Systems, methods and computer program products for modeling uncertain future benefits

Abstract

Systems, methods and computer program products are provided for modeling future benefits. According to the method, modeling future benefits begins by defining a growth rate for the good for each time segment of a period of time, where the period of time includes a plurality of time segments. An uncertainty for the good is then determined for each time segment. Next, a benefit distribution is determined at an end of each time segment based upon the growth rate and uncertainty for the respective time segment. Finally, a benefit value is selected at the end of each time segment by randomly selecting each benefit value based upon a respective benefit distribution to thereby model future benefits over the period of time. The method therefore allows the growth rate and/or the uncertainty to very between time segments. The method can also account for contingencies at the end of previous time segments.

That which is claimed:  
     
         1 . A method of modeling future benefits comprising: 
 determining a benefit distribution at an end of each time segment of a period of time based upon a growth rate and an uncertainty for the respective time segment; and    selecting a benefit value at the end of each time segment by randomly selecting each benefit value based upon a respective benefit distribution to thereby model future benefits over the period of time.    
     
     
         2 . A method according to  claim 1  further comprising defining a growth rate for each time segment before determining a benefit distribution.  
     
     
         3 . A method according to  claim 2 , wherein defining a growth rate for each time segment comprises defining a growth rate for each time segment such that the growth rate for at least one time segment differs from the growth rate of at least one other time segment.  
     
     
         4 . A method according to  claim 2 , wherein defining a growth rate for each time segment comprises defining a growth rate for each time segment independent of an uncertainty for the respective time segment, and wherein the method further comprises determining an uncertainty for each time segment independent of a growth rate for the respective time segment.  
     
     
         5 . A method according to  claim 1  further comprising determining an uncertainty for each time segment such that the uncertainty for at least one time segment differs from the uncertainty of at least one other time segment.  
     
     
         6 . A method according to  claim 1 , wherein selecting a benefit value comprises repeatedly selecting a different benefit value at the end of each time segment to thereby repeatedly model future benefits.  
     
     
         7 . A method according to  claim 1  further comprising modeling bounds of uncertainty of future benefits, wherein modeling the bounds of uncertainty comprises determining a mean value and standard deviation associated with the benefit at the end of each time segment, and modeling an upper and lower bound of uncertainty for each time segment based upon the mean value and standard deviation to thereby model the bounds of uncertainty.  
     
     
         8 . A method according to  claim 1 , wherein modeling the future benefits comprises modeling the future benefits with a processing element operating a spreadsheet software program, and wherein the method further comprises presenting a display of the future benefit model on a display coupled to the processing element.  
     
     
         9 . A method according to  claim 7 , wherein presenting the display comprises presenting a display of the future benefit model comprising a plot of the selected future benefit values and associated time segments.  
     
     
         10 . A method according to  claim 1 , wherein determining a benefit distribution comprises determining a benefit distribution at the end of at least one time segment further based upon execution of a contingent activity.  
     
     
         11 . A method according to  claim 10 , wherein determining a benefit distribution comprises determining a benefit distribution at the end of at least one time segment further based upon execution of a contingent activity at the end of at least one previous time segment.  
     
     
         12 . A method of modeling bounds of uncertainty of future benefits comprising: 
 determining a mean value and standard deviation associated with the benefit for each time segment, wherein the mean value is determined based upon a growth rate associated with the benefit for the respective time segment, and wherein the standard deviation is determined based upon an uncertainty for the good for the respective time segment; and    modeling an upper and lower bound of uncertainty based upon the mean value and standard deviation for each time segment to thereby model the bounds of uncertainty.    
     
     
         13 . A method according to  claim 12  further comprising defining a growth rate for the good for each time segment before determining the mean value.  
     
     
         14 . A method according to  claim 13 , wherein defining a growth rate for each time segment comprises defining a growth rate for each time segment such that the growth rate for at least one time segment differs from the growth rate of at least one other time segment.  
     
     
         15 . A method according to  claim 13 , wherein defining a growth for each time segment comprises defining a growth rate for each time segment independent of an uncertainty for the respective time segment, and wherein the method further comprises determining an uncertainty for each time segment independent of a growth rate for the respective time segment.  
     
     
         16 . A method according to  claim 12  further comprising determining an uncertainty for each time segment such that the uncertainty for at least one time segment differs from the uncertainty of at least one other time segment.  
     
     
         17 . A method according to  claim 12 , wherein modeling an upper and lower bound of uncertainty comprises modeling an upper and lower bound of uncertainty further based upon an inverse of a standard normal cumulative distribution, and wherein the standard normal cumulative distribution is defined by a probability.  
     
     
         18 . A method according to  claim 17 , wherein modeling an upper and lower bound further comprises selecting a lower probability associated with the lower bound and an upper probability associated with the upper bound, wherein selecting the lower probability comprises selecting a lower probability higher than zero, and wherein selecting a higher probability comprises selecting a higher probability lower than one.  
     
     
         19 . A method according to  claim 12  further comprising normalizing the mean value for each time segment based upon the standard deviation and normalizing the standard deviation for each time segment based upon the mean value, wherein modeling an upper and lower bound of uncertainty comprises modeling an upper and lower bound of uncertainty for each time segment based upon the normalized mean value and normalized standard deviation.  
     
     
         20 . A method according to  claim 12 , wherein modeling bounds of uncertainty of future benefits comprises modeling bounds of uncertainty of future benefits with a processing element operating a spreadsheet software program, and wherein the method further comprises presenting a display of the upper and lower bounds of uncertainty on a display coupled to the processing element.  
     
     
         21 . A method according to  claim 20 , wherein presenting the display comprises presenting a display of the upper and lower bounds of uncertainty comprising a plot of the upper and lower bounds of uncertainty and associated time segments.  
     
     
         22 . A method according to  claim 12 , wherein determining a mean value comprises determining a mean value for at least one time segment of the period of time further based upon execution of a contingent activity.  
     
     
         23 . A method according to  claim 22 , wherein determining a mean value comprises determining a mean value for at least one time segment further based upon execution of a contingent activity at an end of at least one previous time segment.  
     
     
         24 . A system for modeling future benefits comprising: 
 a processing element capable of determining a benefit distribution at an end of each time segment of a period of time based upon a growth rate and an uncertainty for the respective time segment, and wherein the processing element is further capable of selecting a benefit value at the end of each time segment by randomly selecting each benefit value based upon a respective benefit distribution to thereby model future benefits over the period of time.    
     
     
         25 . A system according to  claim 24 , wherein the processing element is also capable of defining a growth rate for each time segment.  
     
     
         26 . A system according to  claim 25 , wherein the processing element is capable of defining a growth rate for each time segment such that the growth rate for at least one time segment differs from the growth rate of at least one other time segment.  
     
     
         27 . A system according to  claim 25 , wherein the processing element is capable of defining a growth rate for each time segment independent of an uncertainty for the respective time segment, and wherein the processing element is capable of determining an uncertainty for each time segment independent of a growth rate for the respective time segment.  
     
     
         28 . A system according to  claim 24 , wherein the processing element is also capable of determining an uncertainty for each time segment such that the uncertainty for at least one time segment differs from the uncertainty of at least one other time segment.  
     
     
         29 . A system according to  claim 24 , wherein the processing element is capable of repeatedly selecting a different benefit value at the end of each time segment to thereby repeatedly model future benefits.  
     
     
         30 . A system according to  claim 24 , wherein the processing element is also capable of modeling bounds of uncertainty of future benefits by determining a mean value and standard deviation associated with the benefit at the end of each time segment, and thereafter modeling an upper and lower bound of uncertainty for each time segment based upon the mean value and standard deviation.  
     
     
         31 . A system according to  claim 24 , wherein the processing element is capable of modeling the future benefits by operating at least one function within a spreadsheet software program, and wherein the system further comprises: 
 a display coupled to the processing element, wherein the display is capable of presenting the future benefit model.    
     
     
         32 . A system according to  claim 31 , wherein the display is capable of presenting the future benefit model as a plot of the selected future benefit values and associated time segments.  
     
     
         33 . A system according to  claim 24 , wherein the processing element is capable of determining a benefit distribution at the end of at least one time segment further based upon execution of a contingent activity.  
     
     
         34 . A system according to  claim 33 , wherein the processing element is capable of determining a benefit distribution comprises at the end of at least one time segment further based upon execution of a contingent activity at the end of at least one previous time segment.  
     
     
         35 . A system for modeling bounds of uncertainty of future benefits comprising: 
 a processing element capable of determining a mean value and standard deviation associated with the benefit for each time segment, wherein the processing element determines the mean value based upon a growth rate associated with the benefit for the respective time segment, wherein the processing element determines the standard deviation based upon an uncertainty for the good for the respective time segment, and wherein the processing element is capable of modeling an upper and lower bound of uncertainty based upon the mean value and standard deviation for each time segment to thereby model the bounds of uncertainty.    
     
     
         36 . A system according to  claim 35 , wherein the processing element is also capable of defining a growth rate for the good for each time segment before determining the mean value.  
     
     
         37 . A system according to  claim 36 , wherein the processing element is capable of defining a growth for each time segment independent of an uncertainty for the respective time segment, and wherein the processing element is capable of determining an uncertainty for each time segment independent of a growth rate for the respective time segment.  
     
     
         38 . A system according to  claim 36 , wherein the processing element is capable of defining a growth rate for each time segment such that the growth rate for at least one time segment differs from the growth rate of at least one other time segment.  
     
     
         39 . A system according to  claim 35 , wherein the processing element is capable of determining an uncertainty for each time segment such that the uncertainty for at least one time segment differs from the uncertainty of at least one other time segment.  
     
     
         40 . A system according to  claim 35 , wherein the processing element is capable of modeling the upper and lower bound of uncertainty further based upon an inverse of a standard normal cumulative distribution, and wherein the standard normal cumulative distribution is defined by a probability.  
     
     
         41 . A system according to  claim 40 , wherein the processing element is capable of modeling an upper and lower bound by further selecting a lower probability associated with the lower bound and an upper probability associated with the upper bound, wherein the processing element is capable of selecting a lower probability higher than zero, and wherein the processing element is capable of selecting a higher probability lower than one.  
     
     
         42 . A system according to  claim 35 , wherein the processing element is also capable of normalizing the mean value for each time segment based upon the standard deviation and normalizing the standard deviation for each time segment based upon the mean value, wherein the processing element is capable of modeling an upper and lower bound of uncertainty by modeling an upper and lower bound of uncertainty for each time segment based upon the normalized mean value and normalized standard deviation.  
     
     
         43 . A system according to  claim 35 , wherein the processing element is capable of operating at least one function within a spreadsheet software program to thereby model the bounds of uncertainty of future benefits, and wherein the system further comprises: 
 a display capable of presenting the upper and lower bounds of uncertainty.    
     
     
         44 . A system according to  claim 43 , wherein the display is capable of the upper and lower bounds of uncertainty as a plot of the upper and lower bounds of uncertainty and associated time segments.  
     
     
         45 . A system according to  claim 35 , wherein the processing element is capable of determining a mean value for at least one time segment further based upon execution of a contingent activity.  
     
     
         46 . A system according to  claim 45 , wherein the processing element is capable of determining a mean value for at least one time segment further based upon execution of a contingent activity at an end of at least one previous time segment.  
     
     
         47 . A computer program product for modeling future benefits, the computer program product comprising a computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program portions comprising: 
 a first executable portion for determining a benefit distribution at an end of each time segment of a period of time based upon a growth rate and an uncertainty for the respective time segment; and    a second executable portion for selecting a benefit value at the end of each time segment by randomly selecting each benefit value based upon a respective benefit distribution to thereby model future benefits over the period of time.    
     
     
         48 . A computer program product according to  claim 47  further comprising a third executable portion for defining a growth rate for the good for each time segment.  
     
     
         49 . A computer program product according to  claim 48 , wherein the third executable portion defines a growth rate for each time segment such that the growth rate for at least one time segment differs from the growth rate of at least one other time segment.  
     
     
         50 . A computer program product according to  claim 48 , wherein the third executable portion defines a growth rate for each time segment independent of an uncertainty for the respective time segment, and wherein the computer program product further comprises a fourth executable portion for determining an uncertainty for each time segment independent of a growth rate for the respective time segment.  
     
     
         51 . A computer program product according to  claim 47  further comprising a third executable portion for determining an uncertainty for each time segment such that the uncertainty for at least one time segment differs from the uncertainty of at least one other time segment.  
     
     
         52 . A computer program product according to  claim 47 , wherein the second executable portion repeatedly selects a different benefit value at the end of each time segment to thereby repeatedly model future benefits.  
     
     
         53 . A computer program product according to  claim 47  further comprising a third executable portion for modeling bounds of uncertainty of future benefits, wherein the third executable portion models the bounds of uncertainty by determining a mean value and standard deviation associated with the benefit at the end of each time segment, and modeling an upper and lower bound of uncertainty for each time segment based upon the mean value and standard deviation to thereby model the bounds of uncertainty.  
     
     
         54 . A computer program product according to  claim 47  further comprising a third executable portion for generating a display of the future benefit model.  
     
     
         55 . A computer program product according to  claim 54 , wherein the third executable portion generates a display of the future benefit model comprising a plot of the selected future benefit values and associated time segments.  
     
     
         56 . A computer program product according to  claim 47 , wherein the first executable portion determines a benefit distribution at the end of at least one time segment further based upon execution of a contingent activity.  
     
     
         57 . A computer program product according to  claim 56 , wherein the first executable portion determines a benefit distribution at the end of at least one time segment further based upon execution of a contingent activity at the end of at least one previous time segment.  
     
     
         58 . A computer program product for modeling bounds of uncertainty of future benefits, the computer program product comprising a computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program portions comprising: 
 a first executable portion for determining a mean value and standard deviation associated with the benefit for each time segment, wherein the first executable portion determines the mean value based upon a growth rate associated with the benefit for the respective time segment, and wherein the first executable portion determines the standard deviation based upon an uncertainty for the good for the respective time segment; and    a second executable portion for modeling an upper and lower bound of uncertainty based upon the mean value and standard deviation for each time segment to thereby model the bounds of uncertainty.    
     
     
         59 . A computer program product according to  claim 58  further comprising a third executable portion for defining a growth rate for the good for each time segment.  
     
     
         60 . A computer program product according to  claim 59 , wherein the third executable portion defines a growth rate for each time segment such that the growth rate for at least one time segment differs from the growth rate of at least one other time segment.  
     
     
         61 . A computer program product according to  claim 59 , wherein the third executable portion defines a growth rate for each time segment independent of an uncertainty for the respective time segment, and wherein the computer program product further comprises a fourth executable portion for determining an uncertainty for each time segment independent of a growth rate for the respective time segment.  
     
     
         62 . A computer program product according to  claim 58  further comprising a third executable portion for determining an uncertainty for each time segment such that the uncertainty for at least one time segment differs from the uncertainty of at least one other time segment.  
     
     
         63 . A computer program product according to  claim 58 , wherein the fourth executable portion models an upper and lower bound of uncertainty further based upon an inverse of a standard normal cumulative distribution, and wherein the standard normal cumulative distribution is defined by a probability.  
     
     
         64 . A computer program product according to  claim 63 , wherein the fourth executable portion models an upper and lower bound further by selecting a lower probability associated with the lower bound and an upper probability associated with the upper bound, wherein the fourth executable portion selects the lower probability higher than zero, and wherein the fourth executable selects a higher probability lower than one.  
     
     
         65 . A computer program product according to  claim 58  further comprising a fifth executable portion for normalizing the mean value for each time segment based upon the standard deviation and normalizing the standard deviation for each time segment based upon the mean value, wherein the fourth executable portion models an upper and lower bound of uncertainty by modeling an upper and lower bound of uncertainty for each time segment based upon the normalized mean value and normalized standard deviation.  
     
     
         66 . A computer program product according to  claim 58  further comprising a fifth executable portion for generating a display of the upper and lower bounds of uncertainty.  
     
     
         67 . A computer program product according to  claim 66 , wherein the fifth executable portion generates a display of the upper and lower bounds of uncertainty comprising a plot of the upper and lower bounds of uncertainty and associated time segments.  
     
     
         68 . A computer program product according to  claim 58 , wherein the first executable portion determines a mean value for at least one time segment further based upon execution of a contingent activity.  
     
     
         69 . A computer program product according to  claim 68 , wherein the first executable portion determines a mean value for at least one time segment further based upon execution of a contingent activity at an end of at least one previous time segment.