Patent Publication Number: US-10331818-B2

Title: Simulation system and simulation method

Description:
TECHNICAL FIELD 
     The present invention relates to a simulation system. 
     BACKGROUND ART 
     Simulations using computers have been widely used for the purpose of simulating the behaviors of systems. Normally, the behavior of a system is converted into a mathematical model and the operation of the model is finely adjusted using parameters in order to execute a simulation. If the simulation is actually executed, the simulation is executed a plurality of times while changing initial values and parameters and the results are compared or subjected to statistical processing in many cases. 
     There have been conventionally proposed methods of searching or optimizing at least either parameters or a model on the basis of the difference between a simulation result and a result of a phenomenon that actually occurred (see, for example, Patent Literature 1, paragraphs [0017] to [0024] and FIG. 2). 
     PRIOR ART LITERATURE 
     Patent Literature 
     Patent Literature 1: JP-2005-534192-A 
     SUMMARY OF THE INVENTION 
     Problem to be Solved by the Invention 
     If a notable phenomenon has occurred to a simulation, for example, it is necessary to address the additional analysis of the reason of the occurrence of the phenomenon, a method of controlling the phenomenon, and the like. However, the abovementioned background art concerning the simulation is simply a technique for improving simulation accuracy on the basis of an actual result and not for supporting the user&#39;s positive, additional analysis of the simulation result. 
     The present invention has been achieved in the light of the abovementioned problems and an object of the present invention is to provide a simulation method for supporting the user&#39;s efficient, additional analysis of a simulation if a notable phenomenon has occurred to the simulation. 
     DETAILED DESCRIPTION OF EMBODIMENTS 
     To achieve the object, the present invention includes a simulation system for executing a simulation using a plurality of parameters, including: a processor; and a memory, wherein the memory holds a first evaluation function for executing the simulation by calculating an evaluation value using a first parameter having a plurality of values and at least one second parameter having a plurality of values, and the processor is adapted to: accept information for identifying the plurality of values of the first parameter and the plurality of values of the second parameter; execute a first simulation by calculating a plurality of evaluation values corresponding to the plurality of values of the first parameter using the plurality of values of the second parameter and the first evaluation function; acquire a result group including a plurality of evaluation values to which a predetermined phenomenon occurs from the plurality of evaluation values calculated by the first simulation; acquire a start value and an end value of the first parameter for analyzing the predetermined phenomenon on the basis of the acquired result group; execute a second simulation by calculating a plurality of evaluation values corresponding to the plurality of values of the first parameter from the acquired start value to the acquired end value using the plurality of values of the second parameter and the first evaluation function; and output data for displaying the plurality of evaluation values calculated by the second simulation in such a manner as to be continuous with the evaluation values in the acquired result group corresponding to the acquired start value. 
     Advantage of the Invention 
     According to the present invention, the user&#39;s efficient, additional simulation analysis is supported. 
     Objects other than the abovementioned object, configurations, and advantages will be readily apparent from the description of embodiments given below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram showing a simulation system according to a first embodiment. 
         FIG. 2  is a block diagram showing a hardware configuration of each of a host processing device and a terminal device according to the first embodiment. 
         FIG. 3  is a flowchart showing processing performed by the simulation system according to the first embodiment. 
         FIG. 4  is an explanatory diagram showing initial setting information according to the first embodiment. 
         FIG. 5  is an explanatory diagram showing a time series graph displaying a plurality of evaluation values of a first simulation according to the first embodiment. 
         FIG. 6  is an explanatory diagram showing resetting information according to the first embodiment. 
         FIG. 7  is a flowchart showing a second simulation and result visualization processing according to the first embodiment. 
         FIG. 8  is an explanatory diagram showing a screen output by a result providing unit according to the first embodiment. 
         FIG. 9  is an explanatory diagram showing a time series graph displaying a plurality of results of the first simulation according to a second embodiment. 
         FIG. 10  is an explanatory diagram showing a time series graph displaying standard deviation according to the second embodiment. 
         FIG. 11  is an explanatory diagram showing resetting information according to a third embodiment. 
         FIG. 12  is an explanatory diagram showing a screen output by a result providing unit according to the third embodiment. 
         FIG. 13  is an explanatory diagram showing resetting information according to a fourth embodiment. 
         FIG. 14  is a flowchart showing processing performed by a resetting unit according to a fifth embodiment. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Embodiments will now be described hereinafter with reference to the drawings. 
     First Embodiment 
     In a first embodiment, a method of supporting an additional simulation analysis by providing a cause of the occurrence of a notable phenomenon and parameters or parameter values capable of controlling the phenomenon if the phenomenon has occurred to a simulation. 
       FIG. 1  is a functional block diagram showing a simulation system according to the first embodiment. 
     The simulation system according to the first embodiment includes a host processing device  101  and a terminal device  102 . The terminal device  102  includes, as functional units, a user input unit  103 , an interface unit  104 , and a result providing unit  109 . 
     Furthermore, the host processing device  101  includes, as functional units, an interface unit  105 , an initial setting unit  106 , a simulation unit  107 , a visualization unit  108 , a resetting unit  110 , a sensitivity analysis unit  111 , and a result analysis unit  112 . The host processing device  101  includes a storage unit for initial setting information  300  and resetting information  310 . 
     The terminal device  102  is a device for accepting an input by a user and outputting data to the user. The user in the present embodiment means a person who executes a simulation and acquires a result of the simulation. In addition, an administrator according to the present embodiment means a person who administrates or operates the simulation system. 
     The user input unit  103  is the functional unit that accepts parameters input by the user and a simulation start request instructed by the user. 
     The result providing unit  109  is the functional unit that outputs the result of the simulation. The interface unit  104  is an interface for communicating with the host processing device  101 . 
     The host processing device  101  is a device for executing the simulation. The interface unit  105  is an interface for communicating with the terminal device  102 . 
     The initial setting unit  106  accepts parameters in a simulation for verifying whether a notable phenomenon occurs. The resetting unit  110  accepts parameters in a simulation for analyzing the notable phenomenon. 
     The simulation unit  107  functions to perform a simulation using the parameters set by the initial setting unit  106  or the resetting unit  110  and a simulation method (for example, a numerical formula) held in advance. 
     The result analysis unit  112  functions to determine whether the result of the simulation falls in a preset reference range. The sensitivity analysis unit  111  functions to analyze a parameter having a large influence on the result of the simulation. 
     The visualization unit  108  functions to generate data for providing the user with the result of the simulation by the simulation unit  107 , an analysis result by the result analysis unit  112 , and an analysis result by the sensitivity analysis unit  111 . 
     The initial setting information  300  holds information on the parameters used in the simulation for verifying whether a notable phenomenon occurs. The resetting information  310  holds information on the parameters used in the simulation for analyzing the notable phenomenon. 
     The functional units shown in  FIG. 1  may be implemented by a program or may be implemented by different physical devices. Alternatively, a plurality of functional units may be implemented by one program or by one physical device, or one functional unit may be implemented by a plurality of programs or by a plurality of physical devices. 
       FIG. 2  is a block diagram showing a hardware configuration of each of the host processing device  101  and the terminal device  102  according to the first embodiment. 
     Each of the host processing device  101  and the terminal device  102  is a computer and includes a processor  121 , a memory  122 , and a network interface  124 . Furthermore, the terminal device  102  includes an input/output interface  123 . Likewise, the host processing device  101  includes the input/output interface  123  if being connected to an output device such as a display or a printer. 
     The processor  121  is, for example, a CPU and serves as an arithmetic unit and a control unit. The memory  122  is a storage device that retains data. The processor  121  implements the functions of the terminal device  102  or the host processing device  101  by executing a program using the memory  122 . 
     The network interface  124  is a network interface for transmitting and receiving data. The input/output interface  123  is an interface for connecting to the output device such as the display or the printer and to a keyboard, a mouse, and a touch panel. 
     While the host processing device  101  and the terminal device  102  are implemented by physically different devices in the following description, the host processing device  101  and the terminal device  102  may be implemented by one physical device. Alternatively, the host processing device  101  and the terminal device  102  may be implemented by three or more different devices as long as the functions of the host processing device  101  and the terminal device  102  can be executed. 
       FIG. 3  is a flowchart showing processing performed by the simulation system according to the first embodiment. 
     First, the initial setting unit  106  accepts initial values of the parameters or the like used in a simulation and sets the initial values to the host processing device  101  ( 201 ). Specifically, in Step  201 , the user input unit  103  accepts initial setting information including the initial values. 
     The user input unit  103  transmits the accepted initial setting information to the initial setting unit  106  via the interface unit  104  and the interface unit  105 . Furthermore, the initial setting unit  106  stores the initial setting information which has received in Step  201  in the storage device such as the memory  122  of the host processing device  101  as the initial setting information  300  necessary for the simulation. 
       FIG. 4  is an explanatory diagram showing the initial setting information  300  according to the first embodiment. 
     The initial setting information  300  shown in  FIG. 4  is an example. The initial setting information  300  includes arguments of an evaluation function used by the simulation unit  107  in the simulation. 
     The initial setting information  300  shown in  FIG. 4  includes, as items, start time  301 , end time  302 , and parameters  303 . Furthermore, the initial setting information  300  includes, as values of the start time  301 , the end time  302 , and the parameters  303 , minimum values  304 , maximum values  305 , and strides  306 . 
     The start time  301  is first time of time elapsed in the simulation and time in an initial state in the simulation. The end time  302  is end time of the time elapsed in the simulation. The parameters  303  indicate ranges of values of the arguments of the evaluation function and indicate a plurality of values of at least one argument. 
     The minimum values  304  and the maximum values  305  each indicate a minimum value or a maximum value of each of the items including the start time  301 , the end time  302 , and the parameters  303 , and each indicate a range of a value that each item can have. The strides  306  each indicate a variation in the values of each item in a combination of the values of the item. 
     For example, according to the initial setting information  300  shown in  FIG. 4 , since the maximum value  305  of a parameter A is 5, the minimum value  304  thereof is −5, and the stride  306  thereof is 1, the parameter A is any one of 11 values including −5, −4, −3, −2, −1, 0, 1, 2, 3, 4, and 5. Furthermore, according to the initial setting information  300  shown in  FIG. 4 , the simulation unit  107  executes a simulation for every time from time 0 to time  100 . 
     Moreover, if the evaluation function used in the simulation is a function for calculating sales in retailing, the parameters include a weather, a date, a day of week, and advertisement fee. 
     Note that the initial setting information  300  may include any item as long as the values of the arguments used in the simulation can be held. For example, if the arguments of the evaluation function used in the simulation do not include parameters of time, the initial setting information  300  may include a start value and an end value as an alternative for the start time  301  and the end time  302 . 
     After Step  201 , the simulation unit  107  acquires the initial setting information  300  from the storage device such as the memory  122 , and generates a combination of the values of the parameters  303 . The simulation unit  107  performs the simulation using the start time  301 , the end time  302 , the generated combination, and the evaluation function held in advance ( 202 ). 
     Specifically, the simulation unit  107  executes the simulation by calculating the evaluation function using the generated combination at a plurality of time points indicated by the stride  306  of the start time  301  and the end time  302  between the start time  301  and the end time  302 . 
     Note that the simulation executed in Step  202  will be referred to as first simulation, hereinafter. The first simulation is the simulation for verifying whether a notable phenomenon occurs. Furthermore, the first simulation includes at least one simulation by at least one combination of the values of the parameters. 
     The notable phenomenon in the first embodiment means a phenomenon that simulation results, that is, evaluation values output in the simulation differ from evaluation values that have been predicted or expected by the user. The user may determine, as the notable phenomenon, the simulation in which an average evaluation value is not output at the end time  302  or may determine, as the notable phenomenon, the simulation in which an average evaluation value is output at the end time  302  but a peculiar evaluation value is output at time other than the end time  302 . 
     In Step  202 , the simulation unit  107  outputs a plurality of evaluation values per simulation. To execute the simulation using a plurality of combinations of the values of the parameters  303 , a plurality of evaluation values as a plurality of groups is output by a plurality of simulations. 
     The groups generated by dividing a plurality of evaluation values output when simulations have been executed for every combination of the used values of the parameters  303  will be referred to as result groups, hereinafter. 
     The simulation unit  107  stores a plurality of generated result groups and the combinations of the values of the parameters  303  corresponding to the result groups in at least one of the storage device, such as the memory  122 , included in the host processing device  101  and an external storage device connected to the host processing device  101 . 
     After Step  202 , the visualization unit  108  acquires a plurality of evaluation values in the first simulation via the storage device such as the memory  122 , and generates image data to be output by the result providing unit  109  on the basis of the acquired evaluation values ( 203 ). 
       FIG. 5  is an explanatory diagram showing a time series graph  400   a  displaying a plurality of evaluation values in the first simulation according to the first embodiment. 
     The time series graph  400   a  shown in  FIG. 5  is on a screen displayed on the display or the like by the result providing unit  109  on the basis of the image data generated by the visualization unit  108 . The time series graph  400   a  shown in  FIG. 5  indicates a plurality of result groups (result groups #10 to #24) generated in the first simulation. 
     The result groups (result groups #10 to #24) in the first simulation are a set of evaluation values F that have been calculated on the basis of the initial setting information  300  shown in  FIG. 4  and the evaluation function. In the time series graph  400   a  shown in  FIG. 5 , a horizontal axis indicates the time and a vertical axis indicates the evaluation value F. 
     The visualization unit  108  generates, in Step  203 , the image data for displaying, as the time series graph  400   a , the evaluation values F in one simulation that has been executed using one combination of the parameters  303  and the evaluation function. 
     The result providing unit  109  receives the image data from the visualization unit  108  via the interface units  105  and  104  and outputs the received image data to the user. If the user puts a cursor on a ridge line corresponding to one result group in one simulation, the result providing unit  109  may display identifiers or the used values of the parameters  303  of the result group indicated by the cursor by pop-up. 
     While the result groups #10 to #20 are constant in increasing tendency, the result groups #21 to #24 remarkably increase at and after time “25.” Furthermore, the evaluation values F of the result groups #21 to #24 at end time “100” greatly differ from the evaluation values F of the result groups #10 to #20 at the end time “100.” 
     After Step  203 , it is highly likely that the user determines that a notable phenomenon has occurred to a part of the first simulation if reference is made to the evaluation values in the first simulation shown in  FIG. 5  and the user has predicted or expected the result groups #10 to #20 as the evaluation values in the first simulation. 
     If the user instructs the user input unit  103  to execute a re-simulation for analyzing the notable phenomenon, the user input unit  103  accepts resetting information input from the user. The re-simulation for analyzing the notable phenomenon will be referred to as a second simulation, hereinafter. At this time, the user selects one result group in the first simulation to be analyzed by the second simulation from among the result groups #10 to #24 shown in  FIG. 5 . 
     The first simulation for the selected result group will be referred to as the analysis simulation, hereinafter. The user inputs, as resetting information, the identifiers of the selected result group (that is, identifiers of the analysis simulation) and parameters and the like used in the second simulation to the user input unit  103 . 
     The user input unit  103  transmits the accepted resetting information to the resetting unit  110  via the interface units  104  and  105 . The resetting unit  110  stores the received resetting information in the storage device such as the memory  122  as resetting information  310   a  necessary for the second simulation ( 204 ). 
       FIG. 6  is an explanatory diagram showing the resetting information  310   a  according to the first embodiment. 
     The resetting information  310   a  shown in  FIG. 6  indicates a combination of changed values of the parameters and the like when the values of the parameters used in the analysis simulation for the result group #23 are changed halfway along the analysis simulation for the result group #23. 
     The result group #23 is a set of a plurality of evaluation values in the simulation using a combination of the values of the parameters  303  (A=2, B=3, C=5, . . . ), that is, a set of the evaluation values in the simulation from the start time “0” to the end time “100.” 
     By designating the analysis simulation, the user designates a combination of the values of the parameters  303  used in the simulation to which the notable phenomenon occurs. 
     The user inputs, as the resetting information, the identifiers of the analysis simulation (that is, identifiers of the combination of the values of the parameters  303 ) and start time  311   a , end time  312   a , and values of parameters  313   a  and a reference  317   a  shown in  FIG. 6 . Minimum values  314   a , maximum values  315   a , and strides  316   a  are designated to the start time  311   a , the end time  312   a , and the parameters  313   a.    
     The start time  311   a  indicates time at which the values of the parameters used to execute the second simulation are changed out of time of outputting the analysis simulation. Owing to this, the start time  311   a  indicates the time of starting the second simulation. 
     The end time  312   a  indicates time of ending the second simulation. The reference  317   a  is a range of predicted values or expected values of the evaluation values in the second simulation at the end time  312   a.    
     A controllable simulation in the first embodiment means herein a simulation for outputting the evaluation values falling in the range of the reference  317   a  at the end time  312   a . According to  FIG. 6 , the simulation for which the evaluation values are equal to or smaller than 200 when the time is time “50” is a controllable simulation. 
     Furthermore, if the evaluation values satisfying the reference  317   a  at the end time  312   a  are obtained in the simulation, it means that the simulation unit  107  have been able to control the phenomenon in the simulation. 
     Time at which it is predicted that the simulation can be controlled by changing the values of the parameters at the start time  311   a  is designated as the start time  311   a . Owing to this, the user may designate time before or after the occurrence of the notable phenomenon as the start time  311   a.    
     The time between the minimum value  314   a  and the maximum value  315   a  (time between time “25” and time “40”) is designated to the start time  311   a  of  FIG. 6 . This indicates that the second simulation using a plurality of combinations of the values of the parameters starts at a plurality of time points between the minimum value and the maximum value. 
     The parameters  313   a  indicate ranges of values of arguments of an evaluation function in the second simulation. The values of the parameters  313   a  may be set again in Step  204  or may be the same as those of the parameters  303  set in Step  201 . 
     The reason for designating the minimum value  314   a  and the maximum value  315   a  to the start time of the second simulation is to provide information for identifying the start time at which the evaluation values F can be controlled by performing the simulation a plurality of times from a plurality of start time by the simulation unit  107 . As the value of the reference  317   a , the range of the evaluation values F predicted to have reached at the end time  312   a  if no notable phenomenon occurs. 
     Note that the resetting information  310   a  may include any items as long as the values of the arguments used in the simulation can be held similarly to the initial setting information  300 . For example, if the arguments of the evaluation function used in the simulation do not include parameters of time, the resetting information  310   a  may include a start value and an end value as an alternative for the start time  311   a  and the end time  312   a.    
     After Step  204 , the simulation unit  107  acquires the resetting information  310   a  via the storage device such as the memory  122 , and executes the simulation using the evaluation values F at the start time  311   a  of the analysis simulation, the evaluation function held in advance, and the resetting information  310   a  ( 205 ). 
     The simulation in Step  205  is the second simulation. The second simulation will be described in detail below. 
       FIG. 7  is a flowchart showing the second simulation and result visualization processing according to the first embodiment. 
     In Step  205 , the simulation unit  107  first acquires a combination of the values of the parameters  303  in the analysis simulation and the evaluation values output in the second simulation on the basis of the identifiers of the analysis simulation indicated by the resetting information  310   a , from the storage device such as the memory  122  ( 601 ). 
     For example, if the analysis simulation indicated by the resetting information  310   a  is the result group #23, the simulation unit  107  acquires a combination of the parameters  303  (A=2, B=3, C=5 . . . ) and a plurality of evaluation values corresponding to the identifier “#23” from the memory  122  or the like. 
     After Step  601 , the simulation unit  107  acquires the value of the maximum value  315   a  of the start time  311   a  from the resetting information  310   a  and sets the acquired value of the maximum value  315   a  as the start time T of the second simulation ( 602 ). Since the maximum value  315   a  shown in  FIG. 6  is time “40,” “40” is set to the start time T of the second simulation. 
     After Step  602 , the simulation unit  107  determines whether the start time T is equal to or greater than the value of the minimum value  314   a  of the start time  311   a  in the resetting information  310   a  ( 603 ). This is intended to start the second simulation at a plurality of time included in time between the maximum value  315   a  and the minimum value  314   a  of the start time  311   a.    
     If the start time T is equal to or greater than the minimum value  314   a  of the start time  311   a , the simulation unit  107  continues the second simulation. The simulation unit  107  then extracts the evaluation values F at the start time T of the analysis simulation from the evaluation values acquired in Step  601  ( 604 ). 
     The reasons are as follows. The simulation unit  107  executes the second simulation using a plurality of combinations of the values of the parameters  313   a  at the user&#39;s selected start time T of the simulation. Furthermore, the simulation unit  107  extracts initial values of the evaluation function in the second simulation. 
     After Step  604 , the sensitivity analysis unit  111  and the simulation unit  107  analyze the sensitivity of each of the parameters using the evaluation values extracted by the simulation unit  107  as the initial values ( 605 ). The sensitivity means herein a ratio of a change in the evaluation values of the simulation to a change in each parameter, and a high sensitivity means herein that an amount of the change in the evaluation values of the simulation with respect to the change in the parameter is greater than those for the other parameters. 
     Specifically, if the sensitivity analysis unit  111  instructs one parameter the value of which has been changed slightly to the simulation unit  107 , the simulation unit  107  executes the simulation using the one parameter the value of which has been changed slightly and the other parameters the values of which have not changed since the start time T as well as the evaluation function. 
     Changing the value slightly refers herein to change by a value that is sufficiently small compared with the values in the range between the minimum value  314   a  and the maximum value  315   a  of each parameter and means, for example, increasing the value by as much as the stride  316   a . Furthermore, the administrator of the simulation system according to the first embodiment may instruct a method of changing the parameters at the time of sensitivity analysis to the sensitivity analysis unit  111  in advance. 
     The sensitivity analysis unit  111  changes the value of each parameter in the simulation and then determines the variation in the evaluation values F for every parameter, thereby analyzing the sensitivity. 
     For example, the sensitivity analysis unit  111  holds, as a method of analyzing the sensitivity, a method of adding the value of the stride  316   a ×1 to each parameter. The parameter A corresponding to the result group #23 in the analysis simulation is 2. 
     In this case, the sensitivity analysis unit  111  instructs, as the parameter A, 2.1 to the simulation unit  107 , and the simulation unit  107  executes the simulation from the start time T=40 to the end time  312   a  using the changed parameter A and the values of the other parameters in the result group #23. 
     If the evaluation values F are 300 at the start time T=40 and 315 at the end time  312   a  in this simulation result, the sensitivity analysis unit  111  calculates {(315−300)/300}/0.1=+0.50 as the sensitivity of the parameter A. The sensitivity analysis unit  111  calculates the sensitivities of the other parameters by the same processing. 
     The sensitivity analysis unit  111  notifies the visualization unit  108  of the calculated sensitivities, and the visualization unit  108  generates image data for displaying ranking lists  401   a  on the basis of the notified sensitivities ( 206 ). The visualization unit  108  generates the image data on the ranking lists  401   a  where, for example, the parameters are listed in the descending order of sensitivity. 
     Subsequently, the visualization unit  108  transmits the image data on the ranking lists  401   a  to the result providing unit  109  via the interface units  104  and  105 , and the result providing unit  109  provides the user with the ranking lists  401   a  at the start time T on the basis of the received image data. Furthermore, the visualization unit  108  transfers the ranking lists  401   a  to the resetting unit  110 . 
     The subsequent processing is processing for searching conditions for the parameters and the like to control the simulation. 
     First, the resetting unit  110  extracts top N parameters having the higher sensitivities on the basis of a result of the ranking lists  401   a , and stores the extracted parameters in the storage device such as the memory  122  as information necessary for the second simulation to be executed next. 
     The reason that the resetting unit  110  extracts the N parameters on the basis of the sensitivities is as follows. It is possible to efficiently determine the conditions for making the second simulation controllable with a smaller calculation volume if the simulation is executed with a priority given to the parameters having the higher sensitivities. The conditions for making the second simulation controllable include the values of the parameters and the values of the start time T capable of controlling the simulation. 
     Owing to this, if the analysis with higher accuracy is desired despite the increased calculation volume, the resetting unit  110  may omit the extraction of the parameters based on the sensitivities. 
     Note that the value N is a natural number, for example, 2 and may be arbitrarily set in advance by the administrator or the user. Moreover, the user may designate the value N to the resetting unit  110  via the user input unit  103  as a result of referring to the ranking lists  401   a.    
     Furthermore, the functional unit such as the resetting unit  110  may set the value N depending on the result of the sensitivity analysis and the total number of the parameters. For example, the functional unit such as the resetting unit  110  may set one-third of the total number of the parameters as the value N or the number of the parameters for which the result of the sensitivity analysis indicates that the sensitivities are higher than an average value or a central value as the value N. 
     The simulation unit  107  acquires the abovementioned N parameters from the storage device such as the memory  122 . The simulation unit  107  generates a plurality of combinations in which only the values of the N parameters are changed while the values of the other parameters are the same as those of the parameters corresponding to the analysis simulation, on the basis of the resetting information  310 . 
     The simulation unit  107  executes the simulation a plurality of times from the start time T of the analysis simulation to the time of the end time  312   a  using the generated combinations and the evaluation function ( 606 ). The simulation executed in Step  606  is the second simulation. 
     More specifically, in Step  606 , the simulation unit  107  executes the second simulation from the start time T to the end time  312   a  using the evaluation values calculated in the analysis simulation, the generated combinations, and the evaluation function. If the evaluation function for the simulation is, for example, a function using previous parameters, the simulation unit  107  may use the parameters  313  in the analysis simulation for the second simulation. 
     In this case, the simulation unit  107  calculates the evaluation values in the second simulation with the evaluation values at the start time T that are initial values calculated at the start time T assumed as the initial values. Specifically, the simulation unit  107  may convert the evaluation function used in the simulation so that the evaluation values calculated at the start time T of the second simulation become equal to the evaluation values calculated at the start time T of the analysis simulation. 
     By doing so, the simulation unit  107  can change the parameters  313  to be used halfway along the time course of the analysis simulation and execute a plurality of simulations. 
     In Step  206 , the simulation unit  107  transmits a plurality of result groups output by the second simulation to the visualization unit  108  after executing the second simulation, and the visualization unit  108  generates screen data on the time series graph  402   a  for displaying the result groups similarly to Step  203 . 
     In this case, the visualization unit  108  generates image data on the time series graph  402   a  such that the result groups in the analysis simulation are continuous with a plurality of result groups in the second simulation. As a result, it is possible to allow the user to compare the result of the first simulation with the result of the second simulation. 
     In Step  206 , the visualization unit  108  transmits the image data on the time series graph  402   a  to the result providing unit  109  via the interface units  104  and  105 . The result providing unit  109  outputs the time series graph  402   a  at the start time T on the basis of the transmitted image data. Furthermore, the visualization unit  108  transfers the result groups output in the second simulation to the result analysis unit  112  in Step  206 . 
     If a plurality of result groups is transferred from the visualization unit  108  to the result analysis unit  112 , the result analysis unit  112  identifies the result group in which the evaluation values F at the end time  312   a  correspond to the reference  317   a  from the result groups in the second simulation. The result analysis unit  112  acquires a combination of the values of the parameters used to execute a simulation of the identified result group ( 607 ). 
     For example, the result analysis unit  112  identifies the result group in which the evaluation values F at the end time  312   a “ 50” are equal to or smaller than 200 set as the reference  317   a  from the execution result of the second simulation, and acquires a combination of the values of the parameters used to execute the simulation of the identified result group. 
     Furthermore, in Step  607 , the result analysis unit  112  notifies the visualization unit  108  of the acquired combination of the values of the parameters. If the result analysis unit  112  is unable to acquire the combination of the values of the parameters in Step  607 , the result analysis unit  112  notifies the visualization unit  108  that the result analysis unit  112  has not been able to acquire the combination. 
     If having been notified by the result analysis unit  112  of the combination of the values of the parameters in the second simulation, the visualization unit  108  generates image data for displaying the notified combination of the values of the parameters as a scatter diagram  403   a  ( 206 ). The visualization unit  108  transmits the image data on the scatter diagram  403   a  to the result providing unit  109  via the interface units  104  and  105 . The result providing unit  109  outputs the scatter diagram  403   a  at the start time T to the user on the basis of the transmitted image data. 
     By outputting the scatter diagram  403   a , the result providing unit  109  enables the user to recognize the combination of the values of the parameters capable of controlling the simulation to which the notable phenomenon has occurred. 
     After Step  607 , the simulation unit  107  subtracts the value of the stride  316   a  of the start time  311   a  from the value of the start time T ( 608 ). The simulation unit  107  then executes Step  603 . In this way, the host processing device  101  repeats Steps  603  to  608  until the start time T becomes smaller than the minimum value  314   a  of the start time  311   a.    
       FIG. 8  is an explanatory diagram showing the screen output by the result providing unit  109  according to the first embodiment. 
     The result providing unit  109  outputs a plurality of ranking lists  401   a , a plurality of time series graphs  402   a , and a plurality of scatter diagrams  403   a  at a plurality of start time T. The result providing unit  109  may output the ranking lists  401   a , the time series graphs  402   a , and the scatter diagrams  403   a  in such a manner as to be displayed on one screen or to be sequentially displayed. 
       FIG. 8  shows the ranking lists  401   a , the time series graphs  402   a , and the scatter diagrams  403   a  at the start time T of “40,” “35,” and “30,” respectively. Note, however, that if the second simulation has been executed using the resetting information  310   a  shown in  FIG. 6 , the start time T has 16 values from 25 to 40 and 16 ranking lists  401   a,  16 time series graphs  402   a , and the 16 scatter diagrams  403   a  are, therefore, generated. 
     According to the ranking lists  401   a  shown in  FIG. 8 , the parameters having high sensitivities are parameters A, C, and D at all the start time T. Owing to this, the time series graphs  402   a  and the scatter diagrams  403   a  shown in  FIG. 8  are the screen generated by the simulation that has been executed by a combination where only the parameters A and C have been changed. 
     The time series graphs  402   a  shown in  FIG. 8  indicate the result of the second simulation when values of the parameters A and C have been changed. Moreover, the scatter diagrams  403   a  shown in  FIG. 8  indicate the values of the parameters A and C in the simulation of outputting the evaluation values corresponding to the reference  317   a  by plotting the values with black circles. 
     According to the time series graphs  402   a  and the scatter diagrams  403   a  shown in  FIG. 8 , if the start time T is “40,” the simulation unit  107  is unable to output the evaluation values corresponding to the reference  317   a  in the second simulation. However, if the start time T is “35” or “30,” the simulation unit  107  can output the evaluation values corresponding to the reference  317  in the second simulation by a combination of a part of the parameters. 
     The user can acquire the combination of the values of the parameters and the start time in the simulation of outputting the evaluation values corresponding to the reference  317   a  by referring to the screen shown in  FIG. 8 . In other words, the use can acquire the combination of the values of the parameters and the start time capable of controlling the simulation to output the values in the range of the reference  317   a  when the notable phenomenon has occurred. The user can thereby analyze the notable phenomenon in more detail. 
     According to the first embodiment, the ranking lists  401   a  are output and the result of analyzing the sensitivities is displayed, whereby it is possible to provide the user with the parameters having a large influence on the simulation during the occurrence of the notable phenomenon as an analysis result. 
     Furthermore, outputting the time series graphs  402   a  and the scatter diagrams  403   a  make it possible to indicate whether the notable phenomenon can be controlled. Moreover, in the time series graphs  402   a  and the scatter diagrams  403   a , the combination of the parameters in the simulation of outputting the evaluation values corresponding to the reference  317   a  and the start time T are displayed, thereby making it possible to provide the user with the start time, the parameters, and the values of the parameters capable of controlling the notable phenomenon as the analysis result. 
     Owing to this, by outputting at least any of the ranking lists  401   a , the time series graphs  402   a , and the scatter diagrams  403   a , the simulation system according to the first embodiment can provide the user with the information for efficiently and additionally analyzing the result of the simulation. 
     While in the first embodiment, the set range of the parameters in the simulations is defined by the minimum value, the maximum value, and the stride, the present invention is not limited to the embodiment and the set range of the parameters may be defined by, for example, random variables using an estimated average value and estimated dispersion values. 
     Furthermore, while in the abovementioned embodiment, the minimum value  314   a  of the start time T of the second simulation is defined as the time “25” at which the evaluation values F suddenly change (the notable phenomenon occurs), the present invention is not limited to the embodiment and the minimum value  314   a  of the start time T may be defined as time before or after the time of the occurrence of the notable phenomenon. 
     Furthermore, if time before the time of the occurrence of the notable phenomenon has been set as the start time T, the visualization unit  108  may extract the parameters having the high sensitivities as the parameters that have caused the occurrence of the notable phenomenon by executing the sensitivity analysis of Step  605  at the start time T. The visualization unit  108  may generate the image data for displaying the parameters that have caused the occurrence of the notable phenomenon. 
     Moreover, the end time  312   a  of the second simulation may be arbitrarily set as long as the end time  312   a  is the time at which the user desires to obtain the values of the reference  317   a  by the simulation. 
     Furthermore, while the two axes of the parameters A and C are displayed in the scatter diagrams  403   a  in  FIG. 8 , the number of axes is not limited to two as that shown in  FIG. 8  but one axis or three or more axes may be displayed. Moreover, if the number of axes of the scatter diagrams  403   a  matches the number N of the parameters described above, the user can identify the parameters in the simulation of outputting the evaluation values corresponding to the reference  317   a  from among the changed parameters. 
     Furthermore, while the visualization unit  108  and the result providing unit  109  output the result of the simulation by a format and an output method of the ranking lists  401   a , the time series graphs  402   a , and the scatter diagrams  403   a  shown in  FIG. 8 , the present invention is not limited to the format and the output method. The visualization unit  108  and the result providing unit  109  may output the result of the simulation by an arbitrary format and an arbitrary output method such as lists, graphs, and tables as long as the user can efficiently perform simulation analysis. Moreover, the result providing unit  109  may display the time series graphs  402   a  and the scatter diagrams  403   a  by, for example, animation according to a change in the start time T. 
     Furthermore, the strides  316   a  of the parameters in the second simulation may be arbitrary values. Nevertheless, if the strides  316   a  of the parameters  313   a  in the second simulation are smaller than the strides  306  of the parameters  303  in the first simulation, then the exhaustiveness of the simulation can be efficiently enhanced and yet the probability of discovering a phenomenon other than the notable phenomenon can be enhanced. 
     Moreover, while the time is used as one of the parameters in the abovementioned simulations, any parameters may be used in the simulations according to the first embodiment as long as the parameters are defined as the parameters of the evaluation function. 
     Second Embodiment 
     In the first embodiment, the user directly selects the simulation for analyzing the notable phenomenon and directly designates the reference  317   a . If the notable phenomenon as shown in  FIG. 5  occurs, the use can easily recognize the notable phenomenon and can, therefore, make the selection and designation relatively easily. 
       FIG. 9  is an explanatory diagram showing a time series graph  400   b  displaying a plurality of evaluation values in the first simulation according to a second embodiment. 
     As indicated by the time series graph  400   b  shown in  FIG. 9 , for example, if evaluation values that do not fall in a range predicted from evaluation values calculated before the time “25” occurs at the end time after the time “25” but a plurality of result groups is dispersed uniformly, it is difficult for the user to sensuously select the result group to which the notable phenomenon occurs. 
     To address the problem, according to the second embodiment, a method of providing the user with information capable of positively supporting the selection and designation if it is difficult for the user to select the result of the simulation and to designate the reference  317  will be described. 
     Configurations of functional units, a storage unit, hardware, and the like according to the second embodiment are the same as those according to the first embodiment. The difference between the second embodiment and the first embodiment is a content of the Step  203 . 
     In Step  203 , the visualization unit  108  performs statistical processing on a plurality of results of the simulation in Step  202 . Specifically, the visualization unit  108  calculates an average μ and a standard deviation σ with respect to a distribution of a plurality of evaluation values at each time at which the result of the simulation has been obtained. The visualization unit  108  generates image data for displaying results of the average μ and the standard deviation σ as a time series graph  404  as shown in  FIG. 10 . 
     Note that the averages μ and the standard deviations σ calculated at the respective time differ, the visualization unit  108  may generate image data for displaying the average μ and the standard deviation σ calculated at the end time  312   a  as the time series graph  404 . Furthermore, the visualization unit  108  may generate image data for displaying an average value between each of the average μ and the standard deviation σ calculated at predetermined time before the end time  312   a  and those calculated at the end time  312   a  as the time series graph  404 . 
       FIG. 10  is an explanatory diagram showing the time series graph  404  displaying the standard deviation according to the second embodiment. 
     The visualization unit  108  transmits the image data for displaying the time series graph  400   b  and the image data for displaying the time series graph  404  to the result providing unit  109 . The result providing unit  109  displays, for example, the time series graph  400   b  and the time series graph  404  by overlaying one on the other. The result providing unit  109  thereby allows the user to compare the time series graph  400   b  with time series graph  404  to facilitate user&#39;s grasping the situation of the dispersion of the result groups. 
     With this display method, the user can select, as a notable result, the result group, for example, 2σ or 3σ closest to the preset standard deviation, and determine the evaluation values F closer to an average μ as the reference. 
     The second embodiment exhibits an advantage in that it is possible to output information for positively supporting user&#39;s determination for the selection and setting if it is difficult for the user to select the result of the simulation and to set the reference  317 , in addition to the advantage of the first embodiment. Therefore, it is possible to support the user&#39;s efficient, additional analysis of the result of the simulation. 
     While the average μ and the standard deviation σ have been employed as indexes for the comparison with the time series graph  400   b  of the result of the simulation in the embodiment, the indexes are not limited thereto and the visualization unit  108  may calculate any indexes as long as the user can easily select the result of the simulation and set the reference. 
     Third Embodiment 
     In the first embodiment and the second embodiment, the number of evaluation functions used in the simulations is one and the number of types of the evaluation values F output by the simulations is one. A simulation system according to a third embodiment executes a plurality of simulations using a plurality of evaluation functions while using the same parameters, and changes the parameters halfway along the simulations, thereby providing the user with information for analyzing the notable phenomenon that occurs to each of the simulations. 
     Furthermore, in the first embodiment and the second embodiment, if the combination of the values of the parameters for controlling one simulation is used in a simulation by the other evaluation function, the user is unable to grasp a change in the simulation by the other evaluation function. To address the problem, the third embodiment provides the result of one simulation by a plurality of evaluation functions when conditions for parameters capable of controlling the simulation by one evaluation function are analyzed. 
     Configurations of functional units, hardware, and the like according to the third embodiment are the same as those according to the first embodiment. The difference between the third embodiment and the first embodiment is a part of processing in each of Steps  204  to  206 . 
     The simulation unit  107  according to the third embodiment holds a plurality of evaluation functions (evaluation function F, evaluation function G, and evaluation function H) in advance. These evaluation functions use the same types of parameters. 
     For example, the simulation unit  107  holds the evaluation function F for evaluating the sales of a shop and the evaluation function G for evaluating power consumption cost of the shop. The evaluation functions F and G use the same types of parameters such as the number of customers, a weather of the day, and a temperature. 
     In Step  202  of the third embodiment, the simulation unit  107  executes a plurality of first simulations using a plurality of evaluation functions. The simulation unit  107  generates a plurality of result groups in a plurality of first simulations. 
     In Step  203  of the third embodiment, the visualization unit  108  may generate image data on time series graphs  402  for displaying a plurality of result groups in the first simulations, or may generate image data on the time series graphs  402  for displaying a plurality of results in the single first simulation. 
     Furthermore, in Step  203  of the third embodiment, the user selects the result group to which the notable phenomenon occurs and which is necessary to analyze by referring to a plurality of displayed time series graphs  402 . The user thereby selects a combination of the parameters used in the simulation to which the notable phenomenon occurs. 
     In Step  204 , the resetting unit  110  receives resetting information including a plurality of references corresponding to the respective evaluation functions as resetting information  310   b . Furthermore, the resetting information  310   b  includes an identifier of the result selected by the user (for example, result group #23). 
       FIG. 11  is an explanatory diagram showing the resetting information  310   b  according to the third embodiment. 
     Start time  311   b , end time  312   b , and parameters  313   b  in the third embodiment are the same as the start time  311   a , the end time  312   a , and the parameters  313   a  in the first embodiment. A reference  317   b  in the third embodiment includes a reference KF, a reference KG, and a reference KH. 
     The reference KF indicates a range of predicted or expected evaluation values F in the simulation using the evaluation function F. The reference KG indicates a range of predicted or expected evaluation values G in the simulation using the evaluation function G. The reference KH indicates a range of predicted or expected evaluation values H in the simulation using the evaluation function H. 
     If acquiring the resetting information  310   b , the simulation unit  107  of the third embodiment executes the second simulation using a plurality of evaluation functions in Step  205 . As a result, a plurality of evaluation values (evaluation values F, evaluation value G, and evaluation value H) is output. 
     Specifically, the simulation unit  107  extracts the result groups generated using the same parameters as the parameters selected by the user in Step  203  from the result groups in a plurality of first simulations executed by a plurality of evaluation functions in Step  601  of the third embodiment for every evaluation function. The simulation unit  107  acquires contents of the extracted result groups from the storage device such as the memory  122 . 
     Steps  602  and  603  of the third embodiment are the same as Steps  602  and  603  of the first embodiment. 
     In Step  604  of the third embodiment, the simulation unit  107  extracts the evaluation values at the start time T from the result acquired in Step  601 . Note that if a plurality of evaluation functions is three evaluation functions, the number of extracted results is similarly three. 
     In Step  605  of the third embodiment, the simulation unit  107  and the sensitivity analysis unit  111  analyze the sensitivity of each parameter similarly to the first embodiment. The evaluation function used in this case may be the evaluation function used to generate the result group selected in Step  203  or may be the other evaluation function. 
     In Step  606  of the third embodiment, the simulation unit  107  executes the second simulation using the respective evaluation functions by changing a combination of the top N parameters from the start time T. 
     In Step  607  of the third embodiment, the simulation unit  107  identifies the result groups corresponding to the references indicated by the reference  317   b  from the result groups in the second simulation by a plurality of evaluation functions for every evaluation function. The visualization unit  108  generates image data on scatter diagrams  403   b  indicating a combination of the parameters  313   b  used to generate the identified result group for every evaluation function. 
     In this way, in Step  206  of the third embodiment, the visualization unit  108  generates the image data on a ranking list  401   b , the time series graphs  402   b , and the scatter diagrams  403   b  for displaying a plurality of results by a plurality of evaluation functions. 
     Furthermore, the visualization unit  108  of the third embodiment generates image data on a scatter diagram  405   b  after Step  607 . The scatter diagram  405   b  displays a combination of parameters that have generated the result groups corresponding to all references indicated by the reference  317   b  among the combinations of the parameters  313   b  corresponding to the result groups. 
     The visualization unit  108  of the third embodiment transmits the generated image data to the result providing unit  109 , and the result providing unit  109  displays the ranking list  401   b , the time series graphs  402   b , the scatter diagrams  403   b , and the scatter diagram  405   b  on the basis of the transmitted image data. 
       FIG. 12  is an explanatory diagram showing a screen output by the result providing unit  109  according to the third embodiment. 
     The ranking list  401   b  of  FIG. 12  is the ranking list  401   b  generated when the user has selected the result (result group #23) of one simulation from the first simulation using the evaluation function F in Step  203 . Furthermore, the ranking list  401   b  is generated when the sensitivity analysis unit  111  and the simulation unit  107  have analyzed the sensitivities using the evaluation function F. 
     Moreover, the time series graphs  402   b , the scatter diagrams  403   b , and the start scatter diagram  405   b  in  FIG. 12  are the time series graphs  402   b , the scatter diagrams  403   b , and the scatter diagram  405   b  when the values of the parameters A and C have changed at the start time T. The time series graphs  402   b  shown in  FIG. 12  indicate the evaluation values F, the evaluation values G, and the evaluation values H that are the result of the simulations. 
     The scatter diagrams  403   b  shown in  FIG. 12  indicate combinations of the values of the parameters  313   b  in the simulations that have output the results corresponding to the reference KH, the reference KG, and the reference KH. Moreover, the scatter diagram  405   b  shown in  FIG. 12  indicates a combination of the values of the parameters  313   b  in the simulation that has output the result corresponding to each of the reference KH, the reference KG, and the reference KH in all the simulations by the evaluation function F, the evaluation function G, and the evaluation function H. 
     Note that the screen shown in  FIG. 12  indicates a screen when the start time T is 30. Nevertheless, if the resetting information  310   b  shown in  FIG. 11  is used, the start time T has 16 values from 25 to 40; the result providing unit  109 , thus, may display all screens at the start time T having these 16 values or display screens at a part of the time. 
     As described so far, the third embodiment exhibits an advantage in that the user can confirm, by the time series graphs  402   b , a behavior as to how the results of a plurality of simulations using a plurality of evaluation functions change if changing the parameters, in addition to the advantages of the first embodiment and the second embodiment. Furthermore, the user can confirm, by the scatter diagram  405   b , the values of the parameters  313   b  that can output the results corresponding to the reference  317   b  set for a plurality of evaluation functions. 
     Owing to this, the third embodiment provides a method that enables the user to efficiently and additionally analyze the result of the simulation, which is the object of the present invention, and a system therefor. 
     Note that while the number of the evaluation functions is three in the abovementioned embodiment, the number of the evaluation functions is not limited to three but the number of the evaluation functions in the third embodiment may be an arbitrary number as long as the number is two or more. Furthermore, while the result providing unit  109  displays the ranking list  401   b  only for the evaluation function F in  FIG. 12 , the display of the ranking list  401   b  is not limited to that for the evaluation function F. Specifically, the sensitivity analysis unit  111  and the simulation unit  107  of the third embodiment may analyze the sensitivities of the parameters  313   b  using the evaluation functions G and H, and the result providing unit  109  of the third embodiment may display those results as the ranking lists  401   b.    
     Moreover, in Step  203  in the third embodiment, similarly to Step  203  in the second embodiment, the visualization unit  108  may calculate the average μ and the standard deviation σ of the results of the first simulations, and the result providing unit  109  may display the time series graph  404  displaying the average μ and the standard deviation σ for each of a plurality of evaluation functions. 
     Fourth Embodiment 
     The sensitivity analysis unit  111  in the first to third embodiments analyzes the sensitivities at the start time T, thereby determining the parameters to be changed in the second simulation. However, if the host processing device  101  sets the start time T before the occurrence of the notable phenomenon (for example, before the time “25” shown in  FIG. 6 ) and analyzes the sensitivities, there is a probability that sudden changes in the evaluation values F after the occurrence of the notable phenomenon are not reflected in the sensitivity analysis. As a result, there is a probability that the simulation unit  107  is unable to correctly use the parameters effective for controlling the sudden changes in the evaluation values F. 
     In a fourth embodiment, therefore, a method of correctly extracting parameters effective for controlling the notable phenomenon even in the abovementioned case will be described. 
     Configurations of functional units, hardware, and the like according to the fourth embodiment are the same as those according to the first embodiment. The difference between the fourth embodiment and the first embodiment is a content of the resetting information  310  and a part of processing in each of Steps  204  and  205 . 
       FIG. 13  is an explanatory diagram showing resetting information  310   c  according to the fourth embodiment. 
     The resetting information  310   c  includes start time  311   c , end time  312   c , parameters  313   c , and a reference  317   c . Furthermore, the resetting information  310   c  indicates a minimum value  314   c , a maximum value  315   c , and a stride  316   c  of the start time  311   c , and the minimum values  314   c , the maximum values  315   c , and the strides  316   c  of the parameters  313   c.    
     The resetting information  310   c  of the fourth embodiment, differently from the resetting information  310   a  of the first embodiment, includes sensitivity analysis time  318   c . The sensitivity analysis time  318   c  indicates start time of a simulation for executing sensitivity analysis in Step  605 . The time indicated by the sensitivity analysis time  318   c  does not necessarily match the time indicated by the start time  311   c.    
     In Step  203 , the user inputs a value of the sensitivity analysis time  318   c  to the user input unit  103 . In this case, the user inputs, as the value of the sensitivity analysis time  318   c , time after the occurrence of the notable phenomenon. 
     As described so far, the simulation analysis method of the fourth embodiment exhibits an advantage in that it is possible to accurately extract the parameters the values of which are changed in the second simulation in order to execute the simulation for the sensitivity analysis from the time that is not associated with the start time of the second simulation, in addition to the advantages of the first to third embodiments. As a result, it is possible to correctly extract the parameters effective for controlling the evaluation values. 
     Owing to this, the fourth embodiment provides the method that enables the user to efficiently and additionally analyze the result of the simulation, which is the object of the present invention, and a system therefor. 
     In the fourth embodiment, the user inputs the sensitivity analysis time  318   c ; alternatively, for example, the visualization unit  108  (or result analysis unit  112  or the like) of the host processing device  101  may calculate a variation per unit time in the evaluation values F as the result of the first simulation and the result providing unit  109  may display the result while overlaying the result on the time series graph  400   a . It is thereby possible to positively support the user&#39;s determination as to the input of the sensitivity analysis time. 
     Furthermore, in Step  203  in the fourth embodiment, similarly to Step  203  in the second embodiment, the visualization unit  108  may calculate the average μ and the standard deviation σ of the results of the first simulations, and the result providing unit  109  may display the time series graph  404  displaying the average μ and the standard deviation σ. 
     Moreover, the simulation unit  107  in the fourth embodiment, similarly to the simulation unit  107  in the third embodiment, may execute a plurality of first simulations by a plurality of evaluation functions. The user then may input the sensitivity analysis time for any of the evaluation functions after referring to the result of the first simulations by a plurality of evaluation functions in Step  203 . 
     Fifth Embodiment 
     A simulation system of a fifth embodiment combines the methods described in the first, second, and fourth embodiments and determines the values of resetting information  310 , thereby automatically executing result analysis by the second simulation. 
       FIG. 14  is a flowchart showing processing performed by the resetting unit  110  according to the fifth embodiment. 
     Configurations of functional units, hardware, and the like according to the fifth embodiment are the same as those according to the first embodiment. The difference between the fifth embodiment and the first embodiment is Step  204 . In Step  204  of the fifth embodiment, the user input unit  103  does not accept the resetting information  310  from the user. 
     On the other hand, in Step  204  of the fifth embodiment, the resetting unit  110  reads all of a plurality of result groups in the first simulation ( 1301 ). After Step  1301 , the resetting unit  110  performs statistical processing on the evaluation values F in a plurality of read result groups ( 1302 ). 
     After Step  1302 , the resetting unit  110  determines the result group (that is, a combination of the simulation unit and the values of the parameters  303 ) to be analyzed by the second simulation on the basis of a result of the statistical processing ( 1303 ). 
     The statistical processing in Step  1302  may be the same as the statistical processing executed by the visualization unit  108  in the second embodiment. Specifically, the resetting unit  110  calculates the average μ and the standard deviation σ with respect to the distribution of a plurality of evaluation values F at each time at which the result of the simulation has been obtained in Step  1302 . 
     In Step  1303 , the resetting unit  110  determines the simulation that has output the result group, for example, 2σ or 3σ closest to the preset standard deviation, as the simulation to which the notable phenomenon has occurred and which is to be analyzed by the second simulation. The resetting unit  110  stores the identifier of the determined simulation in the resetting information  310 . 
     After Step  1303 , the resetting unit  110  reads a combination of the values of the parameters  303  used in the determined simulation and the initial setting information  300  from the storage device such as the memory  122  ( 1304 ). After Step  1304 , the resetting unit  110  determines the ranges of the parameters  313  ( 1305 ). 
     Specifically, the resetting unit  110  sets, for example, the values of the parameters  303  in the simulation determined in Step  1303  as central values. In Step  1305 , the resetting unit  110  calculates the ranges of the parameters in the second simulation by multiplying a preset coefficient A (for example, 1/10) by the range (value obtained by subtracting the minimum value  304  from the maximum value  305 ) of each of the parameters indicated by the parameters  303  in the initial setting information  300 . The resetting unit  110  determines the minimum values  314  and the maximum values  315  on the basis of the set central values and the calculated ranges. 
     Furthermore, in Step  1305 , the resetting unit  110  calculates the strides  316  of the parameters in the second simulation by acquiring a preset coefficient B (for example, 1/10) by the stride  306  of each of the parameters  303 . 
     The resetting unit  110  stores the ranges of the parameters  313  determined in Step  1305  in the parameters  313  in the resetting information  310 . 
     After Step  1303  or Step  1305 , the resetting unit  110  calculates the variation in the evaluation values F in the simulation determined in Step  1303  ( 1306 ). The resetting unit  110  then determines start time  311  and end time  312  on the basis of the analysis result ( 1307 ). 
     Specifically, the resetting unit  110  calculates the variation per unit time in the evaluation values F in the determined simulation in Step  1306 . The resetting unit  110  then extracts the time at which the calculated variation is the largest in Step  1307 . The resetting unit  110  determines the extracted time as sensitivity analysis time  318 , and determines the extracted time as the central value of the start time  311  and the end time  312 . 
     In Step  1307 , the resetting unit  110  multiplies a preset coefficient C (for example, 1/10) by the difference between the start time  301  and the end time  302  in the initial setting information  300 . The resetting unit  110  determines the start time  311  and the end time  312  so that the value obtained by the multiplication becomes the difference between the start time  311  and the end time  312 . Alternatively, the resetting unit  110  may determine the same value as, for example, the stride  306  in the initial setting information  300 , as a stride  316  of the start time  311  and the end time  312 . 
     After Step  1302  or Step  1307 , the resetting unit  110  determines a reference  317  ( 1308 ). Specifically, the resetting unit  110  may determine the reference  317  using, for example, the value of the average μ and the range of the standard deviation σ calculated in Step  1302  at the same time as the end time  312  determined in Step  1307 . 
     The resetting unit  110  stores the information determined in Steps  1307  and  1308  in the storage device such as the memory  122  as the resetting information  310 . As a result, the simulation unit  107  can automatically execute the second simulation after the first simulation without accepting the resetting information  310  input from the user. Note that the user input unit  103  may accept the aforementioned coefficients A, B, and C from the user before or during the processing shown in  FIG. 14 . 
     As described so far, the fifth embodiment exhibits an advantage in that the resetting information  310  can be set without relying on user&#39;s input, in addition to the advantages of the first to the fourth embodiments. It is thereby possible to provide the user with the information for analyzing the notable phenomenon promptly after the execution of the first simulation. 
     Therefore, it is possible to provide a simulation analysis method that enables the user to efficiently and additionally analyze the result of the simulation, which is the object of the present invention, and a system therefor. 
     Note that the fifth embodiment may be applied to any of the first to third embodiments. 
     Furthermore, in the first to fifth embodiments, it is desirable that the sensitivities of the parameters used in the simulation are normalized as much as possible for the evaluation values in a normal state. Here, it is considered that the normal state is a state in which the average value μ shown in, for example, the second embodiment is used. 
     Furthermore, actual simulations to which the embodiments of the present invention are applicable are not limited to a specific simulation as long as the result of the simulation can be provided in the form of the time series graph  402 , and may include an interest rate simulation, a chain-reaction bankruptcy simulation, an evacuation guidance simulation, and a circuit simulation. 
     Moreover, the present invention is not limited to the aforementioned embodiments and encompasses various modifications. For example, the abovementioned embodiments have been described in detail for describing the present invention so that the present invention is easy to understand. The present invention is not always limited to the embodiments having all the configurations described so far. 
     Furthermore, the configuration of the certain embodiment can be partially replaced by the configuration of the other embodiment or the configuration of the other embodiment can be added to the configuration of the certain embodiment. Moreover, for a part of the configuration of each embodiment, the other configuration can be added, deleted or replaced. 
     Furthermore, a part of or all of each of the configurations, the functions, the processing units, processing procedures, and the like described above may be realized by hardware by, for example, designing by an integrated circuit. Moreover, each of the configurations, the functions, and the like described above may be realized by software by interpreting a program for causing a processor to realize the respective functions and executing the functions. Information on a program, a table, a file, and the like for realizing each function may be placed in a recording device such as a memory, a hard disk or an SSD (Solid State Drive), or in a recording medium such as an IC card, an SD card or a DVD. 
     Furthermore, control lines or information lines considered to be necessary for the description are illustrated and all the control lines or the information lines are not always illustrated in terms of a product. In actuality, it may be assumed that almost all the configurations are mutually connected.