<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:wfw="http://wellformedweb.org/CommentAPI/">
	<channel>
		<title>Playing RPS game using Lyapunov threshold improves game&#x27;s payout</title>
		<link>/blogentry/31240</link>
		<atom:link href="https://www.lotterypost.com/rss/blogcomments/31240" rel="self" type="application/rss+xml" />
		<description>edge's Blog: Playing RPS game using Lyapunov threshold improves game&#x27;s payout</description>
		<dc:language>en-us</dc:language>
		<generator>Lottery Post RSS Generator</generator>
		<item>
			<title>Original Blog Entry: Playing RPS game using Lyapunov threshold improves game&#x27;s payout</title>
			<link>/blogentry/31240</link>
			<guid isPermaLink="true">/blogentry/31240</guid>
			<pubDate>Sun, 12 Jul 2009 21:47:26 GMT</pubDate>
			<dc:creator>edge</dc:creator>
			<description><![CDATA[<p>After much research and troubleshooting finally some progress in the analysis of RPS (Rock Paper Scissors) game model.<br /><br />Why the RPS game is of so much interest to me? Because RPS represents one of the simplest models (two players with three probabilistic states each) in fact RPS game<br /><br />is used by researches in game theory, statistics and chaos theory to study phenomena in non-linear dynamics.<br /><br />In the game theory the simplest goal is to improve payout matrix (as its in the lottery which is a type of probabilistic game, albeit very extreme case of one!)<br /><br />One thing RPS game has in common with lottery is its random probability distribution between game iterations, of course RPS probability states are only 3 where else in a typical lottery game let say mega lottery, there are 56 different probability states between each iteration, nevertheless its still of value to study simpler models<br /><br />(possible reduction of large dimensional probabilistic state game (such as lottery) to simpler model is unknown at this stage, I don&#x27;t even know if at all possible!)<br /><br />With RPS game, 3 different trials were run, each iterating 4 times across 500 game runs and each run was divided to one of two bets:<br /><br />1. blind bets, using RPS game learning algorithm that at best yields 0.333% improvement over statistical medium<br /><br />2. using Local Lyapunov Threshold to consistently (and selectively) bet on Local Lyapunov Threshold bands<br /><br />Both 1 and 2 game outcome were collected and are displayed below (note that consistent improvement has been made of 2 over 1, yielding some credibility to the fact that using Local Lyapunov Threshold can in fact improve game&#x27;s outcome matrix.<br /><br />This in fact is really surprising to me, to recall RPS game probabilistic states are chosen from the random numbers! So in short, Lyapunov strategy compensates for the random!<br /><br />There are many things not done as yet such as introduction of:<br /><br />1. adaptive threshold (to inject statistical inference to Local Lyapunov Threshold)<br /><br />2. entropy filtering<br /><br />Below is output from rps_game.cpp, improved trials (using LLE threshold) were marked witth GAIN label, there were 12 trials run resulting in 10 improved (GAIN) payouts with LLE over 2 that did not use LLE Threshold.<br /><br />----------------------<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1143 Losses: 898 Ties: 459 Ratio Win/Total: 0.4572<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -0.1, max: 0.1)<br /><br />Total Game Iterations: 2525 Super Agent Played: 586 times. Wins: 274 Losses: 209 Ties: 103 Ratio Win/Total: 0.467577 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1136 Losses: 930 Ties: 434 Ratio Win/Total: 0.4544<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -0.5, max: 0.5)<br /><br />Total Game Iterations: 2525 Super Agent Played: 1688 times. Wins: 813 Losses: 590 Ties: 285 Ratio Win/Total: 0.481635 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1194 Losses: 915 Ties: 391 Ratio Win/Total: 0.4776<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -1, max: 0)<br /><br />Total Game Iterations: 2525 Super Agent Played: 537 times. Wins: 259 Losses: 199 Ties: 79 Ratio Win/Total: 0.482309 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1120 Losses: 927 Ties: 453 Ratio Win/Total: 0.448<br /><br />Strategy with (+) Local Lyapunov Threshold (min: 0, max: 1)<br /><br />Total Game Iterations: 2525 Super Agent Played: 1654 times. Wins: 753 Losses: 610 Ties: 291 Ratio Win/Total: 0.45526 [GAIN]<br /><br />Press any key to continue . . .<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1177 Losses: 943 Ties: 380 Ratio Win/Total: 0.4708<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -0.1, max: 0.1)<br /><br />Total Game Iterations: 2525 Super Agent Played: 617 times. Wins: 301 Losses: 224 Ties: 92 Ratio Win/Total: 0.487844 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1162 Losses: 933 Ties: 405 Ratio Win/Total: 0.4648<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -0.5, max: 0.5)<br /><br />Total Game Iterations: 2525 Super Agent Played: 1693 times. Wins: 828 Losses: 598 Ties: 267 Ratio Win/Total: 0.489073 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1110 Losses: 936 Ties: 454 Ratio Win/Total: 0.444<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -1, max: 0)<br /><br />Total Game Iterations: 2525 Super Agent Played: 596 times. Wins: 304 Losses: 195 Ties: 97 Ratio Win/Total: 0.510067 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1217 Losses: 869 Ties: 414 Ratio Win/Total: 0.4868<br /><br />Strategy with (+) Local Lyapunov Threshold (min: 0, max: 1)<br /><br />Total Game Iterations: 2525 Super Agent Played: 1568 times. Wins: 785 Losses: 535 Ties: 248 Ratio Win/Total: 0.500638 [GAIN]<br /><br />Press any key to continue . . .<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1113 Losses: 940 Ties: 447 Ratio Win/Total: 0.4452<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -0.1, max: 0.1)<br /><br />Total Game Iterations: 2525 Super Agent Played: 568 times. Wins: 270 Losses: 189 Ties: 109 Ratio Win/Total: 0.475352 [GAIN]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1189 Losses: 898 Ties: 413 Ratio Win/Total: 0.4756<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -0.5, max: 0.5)<br /><br />Total Game Iterations: 2525 Super Agent Played: 1738 times. Wins: 810 Losses: 626 Ties: 302 Ratio Win/Total: 0.466053 [LOSS]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1247 Losses: 883 Ties: 370 Ratio Win/Total: 0.4988<br /><br />Strategy with (+) Local Lyapunov Threshold (min: -1, max: 0)<br /><br />Total Game Iterations: 2525 Super Agent Played: 568 times. Wins: 272 Losses: 202 Ties: 94 Ratio Win/Total: 0.478873 [LOSS]<br /><br />Strategy without(-)<br /><br />Total Game Iterations: 2525 Super Agent Played: 2500 times. Wins: 1159 Losses: 904 Ties: 437 Ratio Win/Total: 0.4636<br /><br />Strategy with (+) Local Lyapunov Threshold (min: 0, max: 1)<br /><br />Total Game Iterations: 2525 Super Agent Played: 1497 times. Wins: 709 Losses: 531 Ties: 257 Ratio Win/Total: 0.473614 [GAIN]<br /><br />Press any key to continue . . .<br /><br />... &#x5b;&#xa0;<a href="/blogentry/31240">More</a>&#xa0;&#x5d;</p>]]></description>
			<category>Blog Entry</category>
			<category>edge</category>
			<wfw:comment>https://www.lotterypost.com/blogentry/31240</wfw:comment>
		</item>
	</channel>
</rss>

