# Random Fibonacci-type Sequences in Online Gambling

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1 Random Fibonacci-type Sequences in Online Gambling Adam Biello, CJ Cacciatore, Logan Thomas Department of Mathematics CSUMS Advisor: Alfa Heryudono Department of Mathematics University of Massachusetts Dartmouth December 19,

3 original value of to and increase rate of Though the second value is not always exactly accurate, as the addition and subtraction is distributed randomly and will not be the same every time. 2 Beginning Strategy The first goal with this project was to visualize the events taking place in the previously described section. Our initial reaction was to do this using the Maple programming software. Logan had some experience with the software and decided it would be an efficient way to accomplish or goals. This belief was quickly corrected by Professor Gary Davis, who had advised us to use Mathematica to write our code instead, as it has excellent visualization tools built in. 2.1 Mathematica Code The Mathematica portion of this project was more to understand random fibonacci sequences as well as normal fibonacci sequences as this was the intro to our research and to get to know a bit about Mathematica seeing as none of us had any experience with it in the past. First we wanted to understand the convergence of the rate of increase to Φ= This code shows the n/n-1 term of the fibonacci sequence as it goes up, showing how it converges to Φ. 3

4 This block of code is a code that will create an array of length n that will be a randomly generated fibonacci sequence f n = + f n 1 + f n 2 for n>3 the sign function basically represents +/-, and if we wanted to change the probability of +/-, say for betting, we could just increase the range of the random number farther to one side or the other. For a 2 to 1 chance of winning it would be Sign[RandomReal[-1,2]] That way it is 2 times more likely to pick a positive number than a negative number. 4

5 The do statement at the top of this code block was not run because it was very long but just shows how the sequence s ith term converges to Φ i, the next important block is the N[x[1000] (1/1000) ] portion that shows that the 1000th term is almost exactly Φ 1000 this shows that the random fibonacci is clearly converging to our new Φ value of After a bit more playing around with different values of x just to make sure the code was full, t1 is declared as session time and is printed, alone with that a histogram of T which is a table of the log of the random fibonacci sequence is shown, P represents a list plot of the log of the fibonacci sequence which we expect to resemble Q, which is the expected value of the graph. R is the error plot of how far off it is from the expected line. S is a table of the x[n] 1/n which we would expect to as n gets larger and larger get closer and closer to U. So we plot S and U on the same plot, and P and Q on the same plot, to compare them. 5

6 These are the Results of the Mathematica Code for the 1 iteration of the random fibonacci sequence, after doing the code many times every time we would get variations of the same results showing that it always drifted around the expected log log line, and the median and mean would vary a little bit. The one surprising result to me was the histogram of the log of T for some reason We did not expect that to be near constant, although, after We see it and We think a bit more about it it makes much more sense, though that was a initial surprise. Also, on the S,U plot (top left corner) In this particular trial it did not much converge onto the expected line though We would expect it to as n gets infinitely larger. 2.2 Skewness We were first advised to look into the statistics of a random fibonacci generator. In this case Professor Davis asked us to look into two different attributes, one the those being skewness. Skewness by definition, is the measure of the degree of asymmetry of a distribution. If the tail at the small end of the distribution is more pronounced than the right tail the function is said to have negative skewness, and vice versa. If the two tails are equal then there is a skewness of zero. The general skewness of a distribution is defined by the following where µ i is the ith central moment. 6

7 2.3 Kurtosis γ 1 = µ 3 The second value our group was asked to find was the Kurtosis of our distribution. Kurtosis is defined as the degree of peakedness of a distribution. In other words, it is show as a normalized form of the fourth central moment µ 4 of the distribution. The kurtosis of a distribution is defined by the following where once again µ i is the ith central moment. µ Online Gambling β 2 = µ 4 µ 2 2 After some searching for a real life application for the work we had done, we were approached by Professor Alpha Heryudono about a mathematical model dealing with online gambling. The model includes a certain amount of players each playing an online gambling game in which they begin with a certain sum of money. Professor Heryudono proposed a scenario where each player is designated a specific betting strategy, which they use to bet against the dealer. The scenario we are creating is an unaltering one, in other words, each player is committed to the betting strategy they are given. This can be done using the random fibonacci generator mentioned previously. 4 Python Codes 4.1 Introduction The reason we switched to python programming was mainly because of mathematica s flaws. Specifically, Mathematica was way too difficult to figure out how to do nested loops. A nested loop is when, inside of a loop, another loop is used. It is used a lot in object oriented programming, not so much in functional programming. Mathematica is made for one line codes that work 7

8 off each other, what we wanted for the next section was to have a code that we would write, and then use that code with different numbers plugged in to run statistics. This is much more easily done in a object oriented environment. Though there is a way to do this in Mathematica, it involves downloading many packages and then learning a whole new way of programming. Mathematica was already new to us so we decided to switch to python because Logan had plenty of practice in that and could guide CJ and Adam. When we first started writing the code we had to start from scratch because object oriented programming is completely different from functional programming, and python s programming syntax is completely different than Mathematica s. 4.2 Players First, in the code, we needed to make an object that would keep track of all the statistics, most of our program is using vectors so we will use a vector labeled s to keep track of the statistics of each player. the object s here, initiated right in the beginning of the code is an array of arrays, or a vector of vectors, such that the s[0][0] element would give the first element of the first array of s because python 2.7 is a 0 index language meaning that when counting the language considers 0 the first number as opposed to 1. this s object is made specifically to keep track of key elements of the players. Because we run our code with 4 players, the first element of s accounts for the first player, the second for the second player and so on. Each element of each player s array is a different value as well. The first element of each of the 1x3 sub arrays is the players average final amount the turn before they go broke as the game goes on, the second element is the average amount of rounds the player lasts, and the third is the amount of times the player beats the dealer. In Python, we needed to make objects that would represent players, and they had to be able to be manipulated as the code went on. though the key 8

9 statistics of each player are recorded in s, it is impossible to carry out the game if the non key aspects of each character are not recorded. Each player is clearly represented by objects p1 through p4 each with 5 different attributes. The first attribute, p1[0] is seen as Alive this means that the player is Alive, obviously. Because python can compare strings, we will use this as the tell in our code to allow the player to play each round, if the player runs out of money then this will be switched to Dead. The second attribute p1[1] is seen as a decimal number. This value is a betting value for how much of their last turns amount they will bet. For instance, player 1 will bet 55 percent of what he had the turn before. which allows for this to be a Fibonacci like sequence. the third and fourth amounts are values the player has, the first is the value the player has the turn before the current one, and the second is the value the player has the current turn. These two values can be seen for each player to be the second 50 poorer than the first, this represents each player sending an ante. Finally the last value of each of the players, the p1[4] element represents the players odds of winning. We have each set at a different number to represent different ways of playing. Player 1 has a 37 to 1 chance against winning, representing a player of roulette that only plays on the 0 space. Player 2 has a 19 to 1 chance against winning representing a player of roulette that only plays splits or on two adjoining numbers vertical or horizontal. Player 3 has a 8.5 to 1 chance against winning representing a roulette player who plays with corner bids or any 4 adjoining numbers in a block, and finally player 4 who has a to 1 chance of winning representing a player who plays on evens or odds. In this first code we just check to see if they win as much as they bet then how often would they win and what were their percentages like. The dealer is represented as a floating point number that is how much 9

10 the dealer has in the bank. 4.3 Betting The betting portion of the code determines wether each player looses or wins each bet. This has to be done randomly so that this is indeed a random fibonacci sequence. The first part of that, the while loop with the qualifiers, basically says that all the players have to be alive and the dealer cannot be dead in order for the betting to commence for that round. the first if loop keeps track of what round it is by taking the i counting variable which keeps track of how many bets have been made and using the modulus operator with mod 4, allowing us to see that i is a factor of 4, qualifying it for the start of a new round, and if it is indeed a factor of 4, (i mod 4 ==0) then the round variable, r is set to i The second loop which just checks if the player p[i mod 4][0]== Alive : or, if the player whose turn it is is alive then the betting will commence for that round, if the player is dead, the betting skips over him for the round. Once it has been determined that the player is indeed betting this round, the b variable which represents if the player looses or wins the bet, 1 if they win, -1 if they loose. This is the portion where the players odds of winning comes into play. this is done by making b a random integer between 1 and 1000 times the players odds to win , if the number is less than 1000, then the player wins, if it is greater than 1000 the player looses. This makes sense because basically multiplying the odds by 1000 and adding 1000 is the equivalent of adding 1 to 1times the odds, a random variable between 1 and that number has an equal chance of being on each number, therefore if We say that all number above 1000 (representing 1000*the odds of winning) will 10

11 be a chase to loose, and only 1000 numbers will have the chance to win, that is equivalent to saying 1 to the odds of winning, but because some of the odds are not integers We had to throw in the multiplier of In this next portion of the code, w is just a variable that is declared to hold the amount of money the player has before the bet so that it can be placed into the p[imod4][2] place after all the betting has been done. Then the next portion deals with the changing hands of the money. The second line changes the current value to the current value plus b(1 or -1) times the percent bet times the amount they had last turn, showing how b determines if they win or loose the bet. Then the dealer adds or subtracts the opposite of the player, representing the dealer giving or receiving money. This is where this code would stop if we were just dealing with an infinite sequence but this project is finite so it also deals with when players die, or run out of cash, as well as if the dealer dies, or runs out of cash. This block of code represents solely what happens when the dealer or the players die. the first loop checks if the dealer is dead, if the dealer dies, he is 11

12 set to dead with the first line, the a sub-loop is implemented to check each player if they are alive or dead, and if they are alive, their win count goes up by 1, because they beat the dealer, if they are dead, then the win count doesn t change because they did not beat the dealer. The next loop, checks if the player is at or below 0, and if they are, the player is declared Dead and the dealer gets all the money he had right before he died, but also looses all the money that the dealer won from the player dying, this prevents the player from giving the dealer money he does not have, keeping the net amount of money the same. Then, the player s amount of money is set to 0 and the statistics are kept track of in s, putting in a new average for s[i mod 4][0] and [1]. This portion just deals with the outputs and the increasing of the iteration variables. First the [2] element of the player which is the previous value, gets changed to w, the place holder that we set up before that held the value before betting, and then the output is printed, it returns the s array, which is the array we first initiated, and then prints the amount the dealer has which just tells me that the game was being played right, because there is no net loss because it is a conservative system. 4.4 Results From the Python code we can collect averages for n amounts of runs. First i will run with these parameters set. 12

13 This says that all the players have the same betting value and starting amount, just different odds of winning. We wanted to see how this affected their amount of wins and final value before they die. After running this code for a few values of n, our outputs were: Their average final values seemed to be converging to a certain value as n went up and no body won at all except for player 4 with the best chances of winning, in the n=10,000 player 4 won 84 times, which may look like a lot, but out of 84 times it really isn t too much. Although changing these values can change the game, because every player may not start with the same amount of money or play the same bet percentages, we can change them, showing one example: 13

14 Though these are drastic changes in all the betting percentages and the starting money, really what governs the players chances of winning are their betting odds. as can be seen in the results of this with n=1000. The only player that won again was player 4 even though so much has changed. 4.5 What about payout changes? This is the first time in our lives not being a gambler has hindered me. After writing all this code We started to think, well why would anyone bet on such low odds if they are going to be losing the same amount of money as the person betting on higher odds? and then We realized, they wouldn t. In the game of roulette when people bet on a less likely event they get a multiplier of their bet. because of this We had to revise our code to do this. It really wasn t difficult, just changing a few aspects of the parameters, adding another element to each player, and then adding a if win or if loss loop to the main code, the two sections are: 14

15 This is the new players with the payout changes, the last parameter for each player is the multiplier of their score that they receive if they win. As you can see they are always a bit lower than their odds, the casinos have to make their money. Player 1 with 37 to 1 chances against winning, if he wins, gets 35 times the amount he betted. player two gets 17 times the amount, and so on. This part is the new if loop added in, if the player wins the bet (b=1) then they add p[i mod 4][5] times their bet to their amount and subtract that from the dealer, p[i mod 4][5] is their payout multiplier. If the player looses the bet (p=-1) then the loss is carried out as it was. Hindsight 20/20 I see now a much easier way to do this, as we are assigning b, we could have just written that if b wins change b to be p[i mod 4][5], it would have done the same thing, minus two loops. And the final results of the code with the payouts implemented: This shows a much more accurate to real life result, these players, all beat the dealer at some point, and had MUCH higher amounts of money before dying. This is great, it shows that if you wanna get to most money the fastest go with player 2 s strategy, if you want to last the longest, go with player 4 s strategy. 15

16 5 Conclusions Our final outputs demonstrate certain values for each of our four players as well as the dealer we have created. The values given for the players are; the amount of money they currently have, the percent of their previous total they had been gambling each time (their betting strategy), the amount of money they had just before they lost, the amount of money they have just after they lost (will be 0 every time), and the round in which they lost in. We found that if each of the players continues betting without varying their strategy, they will eventually lose all of their money. The value of the dealer represents how much money the dealer has at the end of the game, this will be 105,000 every time because we start the dealer off at 100,200, and the total amount money that each player has at the beginning will be transferred to the dealer. These add up to a total of 105,000. This will be the case each and every time unless we lower the money possessed by the dealer a considerable amount. The trend our results seem to follow is that the smaller percent of a player s money they bet each time, the longer that player will last in the game, though due to the randomness in nature of our codes, this is not always the case. 6 Closing Thoughts 6.1 Logan Thomas This is my Third semester (including summer semester) in CSUMS and My second semester using Python. I love python just as much as last time as it proves again to be awesome, except for graphing. I also enjoyed this project because It really let me apply my coding knowledge to a real world problem, without any starting block, just free. Programming like that is a real pleasure because you treat every problem like a puzzle and the challenge is to find the most efficient way to solve the puzzle, and it makes it more interesting when I realize I was wrong and had to implement another part to the code (the payout). Though working in a group was a bit more challenging for me than working alone I know that the experience of working in a group will be very helpful in the future of my research career. Also, the topic was very interesting, I have always been interested in Fibonacci type sequences ever since taking a number theory independent 16

17 study in High School. I find it very interesting to examine the properties of a random-type fibonacci sequence as it gets larger it clearly converges and that is fascinating, proving something for a random sequence is always exciting. 6.2 CJ Cacciatore This being my second semester participating in the CSUMS program, I was able to work much more efficiently than I had last semester. I had a basic understanding of how the program operated and feel as if I were able to work more towards the standards of the class. The skills I gained last semester helped this time around as well, though our primary code was written in Python, a language I had never used before, I was able to grasp the general structures and indexes of the programming language with significantly less help than I needed last semester. My personal thoughts on our project this semester were that it ended up being far more interesting than I had originally forseen. Being able to apply mathematical models to real-life situations truly makes them worth exploring and this project would be something I would consider researching further in the future. 6.3 Adam Biello This was my first semester programming in any language. I have learned much of the basics in Mathematica, MATLAB, Python, and Latex. I ve come to love using Latex, and can see myself using it in future courses. The graphing in Mathematica and MATLAB is amazing and easy to use. This was also my first semester to really explore fibonacci numbers and sequences. I had heard of them previously, but did not have as much knowledge of them as I do now. I d like to specially thank Professor Davis for sending out the about CSUMS. Without receiving that I probably would have never taken this course. Also, I d like to thank Professor Kim for checking in with me throughout the semester, and Professor Heryudono for advising us towards an application for random fibonacci-type sequences. 17

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