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advanced analytic techniques skip to main skip to sidebar advanced analytic techniques this blog is part of class project to explore various analytic techniques used by modern intelligence analysts tuesday november 13 2018 summary of findings monte carlo simulation 4 out of 5 stars note this post represents the synthesis of the thoughts procedures and experiences of others as represented in the articles read in advance see previous posts and the discussion among the students and instructor during the advanced analytic techniques class at mercyhurst university in november 2018 regarding monte carlo simulation as an analytic technique specifically this technique was evaluated based on its overall validity simplicity flexibility and its ability to effectively use unstructured data description monte carlo simulation is arguably both a method and a modifier by which analysts can pull together pieces of information in the form of ranges and through random sampling of those ranges produce an estimate it is a highly flexible method due to its ability to handle uncertain data from virtually any discipline the mental modeling required to piece apart the given problem decreases the simplicity of the method but plugging in the ranges themselves is simple indeed monte carlo simulations have the capacity to produce a highly accurate distribution of estimates strengths applicable with simple ranges exact numbers not required increases confidence in estimate through statistical analysis simulations produce a visual statistical distribution allows analyst to identify collection needs to improve estimate allows analyst to create an estimate with uncertain data weaknesses qualitative estimates require coding statistical data can be misleading the result of a mc simulation is a numeric range which is almost never where the analysis itself will stop challenging to explain to decision makers how to identify the variables that are needed to answer the question see application of technique can be ranges or exact numbers enter the variables into a software program capable of running a monte carlo simulation guesstimate microsoft excel risk etc multiply average outputs of simulations together to produce estimate continue adding variables and running simulations until question is answered application of technique utilizing www getguesstimate com we presented the group with a question how many big mac s will mcdonald s sell at locations in erie in a week the group then created a model to determine the number of big mac s sold the group developed a series of questions with estimate ranges to answer each question pictured below is the model the group created to work through the question the arrows connecting boxes within the model indicate that a formula was used in order to produce an answer in the form of a range of possible outcomes i e 4 9 mcdonald s in erie 400 customers per day 2000 people visiting mcdonald s in erie per day for further information get guesstimate posted by unknown at 10 40 am 23 comments monday november 12 2018 monte carlo method in risk analysis for investment projects by victor platona and andreea constantinescua summary investment projects worth over 50 million that are financed with eu support must meet certain conditions including criteria related to the size of the risk for major projects risk analysis indicates whether risks have been taken into account in estimating the costs in this study the authors propose a risk estimation method monte carlo method to be applied in a standardized way on these investment projects in the monte carlo method artificial values of a probabilistic variable are generated through a random uniformly distributed number generator in 0 1 intervals the method algorithm is shown in its succession interactive five steps step 1 creating a parametric model y f x1 x2 xq step 2 generation of random input set of data xi1 xi2 xiq step 3 effective calculations and memorizing results as yi step 4 repeating steps 2 and 3 for i 1 to n n 5000 step 5 analyzing the results using histograms confidence intervals other statistic indicators resulting from the simulation etc the authors used 23 waste management projects and a number of 40 water and wastewater projects which have been contracted and are under implementation they then calculated average standard deviation and relative standard deviation for the two types of projects the water supply projects had larger average value of a project is 106 23 million compared to waste management projects average of 31 7 million they then estimated the risk of exceeding the project value this showed that the average is 50 75 million slightly lower than the chosen project analysis 51 76 million the asymmetry is 0 05 which shows a slightly expanded distribution to the right and the flattening is 0 1 indicating a slight flattening compared to normal distribution the authors also estimated the risk of exceeding the project implementation period some of their over all conclusions included that competitive bidding system can make the offering price lower than the starting price there are higher chances of exceeding the initially established period due to multiple situations occurring during implementation finally that monte carlo method is relatively easy to perform and provides important information regarding the risks of investment projects critique the authors laid out a step by step way evaluate both the risk of exceeding the project value and the risk of exceeding the project implementation period if the monte carlo method is applied correctly and with accurate inputs it can be useful in evaluating the risks in investments like many methods bad data will cause miss leading results that could be very costly in the financial industry link https core ac uk download pdf 82113269 pdf posted by unknown at 9 26 am 11 comments older posts home subscribe to posts atom subscribe to posts atom posts all comments atom all comments contributors billy chelsie kristan j wheaton unknown unknown unknown unknown unknown unknown unknown advat course projects here is a list of links to some advat projects we ve been working on seo analysis now a site for using seo in intelligence analysis intellectual property in intelligence analysis blog archive 2018 47 november 9 summary of findings monte carlo simulation 4 out monte carlo method in risk analysis for investment the effect of number of rating scale categories on monte carlo simulations and increasing confidence modeling for decision making under uncertainty in summary of findings social network analysis 3 5 social network analysis in the study of terrorism the social network of hackers social network analysis of an urban street gang us october 15 september 18 august 5 2017 77 november 15 october 28 september 33 august 1 2016 61 november 15 october 22 september 22 august 2 2015 61 november 12 october 27 september 18 august 4 2014 60 november 13 october 29 september 18 2013 80 may 12 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