Nowadays, grace of computing possibilities that electronic computers offer and namely, big memory volume and computing speed, there is the improving of modeling methods, an important role having complex system modeling using simulation techniques. These osimulation model, validation, sensitivity analysis

COCULESCU CRISTINA

CRISAN DANIELA ALEXANDRA

DESPA RADU

STANICA JUSTINA LAVINIA

English

Research Papers in Economics

918 
ASPECTS ABOUT SIMULATED MODEL TRUSTINESS  
 
Coculescu Cristina 
Romanian-American University, Bucharest, Romania, Faculty of Computer Science for Business Management, 
Street  Durău,  no.  47,  district  1,  Bucharest,  Romania  E-mail:  cristina_coculescu@yahoo.com,  Phone: 
+40740975358 
Crişan Daniela Alexandra 
Romanian-American University, Bucharest, Romania, Faculty of Computer Science for Business Management, 1B 
Expozitiei Blvd., district 1, Bucharest E-mail: dacrisan@yahoo.com, Phone: +40721264908 
Stănică Justina Lavinia 
Romanian-American University, Bucharest, Romania, Faculty of Computer Science for Business Management, 1B 
Expozitiei Blvd., district 1, Bucharest E-mail: lavinia.stanica@cheyenne.ro, Phone: +40721.708.580 
Despa Radu 
Romanian-American  University,  Bucharest,  Romania,  Faculty  of  Internal  and  International  Commercial  and 
Financial-Banking  Relations  Street  Istru,  no.  1,  bl.  P2,  sc.  A,  etj.  2,  ap.5,  district  6,  Bucharest  E-mail: 
radudespa@yahoo.com, Phone: +40744270089   
 
Nowadays, grace of computing possibilities that electronic computers offer and namely, big memory volume and computing 
speed, there is the improving of modeling methods, an important role having complex system modeling using simulation 
techniques. These offer us information about the system before it to be correctly realized through building of mathematical and 
logical models which describe real system behavior or the behavior of some parts of it during long time.  
In this work we purpose to analyze the main kinds of testing and validation of simulation model, starting from that to a 
simulation program it can assign the character of model only when its trustiness is checked and practically proved because 
only relying on the results of such models, decisions can be taken, these results being those which correctly represent studied 
system.  
 
Key words: simulation model, validation, sensitivity analysis  
 
JEL code: C61 
 
1.PROCESS OF PASSING FROM REAL SYSTEM TO THE MODEL OF SIMULATION  
The  knowledge  of  evolution  rules  of  complex  systems,  making  a  link  between  quantity-quality  analysis  and 
formalized representation of processes and phenomena demand application of modeling method for decision act.  
The model made basing on the checking of available information about feature, behavior and functionality of a 
system is an active part of information resource for optimize the activity.  
On the base of making every model, is mainly the exigency of its conception so to reflect simply and clearer and 
clearer the reality that it follow to show  
Nowadays, grace of computing possibilities that electronic computers offer and namely, big memory volume and 
computing speed, there is the improving of modeling methods, an important role having complex system modeling 
using simulation techniques.  
These offer us information about the system before it to be correctly realized through building of mathematical and 
logical models which describe real system behavior or the behavior of some parts of it during long time.  
By the means of simulation, ‘inputs” are generated and considering system internal states, through right algorithms, 
the “outputs” are established and there is shown the evolution in time of the system [1].  
However simulation gives under-optimal solutions, it is an efficient technique for studying complex problems at 
firm level.  
In simulation activity, three important elements are implied and namely: real system, the model, the computer and 
two relations: modeling and simulation relations. It needs the following considerations: 
  “real system” is the system observed using human feelings; 
  “real model” is the replaced real system and that generally correspond to the demands of initial real system; 
  “abstract model” makes the passing from “real system” to “real model”. It remakes real system through 
system decomposition in basic parts and establishes the links between them [9].  
Validation of results is made through a checking of the correspondence between the data from real system and 
those given by model.  
Simulation allows the description of complex system behavior and makes possible experimentation of a system 
without expose the organization to the dangers of real world.  
Simulation model, with its many characteristics, is the link between economic-mathematic model that has the 
possibility of modeling and reality. Searching thoroughly possible changes in real world through simulation model, 
we can often learn how to improve existing behavior without applying in fact the good or bad ideas about the 
system. In a simulation model it is possible the compression of long whiles in seconds at computer [3].  919 
Simulation models are in most cases parts of informatic systems relied on solving system problems based on 
solving conversational system problems. They are used together with informational management and decision 
system. 
 
2.DIFFICULTIES AND TYPES OF VALIDATION  
It should be ideal that simulation model to be compared directly to the system which represents but this thing is not 
always possibly because the comparison is made between two “sets” of observations.  
In the best case, the analysis put in evidence features equal to those of “real” system and can be considered correct 
if it is not negated by certain hypotheses.  
In consequence, a simulation model has no universal validity but can be valid for reaching a certain target and 
within certain hypothesis.   
 
Black Box Validation 
For analyze the kind of model operating compared to real system these are supposed to be ‘black boxes”. In 
consequence, operating data are unknown.  
Test method of the kind ‘black box” is simply. The behavior of real system is viewed in certain conditions and after 
that, the model is put in operation in similar conditions. If the model is valid, observations about it wouldn’t have to 
differ from those of “real” system.  
The comparison of the two sets of observations is made by the means of statistic methods, there are called checking 
tests for some hypotheses and test trustiness is explained through likelihood index.  
Validation errors which can occur are errors of level 1, when a correct hypothesis is wrongly rejected. II-level 
errors appear when false hypothesis is accepted as true. Zero-type errors are very severe because who makes the 
test follows a series of useless aspects – it results a wrong model, ultra-sophisticated [7].  
The model is statistically considered as valid but it proves to be useless because it responds to not important 
problems. This approaching can be made for the whole model or for a part of it.  
 
White Box Validation 
In this case, it is supposed that simulation model and “real” system are transparent. Internal features are very well 
known and understood. Validation of this kind is useful in model building process, nowise in final phase.  
While in validation case of type “black box” prediction power of model is stressed, in “white box” validation, the 
accent is on internal activities of the model.  
The most of simulation include a static logic which shapes system behavior. In a discrete simulation these are rules 
of the kind if (conditions) – then (action). Thus, real system can be controlled.  In a continuous simulation, there is 
more probably to be rules which control system behavior in extreme conditions. If simulation has been base on 
statistical rule of system behavior it wouldn’t be longer useful because simulation is used to mirror the system’s 
dynamic behavior. In fact, dynamic performance (of action) of the model during running must be validated [8]. 
The materials (support), the animation (dynamic exposure of model features) are integrated in simulation program. 
System variables and states can be observed while the programs run, so the errors in action logic can be found.  
As how it is possible, ultra-sophistication of the model which harms in a dynamic approaching must be avoided 
because a lot of changes appear in system wherefore the model must answer favorably.  
Ultra-simplification is another extreme case that must be avoided, because it doesn’t pass to surprise important 
aspects, useful details.  
Zero errors avoidance can be realized either through the fast establishing of the target purposed through the model 
or through the adoption of a kind of approaching explicit and evolutive for the model.  
 
3.SIMULATION MODEL TESTING AND CHECKING  
We assign to simulation program the character of model only when its trustiness is validated and practically 
proved.  Because only relying on the results of such models it can take decisions, they are those which correctly 
represent studied system.  
In the case of a wrong structured problem, a wrong computer program can be made or baffling results can be 
reached. Hence there is useful model checking through testing step by step.  
There  are  two  components  and  namely:  checking  (refers  to  the  rightness  of  process  building,  of  model 
programming) and validation itself (refers to concept checking, there are checked the component parts and the 
relations between them).  
Narrow  linked  to  model  validity  is  its  sensitivity  to  outside  influences.  From  the  study  it  results  the  way  of 
configuration the same time with the ease of model feature.  
For checking right making of model, there were developed a series of software-engineering methods that find and 
remove programming mistakes (structured programming, top-down programming, Program-design).  
Relevant especially for simulation programs, “trace” method is known which consists of the showing of event 
effects over corresponding status changes. Presentation of key-messages and message of pointing error causes in 920 
the case of some procedures or methods „a structured walk through” used in team working case (every command is 
jointly explained and checked).  
Generally, it considers that in every growth of complexity, the model is tested again. For program codes checking 
there are recommended methods of viewing them. Because the importance of validation, in the followings we’ll 
show some characteristics of it.  
The validation is characteristic to every model. Depending on the problematic of each studied model, there are 
established the specific validation criteria for deliver a real results. Validation is a gradual category. In the case of a 
decision of the kind “either-or”, the model is considered valid only how it reaches real problem core. In certain 
cases, the growth of model validity can be realized only using very big costs. In these sense there was established a 
function of the relation between edge value of information and edge cost in the case of the increasing of model 
validity degree. [9] 
Validation is the result of a negotiation process. A model is useful when beneficiary believes in its validity. As 
negotiation process, validity can be viewed considering used validation criteria and methods.  
Practically, this thing is realized as collaboration between implied parts filled in by analytical and methodological 
competencies of expert consultants and with beneficiary’s experience.  
Validation acts during running of the whole project. It is considered even in the phase of element definition till 
model concept framing and model experimentation. Project phases occur interactively.  
In  dedicated  literature  [6]  evaluation  methods  are  grouped  according  to  certain  criteria.  For  user  there  are 
important: functional validity (checks model acceptability, reaction degree of simulated model, model behavior), 
result  validation (gained  results are  compared  and  there  is  analyzed  how  they  are  near to reality  and/or  they 
improve real system) and theoretical validation (validation of simulation results through comparison to theoretical 
results computed using other methods).  
 
4.SENSITIVITY ANALYSIS FOR A SIMULATION MODEL   
Sensitivity analysis allows experimentation of models for follow system reaction under conditions of controlled 
variation of external parameters or of internal processes from system.  
There is not the subject of simulated system validation but of generation scenarios of results running and evolution.   
There are established possible limits of overrunning results and also of deviation and dispersion of these results. 
For this reason, sensitivity analysis is considered to be an analysis of kind “what-if”, “what happens if…”. For this, 
there is built a system of values intervals relied on forecasting efficiency computes [10].  
Sensitivity analysis is used especially when input data are not valid, when repartition of input data is subjectively 
established, when information about inputs are related, when they can be given only fuzzy hypotheses about system 
relations especially in cause-effect relation.  
In all these cases there are so-called “critical points” which strongly lead to the emission of alternative premises 
and the result is got as scenario [11].  
The scenario will be so built that to be also considered occasional influences wherewith the system could fight in 
the future.  
The capacity to fast and efficiently test the most interesting combinations offered by this scenario, also supposes 
real collaboration with system beneficiary.  
 
CONCLUSIONS 
Validation process and sensitivity analysis have an important role in the foundation of easing a simulation model. It 
can  be  cancelled  a  series  of  features  and  relations  that  have  minor  influences  over  the  model  and  also  the 
description of the states of the system having too big number of variables.  
As a consequence of complexity reduction, the effects are turned into simulation costs decreasing, a smaller volume 
of input data, decreasing of the expenses of model implementation, easy interpretation.  
Complexity decreasing for a model is possibly only as how the reality allows this thing. But there are a series of 
simplification principles of a model possibly to be applied and namely: 
  When input values repartition is a repartition function of empirical values, there is checked the existence of 
a family of repartition functions that could lead to a better approximation (thing possible to be realized through a 
sensitivity analysis); 
  The parameters which have small variations during simulation can be considered as constants; 
  The size which equally troubles all the alternatives can be cancelled.  
  All the relation that can be represented through mathematical functions will be included into the model; 
  Sensitivity analysis can be used for building the possible combinations between input and output values of 
for evaluation the consequences of doubt premises; 
  For models having hierarchy structure, there are especially validated the models having easier feature, then 
it also follows an extension over those more sophisticated.  921 
It results that the heaviness of testing process refers to the model wherein we understand and dimension the reality, 
because economic-mathematic model or simulation one, is the creation of an analyst by whose means it represents 
in a kind or other, a certain system.  
 
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ASPECTS ABOUT SIMULATED MODEL TRUSTINESS

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ASPECTS ABOUT SIMULATED MODEL TRUSTINESS

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