downstream from the entry node. The sour~~
ce node for this distribution is considered to be outside of the syst~~
em, in order to account for the partial delay to enter the system. T~~
he resultant deposition probabilities are then conditioned on entry t~~
o the system. This way, the multinomial can be used on the results, ~~
with N equal to the Poisson arrivals.~
~
Transition probabilities are calculated according to a cumulative hyp~~
oexponential distribution adapted for emigration. The formula for th~~
e distribution function for the hypoexponential is taken from Sheldon~~
Ross, "Introduction to Probability Models," 6th ed., p. 245-6. The ~~
transition probabilities, differently adjusted for emigration, are th~~
en subtracted from one another to determine probabilities of landing ~~
in a given state. This form is suitable for multinomial sampling to ~~
determine actual migration from outside the system to downstream node~~
s.
Author: Wynship Hillier
Date: Tue, May 01, 2007 10:02 PM
Defaultsize: 48,24
Nodelocation: 104,168,1
Nodesize: 80,28
Diagstate: 1,605,41,653,670,17
Index Dest1
Title: dest
Description: This is the index denoting the destination node of a popu~~
lant.
Definition: ['Pre-FoF','FoF','On List','Show Up','Volunteer','Donor','~~
On Board']
Nodelocation: 256,80,1
Nodesize: 68,36
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
{!40000|Att_previndexvalue: ['Pre-FoF','FoF','On List','Show Up','Volu~~
nteer','Donor','On Board']}
Index Term1
Title: term
Description: This indexes the terms in the hypoexponential formula, gi~~
ven a source and destination.
Definition: ['Pre-FoF','FoF','On List','Show Up','Volunteer','Donor','~~
Board']
Nodelocation: 408,80,1
Nodesize: 68,36
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
Windstate: 2,102,90,465,368
{!40000|Att_previndexvalue: ['Pre-FoF','FoF','On List','Show Up','Volu~~
nteer','Donor','Board']}
Variable Rr1
Title: Ratios of Rates
Description: This table contains the ratios of rates terms used in the~~
calculation of the C constant in the hypoexponential distribution (s~~
ee module-level description). Diagonal entries contain the cumulativ~~
e exponential probabilities (probability of a populant exiting the no~~
de by the end of the period).~
~
Rates are slightly more complicated than in Ross' formula, because ea~~
ch node has two exit paths, rather than one. to use the hypoexponent~~
ial distribution, each node is considered only to transition to the d~~
ownstream node (i.e. no emigration allowed). Resulting hypoexponenti~~
al probabilities are then conditioned to reflect emigration.~
~
The hypoexponential is calculated several times, once on the basis of~~
entry into the system at each node.~
~
A slight additional complication is that the hypoexponential distribu~~
tion is indended to be used on a standing population, one that is alr~~
eady "in the system", and to distribute that population between that ~~
node and all downstream nodes. It is in fact used this way in the mo~~
dule "dynamic advancement", in which it calculates distributions for ~~
populants that are already resident in one of the nodes in the system~~
. Here, however, it is necessary to imagine "phantom" nodes that occ~~
ur before the actual nodes in the chain, in which the (Poisson distri~~
buted) new entrants are imagined to begin the period. Once these pop~~
ulations are distributed among the other nodes and out of the system,~~
then the populations of the "phantom nodes" can be discarded. This ~~
occurs in the node "Deposition Plus Complement."~
~
The first phantom node is "pre-FoF". Phantom nodes for other entry p~~
oints in the system are simply the previous nodes in the system. So,~~
the "phantom node" for "On List" is "FoF", and so-on. The transitio~~
n rates from the "phantom nodes" to the "source nodes" are simply the~~
arrival rates for the source nodes.
Definition: Table(Dest1,Term1,Source_2)(~
1-exp(-fofar),0,0,0,0,0,~
fofar/(fofar-fofad-fofdo),1,0,0,0,0,~
fofar/(fofar-olad-oldo),1,1,0,0,0,~
fofar/(fofar-suad-sudo),1,1,1,0,0,~
fofar/(fofar-voad-vodo),1,1,1,1,0,~
fofar/(fofar-doad-dodo),1,1,1,1,1,~
fofar/(fofar-bodo),1,1,1,1,1,~
(fofad+fofdo)/(fofad+fofdo-fofar),0,0,0,0,0,~
1-exp(-fofad-fofdo),1-exp(-olar),0,0,0,0,~
(fofad+fofdo)/(fofad+fofdo-olad-oldo),olar/(olar-olad-oldo),1,0,0,0,~
(fofad+fofdo)/(fofad+fofdo-suad-sudo),olar/(olar-suad-sudo),1,1,0,0,~
(fofad+fofdo)/(fofad+fofdo-voad-vodo),olar/(olar-voad-vodo),1,1,1,0,~
(fofad+fofdo)/(fofad+fofdo-doad-dodo),olar/(olar-doad-dodo),1,1,1,1,~
(fofad+fofdo)/(fofad+fofdo-bodo),olar/(olar-bodo),1,1,1,1,~
(olad+oldo)/(olad+oldo-fofar),0,0,0,0,0,~
(olad+oldo)/(olad+oldo-fofad-fofdo),(olad+oldo)/(olad+oldo-olar),0,0,~~
0,0,~
1-exp(-olad-oldo),1-exp(-olad-oldo),1-exp(-suar),0,0,0,~
(olad+oldo)/(olad+oldo-suad-sudo),(olad+oldo)/(olad+oldo-suad-sudo),s~~
uar/(suar-suad-sudo),1,0,0,~
(olad+oldo)/(olad+oldo-voad-vodo),(olad+oldo)/(olad+oldo-voad-vodo),s~~
uar/(suar-voad-vodo),1,1,0,~
(olad+oldo)/(olad+oldo-doad-dodo),(olad+oldo)/(olad+oldo-doad-dodo),s~~
uar/(suar-doad-dodo),1,1,1,~
(olad+oldo)/(olad+oldo-bodo),(olad+oldo)/(olad+oldo-bodo),suar/(suar-~~
bodo),1,1,1,~
(suad+sudo)/(sudo+suad-fofar),0,0,0,0,0,~
(suad+sudo)/(sudo+suad-fofad-fofdo),(suad+sudo)/(sudo+suad-olar),0,0,~~
0,0,~
(suad+sudo)/(sudo+suad-olad-oldo),(suad+sudo)/(sudo+suad-olad-oldo),(~~
suad+sudo)/(sudo+suad-suar),0,0,0,~
1-exp(-suad-sudo),1-exp(-suad-sudo),1-exp(-suad-sudo),1-exp(-voar),0,~~
0,~
(suad+sudo)/(sudo+suad-voad-vodo),(suad+sudo)/(sudo+suad-voad-vodo),(~~
suad+sudo)/(sudo+suad-voad-vodo),voar/(voar-voad-vodo),1,0,~
(suad+sudo)/(sudo+suad-doad-dodo),(suad+sudo)/(sudo+suad-doad-dodo),(~~
suad+sudo)/(sudo+suad-doad-dodo),voar/(voar-doad-dodo),1,1,~
(suad+sudo)/(sudo+suad-bodo),(suad+sudo)/(sudo+suad-bodo),(suad+sudo)~~
/(sudo+suad-bodo),voar/(voar-bodo),1,1,~
(voad+vodo)/(vodo+voad-fofar),0,0,0,0,0,~
(voad+vodo)/(vodo+voad-fofad-fofdo),(voad+vodo)/(vodo+voad-olar),0,0,~~
0,0,~
(voad+vodo)/(vodo+voad-olad-oldo),(voad+vodo)/(vodo+voad-olad-oldo),(~~
voad+vodo)/(vodo+voad-suar),0,0,0,~
(voad+vodo)/(vodo+voad-suad-sudo),(voad+vodo)/(vodo+voad-suad-sudo),(~~
voad+vodo)/(vodo+voad-suad-sudo),(voad+vodo)/(vodo+voad-voar),0,0,~
1-exp(-voad-vodo),1-exp(-voad-vodo),1-exp(-voad-vodo),1-exp(-voad-vod~~
o),1-exp(-doar),0,~
(voad+vodo)/(vodo+voad-doad-dodo),(voad+vodo)/(vodo+voad-doad-dodo),(~~
voad+vodo)/(vodo+voad-doad-dodo),(voad+vodo)/(vodo+voad-doad-dodo),do~~
ar/(doar-doad-dodo),1,~
(voad+vodo)/(vodo+voad-bodo),(voad+vodo)/(vodo+voad-bodo),(voad+vodo)~~
/(vodo+voad-bodo),(voad+vodo)/(vodo+voad-bodo),doar/(doar-bodo),1,~
(doad+dodo)/(doad+dodo-fofar),0,0,0,0,0,~
(doad+dodo)/(doad+dodo-fofad-fofdo),(doad+dodo)/(doad+dodo-olar),0,0,~~
0,0,~
(doad+dodo)/(doad+dodo-olad-oldo),(doad+dodo)/(doad+dodo-olad-oldo),(~~
doad+dodo)/(doad+dodo-suar),0,0,0,~
(doad+dodo)/(doad+dodo-suad-sudo),(doad+dodo)/(doad+dodo-suad-sudo),(~~
doad+dodo)/(doad+dodo-suad-sudo),(doad+dodo)/(doad+dodo-voar),0,0,~
(doad+dodo)/(doad+dodo-voad-vodo),(doad+dodo)/(doad+dodo-voad-vodo),(~~
doad+dodo)/(doad+dodo-voad-vodo),(doad+dodo)/(doad+dodo-voad-vodo),(d~~
oad+dodo)/(doad+dodo-doar),0,~
1-exp(-doad-dodo),1-exp(-doad-dodo),1-exp(-doad-dodo),1-exp(-doad-dod~~
o),1-exp(-doad-dodo),1-exp(-boar),~
(doad+dodo)/(doad+dodo-bodo),(doad+dodo)/(doad+dodo-bodo),(doad+dodo)~~
/(doad+dodo-bodo),(doad+dodo)/(doad+dodo-bodo),(doad+dodo)/(doad+dodo~~
-bodo),boar/(boar-bodo),~
bodo/(bodo-fofar),0,0,0,0,0,~
bodo/(bodo-fofad-fofdo),bodo/(bodo-olar),0,0,0,0,~
bodo/(bodo-olad-oldo),bodo/(bodo-olad-oldo),bodo/(bodo-suar),0,0,0,~
bodo/(bodo-suad-sudo),bodo/(bodo-suad-sudo),bodo/(bodo-suad-sudo),bod~~
o/(bodo-voar),0,0,~
bodo/(bodo-voad-vodo),bodo/(bodo-voad-vodo),bodo/(bodo-voad-vodo),bod~~
o/(bodo-voad-vodo),bodo/(bodo-doar),0,~
bodo/(bodo-doad-dodo),bodo/(bodo-doad-dodo),bodo/(bodo-doad-dodo),bod~~
o/(bodo-doad-dodo),bodo/(bodo-doad-dodo),bodo/(bodo-boar),~
1-exp(-bodo),1-exp(-bodo),1-exp(-bodo),1-exp(-bodo),1-exp(-bodo),1-ex~~
p(-bodo)~
)
Nodelocation: 104,184,1
Nodesize: 68,36
Defnstate: 2,135,443,1122,279,0,MIDM
Valuestate: 2,66,164,628,352,0,MIDM
Reformdef: [Term1,Dest1]
Reformval: [Term1,Dest1]
{!40000|Att_resultslicestate: [Source1,1,Dest1,1,Term1,1]}
{!40000|Att_editslicestate: [Source_2,6,Dest1,1,Term1,1]}
Variable C_s1
Title: C and exp terms
Description: This table contains the cumulative products of each of th~~
e terms in the "Ratios of Rates" table. Each of these products is on~~
e of the "C" and exponential terms in Ross' formula (see module-level~~
description).
Definition: cumProduct(Rr1,Dest1)
Nodelocation: 104,272,1
Nodesize: 68,36
Windstate: 2,102,90,489,267
Valuestate: 2,48,190,603,264,0,MIDM
Reformval: [Term1,Dest1]
{!40000|Att_resultslicestate: [Source1,1,Dest1,1,Term1,1]}
Index Source_2
Title: source 2
Description: This indexes the source node of a hypoexponential distrib~~
ution. This index contains an extra source node for use as a "phanto~~
m" node to hold populants that never enter the system under the hypoe~~
xponential. The results are then normalized to exclude these populan~~
ts. The "phantom" nodes for nodes other than FoF are just the immedi~~
ately previous nodes in the chain.
Definition: ['Pre-FoF','FoF','On List','Show Up','Volunteer','Donor']
Nodelocation: 104,80,1
Nodesize: 68,36
Windstate: 2,102,90,464,353
{!40000|Att_previndexvalue: ['Pre-FoF','FoF','On List','Show Up','Vol~~
unteer','Donor']}
Variable Af1
Title: Incoming Advancement Factors
Description: This table is the basis for modifying the hypoexponential~~
to account for emigration from nodes. Populants advancing to the ne~~
xt node in the chain occur in ratio proportional to the ratio of the ~~
advancement rate to the total exit rate from the node (sum of the adv~~
ancement rate and emigration rate). Please note that the nodes used ~~
as "phantom nodes" in a particular chain have only one exit, and they~~
exit at the arrival rate.~
~
"Incoming" denotes that the advancement factor applies to populants a~~
rriving in the destination node from the previous node in the chain.
Definition: Table(Dest1,Source_2)(~
1,1,1,1,1,1,~
1,1,1,1,1,1,~
fofad/(fofad+fofdo),1,1,1,1,1,~
olad/(olad+oldo),olad/(olad+oldo),1,1,1,1,~
suad/(suad+sudo),suad/(suad+sudo),suad/(suad+sudo),1,1,1,~
voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),1~~
,1,~
doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),d~~
oad/(doad+dodo),1~
)
Nodelocation: 256,272,1
Nodesize: 68,36
Defnstate: 2,225,311,891,273,0,MIDM
Valuestate: 2,50,1,416,303,0,MIDM
Reformdef: [Source_2,Dest1]
Reformval: [Source_2,Dest1]
Variable Ap1
Title: Incoming Advancement Product
Description: This table contains the products of successive advancemen~~
t factors. These products are the probabilities that a populant begi~~
nning in a source node and advancing to a destination node actually m~~
ake it to that node, rather than emigrating out of the system along t~~
he way.
Definition: cumProduct(Af1,Dest1)
Nodelocation: 256,360,1
Nodesize: 68,36
Valuestate: 2,21,372,484,203,0,MIDM
Reformval: [Source_2,Dest1]
Variable Soc1
Title: Sums of C and exp terms
Description: This is the sum of the "C" and exponential terms in Ross'~~
formula (see module-level description).
Definition: cumulate(C_s1,Term1)
Nodelocation: 104,360,1
Nodesize: 68,36
Valuestate: 2,62,514,502,245,0,MIDM
Reformval: [Term1,Dest1]
{!40000|Att_resultslicestate: [Source1,1,Dest1,1,Term1,1]}
Variable He1
Title: Hypoexponential
Description: This is the cumulative hypoexponential distribution calcu~~
lated over sources and destinations. In other words, cells in the ta~~
ble contain probabilities that a populant that enters the system at t~~
he source node (or "began" the period inside the "phantom node" immed~~
iately preceding the source node) will have escaped the destination n~~
ode (which could be the same as the source node) by the end of the pe~~
riod.
Definition: Table(Dest1,Source_2)(~
Soc1[Term1 = 'Pre-FoF' ],0,0,0,0,0,~
Soc1[Term1 = 'FoF' ],Soc1[Term1 = 'FoF' ],0,0,0,0,~
Soc1[Term1 = 'On List' ],Soc1[Term1 = 'On List' ],Soc1[Term1 = 'On Li~~
st' ],0,0,0,~
Soc1[Term1 = 'Show Up' ],Soc1[Term1 = 'Show Up' ],Soc1[Term1 = 'Show ~~
Up' ],Soc1[Term1 = 'Show Up' ],0,0,~
Soc1[Term1 = 'Volunteer' ],Soc1[Term1 = 'Volunteer' ],Soc1[Term1 = 'V~~
olunteer' ],Soc1[Term1 = 'Volunteer' ],Soc1[Term1 = 'Volunteer' ],0,~
Soc1[Term1 = 'Donor' ],Soc1[Term1 = 'Donor' ],Soc1[Term1 = 'Donor' ],~~
Soc1[Term1 = 'Donor' ],Soc1[Term1 = 'Donor' ],Soc1[Term1 = 'Donor' ],~~
~
Soc1[Term1 = 'Board' ],Soc1[Term1 = 'Board' ],Soc1[Term1 = 'Board' ],~~
Soc1[Term1 = 'Board' ],Soc1[Term1 = 'Board' ],Soc1[Term1 = 'Board' ]~
)
Nodelocation: 104,448,1
Nodesize: 68,36
Defnstate: 2,57,436,1217,259,0,MIDM
Valuestate: 2,512,422,518,231,0,MIDM
Reformdef: [Source_2,Dest1]
Reformval: [Source_2,Dest1]
Variable We1
Title: With Emigration
Description: This is the hypoexponential distribution after conditioni~~
ng to account for emigration.
Definition: Ap1*He1
Nodelocation: 256,448,1
Nodesize: 68,36
Valuestate: 2,500,192,611,242,0,MIDM
Reformval: [Source_2,Dest1]
Variable Oaf1
Title: Outgoing Advancement Factors
Description: These are the probabilities that a populant exits a node ~~
in the direction of advancing to the next node in the chain, rather t~~
han emigrating from the system.
Definition: Table(Dest1,Source_2)(~
1,1,1,1,1,1,~
fofad/(fofad+fofdo),1,1,1,1,1,~
olad/(olad+oldo),olad/(olad+oldo),1,1,1,1,~
suad/(suad+sudo),suad/(suad+sudo),suad/(suad+sudo),1,1,1,~
voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),voad/(voad+vodo),1~~
,1,~
doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),doad/(doad+dodo),d~~
oad/(doad+dodo),1,~
1,1,1,1,1,1~
)
Nodelocation: 408,360,1
Nodesize: 68,36
Defnstate: 2,218,70,959,281,0,MIDM
Valuestate: 2,207,541,520,224,0,MIDM
Reformdef: [Source_2,Dest1]
Reformval: [Source_2,Dest1]
Variable Oap1
Title: Outgoing Advancement Probability
Description: These are the probabilities that populants will traverse ~~
from the source node to the destination node, with emigration, and th~~
en exit in the direction of further advancement, rather than emigrati~~
on.
Definition: We1*Oaf1
Nodelocation: 408,448,1
Nodesize: 68,36
Valuestate: 2,529,472,590,249,0,MIDM
Reformval: [Source_2,Dest1]
Variable Depro1
Title: Deposition Probabilities
Description: These are the probabilities that populants will, after ad~~
vancing to the destination node "from" the "phantom" node, remain at ~~
the destination node at the end of the period.~
~
They are calculated by simply subtracting the probability that a popu~~
lant will traverse to the destination node and then exit it from the ~~
probability that a populant will traverse to the destination node.
Definition: Table(Dest1,Source_2)(~
1-We1,0,0,0,0,0,~
Oap1[Dest1='Pre-FoF']-We1,1-We1,0,0,0,0,~
Oap1[Dest1='FoF']-We1,Oap1[Dest1='FoF']-We1,1-We1,0,0,0,~
Oap1[Dest1='On List']-We1,Oap1[Dest1='On List']-We1,Oap1[Dest1='On Li~~
st']-We1,1-We1,0,0,~
Oap1[Dest1='Show Up']-We1,Oap1[Dest1='Show Up']-We1,Oap1[Dest1='Show ~~
Up']-We1,Oap1[Dest1='Show Up']-We1,1-We1,0,~
Oap1[Dest1='Volunteer']-We1,Oap1[Dest1='Volunteer']-We1,Oap1[Dest1='V~~
olunteer']-We1,Oap1[Dest1='Volunteer']-We1,Oap1[Dest1='Volunteer']-We~~
1,1-We1,~
Oap1[Dest1='Donor']-We1,Oap1[Dest1='Donor']-We1,Oap1[Dest1='Donor']-W~~
e1,Oap1[Dest1='Donor']-We1,Oap1[Dest1='Donor']-We1,Oap1[Dest1='Donor'~~
]-We1~
)
Nodelocation: 408,536,1
Nodesize: 68,36
Defnstate: 2,94,438,1104,288,0,MIDM
Valuestate: 2,256,105,571,263,0,SAMP
Reformdef: [Source_2,Dest1]
Reformval: [Source_2,Dest1,Undefined,Undefined,Undefined,0]
Att__totalsindex: Index Dest1
{!40000|Att_resultslicestate: [Run,1,Dest1,1,Source_2,2]}
Index Dest__1
Title: dest ++
Description: Same as dest, but an additional entry for the complementa~~
ry probability (1 - sum), needed to use multinomial function.
Definition: ['FoF','On List','Show Up','Volunteer','Donor','On Board',~~
'Out']
Nodelocation: 552,80,1
Nodesize: 64,36
Windstate: 2,102,90,466,429
{!40000|Att_previndexvalue: ['FoF','On List','Show Up','Volunteer','Do~~
nor','On Board','Out']}
Variable Dpc1
Title: Deposition Plus Complement
Description: This array adds the complementary probability (which, in ~~
this case, is the probability of exiting the system entirely) to the ~~
end of the destinations of the deposition probabilities, and removes ~~
the deposition probabilities for the "phantom" nodes.~
~
This is done in order to properly scale the probabilities. We wish t~~
o include the complementary probability, because that is the actual p~~
robability of exiting the system. However, we also wish to exclude t~~
he deposition probability for the first node in each chain. That is ~~
because there is no actual probability of a populant being in this "n~~
ode." These "phantom nodes" are only included in order to use the hy~~
poexponential distribution to spread populants among nodes in such a ~~
way as to take into account the amount of time it takes them to reach~~
the first node. Once the hypoexponential is calculated, *and* we ha~~
ve calculated the complementary probability (of exiting the system), ~~
then we can dispense with the starting "phantom" nodes. The remainin~~
g distribution is improper, but the multinomial function automaticall~~
y normalizes it, effectively conditioning on the removal of the "phan~~
tom" nodes here.
Definition: Table(Source_2,Dest__1)(~
Depro1[Dest1='FoF'],Depro1[Dest1='On List'],Depro1[Dest1='Show Up'],D~~
epro1[Dest1='Volunteer'],Depro1[Dest1='Donor'],Depro1[Dest1='On Board~~
'],1-sum(Depro1,Dest1),~
0,Depro1[Dest1='On List'],Depro1[Dest1='Show Up'],Depro1[Dest1='Volun~~
teer'],Depro1[Dest1='Donor'],Depro1[Dest1='On Board'],1-sum(Depro1,De~~
st1),~
0,0,Depro1[Dest1='Show Up'],Depro1[Dest1='Volunteer'],Depro1[Dest1='D~~
onor'],Depro1[Dest1='On Board'],1-sum(Depro1,Dest1),~
0,0,0,Depro1[Dest1='Volunteer'],Depro1[Dest1='Donor'],Depro1[Dest1='O~~
n Board'],1-sum(Depro1,Dest1),~
0,0,0,0,Depro1[Dest1='Donor'],Depro1[Dest1='On Board'],1-sum(Depro1,D~~
est1),~
0,0,0,0,0,Depro1[Dest1='On Board'],1-sum(Depro1,Dest1)~
)
Nodelocation: 408,624,1
Nodesize: 68,36
Defnstate: 2,102,416,1071,307,0,MIDM
Valuestate: 2,285,196,572,266,0,SAMP
Reformdef: [Source_2,Dest__1]
Reformval: [Source_2,Dest__1,Undefined,Undefined,Undefined,0]
Att__totalsindex: Index Dest__1
{!40000|Att_resultslicestate: [Run,1,Dest__1,5,Source_2,1]}
Chance Multinomial_sample1
Title: Multinomial Sample
Description: For each source node, this takes a multinomial sample ove~~
r the set of possible destination nodes, using the starting populatio~~
n as N. In other words, each simulated run of this function contains~~
one scenario of the distribution of populants over the source node, ~~
all downstream nodes, and outside the system after exit (the last is ~~
not used in results).
Definition: multinomial(pa,mnn,dest__1)
Nodelocation: 408,800,1
Nodesize: 68,36
Defnstate: 2,41,420,1260,244,0,MIDM
Valuestate: 2,518,521,730,286,0,SAMP
Reformdef: [Undefined,Source]
Reformval: [Source_2,Dest__1,Undefined,Undefined,Undefined,0]
Att__totalsindex: Index Source_2, Index Dest__1
{!40000|Att_resultslicestate: [Run,10,Dest__1,1,Source_2,2]}
Variable Arrivals
Title: Sum by Destination
Description: This node sums the multinomial sample over entry nodes. ~~
This gives a population of each node, at the end of the period, of p~~
opulants that began the period outside of the system.
Definition: sum(Multinomial_sample1,Source_2)
Nodelocation: 408,888,1
Nodesize: 68,36
Valuestate: 2,248,258,756,261,0,SAMP
Graphsetup: {!40000|Att_catlinestyle Graph_cumprob_valdim:1}
Reformval: [Undefined,Dest__1,Undefined,Undefined,Undefined,0]
{!40000|Att_resultslicestate: [Run,72,Dest__1,1]}
Chance Pa
Title: Poisson Arrivals
Description: This well-known distribution calculates the number of arr~~
ivals at each node from outside the system. The only assumption made~~
is that individual arrivals are each uniformly distributed over time~~
. A consequence of this is that the interarrival times are exponenti~~
ally distributed. The arrival rate is the only parameter.
Definition: Table(Source_2)(~
Poisson(fofar),Poisson(olar),Poisson(suar),Poisson(voar),Poisson(doar~~
),Poisson(boar))
Nodelocation: 256,712,1
Nodesize: 68,36
Valuestate: 2,455,264,651,135,0,SAMP
Reformval: [Source_2,Undefined,Undefined,Undefined,Undefined,0]
Att__totalsindex: Index Source_2
{!40000|Att_resultslicestate: [Run,1,Source_2,1]}
Function Makenonneg(N: Numeric[Run])
Title: Make NonNegative
Definition: IF n<0~
THEN n:=0;~
n
Nodelocation: 408,272,1
Nodesize: 64,36
Windstate: 2,102,90,497,311
Paramnames: N
Variable Mnn
Title: Make NonNegative
Description: Because we are subtracting nearly equal numbers calculate~~
d on the basis of complicated formulas, one case in a thousand will e~~
nd with a negative result for the deposition probability. As this wo~~
uld cause an error, this node repairs these negatives back to zero be~~
fore they are used in multinomial sampling.
Definition: MakeNonNeg (dpc1)
Nodelocation: 408,712,1
Nodesize: 72,36
Valuestate: 2,88,98,416,303,0,SAMP
Reformval: [Source_2,Dest__1]
{!40000|Att_resultslicestate: [Source_2,1,Dest__1,1,Run,1]}
Close Dp1
Library Multivariate_distrib
Title: Multivariate Distributions
Description: A library of multivariate distributions.
Author: Lonnie Chrisman, Ph.D.~
Lumina Decision Systems~
~
With contributions by John Bowers, US FDA.
Date: Fri, Aug 01, 2003 7:12 PM
Saveauthor: Lonnie
Savedate: Thu, Mar 16, 2006 3:51 PM
Defaultsize: 48,24
Nodelocation: 104,272,1
Nodesize: 80,28
Nodeinfo: 1,1,1,1,1,1,0,0,0,0
Diagstate: 1,57,306,666,300,17
Windstate: 2,311,89,476,224
Function Gaussian(meanVec : numeric[I],covar : numeric[I,J]; I,J:Inde~~
xType)
Title: Gaussian
Description: A multi-variate Gaussian distribution based on a mean vec~~
tor and covariance matrix. The covariance matrix must symmetric and ~~
positive-definite. The meanVec is indexed by I. The covariance matr~~
ix is 2-D, indexed by I & J. Indexes I & J should be the same length~~
.
Definition: var S := Decompose(covar,I,J);~
var U := ifall J then 0 else 0;~
var Z := Normal(U,1);~
sum( S*Z,J ) + meanVec
Nodelocation: 104,56,1
Nodesize: 48,24
Windstate: 2,36,128,486,314
Paramnames: meanVec,covar,I,J
Function Dirichlet(alpha : Numeric[I]; I:IndexType)
Title: Dirichlet
Description: A Dirichlet distribution with parameters alpha_i>0~
Each sample of a Dirichlet distribution produces a random vector whos~~
e elements sum to 1. It is commonly used to represent second order p~~
robability information.~
~
The Dirichlet distribution has a density given by ~
k * Product( X^(alpha-1), I)~
where k is a normalization factor equal to~
GammaFn( sum(alpha,I )) / Sum(GammaFn(alpha),I)~
~
The parameters, alpha, can be interpreted as observation counts. The~~
mean is given by the relative values of alpha (normalized to 1), but~~
the variance narrows as the alphas get larger, just as your confiden~~
ce in a distribution would narrow as you get more samples.~
~
The Dirichlet lends itself to easy Bayesian updating. If you have a ~~
prior of alpha0, and you observe N
Definition: var a:=Gamma(alpha);~
a/sum(a,I)
Nodelocation: 232,56,1
Nodesize: 48,24
Windstate: 2,26,18,900,615
Paramnames: alpha,I
Function Binormal(MeanVec :numeric[I]; Sdeviations : positive[I]; I:In~~
dexType; correlationCoef : numeric atomic)
Title: BiNormal
Description: A 2-D Normal (or Bi-variate Gaussian) distribution with t~~
he indicated individual standard deviations (>0) and the indicated co~~
rrelation coefficient. The index, I, must have exactly 2 elements, S~~
deviations must be indexed by I.
Definition: if size(I)<>2 then ~
Error("Index to BiNormal must have 2 elements")~
else~
var s := product(Sdeviations,I) * correlationCoef;~
Index J:=CopyIndex(I);~
Gaussian( meanVec, if I<>J then s else Sdeviations^2 , I,J )
Nodelocation: 360,56,1
Nodesize: 48,24
Windstate: 2,2,24,525,540
Paramnames: MeanVec,Sdeviations,I,correlationCoef
Function Samplecovariance(X ; I,J,R : IndexType)
Title: Sample Covariance
Description: Returns a covariance matrix based on the sampled data, X,~~
indexed by I and R. (I is the dimensionality of X, R corresponds to~~
the samples). The result will be indexed by I and J -- supply J to ~~
be the same length as I.~
~
Note that the mean is simply Average(X,R), and doen't warrant a separ~~
ate function.
Definition: var Z:=X-Average(X,R);~
var Zt := slice(Z,I,cumulate(1,J));~
sum(Z*Zt,R)/(size(R)-1)
Nodelocation: 104,208,1
Nodesize: 48,24
Windstate: 2,263,84,476,304
Paramnames: X,I,J,R
Function Multinomial(N,theta:Numeric ; I : IndexType)
Title: Multinomial
Description: Returns the Multinomial Distribution.~
~
The multinomial distribution is a generalization of the Binomial dist~~
ribution to N possible outcomes. For example, if you were to roll a ~~
fair die N times, an outcome would be the number of times each of the~~
six numbers appears. Theta would be the probability of each outcome~~
, where sum(theta,I)=1, and index I is the list of possible outcome. ~~
If theta doesn't sum to 1, it is normalized.~
~
Each sample is a vector indexed by I indicating the number of times t~~
he corresponding outcome (die number) occurred during that sample poi~~
nt. Each sample will have the property that sum( result, I ) = N.
Definition: var z := n;~
var k := size(I);~
~
var j:=cumulate(1,I) in I do begin~
Index I2 := j..k;~
var theta2 := Slice(theta,I,I2); /* unnormalized sub-process */~
var p := theta2/sum(theta2, I2);~
var xj := Binomial( z, p[I2=j]);~
z := z - xj;~
xj~
end
Nodelocation: 472,56,1
Nodesize: 48,24
Windstate: 2,56,42,476,522
Paramnames: N,theta,I
Function Correlate_dists(dists : samp[I,Run] ; rankcorrs : numeric arr~~
ay[I,J] ; I,J : IndexType )
Title: Correlate Dists
Description: Reorders the samples in dists so as to match the desired ~~
rank correlations between distributions as closely as possible. Rank~~
Corrs must be positive definite, and the diagonal should contain all ~~
ones.~
~
The result will be distributions having the same margins as the origi~~
nal input, but with rank correlations close to those of the rankcorrs~~
matrix.
Definition: var u := Sample(Gaussian(0,rankcorrs,I,J));~
var dsort := sortIndex(dists,Run);~
var urank := Rank(u,Run);~
dists[Run=dsort[Run=urank]]
Nodelocation: 360,208,1
Nodesize: 48,24
Windstate: 2,315,33,494,399
Paramnames: dists,rankcorrs,I,J
Function Correlate_with( S, referenceS : samp ; rankcorr : numeric )
Title: Correlate With
Description: Reorders the samples of S so that the result is correlate~~
d with the reference sample with a rank correlation close to rankcorr~~
. ~
~
Example: To generate a logNormal distribution that is highly correlat~~
ed with Ch1, use, e.g.,: Correlate_With( LogNormal(2,3), Ch1, 0.8 )
Definition: Index q := 1..2;~
var u := sample(binormal( 0, 1, q, rankcorr ));~
var rrank := Rank(referenceS,Run);~
var u1sort := sortIndex(u[q=1],Run);~
var u2rank := Rank(u[q=2],Run);~
var ssort := sortIndex(S,Run);~
S[Run=ssort[Run=u2rank[Run=u1sort[Run=rrank]]]]
Nodelocation: 472,208,1
Nodesize: 48,24
Windstate: 2,445,194,470,272
Paramnames: S,referenceS,rankcorr
Function Uniformspherical(I : IndexType ; R : optional Numeric[I])
Title: Uniform Spherical
Description: Generates points uniformly on a sphere (or circle or hype~~
rsphere).~
Each sample generated is indexed by I -- so if I has 3 elements, the ~~
points will lie on a sphere.~
~
The mid value is a bit strange here since there isn't really a median~~
that lies on the sphere. Obviously the center of the sphere is the ~~
middle value, but that isn't in the allowable range. So, an arbitrar~~
y point on the sphere is used.
Definition: if IsNotSpecified(R) then R:=1;~
var u := normal(Array(I,0),1);~
var d := sqrt( sum(u^2,I) );~
if d=0 then Array(I,R/sqrt(size(I))) else r*u/d
Nodelocation: 104,128,1
Nodesize: 48,24
Windstate: 2,294,375,476,424
Paramnames: I,R
Function Multiuniform(corr : Numeric[I,J] ; I,J : IndexType ; lb,ub : ~~
optional Numeric[I,J] )
Title: MultiUniform
Description: The multi-variate uniform distribution.~
Generates vector samples (indexed by I) such that each component has ~~
a uniform marginal distribution, and such that each component have th~~
e pair-wise correlations given by corr. Indexes I and J must have th~~
e same number of elements, corr needs to be symmetric and must obey a~~
certain semidefinite condition (namely that the transformed matrix [~~
2*sin(30*cov) ] is positive semidefinite. In most cases, this rough~~
ly the same as corr being, or not being, positive semidefinite). Lb ~~
and ub can be used to specify upper and lower bounds, either for all ~~
components, or individually if these bounds are indexed by I. If lb ~~
& ub are omitted, each component will have marginal Uniform(0,1).~
~
The correlation specified in corr is true sample correlation - not ra~~
nk correlation. ~
~
The transformation here is based on:~
* Falk, M. (1999), "A simple approach to the generation of uniformly ~~
distributed random variables with prescribed correlations," Comm. in ~~
Stats - Simulation and Computation 28: 785-791.
Definition: if IsNotSpecified(lb) then lb:=0;~
if IsNotSpecified(ub) then ub := 1;~
var R := if I=J then 1 else 2*sin(30*corr);~
Cumnormal( Gaussian(0,R,I,J) ) * (ub-lb) + lb
Nodelocation: 232,128,1
Nodesize: 52,24
Windstate: 2,102,90,600,532
Paramnames: corr,I,J,lb,ub
Function Samplecorrelation(X : array[I,R] ; I,J,R : IndexType)
Title: sample correlation
Description: Returns a correlation matrix based on data in X, where ea~~
ch data point is a vector indexed by I, and the entries in the correl~~
ation matrix are the pair-wise correlations of the columns of data. ~~
A second index, J, of size identical to I, is required in order to in~~
dex the 2-dimensional result.
Definition: var z:=x-average(x,R);~
var zt := slice(z,I,cumulate(1,J));~
sum(z*zt,R) / sqrt(sum(z^2,R) * sum(zt^2,R))~
Nodelocation: 232,208,1
Nodesize: 48,24
Windstate: 2,290,388,523,377
Paramnames: X,I,J,R
Close Multivariate_distrib
Close Details
Decision De1
Definition: 0
Nodelocation: 880,752,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,1,,0,
Nodecolor: 65535,59795,19661
Nodefont: Arial, 30
Text Te1
Description: Friends-of-Friends/Presenters Identified
Nodelocation: 376,448,-1
Nodesize: 264,24
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 23
Decision De2
Description: All of these fields, and the result graphs, deal only wit~~
h the first step in the pipeline, the step denoting "friends of frien~~
ds" or "presenters who I have identified." You will probably never b~~
e certain how many friends of friends you have, nor how quickly you a~~
re gaining them, nor how long they have been in this state before the~~
y disappear or else join your mailing list. The last point addresses~~
an important point: this stage is exclusive of all of the others. ~~
That is to say: this state only includes friends-of-friends who are ~~
NOT on your mailing list, and who are not part of your regular audien~~
ce, etc. That is why we also measure cumulative populations (see com~~
ments on the righthand magenta buttons at the top and bottom of this ~~
screen).~
~
Because it would be absurd to keep accurate records of the actual par~~
ameters of this stage (one could imagine the following rather strange~~
telephone conversation: "John? This is Jane. Say, I just want to ~~
catch up with you about a few things. Are you still my friend? If s~~
o, have you gained any new friends in the past three months? How oft~~
en do people who never get onto my mailing list remain friends with y~~
ou before terminating friendship with you, on average? Could you ple~~
ase check your actual records? And how often do people remain friend~~
s with you before joining my mailing list, among those who eventually~~
do?"), it is well-suited to the estimates required as inputs here. ~~
See the descriptions of the individual attributes, and fields within ~~
each attribute, contained within for more.
Definition: 0
Nodelocation: 576,512,1
Nodesize: 472,96
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,65535,65535
Text Te2
Description: Mailing List/Presenters Invited
Nodelocation: 296,648,-1
Nodesize: 184,20
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 23
Decision De3
Description: This is the stage that people get to, usually after joini~~
ng your mailing list, but not always, at which they become part of yo~~
ur "regular audience." Like "friends of friends," who counts as bein~~
g in this stage is somewhat subjective, and uncertain. However, this~~
stage has the benfit of empirical evidence in that the people actual~~
ly come to your shows. Showing up applies equally to presenters and ~~
to regular audience. However, people who show up regularly but also ~~
do other things, such as volunteer for you, donate, present your work~~
, or sit on your board, or any combination of these, should be exclud~~
ed from this stage and included in the appropriate stage below.
Definition: 3
Nodelocation: 576,928,1
Nodesize: 472,96
Nodeinfo: 1,1,1,0,1,0,0,,0,
Decision De4
Description: These fields apply to donors. Donors, like regular audie~~
nce, is a somewhat subjective term, since some people might only dona~~
te every few years or longer. They are assumed to fulfill all the re~~
quiements of the previous stages (i.e. they are friends of friends, o~~
n your mailing list, part of your regular audience (whatever that mea~~
ns to you), and do volunteer work for you. If they don't do all of t~~
hese things, then make appropriate adjustments to the "cumulative pop~~
ulations" graph invoked by the buttons at the top and bottom of this ~~
screen.
Definition: 3
Nodelocation: 576,1344,1
Nodesize: 472,96
Nodeinfo: 1,1,1,0,1,0,0,,0,
Decision De5
Definition: 3
Nodelocation: 272,752,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,59795,19661
Decision De6
Definition: 3
Nodelocation: 576,752,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,59795,19661
Decision De7
Definition: 3
Nodelocation: 880,1376,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,31131,19661
Decision De8
Description: These fields deal with the average amount of time between~~
the time that someone enters the fifth state of the pipeline and the~~
time that they leave this state without going to the next one.~
~
You may think, "When people enter this state, they don't come in wear~~
ing a lapel-pin stating that they will eventually drop out of it." T~~
his is true. However, imagine that you have a list of people and how~~
long each one of them spent in this state, and whether they went on ~~
to the next state or dropped out. Imagine taking only those people w~~
ho eventually dropped out and counting the average amount of time bef~~
ore they did so. You should put your highest imaginable estimate of ~~
what this number could be in this box.
Definition: 3
Nodelocation: 576,1376,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,1,,0,
Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Decision De9
Description: These fields deal with the average amount of time between~~
the time that someone enters the first state of the pipeline and the~~
time that they leave this state without going to the next one.~
~
You may think, "When people enter this state, they don't come in wear~~
ing a lapel-pin stating that they will eventually drop out of it." T~~
his is true. However, imagine that you have a list of people and how~~
long each one of them spent in this state, and whether they went on ~~
to the next state or dropped out. Imagine taking only those people w~~
ho eventually dropped out and counting the average amount of time bef~~
ore they did so. You should put your highest imaginable, lowest imag~~
inable, and most likely estimates of what this number could be in the~~
se boxes.
Definition: 3
Nodelocation: 576,544,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,65532,19661
Decision De10
Description: These fields are where you enter information about the ar~~
rival rate to this state, the first state of the pipeline. The arriv~~
al rate is measured in people per unit time. Use whatever unit of ti~~
me that you want, but use the same unit of time here as you use to me~~
asure the average time in the other two groups of fields, and in all ~~
of the groups of fields in the other stages of the pipeline.
Definition: 3
Nodelocation: 272,544,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,65532,19661
Decision De11
Definition: 3
Nodelocation: 272,1376,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,31131,19661
Decision De12
Description: This is the average amount of time, for people who are in~~
this stage of the pipeline, that they stay in it, given that they ev~~
entually advance to the next stage (of joining the mailing list).
Definition: 3
Nodelocation: 880,544,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,65532,19661
Decision De13
Definition: 3
Nodelocation: 272,1584,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,19661,19661
Decision De15
Description: This is the last stage in the pipeline, and generally the~~
most exclusive. It is intended to apply to members of your actual b~~
oard. Unlike other stages, membership in which is somewhat diffuse, ~~
it is always clear who is, and who is not, on your board. If should ~~
be fairly easy to calculate actual numbers for these fields once your~~
organization has been going for a few decades. However, in advance ~~
of that time, you are dealing with a small sample size and it is unwi~~
se the generalize overly much from it. Therefore, fields are provide~~
d that allow you to account for uncertainty.~
~
There are no fields for "Average Time Until Advance" because there is~~
nowhere to which to advance.
Definition: 3
Nodelocation: 576,1552,1
Nodesize: 472,96
Nodeinfo: 1,1,1,0,1,0,0,,0,
Decision De16
Description: These fields deal with the average amount of time between~~
the time that someone enters the sixth state of the pipeline and the~~
time that they leave this state without going to the next one.~
~
You may think, "When people enter this state, they don't come in wear~~
ing a lapel-pin stating that they will eventually drop out of it." T~~
his is true. However, imagine that you have a list of people and how~~
long each one of them spent in this state, and whether they went on ~~
to the next state or dropped out. Imagine taking only those people w~~
ho eventually dropped out and counting the average amount of time bef~~
ore they did so. You should put your highest imaginable estimate of ~~
what this number could be in this box.
Definition: 3
Nodelocation: 576,1584,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,19661,19661
Decision De17
Description: These fields deal with the average amount of time between~~
the time that someone enters the third state of the pipeline and the~~
time that they leave this state without going to the next one.~
~
You may think, "When people enter this state, they don't come in wear~~
ing a lapel-pin stating that they will eventually drop out of it." T~~
his is true. However, imagine that you have a list of people and how~~
long each one of them spent in this state, and whether they went on ~~
to the next state or dropped out. Imagine taking only those people w~~
ho eventually dropped out and counting the average amount of time bef~~
ore they did so. You should put your highest imaginable estimate of ~~
what this number could be in this box.
Definition: 3
Nodelocation: 576,960,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,54067,19661
Decision De18
Definition: 3
Nodelocation: 880,960,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,54067,19661
Decision De19
Description: This is the arrival rate for people who are your regular ~~
audience. This DOES NOT include people who "advance" from being on y~~
our mailing list to being in your regular audience. It DOES include ~~
people who appear out of the blue and become part of your regular aud~~
ience without first being on your mailing list. "Arrival rate" alway~~
s means, in this model, people who come from somewhere other than the~~
previous stage.
Definition: 3
Nodelocation: 272,960,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,54067,19661
Decision De20
Description: This is the mailing-list stage. People in this stage are~~
on your mailing list (either paper, email, or both), but they have n~~
ot (yet) become part of your regular audience, become a donor, a pres~~
enter, or a board member (or absconded completely). These people may~~
become part of your mailing list after seeing you perform once (in w~~
hich case they would be included in the "arrival rate" figure), OR th~~
ey join your mailing list through friends, in which case they are inc~~
luded in the "average time until advance" figure of the previous stag~~
e ("friends of friends").
Definition: 3
Nodelocation: 576,720,1
Nodesize: 472,96
Nodeinfo: 1,1,1,0,1,0,0,,0,
Decision De21
Definition: 3
Nodelocation: 272,1168,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,42597,19661
Decision De22
Definition: 3
Nodelocation: 576,1168,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,42597,19661
Decision De23
Definition: 3
Nodelocation: 880,1168,1
Nodesize: 148,52
Nodeinfo: 1,1,1,0,1,0,0,,0,
Nodecolor: 65535,42597,19661
Decision De24
Description: This is considered to be the next stage that someone move~~
s to after they have been a part of your regular audience (the previo~~
us state). Unfortunately, there is no provision for "leapfrogging" i~~
n the current model -- people can progress sequentially, or they can ~~
jump in or out of the pipeline at any stage, but they cannot move dir~~
ectly from one stage in the pipeline to another that is not immediate~~
ly following it. If this happens a lot of the time between particula~~
r stages of your pipeline, please let me know and I will include it i~~
n the model.~
~
This stage includes people who volunteer or offer to present but do n~~
ot donate and do not serve on the board (those are seperate states or~~
stages, and are handled seperately, below.
Definition: 3
Nodelocation: 576,1136,1
Nodesize: 472,96
Nodeinfo: 1,1,1,0,1,0,0,,0,
Text Te3
Description: Donor/Presenter Dashboard
Nodelocation: 600,40,-1
Nodesize: 276,36
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 40
Text Te4
Description: Arrival Rate
Nodelocation: 360,480,-1
Nodesize: 68,16
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 18
Formnode Highest_possible_va2
Title: highest possible value
Definition: 0
Nodelocation: 272,512,1
Nodesize: 140,12
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofarhi
Formnode Most_likely_value7
Title: most likely value
Definition: 0
Nodelocation: 288,544,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofarml
Formnode Lowest_possible_val6
Title: lowest possible value
Definition: 0
Nodelocation: 272,576,1
Nodesize: 140,12
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofarlo
Formnode Highest_possible_va3
Title: highest possible value
Definition: 0
Nodelocation: 576,512,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofdohi
Formnode Most_likely_value8
Title: most likely value
Definition: 0
Nodelocation: 592,544,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofdoml
Formnode Lowest_possible_val7
Title: lowest possible value
Definition: 0
Nodelocation: 576,576,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofdolo
Text Te5
Description: Average Time Until Drop Out
Nodelocation: 592,480,-1
Nodesize: 136,16
Nodeinfo: 1,0,0,1,0,0,0,,0,
Text Te6
Description: Average Time Until Advance
Nodelocation: 888,480,-1
Nodesize: 144,16
Nodeinfo: 1,0,0,1,0,0,0,,0,
Nodefont: Arial, 20
Formnode Lowest_possible_ave2
Title: Lowest Possible Average Time
Definition: 0
Nodelocation: 880,576,1
Nodesize: 140,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofadlo
Formnode Most_likely_average2
Title: Most Likely Average Time
Definition: 0
Nodelocation: 896,544,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofadml
Formnode Highest_possible_av2
Title: Highest Possible Average Time
Definition: 0
Nodelocation: 880,512,1
Nodesize: 140,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Fofadhi
Formnode Highest_possible_va5
Title: highest possible value
Definition: 0
Nodelocation: 272,720,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,74,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Olarhi
Formnode Most_likely_value10
Title: most likely value
Definition: 0
Nodelocation: 288,752,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Olarml
Formnode Lowest_possible_val9
Title: lowest possible value
Definition: 0
Nodelocation: 272,784,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Olarlo
Text Te7
Description: Arrival Rate
Nodelocation: 360,688,-1
Nodesize: 64,16
Nodecolor: 65535,65532,19661
Formnode Lowest_possible_va10
Title: lowest possible value
Definition: 0
Nodelocation: 576,784,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Oldolo
Formnode Most_likely_value11
Title: most likely value
Definition: 0
Nodelocation: 592,752,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Oldoml
Formnode Highest_possible_va6
Title: highest possible value
Definition: 0
Nodelocation: 576,720,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Oldohi
Text Te8
Description: Average Time Until Drop Out
Nodelocation: 592,688,-1
Nodesize: 136,16
Nodecolor: 65535,65532,19661
Text Te9
Description: Average Time Until Advance
Nodelocation: 880,688,-1
Nodesize: 136,16
Nodecolor: 65535,65532,19661
Formnode Highest_possible_ave
Title: Highest Possible Average Time
Definition: 0
Nodelocation: 880,720,1
Nodesize: 140,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Oladhi
Formnode Most_likely_average_
Title: Most Likely Average Time
Definition: 0
Nodelocation: 896,752,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Oladml
Formnode Lowest_possible_aver
Title: Lowest Possible Average Time
Definition: 0
Nodelocation: 880,784,1
Nodesize: 140,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,59795,19661
Nodefont: Arial, 15
Original: Oladlo
Formnode Show_up_arrival_rate
Title: show up arrival rate: highest possible value
Definition: 0
Nodelocation: 272,928,1
Nodesize: 140,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Suarhi
Formnode Most_likely_value13
Title: most likely value
Definition: 0
Nodelocation: 288,960,1
Nodesize: 124,12
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Suarml
Formnode Lowest_possible_va12
Title: lowest possible value
Definition: 0
Nodelocation: 272,992,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Suarlo
Text Te10
Description: Arrival Rate
Nodelocation: 352,896,-1
Nodesize: 64,16
Text Te11
Description: Average Time Until Drop Out
Nodelocation: 592,896,-1
Nodesize: 136,16
Text Te12
Description: Average Time Until Advance
Nodelocation: 880,896,-1
Nodesize: 136,16
Text Te13
Description: Show Up
Nodelocation: 176,856,-1
Nodesize: 60,20
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 23
Formnode Highest_possible_v10
Title: highest possible value
Definition: 0
Nodelocation: 576,928,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Sudohi
Formnode Lowest_possible_va14
Title: lowest possible value
Definition: 0
Nodelocation: 576,992,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Sudolo
Formnode Most_likely_value15
Title: most likely value
Definition: 0
Nodelocation: 592,960,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Sudoml
Formnode Highest_possible_v12
Title: highest possible value
Definition: 0
Nodelocation: 880,928,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Suadhi
Formnode Most_likely_value17
Title: most likely value
Definition: 0
Nodelocation: 896,960,1
Nodesize: 124,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Suadml
Formnode Lowest_possible_va16
Title: lowest possible value
Definition: 0
Nodelocation: 880,992,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,54067,19661
Nodefont: Arial, 15
Original: Suadlo
Text Te14
Description: Volunteer/Offer to Present
Nodelocation: 272,1064,-1
Nodesize: 160,16
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 23
Text Te15
Description: Arrival Rate
Nodelocation: 352,1104,-1
Nodesize: 60,16
Text Te16
Description: Average Time Until Drop Out
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Nodesize: 136,16
Text Te17
Description: Average Time Until Advance
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Nodesize: 136,16
Text Te18
Description: Arrival Rate
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Nodesize: 60,16
Text Te19
Description: Average Time Until Drop Out
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Nodesize: 136,16
Text Te20
Description: Average Time Until Advance
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Nodesize: 132,16
Text Te21
Description: Arrival Rate
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Nodesize: 60,16
Text Te22
Description: Average Time Until Drop Out
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Nodesize: 136,16
Text Te24
Description: Donors/Presenters
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Nodefont: Arial, 23
Text Te25
Description: Board Members
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Nodesize: 104,20
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 23
Formnode Volunteer_arrival_ra
Title: volunteer arrival rate: highest possible value
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Original: Voarhi
Formnode Most_likely_value18
Title: most likely value
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Formnode Lowest_possible_va17
Title: lowest possible value
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Nodefont: Arial, 15
Original: Voarlo
Formnode Highest_possible_v15
Title: highest possible value
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Nodelocation: 576,1136,1
Nodesize: 140,14
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Nodecolor: 65535,42597,19661
Nodefont: Arial, 15
Original: Vodohi
Formnode Most_likely_value20
Title: most likely value
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Nodelocation: 592,1168,1
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Nodecolor: 65535,42597,19661
Nodefont: Arial, 15
Original: Vodoml
Formnode Lowest_possible_va19
Title: lowest possible value
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Nodelocation: 576,1200,1
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Nodecolor: 65535,42597,19661
Nodefont: Arial, 15
Original: Vodolo
Formnode Highest_possible_v17
Title: highest possible value
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Nodelocation: 880,1136,1
Nodesize: 140,14
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Nodecolor: 65535,42597,19661
Nodefont: Arial, 15
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Formnode Most_likely_value22
Title: most likely value
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Nodelocation: 896,1168,1
Nodesize: 124,16
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Original: Voadml
Formnode Lowest_possible_va21
Title: lowest possible value
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Nodelocation: 880,1200,1
Nodesize: 140,14
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Nodecolor: 65535,42597,19661
Nodefont: Arial, 15
Original: Voadlo
Formnode Donor_arrival_rate__
Title: donor arrival rate: highest possible value
Definition: 0
Nodelocation: 272,1344,1
Nodesize: 140,16
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Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Doarhi
Formnode Most_likely_value23
Title: most likely value
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Nodelocation: 288,1376,1
Nodesize: 124,16
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Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Doarml
Formnode Lowest_possible_va22
Title: lowest possible value
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Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
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Formnode Highest_possible_v20
Title: highest possible value
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Nodelocation: 576,1344,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Dodohi
Formnode Most_likely_value25
Title: most likely value
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Formnode Lowest_possible_va24
Title: lowest possible value
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Nodelocation: 576,1408,1
Nodesize: 140,14
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Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Dodolo
Formnode Highest_possible_v22
Title: highest possible value
Definition: 0
Nodelocation: 880,1344,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Doadhi
Formnode Most_likely_value27
Title: most likely value
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Nodelocation: 896,1376,1
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Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Doadml
Formnode Lowest_possible_va26
Title: lowest possible value
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Nodelocation: 880,1408,1
Nodesize: 140,14
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Nodecolor: 65535,31131,19661
Nodefont: Arial, 15
Original: Doadlo
Formnode On_board_arrival_rat
Title: on board arrival rate
Definition: 0
Nodelocation: 272,1552,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,74,0,1
Nodecolor: 65535,19661,19661
Nodefont: Arial, 15
Original: Boarhi
Formnode Obarmi1
Title: Obarmi
Definition: 0
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Formnode Obarlo1
Title: Obarlo
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Formnode Highest_possible_v25
Title: highest possible value
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Nodelocation: 576,1552,1
Nodesize: 140,14
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,19661,19661
Nodefont: Arial, 15
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Formnode Most_likely_value30
Title: most likely value
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Nodelocation: 592,1584,1
Nodesize: 124,16
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Original: Bodoml
Formnode Lowest_possible_va29
Title: lowest possible value
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Nodelocation: 576,1616,1
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Nodecolor: 65535,19661,19661
Nodefont: Arial, 15
Original: Bodolo
Formnode Friends_of_friends_1
Title: Friends of Friends Steady State
Definition: 1
Nodelocation: 912,440,1
Nodesize: 116,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,52424,1
Nodefont: Arial, 23
Original: Steady_state_fof_pre
Formnode Steady_state2
Title: Steady State
Definition: 1
Nodelocation: 912,648,1
Nodesize: 116,16
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Original: Ol
Formnode Steady_state4
Title: Steady State
Definition: 1
Nodelocation: 912,856,1
Nodesize: 116,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
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Original: Su
Formnode Steady_state6
Title: Steady State
Definition: 1
Nodelocation: 912,1064,1
Nodesize: 116,16
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Original: Vo
Formnode Steady_state8
Title: Steady State
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Nodelocation: 912,1272,1
Nodesize: 116,16
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Original: Don
Formnode Steady_state10
Title: Steady State
Definition: 1
Nodelocation: 912,1480,1
Nodesize: 116,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
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Original: Bo
Formnode Population_size1
Title: Population Size
Definition: 1
Nodelocation: 304,1680,1
Nodesize: 160,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
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Original: Steady_state_populat
Formnode Population_size2
Title: Population Size
Definition: 1
Nodelocation: 296,384,1
Nodesize: 164,20
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
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Original: Steady_state_populat
Formnode Cumulative_populati1
Title: Cumulative Populations
Definition: 1
Nodelocation: 808,1680,1
Nodesize: 208,16
Nodeinfo: 1,0,0,1,0,0,0,66,0,1
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Original: Cumulative_populatio
Formnode Cumulative_populati2
Title: Cumulative Populations
Definition: 1
Nodelocation: 808,384,1
Nodesize: 208,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
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Original: Cumulative_populatio
Formnode On_list_initial1
Title: On list Initial
Definition: 0
Nodelocation: 208,680,1
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Original: On_list_initial
Formnode Show_up_initial1
Title: Show Up Initial
Definition: 0
Nodelocation: 208,888,1
Nodesize: 92,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
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Nodefont: Arial, 15
Original: Show_up_initial
Formnode Volunteer_initial1
Title: Volunteer Initial
Definition: 0
Nodelocation: 208,1096,1
Nodesize: 92,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,42597,19661
Nodefont: Arial, 15
Original: Volunteer_initial
Formnode Donor_initial1
Title: Donor Initial
Definition: 0
Nodelocation: 208,1304,1
Nodesize: 92,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
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Original: Donor_initial
Formnode On_board_initial1
Title: On Board Initial
Definition: 0
Nodelocation: 208,1512,1
Nodesize: 92,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,19661,19661
Nodefont: Arial, 15
Original: On_board_initial
Formnode Friends_of_friends_2
Title: Friends of Friends Initial
Definition: 0
Nodelocation: 208,472,1
Nodesize: 92,16
Nodeinfo: 1,0,0,1,0,0,1,72,0,1
Nodecolor: 65535,65532,19661
Nodefont: Arial, 15
Original: Friends_of_friends_i
Formnode Friends_of_friends_3
Title: Friends of Friends Start
Definition: 1
Nodelocation: 688,440,1
Nodesize: 108,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,52424,1
Original: Fofas
Formnode On_list_start1
Title: On List Start
Definition: 1
Nodelocation: 688,648,1
Nodesize: 108,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,45872,1
Original: Olas
Formnode Show_up_start1
Title: Show Up Start
Definition: 1
Nodelocation: 688,856,1
Nodesize: 108,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,39321,1
Original: Suas
Formnode Volunteer_start1
Title: Volunteer Start
Definition: 1
Nodelocation: 688,1064,1
Nodesize: 108,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,26212,1
Original: Volunteer
Formnode Donor_start1
Title: Donor Start
Definition: 1
Nodelocation: 688,1272,1
Nodesize: 108,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,13109,1
Original: Doas
Formnode On_board_start1
Title: On Board Start
Definition: 1
Nodelocation: 688,1480,1
Nodesize: 108,16
Nodeinfo: 1,0,0,1,0,0,0,72,0,1
Nodecolor: 52427,1,1
Original: Obas
Text Te26
Description: This is free software and is not for sale.
Nodelocation: 608,80,-1
Nodesize: 176,24
Text Te27
Description: Warning: This software makes certain assumptions about t~~
he nature of your problem that are likely not to be true. Probably, ~~
they are close enough to reality that the results will be useful. If~~
they aren't, or you aren't sure, contact me for analysis or modifica~~
tion. The assumptions are as follows:~
1. I have assumed that the amount of time which a person spends in a~~
ny state is independent of how much time they have already spent ther~~
e. This assumption, called "the Markov property," is made in a wide ~~
variety of models.~
2. I have assumed that the transitions between states described belo~~
w are the only valid transitions. I can modify the model to include ~~
other transitions if there is a need.~
3. I have assumed that subjective probability of each parameter is d~~
istributed in the form of a triangle with the specified parameters. ~~
Triangular distributions such as these are often used in engineering ~~
applications when the actual shape of the distribution is not known.~
4. I have assumed that each of the parameters is independent of each~~
of the others. i.e. If you found out that the average time until d~~
rop out on the first population (friends of friends/presenters) was a~~
t what you had identified as the lowest possible (or highest possible~~
, or any other) value, it wouldn't affect any of your estimates of an~~
y of the other parameters in the model.~
If any of the above are not close to being true, please contact me be~~
fore acting on the results of this model.
Nodelocation: 576,224,-1
Nodesize: 472,132
Nodeinfo: 1,0,0,1,0,0,1,,0,
Nodefont: Arial, 15
Close Donor_presenter_dash