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python monopoly an account of one man s attempt to build a monopoly simulator skip to primary content skip to secondary content python monopoly an account of one man s attempt to build a monopoly simulator search main menu home about buyers and renters posted on august 14 2016 by jmcontreras 4 after a months long break from working on the monopoly simulator i am returning to this project with the intention of programming on a more regular basis to help speed things along i will be updating this blog with shorter more frequent posts the latest series of commits to the code repository accomplished four improvements code development using jupyter notebook as opposed to py script file format minimum game mechanics to enable a game of players that buy and rent ad infinitum a global verbose argument that declares explicitly what happens in the game a visualization of players cash that updates dynamically at the end of every turn interestingly a quick simulation of this minimum game shows that players never run out of cash as buyers and renters who cannot buy houses and hotels my next goal is to build methods that allow players to buy houses and hotels to confirm that it is only when this game feature is enabled that players can go bankrupt posted in uncategorized tagged bankrupt buy cash hotels houses jupyter notebook methods rent verbose visualization 4 replies go to jail posted on february 8 2015 by jmcontreras reply the simulator has been improved to model the mechanics of monopoly jail the player class has a method that sends player objects to jail and on their next turn selects a strategy to leave jail paying 50 rolling doubles or playing a get out of jail free card the game flow has been improved accordingly to allow players who are in jail to spend several turns there where they must roll and wait for doubles if they so choose human players human players select a strategy to leave jail pay roll or card by considering the state of the game if most properties are unowned then players opt to leave jail quickly to buy properties in this situation most players pay or use a card if liquidity is an issue in contrast human players choose to roll the die and idle in jail for as long as they can if most of the other players have monopolies in this circumstance jail provides a temporary respite from expensive visits to these properties the simulator currently the simulator uses a simple logic to get a player out of jail a get out of jail free card is played if available otherwise the player pays the 50 fee as soon as possible players who cannot afford the fee roll the die until they can leave jail this fixed strategy is not ideal because it is agnostic to the state of the game unlike human players the simulated players do take into account the dangers of moving around the board when they choose how quickly to leave jail simulated players lack a method that allows them to examine the board considering whether the current layout of monopolies and unowned properties makes it advantageous to leave jail expeditiously this method will also be useful for other types of strategizing like deciding whether or not to purchase a specific property a future improvement to the simulator will involve the creation of such a method making the decision to get out of jail a rational response to the state of the game posted in uncategorized tagged computer simulation decision making die jail liquidity methods monopoly objects python software leave a reply game elements as programmatic objects posted on november 19 2014 by jmcontreras reply a computer simulation of a board game must be a faithful representation of the elements and rules of the game the basic elements of monopoly include a board the bank cards properties and players the interaction of these elements is governed by rules that dictate how each element may behave for example players who own properties can collect rent to represent game elements and rules i designed the monopoly simulation around programmatic objects an object of this type has fields which describe the object and methods which describe what the object can do methods often work by updating values in the object s fields for example a player object has fields that describe its status like board position additionally it has methods that describe the actions it can perform like a move method that moves the player across the board by updating the field that describes the player s board position as a second example each of the 40 spaces in the board is represented by a programmatic object with fields that describe it e g price if it is a property the simulation represents spaces as elements in an ordered list e g free parking is the 20th space that players traverse throughout the course of the game for the sake of simplicity i designed space objects without methods any interaction between a player and a space is executed by a method in the player object for example properties do not have a collect method to extract rent from players who land on them instead the player object has a pay method to compensate the property owners in sum the simulation uses programming objects to represent the elements of the game as well as the rules that dictate how these objects can interact with one another programming objects afford the simulation an economical and intuitive way to represent the dynamics of monopoly posted in uncategorized tagged computer simulation fields lists methods monopoly object oriented programming objects python software leave a reply python monopoly posted on october 13 2014 by jmcontreras 11 many years ago a friend and i were discussing monopoly strategy as one does of course when we came to a disagreement he believed that the best players are risk seeking they buy as many properties as quickly as possible always teetering on the edge of bankruptcy i believed and i still do that the best players are risk averse they buy cautiously saving their money to buy superior properties and make all their payments i don t know who is right though my money is on me but i know that the way to find out is to collect data ideally we would have risk seeking players and risk averse players compete in thousands of games and see which type of player does better than the other unfortunately a monopoly game lasts about 1 000 hours not really but doesn t it feel like it sometimes so this little experiment would take a very long time to conduct thankfully computers can give us the next best thing a computer program could simulate a monopoly game between risk seeking and risk averse players thousands if not millions of times and determine which type of player does best such a program does not currently exist but i am in the process of writing one in python to figure it out i m not a software engineer so this program may be beyond my ability but i m going to give it a shot this blog will document my attempt to build this simulator the code for which is hosted on github part of it will deal with the technical hurdles but most of it will discuss my thought process as i grapple with how to capture the complex human psychology involved in a game of monopoly in a few lines of code so should we seek or avoid risk to be the best monopoly players let s find out posted in uncategorized tagged computer simulation decision making monopoly psychology python risk software 11 replies search recent posts buyers and renters go to jail game elements as programmatic objects python monopoly recent comments diana on python monopoly jmcontreras on buyers and renters kyle foley on buyers and renters jmcontreras on buyers and renters adamcripps on buyers and renters archives august 2016 february 2015 november 2014 october 2014 categories uncategorized meta create account log in entries feed comments feed wordpress com blog at wordpress com subscribe subscribed python monopoly sign me up already have a wordpress com account log in now privacy python monopoly subscribe subscribed sign 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