output the program will provide series of initial prompts such as the followingwhat is the prob player wins servewhat is the prob player wins servehow many games to simulatethe program will print out nicely formatted report showing the number of games simulated and the number of wins and winning percentage for each player here is an examplegames simulated wins for ( %wins for ( %notesall inputs are assumed to be legal numeric valuesno error or validity checking is required in each simulated gameplayer serves first pseudo random numbers our simulation program will have to deal with uncertain events when we say that player wins of the servesthat does not mean that every other serve is winner it' more like coin toss overallwe expect that half the time the coin will come up heads and half the time it will come up tailsbut there is nothing to prevent run of five tails in row similarlyour racquetball player should win or lose rallies randomly the service probability provides likelihood that given serve will be wonbut there is no set pattern many simulations share this property of requiring events to occur with certain likelihood driving simulation must model the unpredictability of other driversa bank simulation has to deal with the random arrival of customers these sorts of simulations are sometimes called monte carlo algorithms because the results depend on "chanceprobabilities of courseyou know that there is nothing random about computersthey are instruction-following machines how can computer programs model seemingly random happeningssimulating randomness is well-studied problem in computer science remember the chaos program from the numbers produced by that program seemed to jump around randomly between zero and one this apparent randomness came from repeatedly applying function to generate sequence of numbers similar approach can be used to generate random (actually pseudo randomnumbers pseudo random number generator works by starting with some seed value this value is fed to function to produce "randomnumber the next time random number is neededthe current value is fed back into the function to produce new number with carefully chosen functionthe resulting sequence of values looks essentially random of courseif you start the process over again with the same seed valueyou end up with exactly the same sequence of numbers it' all determined by the generating function and the value of the seed so probabilistic simulations written in python could be called monte python programs (nudgenudgewink,wink