Poker is a popular gambling game, but most players lose money because they appear to make bets emotionally rather than rationally. Betting by emotion can lead to gambling addiction, where players repeatedly place bets to seek thrills or highs, often resulting in financial losses. The purpose of this paper is to study poker probability using a 24-card AKQJ partial deck for realistic poker situations. The AKQJ game reduces the winning hands to Four of a Kind, Full House, Three of a Kind, and Two Pairs. The experimenters used probability and combinatorics concepts to derive general formulae for each matching card pattern. To simplify probability simulation, a partial deck poker game was designed to increase the matching probability on some higher ranked hands such as the Full House. The system was enhanced to accommodate five players and multiple betting rounds, since these are more realistic poker real-time scenarios that capture how gambling disorder may exacerbate. The experimenters modeled this by assigning players characteristics based on a degree of risk-taking. The experimental results have shown that more conservative players perform better than other aggressive players. This partial poker game and the results can characterize the choices that spur irrational gambling disorder through exploration of fundamental probability scenarios in poker under the AKQJ model.
This project applies the Monte Carlo Simulation, the authors were able to take a deeper look inside the human mind, with modern approaches to computer programming. The game of poker uses key strategies of wit and deception, all in all ending with the sickness of gambling. The Monte Carlo Simulation uses the programming language Python to set up a 6-player game of Poker with a 16 card partial deck using the various face cards. This game counts only the full-house, 3 of a kind, pair, and high cards for winning combinations to maximize the data significance. In this game these characters have different thresholds in categories like checking, folding, and even betting, which is edited using various variables and wagers applied through a complex malleable percent confidence level interface in an effort to model real human psychology. These confidence levels affect how these players participate in the game. This results in players that are concise and specific, only betting when the cards have higher chances of winning, to players that bet no matter the cards that are presented. This program allows for numerous simulated rounds in seconds, while making advanced calculations on winning probability, and intricate hypothesis/correlation tests to deduce that gambling is not only harmful to the mind, but economically non-beneficial. This is only the first stepping stone for the possibilities that this simulation can explore, offering countless applications that simply have no limits.
Poker is a very popular gambling game in Casinos. Except professionals, most Poker players lose money without knowing basic poker probability. This project studies Poker probability by using the 16-cards partial deck with 6 players. The authors have used combination formula and derived the general formulas of matching probability for each matching pattern. With this unique AKQJ game, the winning patterns are down to “Four of a Kind”, “Full House”, “Three of a Kind” and “Two Pairs”. If all 6 players are playing, this Poker AKQJ game would be exciting since basic Poker probability can be simply calculated to guide each poker player on their betting decision to avoid any risky move. To study the players’ personality and psychology character, each player has been assigned a different playing character quantified by the risk level. Through this Poker AKQJ game, player data were simulated based on the worst-case probability algorithm. Worst-case algorithm could shorten the probability calculation time in real poker betting situations. Through playing this AKQJ game, poker players may become more rational and avoid gambling disorder.
|