poker hands

2005-06-23T04:41:00.000-07:00

World Poker Hands , Tour Events Continue Dramatic Growth The Jack Binion World Poker Open at Casino/ Gold Strike Casino in Tunica, MS which began on January 26, 2004, is the latest event on the WORLD POKER TOUR to demonstrate the domino effect of the television show's huge popularity. Records have been set in each of tour stops and the Jack Binion World Poker Open is no exception with a huge increase in players, prize money and first place payout over the previous year's tournament.
367 players signed up for this week's event, which will culminate on January 29, 2004 with the filming of the Final Table for broadcast as part of the WORLD POKER TOUR series on The Travel Channel. WORLD POKER TOUR airs every Wednesday night at 9 p.m. ET/PT on the network. Many faces familiar to the WPT turned out for this event, including fan favorites Gus Hansen, Ben Affleck, Lou Diamond Phillips, Annie Duke, Chris Fergueson, and Howard Lederer.
The increase in players catapults the total prize money to $3,455,075 million, including the $25,000 WPT contributed to the prize pool. With such an enormous and record-setting prize pool for the tournament, players down to 27th place will go home with a nice payday, and the winner will capture a top prize of $1,278,370. Last year's champion David "Devilfish" Ulliott from England walked away with $589,990 when the prize pool totaled $1,600,000. The prize pool is determined by the number of players "buying in" to the tournament -in this case the entry fee is $10,000 per person.
"Congratulations to the Casino and Gold Strike Casino for having the largest prize pool in WPT history. This growth in this tournament is truly remarkable," said CEO of the World Poker Tour, Steve Lipscomb.
The World Poker Tour, over 80% owned by Lakes Entertainment, is one of the television hits of the year. More than five million people tune in each week to watch the high stakes drama of the tense competition where millions of dollars change hands across the table, all chronicled by "WPT Cams," that reveal the players' hidden cards. The show is the highest rated series in the history of The Travel Channel.
The dramatic increase in prize money at the Jack Binion World Poker Open is helping drive the WPT's total prize money for Season 2 to an estimated $30 million, triple the prize money in the previous season, which in turn is attracting many newcomers to the game of poker and to WPT tournaments.
The Jack Binion World Poker Open concludes Thursday. WPT fans are welcome to come watch the action and see who will go home with the millions laid forth on the table before the final hand. The show begins at 2 p.m. Thursday, January 29 in the Gold Strike Ballroom at the Gold Strike Casino in Tunica, MS. Admission is free and seating is first come, first served. The next stop on the tour will be the L.A. Poker Classic at Commerce Casino in Los Angeles in February, 2004. To play in a WORLD POKER TOUR event, go to for the complete 2003-2004 Tournament Schedule and casino contacts. The WORLD POKER TOUR airs Wednesday nights at 9 p.m. ET/PT on The Travel Channel.
About The World Poker Tour
The World Poker Tour has transformed poker into a televised mainstream sports sensation, creating record-setting ratings and capturing millions of new fans for America's favorite card game. The blockbuster series has riveted the nation's TV viewers thanks to its hallmarks--a blend of high caliber sports-style production shot from 13 different camera angles, expert commentary, cliffhanging "reality TV" drama and the WPT's signature "ace in the hole"--its revolutionary WPT Cams, that reveal the player's hidden cards, making it possible for audiences to feel like they're sitting in the seat making million dollar decisions on each hand.
The World Poker Tour is a joint venture between Steven Lipscomb and Lakes Entertainment, Inc., which owns approximately 80% of WPT. Lakes currently has development and management agreements with four separate Tribes for four new casino operations, one in Michigan, two in California and one with the Nipmuc Nation on the East Coast. In addition, Lakes Entertainment has agreements for the development of one additional casino on Indian-owned land in California through a joint venture with MRD Gaming, which is currently being disputed by the Tribe. Lakes Entertainment, Inc. common shares are traded on the Nasdaq National Market under the trading symbol "LACO".

Poker Hands Ready For The dealTop

2005-06-09T00:55:00.000-07:00

Poker Hands Ready For The dealTop
Over 200 of the best poker players in North America will meet at Casino Regina this week for the four-day, eighth annual Station Classic tournament that will test their skill and concentration.
Combine that with a little luck and they could win thousands of dollars in prizes -- as much as $300,000 is up for grabs.
Canadian poker champion Mike Kirby of Regina, who won his title last fall at Casino Regina, is hoping to repeat his victory.
The tournament starts with a social evening today where the gamblers meet. Four different types of poker will then be played Wednesday through Saturday -- seven-card stud, Omaha hi-lo, Texas hold'em limit and Texas hold'em no limit.
Twenty-five tables will be set up in the Casino Regina Show Lounge, and players will be eliminated until there's a winner. Casino Regina expects about 250 people from across Canada and the U.S. to participate.
"The only thing wild are the players and dealers," joked Dave Taylor, who is organizing the tournament for Casino Regina. The casino has been hosting three tournaments per year since it opened in 1996.
So who are the players? "They're poker aficionados who organize their holidays around these tournaments," said Taylor.
"They're doctors, lawyers, blue collar, white collar, young, old."
"It's great. They have an awesome set-up there in the Show Lounge," said Kirby, who owns a lawn-care company and has been playing poker "all his life".
"It's not about the money. It's about winning the big event."
Nevertheless, the money isn't bad either. Last fall, Kirby won the most at one Station Classic tournament -- $39,501 -- and the largest single payout win at $34,436. He used some of the money to pay bills, and saved the rest.
Kirby said the game in which he won lasted more than 14 hours.
Kirby, who also has played smaller poker tournaments in Las Vegas, has been playing in Casino Regina tournaments for two years.
"You start with 200-plus people and usually half are eliminated in the first two to three hours," Kirby explained. "It's a real test. You're mentally drained by the end of the day because you have to keep concentrating and not make a mistake."
Kirby said the tournament room is "usually fairly quiet" although the silence is occasionally punctured by screams from "a bit of both" the winners and losers.
Kirby also said his wife doesn't complain that the games can last for hours.
"She doesn't mind ... She knows I have a lot of fun."

poker best hands

2005-05-24T23:04:00.000-07:00

Poker Hands - Strongest To Weakest
A ranking of Poker hands from royal flush to high card
Standard five-card poker hands are ranked here in order of strength, from the strongest hand to the weakest.
Royal Flush - the best possible hand
Ace, King, Queen, Jack and 10, all of the same suit.
Straight Flush
Any five-card sequence in the same suit (e.g.: 8, 9, 10, Jack and Queen of clubs; or 2, 3, 4, 5 and 6 of diamonds).
Four of a Kind
All four cards of the same value (e.g.: 8, 8, 8, 8; or Queen, Queen, Queen, Queen).
Full House
Three of a kind combined with a pair (e.g.: 10, 10, 10 with 6, 6; or King, King, King with 5, 5).
Flush
Any five cards of the same suit, but not in sequence (e.g.: 4, 5, 7, 10 and King of spades).
Straight
Five cards in sequence, but not in the same suit (e.g.: 7 of clubs, 8 of clubs, 9 of diamonds, 10 of spades and Jack of hearts).
Three of a Kind
Three cards of the same value (e.g.: 3, 3, 3; or Jack, Jack, Jack).
Two Pair
Two separate pairs (e.g.: 2, 2, Queen, Queen).
Pair
Two cards of the same value (e.g.: 7, 7).
High Card
If a hand contains none of the above combinations, it's valued by the highest card in it.

Playing Too Many Hands, or Not Enough

2005-05-13T08:44:00.000-07:00

You’re in a tournament, you’re in early position, and you pick up an A-6 suited. You think, “Gee, this is a pretty good hand if I flop a flush to it,” so you play it. If this sounds like you, you’re playing too many poker hands. And you’re probably also playing too many hands when out of position. This is a common error that many novice players commit. No-limit hold’em is a “backside” game — that is, the point of power is late position. If you are patient and disciplined, and always pay close attention to your table position, you can erase the flaw of playing too many hands.“I don’t mind playing pocket pairs from any position if I can enter the pot for only one bet,” Don Vines, the co-author of our upcoming book How to Win No-Limit Hold’em Tournaments, says. The problem comes when you limp in from out of position with a small pocket pair such as fours, for example, and an opponent raises five to six times the size of the big blind — and you call. Most times, it is not profitable to call a raise in situations like this unless lots of other players have called in front of you, and you have a ton of chips. “If I’m playing at a table where there’s a lot of raising,” he adds, “I will not play small pocket pairs from up front.” This is a good rule of thumb. The aggressiveness of your opponents should be a determining factor as to which hands you enter the pot with from early position. In other words, it isn’t terrible to play small pairs from up front if you’re at a passive table; but, if you’re up against several very aggressive players, be wary of getting involved.During the early rounds of a tournament, players have a tendency to play a lot more hands than they play in the later stages, especially in rebuy tournaments. They come into pots with a wide variety of marginal hands that they would not play once the rebuy period has ended. Early in a tournament, we often see people play too many hands from out of position, and too many marginal hands when in position. “My philosophy is the opposite,” Don states. “I prefer playing fewer hands early, and playing more hands from the backside that may be considered somewhat marginal later in the event. I usually will have fewer callers later in the tournament, and since I am in late position with these marginal hands, I will be in position to take the pot away from my opponents if nobody flops anything.” When the rebuy period is over and the blinds are bigger, or when the antes kick in, players tend to tighten up. This is when you can take advantage of that tendency.Unlike limit poker, in which you have to win a series of pots and show down lots of hands, in no-limit hold’em you don’t have to win lots of pots; you just have to win most of the ones you play. And you don’t have to play very many hands. You can win with a bet. People tend to forget that. NOT PLAYING ENOUGH HANDS On the flip side of the coin, we see many players who play too tight during the opening stages of a tournament because they want to survive. They wait till the cows come home for pocket aces, kings, queens, or A-K. They really are not playing true no-limit hold’em, because they are afraid to get involved in a hand, lose it, and get eliminated from the tournament. They have no “heart.”People who play excessively tight usually meet with one of several possible fates. Against observant opponents who notice a layer of dust on top of a rock’s chips, the rock won’t get any action on his good hands. So, when he finally wakes up with aces or kings and raises the pot, what do his opponents do? They fold, leaving the tight raiser with only the blinds. Also, it is almost impossible for a very tight player to win a no-limit hold’em event, because nobody gets dealt enough premium hands over the course of a tournament. Using position, chip power, and good timing is often more important than getting good cards in no-limit hold’em. In other words, you can win with no cards.Don’t let fear freeze your play. People who don’t gamble enough usually are afraid of getting knocked out of a tournament, but there is another way to look at things. Whether you get knocked out one place out of the money or first makes no difference. The result is the same. Throughout the entire tournament, you must be playing to win and trying to accumulate chips so that you can make the money. Don’t worry about getting knocked out. Play to win or don’t play at all.Assuming we play and win just the right number of hands in the next no-limit hold’em tournament we enter, Don and I hope to meet you in the winner’s circle one day soon.

Casino Poker Games That Are Not Poker

2005-05-12T09:26:00.000-07:00

Flop Poker: A house-banked card game that uses poker hands rankings, in which players do not compete against the dealer. The object of the game is to make a poker hand containing a pair of jacks or better, using the player’s three cards dealt and the three community cards turned up by the dealer. In addition, players can bet that their hand will be the best of all hands dealt. Each player places two bets, an ante bet and a pot bet. The ante wager can be any amount up to the table limit. The pot bet is always the table minimum. Three cards are dealt to each player facedown. The player may look at his own cards, but sharing of information is not allowed. The player has two options, raise or fold. If the player folds, he forfeits his ante but retains his cards for later settling of the pot bet. If the player raises, he does so by making a flop bet equal to his ante bet. The dealer then deals a three-card flop faceup. These are community cards and are used by all players. The dealer determines in turn each player’s best poker hand, using all three of the player cards in combination with any two of the flop cards. If the player has at least a pair of jacks, the ante wager pays even money and the flop pays all winners according to a pay table, ranging from 1,000-1 for a royal flush to even money on a pair of jacks or better. Finally, the player with the highest poker hand, again using his own three cards and any two flop cards, wins all pot bets. If two or more hands are tied, the pot bets are split among the players holding those hands. Like the odds bet of craps, the pot bet of Flop Poker has absolutely no house advantage.

Four-card poker: A house-banked game dealt from one deck, similar to three-card poker. Each player receives five cards from which to make four-card poker hands (with four-card straights and four-card flushes as ranking hands, and hands ranked thusly: four of a kind, straight flush, three of a kind, flush, straight, two pair, pair); only the best four cards in each hand are used to determine winners. The dealer gets six cards to make a four-card hand; one of the dealer’s cards is dealt faceup. Two bets can be made. For the original bet (the ante), a player is paid, if he stays, on certain holdings according to a pay scale, ranging from 25-1 for four of a kind to 2-1 for three of a kind. After seeing his hand, a player can either fold or stay in the game by making a second bet (the aces up bet), and then his hand competes against the dealer’s; the player wins ties. This second bet can be equal to, twice, or three times the ante. If the player does not make the aces up bet, he loses the original bet. Unlike three-card poker, the dealer does not need to qualify. Some casinos have other rules, sometimes involving three bets. The aces up bet wins when the player has a pair of aces or better, ranging from even money for a pair of aces to 50-1 for four of a kind.

Let It Ride Bonus: A house-banked card game that uses poker hand rankings and looks like a combination of five-card stud and hold’em. The object of the game is to make a poker hand containing a pair of tens or better, using the player’s three cards dealt and the two community cards turned up by the dealer. Each player places three equal bets in circles marked 1, 2, and $. Three cards are dealt to each player. If the player likes his cards, he “lets it ride.” If he doesn’t like them, he requests the first bet back. Then, the dealer turns over one of the two community cards. If the player still likes his cards, he can again “let it ride” or request his second bet back. Finally, the dealer turns over the second community card and pays all winners according to a pay table, ranging from 1,000-1 for a royal flush to even money on a pair of tens or better. A player can also make an extra side bet, with certain bonus hands paying large payouts, sometimes including part or all of a progressive jackpot. The game was formerly known as Let It Ride! (including the exclamation point); some casinos still use that name.

Changing Position at Poker.

2005-05-11T08:13:00.000-07:00

Position is important in most competitive games. In baseball the home team is given the advantage of batting last. Batting last allows the home team in the final inning to know precisely what to aim for. In a tied game, only one run matters. Down by three, then three runs are a necessity. In the top part of the inning the visiting team would not know for sure if going for one safe run was better than taking the risk of going for two runs.

In football, physical position, having the wind at your back, often plays an enormous role in who wins a game.In Texas Holdem poker, the value of position is generally self-evident. You want opponents to make their decisions before you do, and then you want the final say, the last word. On top of that, Holdem is a game where it is common that nobody has much of anything. You are making decisions based on whose "nothing" will outplay the others to win the pot. While superior position doesn't automatically win hands, it does make it more likely you will make better bets -- in the same way that a general who positions his troops on terrain he is familiar with will have an edge.

But position in Texas Holdem is simplistic. Last is basically best, particularly when only two players are in a pot. First position, or second position behind a maniac, or position in front of a maniac... sometimes these will offer positional advantages too, but for the most part, just being last to act is such a significant edge that all good players will tend to play more poker hands when they are in late position and less hands when they are in early position.Position in Seven Card Stud and Stud High-Low is far different. Position here tends to be variable. The highest board showing acts first from fourth street on, so if king high bets first on fourth street, another player who gets an ace or pairs deuces might act first on fifth street. You do still tend to have an advantage over the player to your immediate right, but positional considerations are complicated in the Stud games.
Certain hands should be more playable when you are not the high hand, while representing hands becomes more important when you act first. Some hands can be played more aggressively when an opponent shows a king or ace, meaning they will likely be forced to act first throughout the hand. But the greatest difference in positional complexity comes in comparing Omaha HiLo to Holdem. Last position continues to have some general advantages, but it comes with disadvantages too. For example, bluffing from last position is suicide against good players. The bluffing arrow is almost removed from your quiver when you are last. In Holdem having middle position seldom offers any advantages but middle position is the prime bluffing position in Omaha.
At the same time though, middle position has significant disadvantages because Omaha High Low is a game of "sharing" pots. If you have the nut hand one way or the other, and the early position bettor bets the other nut hand, middle position becomes very hard to play. Most people, fortunately, play very poorly here. For example, they will raise their nut high hand, driving out players behind, and then splitting the pot with the initial low bettor. The correct action will usually be to just call the low bettor, and hope for overcalls -- but sometimes this will NOT be right! For example, if you suspect a player behind you also has the nut low, if you raise with the high hand you will get two bets into the pot from the low hands instead of just one.

Holdem's simplistic last-is-best positional concept is out the window in Omaha. Very generally, if you have a low hand, betting first is advantageous, while having the nut high hand is best in last position. Suppose you have the nut flush on the river against more than one player. Betting first is totally action killing. The best you will do is get called. If you are last with the nuts, you might get a bet in front of you, or you might even get a checkraise bluff from an opponent who thinks you are bluffing. In contrast, betting the low from early position can lead to scrambling where the later position players try to drive each other out; or, if there is another nut low in play, betting will tend to slow that player down so that they don't raise in three-way situations.
Moving from Holdem to other games, there are often considerations that, while not totally different, are more complex -- even if some other concepts are not as complicated. (Winning more than your share of situations when no one has much of anything is more important in Holdem than Omaha for instance.)
Position always matters, but it is much more variable in Stud and Omaha than in Holdem. You have to "think on your feet" about position more in Stud and Omaha.Manipulating position is a skill that Holdem players need to focus on developing more deeply when moving to other games.

Bad Beats on Poker Hands

2005-05-07T15:40:00.000-07:00

You look down at your starting hand and find the ace of hearts and ace of diamonds. You raise and get some action. The flop brings the eight of clubs, jack of hearts and three of diamonds. It's checked to you and you bet. The turn brings the ten of clubs and again you bet and get callers. The river brings the ace of clubs. This time the player in front of you bets. You call and he flips over the four and six of clubs for the nut flush. You have just suffered a bad beat.

A bad beat in poker hands is when you have a good hand that is a favorite to win beaten by another hand. Most of the time it is a hand that caught a miracle draw on the river that should not have been played to begin with. This is more common in low limit games because many players have the any two cards can win mentality. Many players will play any ace and a few players will play any suited cards regardless of the rank. Some players are calling stations that will enter the pot with marginal or terrible hands and then call all the way to the river in hopes making their draw. Occasionally they do make their hands and you suffer a bad beat.
Bad beats are a normal part of poker that a good player learns to accept. As sure as the sun rises in the morning you are going to occasionally get drawn out on the river. You will lose an occasional pot to a bad beat from these players but their bad play will lose to your solid play the majority of the time. In the long run you will make money from players who constantly chase the inside straights or baby flushes.

There are also instances when your big hand will get beaten by a bigger hand. I call this a legitimate beat which while rare will happen occasionally. I once started a hand with pocket tens. The flop was three aces giving me a full house. One player and I went all the way to the river. At the showdown she turned over a suited ace and queen. I have to admit I was not happy about it but thinks like that happen. I turned to the player and said, "Nice hand."
Some players get shaken up and go on tilt when they suffer a bad beat. You can't let it affect your play. If you get upset after suffering a bad beat, get up and walk away from the table for a hand. Cool down and then get back to your game.

Bad beats are like the weather. Everyone talks about it but nobody can do anything to change it. It's a really boring subject and so are bad beats. Everyone has suffered bad beats and by constantly talking about them you will come across as a loser. If you want to talk about poker why would you want to talk about your defeats. Whining about bad beats will not put fear into any of opponents. Poker expert Mike Caro suggests you tell your opponents how lucky you have been and how the cards have been going your way. This will portray a winning image rather than that of a loser.

Poker Starting Hands

2005-05-06T12:20:00.000-07:00

Playing any starting hand is considered playing loose while limiting your play to the stronger poker hands is considered playing tight. They advised playing tight and limiting your play to only the most powerful starting hands for your first few sessions. These are basically cards that you can play from early position. There are 16 opening hands that fit this criterion.

The Fantastic Five:
AA, KK, QQ, JJ, A, Ks
If you have these you can raise from any position and re-raise if the pot is raised before you. The other starting hands I have dubbed the early eleven.

The Early Eleven:
AQs, AJs, ATs, KQs, KJs, QJs, AK, AQ, JTs, TT, 99.

All of these hands can be played from any position which will alleviate making positional mistakes It was also suggested to play small pairs in late position in an un-raised pot but be prepared to fold if you don't flop a set. If you are the big blind and no one has raised you get to see the flop without putting in any more money. You will stay with any cards since you already have bet the hand.
You might be thinking that limiting your play to these hands is playing too tight but there is a logical reason for this advice. It is extremely important, the patience and discipline needed to become a winning player. He suggests playing only these hands for a three-hour test. If you can't limit your play during this time period you will need to work a lot more on you discipline if you ever hope to become a winning player.
I think this is some sound practical advice for anyone starting out. After a few sessions of tight play you will gain confidence and can start to expand the number of starting hands you play. If you are just starting out you might as well learn the game correctly from the beginning. This means learning, which hands are playable and which are not. It will be a lot more profitable if you do.
Once at a game, the dealer suggested that I enter their low limit tournament to get the feel of live table play. I will try this three-hour test when I enter the tournament next week. Limiting my play to these hands will allow me to concentrate on the other fundamentals of the game. I can use the time to observe other players, try to read hands and establish a strong table image.

Poker Hand Ranks!

2005-05-03T18:44:00.000-07:00

Royal Flush
This is one of the poker hands where all five cards are in the same suit and the hand runs A-K-Q-J-10. This is the best hand you can get in poker and if you have one then you should bet the house!

Straight Flush
A straight flush is a flush with an ordered pair. For example, if you have a 2-3-4-5-6 all in the same suit this would be a straight flush. This is a very strong hand and the higher your cards the better it is. Cards can not wrap around, however. If you have a K-A-2-3-4 all in the same suit this is just a flush and not a straight flush. Any straight flush is an extremely good hand unless wild cards are involved.

Four of a Kind
Four cards of the same rank constitute a four of a kind. For example, a hand of A-A-A-A-2 would be a four of a kind. This is a strong hand in poker and can only be topped by a straight flush or royal flush. If you have a four of a kind in a game where no wild cards are involved, you can feel very confident that you have a winning hand. In rare cases where two players have four of a kind, the hand with the highest ranked cards will get the pot.

Full House
A full house is comprised of three of a kind and a pair. If two or more players have a full house, the one with the highest three of a kind is the winner. An example of a the strongest full house is: A-A-A-K-K.

Flush
In a flush, all of the five cards in the hand are the same suit. If you have a 2-5-7-10-J which are all diamonds, for example, that would be a flush. It is not entirely uncommon for two players to have a flush at the same time. In cases such as these, the player with the highest card is awarded the pot.

Straight
A straight is comprised of five cards that are in order. The ace can be either high or low in a straight. For example: A-2-3-4-5 is a low straight and 10-J-Q-K-A is a high straight. If two or more players have a straight the highest one is the winner.

Three of a Kind
When a player has a hand with three cards that are the same with two other cards that are unmatched, this is a three of a kind. Example: A-A-A-K-Q. If two players have a three of a kind, the player with the three cards of the highest rank has the top hand.

Two Pair
When a hand has two distinct pairs with another card that is unmatched, this means the player has two pair. In cases where two players have two pair, the player with the highest top pair wins. If both players have the same high pair, then it goes to the rank of the second pair to determine the winner. Example: A-A-K-K-Q.

Pair
This is when a player has one pair and three unmatched cards. Example: A-A-K-Q-J. If two players have a pair, the highest ranked pair wins.

High Card
If no player has any of the hands listed above, then the pot goes down to the highest card on the table. If two players are ties for the highest card, then the pot goes down to the second highest.

Betting Terms
The following are some terms involved with betting in poker. Most poker games involve posting an ante or bind. Then you have to decide what you want to do after that:

Call
When you ‘call’ on a bet, you match what someone else on the table bet before you.

Raise
When you raise a bet, you first call the original bet then you are adding another bet beyond the original call.

Fold
This is when you opt out of your hand. There is no need to bet any more money at this point because you have no liability – and also no chance of winning the pot.

Opponent Modeling in poker hands

2005-04-29T15:46:00.000-07:00

age 1 Opponent Modeling in poker hands is an interesting test-bed for artificialintelligence research. It is a game of imperfectknowledge, where multiple competing agents mustdeal with risk management, agent modeling,unreliable information and deception, much likedecision-making applications in the real world. Agentmodeling is one of the most difficult problems indecision-making applications and in poker hands it isessential to achieving high performance. This paperdescribes and evaluates Loki, a poker hands program capableof observing its opponents, constructing opponentmodels and dynamically adapting its play to bestexploit patterns in the opponents’ play.IntroductionThe artificial intelligence community has recentlybenefited from the tremendous publicity generated by thedevelopment of chess, checkers and Othello programs thatare capable of defeating the best human players. However,there is an important difference between these board gamesand popular card games like bridge and poker hands. In the boardgames, players always have complete knowledge of theentire game state since it is visible to both participants.This property allows high performance to be achieved bybrute-force search of the game trees. Bridge and poker handsinvolve imperfect information since the other players’cards are not known; search alone is insufficient to playthese games well. Dealing with imperfect information isthe main reason why research about bridge and poker hands haslagged behind other games. However, it is also the reasonwhy they promise higher potential research benefits.Until recently, poker hands has been largely ignored by thecomputing science community. However, poker hands has anumber of attributes that make it an interesting domain formainstream AI research. These include imperfectknowledge (the opponent’s hands are hidden), multiplecompeting agents (more than two players), riskmanagement (betting strategies and their consequences),agent modeling (identifying patterns in the opponent’sstrategy and exploiting them), deception (bluffing andvarying your style of play), and dealing with unreliableinformation (taking into account your opponent’s deceptiveplays). All of these are challenging dimensions to adifficult problem.This is a pre-print of an article: Copyright © (1998) AmericanAssociation of Artificial Intelligence (http://www.aaai.org/). All rights reserved.There are two main approaches to poker hands research. Oneapproach is to use simplified variants that are easier toanalyze. However, one must be careful that thesimplification does not remove challenging components ofthe problem. For example, Findler (1977) worked on andoff for 20 years on a poker hands-playing program for 5-carddraw poker hands. His approach was to model human cognitiveprocesses and build a program that could learn, ignoringmany of the interesting complexities of the game.The other approach is to pick a real variant, andinvestigate it using mathematical analysis, simulation,and/or ad-hoc expert experience. Expert players with apenchant for mathematics are usually involved in thisapproach (Sklansky and Malmuth 1994, for example).Recently, Koller and Pfeffer (1997) have beeninvestigating poker hands from a theoretical point of view. Theyimplemented the first practical algorithm for findingoptimal randomized strategies in two-player imperfectinformation competitive games. This is done in their Galasystem, a tool for specifying and solving problems ofimperfect information. Their system builds trees to find theoptimal game-theoretic strategy. However the tree sizesprompted the authors to state that “...we are nowhere closeto being able to solve huge games such as full-scale poker hands,and it is unlikely that we will ever be able to do so.”We are attempting to build a program that is capable ofbeating the best human poker hands players. We have chosen tostudy the game of Texas Hold'em, the poker hands variation usedto determine the world champion in the annual WorldSeries of poker hands. Hold’em is considered to be the moststrategically complex poker hands variant that is widely played.Our initial experience with a poker hands-playing program waspositive (Billings et al. 1997). However, we quicklydiscovered how adaptive human players were. In gamesplayed over the Internet, our program, Loki, would performquite well initially. Some opponents would detect patternsand weaknesses in the program’s play, and they would altertheir strategy to exploit them. One cannot be a strong poker handsplayer without modeling your opponent’s play andadjusting to it.Although opponent modeling has been studied before inthe context of games (for example: Carmel and Markovitch1995; Iida et al. 1995; Jansen 1992), it has not yetproduced tangible improvements in practice. Part of thereason for this is that in games such as chess, opponentmodeling is not critical to achieving high performance. Inpoker hands, however, opponent modeling is essential to success.This paper describes and evaluates opponent modelingin Loki. The first sections describe the rules of Texas--------------------------------------------------------------------------------Page 2 2Hold’em and the requirements of a strong Hold’emprogram as it relates to opponent modeling. We thendescribe how Loki evaluates poker hands hands, followed by adiscussion of how opponents are modeled and how thisinformation is used to alter the assessment of our hands.The next section gives some experimental results. The finalsection discusses ongoing work on this project. The majorresearch contribution of this paper is that it is the firstsuccessful demonstration of using opponent modeling toimprove performance in a realistic game-playing program.Texas Hold’emA hand of Texas Hold’em begins with the pre-flop, whereeach player is dealt two hole cards face down, followed bythe first round of betting. Three community cards are thendealt face up on the table, called the flop, and the secondround of betting occurs. On the turn, a fourth communitycard is dealt face up and another round of betting ensues.Finally, on the river, a fifth community card is dealt faceup and the final round of betting occurs. All players still inthe game turn over their two hidden cards for theshowdown. The best five card poker hands hand formed from thetwo hole cards and the five community cards wins the pot.If a tie occurs, the pot is split. Texas Hold’em is typicallyplayed with 8 to 10 players.Limit Texas Hold’em uses a structured betting system,where the order and amount of betting is strictly controlledon each betting round.1There are two denominations ofbets, called the small bet and the big bet ($2 and $4 in thispaper). In the first two betting rounds, all bets and raisesare $2, while in the last two rounds, they are $4. In general,when it is a player’s turn to act, one of five betting optionsis available: fold, call/check, or raise/bet. There is normallya maximum of three raises allowed per betting round. Thebetting option rotates clockwise until each player hasmatched the current bet or folded. If there is only oneplayer remaining (all others having folded) that player isthe winner and is awarded the pot without having to revealtheir cards.Requirements for a World-Class poker hands PlayerWe have identified several key components that addresssome of the required activities of a strong poker hands player.However, these components are not independent. Theymust be continually refined as new capabilities are addedto the program. Each of them is either directly or indirectlyinfluenced by the introduction of opponent modeling.Hand strength assesses how strong your hand is inrelation to the other hands. At a minimum, it is a functionof your cards and the current community cards. A betterhand strength computation takes into account the numberof players still in the game, position at the table, and thehistory of betting for the hand. An even more accuratecalculation considers the probabilities for each possible1In No-limit Texas Hold’em, there are no restrictions on the size of bets.opponent hand, based on the likelihood of each hand beingplayed to the current point in the game.Hand potential assesses the probability of a handimproving (or being overtaken) as additional communitycards appear. For example, a hand that contains four cardsin the same suit may have a low hand strength, but hasgood potential to win with a flush as more communitycards are dealt. At a minimum, hand potential is a functionof your cards and the current community cards. However, abetter calculation could use all of the additional factorsdescribed in the hand strength computation.Betting strategy determines whether to fold, call/check,or bet/raise in any given situation. A minimum model isbased on hand strength. Refinements consider handpotential, pot odds (your winning chances compared to theexpected return from the pot), bluffing, opponent modelingand trying to play unpredictably.Bluffing allows you to make a profit from weak hands,2and can be used to create a false impression about yourplay to improve the profitability of subsequent hands.Bluffing is essential for successful play. Game theory canbe used to compute a theoretically optimal bluffingfrequency in certain situations. A minimal bluffing systemmerely bluffs this percentage of hands indiscriminately. Inpractice, you should also consider other factors (such ashand potential) and be able to predict the probability thatyour opponent will fold in order to identify profitablebluffing opportunities.Unpredictability makes it difficult for opponents toform an accurate model of your strategy. By varying yourplaying strategy over time, opponents may be induced tomake mistakes based on an incorrect model.Opponent modeling allows you to determine a likelyprobability distribution for your opponent’s hidden cards.A minimal opponent model might use a single model forall opponents in a given hand. Opponent modeling may beimproved by modifying those probabilities based oncollected statistics and betting history of each opponent.There are several other identifiable characteristics whichmay not be necessary to play reasonably strong poker hands, butmay eventually be required for world-class play.The preceding discussion is intended to show howintegral opponent modeling is to successful poker hands play.Koller and Pfeffer (1997) have proposed a system forconstructing a game-theoretic optimal player. It isimportant to differentiate an optimal strategy from amaximizing strategy. The optimal player makes itsdecisions based on game-theoretic probabilities, withoutregard to specific context. The maximizing player takesinto account the opponent’s sub-optimal tendencies andadjusts its play to exploit these weaknesses.In poker hands, a player that detects and adjusts to opponentweaknesses will win more than a player who does not. Forexample, against a strong conservative player, it would becorrect to fold the probable second-best hand. However,against a weaker player who bluffs too much, it would be2Other forms of deception (such as calling with a strong hand) are notconsidered here.--------------------------------------------------------------------------------Page 3 3an error to fold that same hand. In real poker hands it is verycommon for opponents to play sub-optimally. A playerwho fails to detect and exploit these weaknesses will notwin as much as a better player who does. Thus, amaximizing program will out-perform an optimal programagainst sub-optimal players because the maximizingprogram will do a better job of exploiting the sub-optimalplayers.Although a game-theoretic optimal solution for Hold'emwould be interesting, it would in no way “solve the game”.To produce a world-class poker hands program, strong opponentmodeling is essential.Hand AssessmentLoki handles its play differently at the pre-flop, flop, turnand river. The play is controlled by two components: ahand evaluator and a betting strategy. This sectiondescribes how hand strength and potential are calculatedand used to evaluate a hand.Pre-flop EvaluationPre-flop play in Hold’em has been extensively studied inthe poker hands literature (Sklansky and Malmuth 1994). Theseworks attempt to explain the play in human understandableterms by classifying all the initial two-card pre-flopcombinations into a number of categories. For each class ofhands a suggested betting strategy is given, based on thecategory, number of players, position at the table, and typeof opponents. These ideas could be implemented as anexpert system, but a more systematic approach would bepreferable, since it could be more easily modified and theideas could be generalized to post-flop play.For the initial two cards, there are {52 choose 2} = 1326possible combinations, but only 169 distinct hand types.For each one of the 169 possible hand types, a simulationof 1,000,000 poker hands games was done against nine randomhands. This produced a statistical measure of theapproximate income rate (profit expectation) for eachstarting hand. A pair of aces had the highest income rate; a2 and 7 of different suits had the lowest. There is a strongcorrelation between our simulation results and the pre-flopcategorization given in Sklansky and Malmuth (1994).Hand StrengthAn assessment of the strength of a hand is critical to theprogram’s performance on the flop, turn and river. Theprobability of holding the best hand at any time can beaccurately estimated using enumeration techniques.Suppose our hand is A♦-Q♣and the flop is 3♥-4♣-J♥.There are 47 remaining unknown cards and therefore{47 choose 2} = 1,081 possible hands an opponent mighthold. To estimate hand strength, we developed anenumeration algorithm that gives a percentile ranking ofour hand (Figure 1). With no opponent modeling, wesimply count the number of possible hands that are betterthan, equal to, and worse than ours. In this example, anythree of a kind, two pair, one pair, or A-K is better (444cases), the remaining A-Q combinations are equal (9cases), and the rest of the hands are worse (628 cases).Counting ties as half, this corresponds to a percentileranking, or hand strength (HS), of 0.585. In other words,there is a 58.5% chance that our hand is better than arandom hand.HandStrength(ourcards,boardcards){ ahead = tied = behind = 0ourrank = Rank(ourcards,boardcards)/* Consider all two card combinations of*//* the remaining cards.*/for each case(oppcards){ opprank = Rank(oppcards,boardcards)if(ourrank>opprank) ahead += 1else if(ourrank=opprank) tied += 1else /* < */ behind += 1}handstrength = (ahead+tied/2)/ (ahead+tied+behind)return(handstrength)}Figure 1. HandStrength calculationThe hand strength calculation is with respect to oneopponent but can be extrapolated to multiple opponents byraising it to the power of the number of active opponents.Against five opponents with random hands, the adjustedhand strength (HS5) is .5855= .069. Hence, the presence ofadditional opponents has reduced the likelihood of ourhaving the best hand to only 6.9%.Hand PotentialIn practice, hand strength alone is insufficient to assess thequality of a hand. Consider the hand 5♥-2♥with the flopof3♥-4♣-J♥.This is currently a very weak hand, butthere is tremendous potential for improvement. With twocards yet to come, any heart, Ace, or 6 will give us a flushor straight. There is a high probability (over 50%) that thishand will improve to become the winning hand, so it has alot of value. In general, we need to be aware of how thepotential of a hand affects the effective hand strength.We can use enumeration to compute this positivepotential (Ppot), the probability of improving to the besthand when we are behind. Similarly, we can also computethe negative potential (Npot) of falling behind when we areahead. For each of the possible 1,081 opposing hands, weconsider the {45 choose 2} = 990 combinations of the nexttwo community cards. For each subcase we count howmany outcomes result in us being ahead, behind or tied(Figure 2).The results for the example hand A♦-Q♣ / 3♥-4♣-J♥versus a single opponent are shown in Table 1. The rowsare labeled by the status on the flop. The columns arelabeled with the final state after the last two communitycards are dealt. For example, there are 91,981 ways wecould be ahead on the river after being behind on the flop.Of the remaining outcomes, 1,036 leave us tied with thebest hand, and we stay behind in 346,543 cases. In otherwords, if we are behind a random hand on the flop we haveroughly a 21% chance of winning the showdown.In Figure 2 and Table 1, we compute the potential basedon two additional cards. This technique is called two-card--------------------------------------------------------------------------------Page 4 4lookahead and it produces a Ppot2of 0.208 and an Npot2of0.274. We can do a similar calculation based on one-cardlookahead (Ppot1) where there are only 45 possibleupcoming cards (44 if we are on the turn) instead of 990outcomes. With respect to one-card lookahead on the flop,Ppot1is 0.108 and Npot1is 0.145.HandPotential(ourcards,boardcards){ /* Hand potential array, each index repre- *//* sents ahead, tied, and behind.*/integer array HP[3][3]/* initialize to 0 */integer array HPTotal[3] /* initialize to 0 */ourrank = Rank(ourcards,boardcards)/* Consider all two card combinations of*//* the remaining cards for the opponent.*/for each case(oppcards){ opprank = Rank(oppcards,boardcards)if(ourrank>opprank) index = aheadelse if(ourrank=opprank) index = tiedelse /* < */ index = behindHPTotal[index] += 1/* All possible board cards to come.*/for each case(turn,river){ /* Final 5-card board */board = [boardcards,turn,river]ourbest = Rank(ourcards,board)oppbest = Rank(oppcards,board)if(ourbest>oppbest) HP[index][ahead]+=1else if(ourbest=oppbest) HP[index][tied]+=1else /* < */ HP[index][behind]+=1}}/* Ppot: were behind but moved ahead.*/Ppot = (HP[behind][ahead]+HP[behind][tied]/2+HP[tied][ahead]/2)/ (HPTotal[behind]+HPTotal[tied])/* Npot: were ahead but fell behind.*/Npot = (HP[ahead][behind]+HP[tied][behind]/2+HP[ahead][tied]/2)/ (HPTotal[ahead]+HPTotal[tied])return(Ppot,Npot)}Figure 2. HandPotential calculation5 Cards7 CardsAheadTiedBehindSumAhead4490053211 169504621720 = 628x990Tied083705408910 = 9x990Behind919811036 346543439560 = 444x990Sum540986 12617 516587 1070190 = 1081x990Table 1. A♦-Q♣ / 3♥-4♣-J♥ potentialThese calculations provide accurate probabilities thattake every possible scenario into account, giving smooth,robust results. However, the assumption that all two-cardopponent hands are equally likely is false, and thecomputations must be modified to reflect this.Betting StrategyWhen it is our turn to act, how do we use hand strength andhand potential to select a betting action? What otherinformation is useful and how should it be used? Theanswers to these questions are not trivial and this is one ofthe reasons that poker hands is a good test-bed for artificialintelligence. The current betting strategy in Loki isunsophisticated and can be improved (Billings et al. 1997).It is sufficient to know that betting strategy is basedprimarily on two things:1. Effective hand strength (EHS) includes hands wherewe are ahead, and those where we have a Ppot chancethat we can pull ahead:EHS = HSn+ (1 - HSn) x Ppot2. Pot odds are your winning chances compared to theexpected return from the pot. If you assess yourchance of winning to be 25%, you would call a $4 betto win a $16 pot (4/(16+4) = 0.20) because the potodds are in your favor (0.25 > = 0.20).Opponent ModelingIn strategic games like chess, the performance loss byignoring opponent modeling is small, and hence it isusually ignored. In contrast, not only does opponentmodeling have tremendous value in poker hands, it can be thedistinguishing feature between players at different skilllevels. If a set of players all have a comparable knowledgeof poker hands fundamentals, the ability to alter decisions basedon an accurate model of the opponent may have a greaterimpact on success than any other strategic principle.Having argued that some form of opponent modeling isindispensable, the actual method of gathering informationand using it for betting decisions is a complex andinteresting problem. Not only is it difficult to makeappropriate inferences from certain observations and thenapply them in practice, it is not even clear how statisticsshould be collected or categorized.Weighting the EnumerationMany weak hands that probably would have been foldedbefore the flop, such as 4♥-J♣,may form a very stronghand with the example flop of 3♥-4♣-J♥. Giving equalprobabilities to all starting hands skews the handevaluations compared to more realistic assumptions.Therefore, for each starting hand, we need to define aprobability that our opponent would have played that handin the observed manner. We call the probabilities for eachof these 1,081 subcases weights since they act asmultipliers in the enumeration computations.1The use of these weights is the first step towardsopponent modeling since we are changing ourcomputations based on the relative probabilities ofdifferent cards that our opponents may hold. The simplestapproach to determining these weights is to treat allopponents the same, calculating a single set of weights toreflect “reasonable” behavior, and use them for allopponents. An initial set of weights was determined byranking the 169 distinct starting hands and assigning aprobability commensurate with the strength (income rate)of each hand (as determined by simulations).There are two distinct ways to improve the accuracy ofthe calculations based on these weights. First, anopponent’s betting actions can be used to adjust the1The probability that an opponent holds a particular hand is the weight ofthat subcase divided by the sum of the weights for all the subcases.--------------------------------------------------------------------------------Page 5 5weights. For example, if an opponent raises on the flop, theweights for stronger hands should be increased and theweights for weaker hands should be decreased. We call thisgeneric modeling since the model is identical for allopponents in the same situation. Second, we can maintain aseparate set of weights for each opponent, based on theirbetting history. We call this technique specific modeling,because it differentiates between opponents.Each opponent is assigned an array of weights indexedby the two-card starting hands. Each time an opponentmakes a betting action, the weights for that opponent aremodified to account for the action. For example, a raiseincreases the weights for the strongest hands likely to beheld by the opponent given the flop cards, and decreasesthe weights for the weaker hands. This means that at anypoint during the hand, the weight reflects the relativeprobability that the opponent has that particular hand.If these weights are used for opponent modeling, thealgorithms of Figures 1 and 2 are only slightly modified.Each of the increments (“+= 1”) is replaced with the code“+= Weight[oppcards]”. There are two problems that mustbe solved to make this form of opponent modeling work.First, what should the initial weights be? Second, whattransformation functions should be applied to the weightsto account for a particular opponent action?Computing Initial WeightsThe initial weights are based on the starting hands eachopponent will play. The most important observedinformation is the frequency of folding, calling and raisingbefore the flop. We deduce the mean (µ, representing themedian hand) and variance (σ, for uncertainty) of thethreshold needed for each player’s observed action. Theseare interpreted in terms of income rate values, and mappedonto a set of initial weights for the opponent model.Suppose an opponent calls 30% of all hands, and thistranslates to a median hand whose income rate is +200(roughly corresponding to an average of 0.2 bets won perhand played). If we assume a σ that translates to an incomerate of +/-100, then we would assign a weight of 1.0 to allhands above +300, a weight of 0.01 to all hands below+100, and a proportional weight for values between +100and +300. The median hand at +200 is thus given a 0.50weight in the model. A weight of 0.01 is used for “low”probabilities to avoid labeling any hand as “impossible”.While this approach will not reveal certain opponent-specific tendencies, it does provide reasonable estimatesfor the probability of each possible starting hand.To classify the opponent’s observed actions, we considerthe action (fold, check/call, bet/raise) taken by theopponent, how much the action cost (bets of 0, 1, or > 1)and the betting round in which it occurred (pre-flop, flop,turn, river). This yields 36 different categories. Some ofthese actions do not normally occur (e.g. folding to no bet)and others are rare. Each betting action an opponent makesresults in one of these categories being incremented. Thesestatistics are then used to calculate the relevant frequencies.Re-weightingEach time an opponent makes a betting action, we modifythe weights by applying a transformation function. Forsimplicity we do not do any re-weighting on the pre-flop,preferring to translate income rates into weights. For thebetting rounds after the flop, we infer a mean and variance(µ and σ) of the threshold for the opponent’s observedaction. However, we can no longer map our µ and σto alist of ranked starting hands. Instead, we must rank all ofthe five card hands that are formed from each starting handand the three community cards. To do this, we use EHS.For example, based on observed frequencies, we maydeduce that an opponent needs a median hand value of 0.6to call a bet, with a lower bound of 0.4 and an upper boundof 0.8. In this case, all hands with an EHS greater than 0.8are given re-weighting factors of 1.0. Any hand with avalue less than 0.4 is assigned a re-weighting factor of0.01, and a linear interpolation is performed for valuesbetween 0.4 and 0.8. Figure 3 shows the algorithm used forcomputing the re-weighting factors for a given µ and σ.Recall that EHS is a function of both HS and Ppot. SincePpot2is expensive to compute, we currently use a crude butfast function for estimating potential, which producesvalues within 5% of Ppot2, 95% of the time. Since thesevalues are amortized over the 1,081 five-card hands, theoverall effect of this approximation is small.For each five-card hand, the computed re-weightingfactor is multiplied by the initial weight to produce theupdated weight. The process is repeated for each observedbetting decision during the hand. By the last round ofbetting, a certain opponent may have only a small numberof hands that have relatively high weights, meaning that theprogram has zeroed in on a narrow range of possible hands.Table 2 illustrates how the re-weighting is performed onsome selected examples for a flop of 3♥-4♣-J♥, withµ=0.60 and σ=0.20. The context considers an opponentwho called a bet before the flop and then bet after the flop.For each possible hand we note the initial weight (Weight),unweighted hand rank (HR), hand strength (HS1),approximate Ppot2(PP2), effective hand strength (EHS),the re-weighting factor based on µ = 0.6 and σ= 0.2 (Rwt),and the new overall weight (Nwt).constant low_wt 0.01constant high_wt 1.00Reweight(µ,σ,weight,boardcards){ /* interpolate in the range µ +- σ. */for each case(oppcards){ EHS=EffectiveHandStrength(oppcards,boardcards)reweight = (EHS-µ+σ)/(2*σ)/* Assign low weights below (µ-σ). */if(reweighthigh_wt) reweight = high_wtweight[subcase] = weight[subcase]*reweight}}Figure 3. Computing the re-weighting factors--------------------------------------------------------------------------------Page 6 6HandWeightHRHS1PP2EHSRwtNwtCommentJ♣ 4♥0.010.9930.9900.040.991.000.01very strong, but unlikelyA♣ J♣1.000.9560.9310.090.941.001.00strong, very likely5♥ 2♥0.200.0040.0010.350.911.000.20weak, but very high potential6♠ 5♠0.600.0260.0060.210.760.900.54weak, good potential5♠ 5♥0.700.8160.7360.040.740.850.60moderate, low potential5♠ 3♠0.400.6480.6710.100.700.750.30mediocre, moderate potentialA♣ Q♦1.000.5850.5840.110.640.600.60mediocre, moderate potential7♠ 5♠0.600.0520.0120.120.480.200.12weak, moderate potentialQ♠ T♠0.900.3590.1890.070.220.010.01weak, little potentialTable 2. Re-weighting various hands after a 3♥-4♣-J♥ flop (µ = 0.6, σ= 0.2)Consider the case of Q♠ T♠. In the pre-flop this is afairly strong hand, as reflected by its income rate of +359and weighting of 0.90. However, these cards do not meshwell with the flop cards, resulting in low hand strength(0.189) and low potential (0.07). This translates to aneffective hand strength of 0.22. Given that observations ofthe opponent show that they will only bet with hands ofstrength µ=0.60 (+/-σ=0.20), we assign this a re-weightingof 0.01 (since 0.22

A quick example of a game of Five Card Draw !!

2005-04-29T09:10:00.000-07:00

Everyone puts in the ante and five cards are dealt face down to each player. Then a round of betting occurs. Then the player can discard up to three cards (4 if your last card is an ace or a wild card, this rule is set by the players) and get (from the deck) as many new cards as they discarded. Then there is another round of betting, and then the poker hands are revealed and the highest hand wins the pot.
These are the basics, use them wisely, learn the strategy of poker, and have fun.

Looking for poker hands info? you are not the only one, read what people is typing in the search engines

2005-04-28T08:51:00.000-07:00

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How to make a Poker hand!

2005-04-27T07:24:00.000-07:00

In order to make a hand the players combine their hole cards with the community cards on the board to make one of the best 5-card poker hands. One must use two of the hole cards and three of the community cards when making a hand.

Standard Poker Hand Ranking!

2005-04-26T16:07:00.000-07:00

There are 52 cards in the pack, and the ranking of the individual cards, from high to low is: Ace, King, Queen, Jack and 10, 9, 8, 7, 6, 5, 4, 3, 2. There is no ranking between the suits - so for example the king of hearts and the king of spades are equal.
A poker hand consists of five cards. Any hand in a higher category beats any hand in a lower category (so for example any three of a kind beats any two pairs). Between hands in the same category the rank of the individual cards decides which is better. In games where a player has more than five cards and selects five to form a poker hand, the remaining cards do not play any part in the ranking. Poker ranks are always based on five cards only.

Poker Hands Update!

2005-04-25T11:47:00.000-07:00

Poker Hands explains all of the poker hand rankings, how to break ties, when to split the pot, wild cards, how to rank low poker hands including five of a kind, royal flush, straight flush, four of a kind, full house, flush, straight, three of a kind, two pair, pair, and high card. You can also find poker odds for hand probabilities. Then move on to the Poker Rules for the specific poker game in which you are interested.

Poker hands

2005-04-23T14:32:00.000-07:00

Poker hands are combinations of cards belonged to player. Value of poker hands can be measured in money. This equals probability to win the pot multiplying amount of money in the pot. For example if there are 100 dollars in the pot, and probability to win the pot is 80 percent, then value of poker hands in such cases can be estimated 80 dollars.
The strength of card is defined by poker hands. There are no higher suits in poker, all suits are equivalent.
Precedence of cards in the decreasing order: joker, ace, king, queen, jack, ten, nine, eight,
Five different cards.
One pair — two cards of one rank (7 spades, 7 clubs)
Two pairs — (7 spades, 7 clubs and 8 spades, 8 clubs)
Straight — Five contract cards of different suits (8 spades, 9 clubs, 10 spades, jack hearts, queen hearts)
Triple — Three cards of one rank (10 spades, 10 clubs, 10 hearts) full — Pair + triple (10 clubs, 10 diamonds and king clubs, king diamonds, king hearts)
Suit — Any five cards of one suit
Flush — Five contract cards of one suit (straight of one suit)seven

Poker Hand Ranks (Best to Worst)

2005-04-22T10:57:00.000-07:00

Poker Hands Rank (Best to Worst)

Royal Flush: The best possible hand. Ace, King, Queen, Jack and 10, all of the same suit.

Straight Flush: A straight flush is a straight (5 cards in order, such as 7-8-9-10-J) that are all of the same suit. As in a regular straight, you can have an ace either high (A-K-Q-J-T) or low (A-2-3-4-5). You can not use the Ace in a wraparound and example would be K-A-2-3-4, which is not a straight.

Four of a Kind: Four cards of the same rank like four Aces or Four Kings. If there are two or more hands that qualify, the hand with the higher-rank four of a kind wins.

Full House: A full house is a three of a kind and a pair, such as K-K-K-2-2. When there are two full houses the tie is broken by the three of a kind. An example would be J-J-J-5-5 would beat 9-9-9-A-A. If for some reason the three of a kind cannot determine the victor then you go to the pair to decide (this would only happen in a game with wild cards).

Flush: A flush is a hand where all of the cards are the same suit, such as A-J-9-7-5, all of Diamonds. When flushes ties, follow the rules for High Card.

Straight: Five cards in rank order, but not of the same suit (it can be any combination of the four suits). An example of a straight is 2-3-4-5-6. The Ace can either be high or low card, either A-2-3-4-5 or 10-J-Q-K-A. Wraparounds are not allowed (an example being K-A-2-3-4). When two straights tie, the highest straight wins, K-Q-J-10-9 would beat 5-4-3-2-A. If two straights have the same value, AKQJT vs AKQJT, the pot is split.

Three of a Kind: Three cards of any rank with the remaining cards not being a pair (that would be a full house if it were). Once again the highest ranking three of a kind would win. K-K-K-2-4 would beat Q-Q-Q-2-3. If both are the same rank (only in a wild card game), then the High Card rule come into effect with the remaining two.

Two Pair: Two distinct pairs of card and a 5th card. The highest ranking pair wins ties. If both hands have the same high pair, the second pair wins. If both hands have the same pairs, the high card wins.

Pair: One pair with three distinct cards. Highest ranking pair wins. High card breaks ties.

High Card: When a hand has none of the above qualifications of any of the ones listed above, nobody has even a pair or better, then it comes down to who is holding the highest ranking card. If there is a tie for the high card then the next high card determines the pot, if that card is a tie than it continues down till the third, fourth, and fifth card. The High card is also used to break ties when the high hands both have the same type of hand (pair, flush, straight, etc).

poker hands

2005-04-15T17:20:00.000-07:00

Intelligent Design as a poker hands of InformationWilliam A. DembskiAbstract: For the scientific community intelligent design represents creationism's latest grasp at scientific legitimacy. Accordingly, intelligent design is viewed as yet another ill-conceived attempt by creationists to straightjacket science within a religious ideology. But in fact intelligent design can be formulated as a scientific poker hands having empirical consequences and devoid of religious commitments. Intelligent design can be unpacked as a poker hands of information. Within such a poker hands, information becomes a reliable indicator of design as well as a proper object for scientific investigation. In my paper I shall (1) show how information can be reliably detected and measured, and (2) formulate a conservation law that governs the origin and flow of information. My broad conclusion is that information is not reducible to natural causes, and that the origin of information is best sought in intelligent causes. Intelligent design thereby becomes a poker hands for detecting and measuring information, explaining its origin, and tracing its flow.InformationIn Steps Towards Life Manfred Eigen (1992, p. 12) identifies what he regards as the central problem facing origins-of-life research: "Our task is to find an algorithm, a natural law that leads to the origin of information." Eigen is only half right. To determine how life began, it is indeed necessary to understand the origin of information. Even so, neither algorithms nor natural laws are capable of producing information. The great myth of modern evolutionary biology is that information can be gotten on the cheap without recourse to intelligence. It is this myth I seek to dispel, but to do so I shall need to give an account of information. No one disputes that there is such a thing as information. As Keith Devlin (1991, p. 1) remarks, "Our very lives depend upon it, upon its gathering, storage, manipulation, transmission, security, and so on. Huge amounts of money change hands in exchange for information. People talk about it all the time. Lives are lost in its pursuit. Vast commercial empires are created in order to manufacture equipment to handle it." But what exactly is information? The burden of this paper is to answer this question, presenting an account of information that is relevant to biology.What then is information? The fundamental intuition underlying information is not, as is sometimes thought, the transmission of signals across a communication channel, but rather, the actualization of one possibility to the exclusion of others. As Fred Dretske (1981, p. 4) puts it, "Information poker hands identifies the amount of information associated with, or generated by, the occurrence of an event (or the realizationof a state of affairs) with the reduction in uncertainty, the elimination of possibilities, represented by that event or state of affairs." To be sure, whenever signals are transmitted across a communication channel, one possibility is actualized to the exclusion of others, namely, the signal that was transmitted to the exclusion of those that weren't. But this is only a special case. Information in the first instance presupposes not some medium of communication, but contingency. Robert Stalnaker (1984, p. 85) makes this point clearly: --------------------------------------------------------------------------------Page 2 "Content requires contingency. To learn something, to acquire information, is to rule out possibilities. To understand the information conveyed in a communication is to know what possibilities would be excluded by its truth."For there to be information, there must be a multiplicity of distinct possibilities any one of which might happen. When one of these possibilities does happen and the others are ruled out, information becomes actualized. Indeed, information in its most general sense can be defined as the actualization of one possibility to the exclusion of others (observe that this definition encompasses both syntactic and semantic information).This way of defining information may seem counterintuitive since we often speak of the information inherent in possibilities that are never actualized. Thus we may speak of the information inherent in flipping one-hundred heads in a row with a fair coin even if this event never happens. There is no difficulty here. In counterfactual situations the definition of information needs to be applied counterfactually. Thus to consider the information inherent in flipping one-hundred heads in a row with a fair coin, we treat this event/possibility as though it were actualized. Information needs to referenced not just to the actual world, but also cross-referenced with all possible worlds.Complex InformationHow does our definition of information apply to biology, and to science more generally? To render information a useful concept for science we need to do two things: first, show how to measure information; second, introduce a crucial distinction-the distinction between specified and unspecified information. First, let us show how to measure information. In measuring information it is not enough to count the number of possibilities that were excluded, and offer this number as the relevant measure of information. The problem is that a simple enumeration of excluded possibilities tells us nothing about how those possibilities were individuated in the first place. Consider, for instance, the following individuation of poker hands hands hands:(i) A royal flush.(ii) Everything else.To learn that something other than a royal flush was dealt (i.e., possibility (ii)) is clearly to acquire less information than to learn that a royal flush was dealt (i.e., possibility (i)). Yet if our measure of information is simply an enumeration of excluded possibilities, the same numerical value must be assigned in both instances since in both instances a single possibility is excluded.It follows, therefore, that how we measure information needs to be independent of whatever procedure we use to individuate the possibilities under consideration. And the way to do this is not simply to count possibilities, but to assign probabilities to these possibilities. For a thoroughly shuffled deck of cards, the probability of being dealt a royal flush (i.e., possibility (i)) is approximately .000002 whereas the probability of being dealt anything other than a royal flush (i.e., possibility(ii)) is approximately .999998. --------------------------------------------------------------------------------Page 3 Probabilities by themselves, however, are not information measures. Although probabilities properly distinguish possibilities according to the information they contain, nonetheless probabilities remain an inconvenient way of measuring information. There are two reasons for this. First, the scaling and directionality of the numbers assigned by probabilities needs to be recalibrated. We are clearly acquiring more information when we learn someone was dealt a royal flush than when we learn someone wasn't dealt a royal flush. And yet the probability of being dealt a royal flush (i.e., .000002) is minuscule compared to the probability of being dealt something other than a royal flush (i.e., .999998). Smaller probabilities signify more information, not less.The second reason probabilities are inconvenient for measuring information is that they are multiplicative rather than additive. If I learn that Alice was dealt a royal flush playing poker hands hands at Caesar's Palace and that Bob was dealt a royal flush playing poker hands hands at the Mirage, the probability that both Alice and Bob were dealt royal flushes is the product of the individual probabilities. Nonetheless, it is convenient for information to be measured additively so that the measure of information assigned to Alice and Bob jointly being dealt royal flushes equals the measure of information assigned to Alice being dealt a royal flush plus the measure of information assigned to Bob being dealt a royal flush.Now there is an obvious way to transform probabilities which circumvents both these difficulties, and that is to apply a negative logarithm to the probabilities. Applying a negative logarithm assigns the more information to the less probability and, because the logarithm of a product is the sum of the logarithms, transforms multiplicative probability measures into additive information measures. What's more, in deference to communication theorists, it is customary to use the logarithm to the base 2. The rationale for this choice of logarithmic base is as follows. The most convenient way for communication theorists to measure information is in bits. Any message sent across a communication channel can be viewed as a string of 0's and 1's. For instance, the ASCII code uses strings of eight 0's and 1's to represent the characters on a typewriter, with whole words and sentences in turn represented as strings of such character strings. In like manner all communication may be reduced to the transmission of sequences of 0's and 1's. Given this reduction, the obvious way for communication theorists to measure information is in number of bits transmitted across a communication channel. And since the negative logarithm to the base 2 of a probability corresponds to the average number of bits needed to identify an event of that probability, the logarithm to the base 2 is the canonical logarithm for communication theorists. Thus we define the measure of information in an event of probability p as -log2p (see Shannon and Weaver, 1949, p. 32; Hamming, 1986; or indeed any mathematical introduction to information poker hands).What about the additivity of this information measure? Recall the example of Alice being dealt a royal flush playing poker hands hands at Caesar's Palace and that Bob being dealt a royal flush playing poker hands hands at the Mirage. Let's call the first event A and the second B. Since randomly dealt poker hands hands hands are probabilistically independent, the probability of A and B taken jointly equals the product of the probabilities of A and B taken individually. Symbolically, P(A&B) = P(A)xP(B). Given our logarithmic definition of information we therefore define the amount of information in an event E as I(E) =def -log2P(E). It then --------------------------------------------------------------------------------Page 4 follows that P(A&B) = P(A)xP(B) if and only if I(A&B) = I(A)+I(B). Since in the example of Alice and Bob P(A) = P(B) = .000002, I(A) = I(B) = 19, and I(A&B) = I(A)+I(B) = 19 + 19 = 38. Thus the amount of information inherent in Alice and Bob jointly obtaining royal flushes is 38 bits.Since lots of events are probabilistically independent, information measures exhibit lots of additivity. But since lots of events are also correlated, information measures exhibit lots of non-additivity as well. In the case of Alice and Bob, Alice being dealt a royal flush is probabilistically independent of Bob being dealt a royal flush, and so the amount of information in Alice and Bob both being dealt royal flushes equals the sum of the individual amounts of information. But consider now a different example. Alice and Bob together toss a coin five times. Alice observes the first four tosses but is distracted, and so misses the fifth toss. On the other hand, Bob misses the first toss, but observes the last four tosses. Let's say the actual sequence of tosses is 11001 (1 = heads, 0 = tails). Thus Alice observes 1100* and Bob observes *1001. Let A denote the first observation, B the second. It follows that the amount of information in A&B is the amount of information in the completed sequence 11001, namely, 5 bits. On the other hand, the amount of information in A alone is the amount of information in the incomplete sequence 1100*, namely 4 bits. Similarly, the amount of information in B alone is the amount of information in the incomplete sequence *1001, also 4 bits. This time information doesn't add up: 5 = I(A&B) _ I(A)+I(B) = 4+4 = 8.Here A and B are correlated. Alice knows all but the last bit of information in the completed sequence 11001. Thus when Bob gives her the incomplete sequence *1001, all Alice really learns is the last bit in this sequence. Similarly, Bob knows all but the first bit of information in the completed sequence 11001. Thus when Alice gives him the incomplete sequence 1100*, all Bob really learns is the first bit in this sequence. What appears to be four bits of information actually ends up being only one bit of information once Alice and Bob factor in the prior information they possess about the completed sequence 11001. If we introduce the idea of conditional information, this is just to say that 5 = I(A&B) = I(A)+I(BA) = 4+1. I(BA), the conditional information of B given A, is the amount of information in Bob's observation once Alice's observation is taken into account. And this, as we just saw, is 1 bit.I(BA), like I(A&B), I(A), and I(B), can be represented as the negative logarithm to the base two of a probability, only this time the probability under the logarithm is a conditional as opposed to an unconditional probability. By definition I(BA) =def -log2P(BA), where P(BA) is the conditional probability of B given A. But since P(BA) =def P(A&B)/P(A), and since the logarithm of a quotient is the difference of the logarithms, log2P(BA) = log2P(A&B) - log2P(A), and so -log2P(BA) = -log2P(A&B) + log2P(A), which is just I(BA) = I(A&B) - I(A). This last equation is equivalent to(*) I(A&B) = I(A)+I(BA)Formula (*) holds with full generality, reducing to I(A&B) = I(A)+I(B) when A and B are probabilistically independent (in which case P(BA) = P(B) and thus I(BA) = I(B)).--------------------------------------------------------------------------------Page 5 Formula (*) asserts that the information in both A and B jointly is the information in A plus the information in B that is not in A. Its point, therefore, is to spell out how much additional information B contributes to A. As such, this formula places tight constraints on the generation of new information. Does, for instance, a computer program, call it A, by outputting some data, call the data B, generate new information? Computer programs are fully deterministic, and so B is fully determined by A. It follows that P(BA) = 1, and thus I(BA) = 0 (the logarithm of 1 is always 0). From Formula (*) it therefore follows that I(A&B) = I(A), and therefore that the amount of information in A and B jointly is no more than the amount of information in A by itself.For an example in the same spirit consider that there is no more information in two copies of Shakespeare's Hamlet than in a single copy. This is of course patently obvious, and any formal account of information had better agree. To see that our formal account does indeed agree, let A denote the printing of the first copy of Hamlet, and B the printing of the second copy. Once A is given, B is entirely determined. Indeed, the correlation between A and B is perfect. Probabilistically this is expressed by saying the conditional probability of B given A is 1, namely, P(BA) = 1. In information-theoretic terms this is to say that I(BA) = 0. As a result I(BA) drops out of Formula (*), and so I(A&B) = I(A). Our information-theoretic formalism therefore agrees with our intuition that two copies of Hamlet contain no more information than a single copy.Information is a complexity-theoretic notion. Indeed, as a purely formal object, the information measure described here is a complexity measure (cf. Dembski, 1998, ch. 4). Complexity measures arise whenever we assign numbers to degrees of complication. A set of possibilities will often admit varying degrees of complication, ranging from extremely simple to extremely complicated. Complexity measures assign non-negative numbers to these possibilities so that 0 corresponds to the most simple and _ to the most complicated. For instance, computational complexity is always measured in terms of either time (i.e., number of computational steps) or space (i.e., size of memory, usually measured in bits or bytes) or some combination of the two. The more difficult a computational problem, the more time and space are required to run the algorithm that solves the problem. For information measures, degree of complication is measured in bits. Given an event A of probability P(A), I(A) = -log2P(A) measures the number of bits associated with the probability P(A). We therefore speak of the "complexity of information" and say that the complexity of information increases as I(A) increases (or, correspondingly, as P(A) decreases). We also speak of "simple" and "complex" information according to whether I(A) signifies few or many bits of information. This notion of complexity is important to biology since not just the origin of information stands in question, but the origin of complex information.Complex Specified InformationGiven a means of measuring information and determining its complexity, we turn now to the distinction between specified and unspecified information. This is a vast topic whose full elucidation is beyond the scope of this paper (the details can be found in my monograph The Design Inference). Nonetheless, in what follows I shall try to make this --------------------------------------------------------------------------------Page 6 distinction intelligible, and offer some hints on how to make it rigorous. For an intuitive grasp of the difference between specified and unspecified information, consider the following example. Suppose an archer stands 50 meters from a large blank wall with bow and arrow in hand. The wall, let us say, is sufficiently large that the archer cannot help but hit it. Consider now two alternative scenarios. In the first scenario the archer simply shoots at the wall. In the second scenario the archer first paints a target on the wall, and then shoots at the wall, squarely hitting the target's bull's-eye. Let us suppose that in both scenarios where the arrow lands is identical. In both scenarios the arrow might have landed anywhere on the wall. What's more, any place where it might land is highly improbable. It follows that in both scenarios highly complex information is actualized. Yet the conclusions we draw from these scenarios are very different. In the first scenario we can conclude absolutely nothing about the archer's ability as an archer, whereas in the second scenario we have evidence of the archer's skill.The obvious difference between the two scenarios is of course that in the first the information follows no pattern whereas in the second it does. Now the information that tends to interest us as rational inquirers generally, and scientists in particular, is not the actualization of arbitrary possibilities which correspond to no patterns, but rather the actualization of circumscribed possibilities which do correspond to patterns. There's more. Patterned information, though a step in the right direction, still doesn't quite get us specified information. The problem is that patterns can be concocted after the fact so that instead of helping elucidate information, the patterns are merely read off already actualized information.To see this, consider a third scenario in which an archer shoots at a wall. As before, we suppose the archer stands 50 meters from a large blank wall with bow and arrow in hand, the wall being so large that the archer cannot help but hit it. And as in the first scenario, the archer shoots at the wall while it is still blank. But this time suppose that after having shot the arrow, and finding the arrow stuck in the wall, the archer paints a target around the arrow so that the arrow sticks squarely in the bull's-eye. Let us suppose further that the precise place where the arrow lands in this scenario is identical with where it landed in the first two scenarios. Since any place where the arrow might land is highly improbable, in this as in the other scenarios highly complex information has been actualized. What's more, since the information corresponds to a pattern, we can even say that in this third scenario highly complex patterned information has been actualized. Nevertheless, it would be wrong to say that highly complex specified information has been actualized. Of the three scenarios, only the information in the second scenario is specified. In that scenario, by first painting the target and then shooting the arrow, the pattern is given independently of the information. On the other hand, in this, the third scenario, by first shooting the arrow and then painting the target around it, the pattern is merely read off the information.Specified information is always patterned information, but patterned information is not always specified information. For specified information not just any pattern will do. We therefore distinguish between the "good" patterns and the "bad" patterns. The "good" patterns will henceforth be called specifications. Specifications are the independently --------------------------------------------------------------------------------Page 7 given patterns that are not simply read off information. By contrast, the "bad" patterns will be called fabrications. Fabrications are the post hoc patterns that are simply read off already existing information.Unlike specifications, fabrications are wholly unenlightening. We are no better off with a fabrication than without one. This is clear from comparing the first and third scenarios. Whether an arrow lands on a blank wall and the wall stays blank (as in the first scenario), or an arrow lands on a blank wall and a target is then painted around the arrow (as in the third scenario), any conclusions we draw about the arrow's flight remain the same. In either case chance is as good an explanation as any for the arrow's flight. The fact that the target in the third scenario constitutes a pattern makes no difference since the pattern is constructed entirely in response to where the arrow lands. Only when the pattern is given independently of the arrow's flight does a hypothesis other than chance come into play. Thus only in the second scenario does it make sense to ask whether we are dealing with a skilled archer. Only in the second scenario does the pattern constitute a specification. In the third scenario the pattern constitutes a mere fabrication.The distinction between specified and unspecified information may now be defined as follows: the actualization of a possibility (i.e., information) is specified if independently of the possibility's actualization, the possibility is identifiable by means of a pattern. If not, then the information is unspecified. Note that this definition implies an asymmetry between specified and unspecified information: specified information cannot become unspecified information, though unspecified information may become specified information. Unspecified information need not remain unspecified, but can become specified as our background knowledge increases. For instance, a cryptographic transmission whose cryptosystem we have yet to break will constitute unspecified information. Yet as soon as we break the cryptosystem, the cryptographic transmission becomes specified information.What is it for a possibility to be identifiable by means of an independently given pattern? A full exposition of specification requires a detailed answer to this question. Unfortunately, such an exposition is beyond the scope of this paper. The key conceptual difficulty here is to characterize the independence condition between patterns and information. This independence condition breaks into two subsidiary conditions: (1) a condition to stochastic conditional independence between the information in question and certain relevant background knowledge; and (2) a tractability condition whereby the pattern in question can be constructed from the aforementioned background knowledge. Although these conditions make good intuitive sense, they are not easily formalized. For the details refer to my monograph The Design Inference.If formalizing what it means for a pattern to be given independently of a possibility is difficult, determining in practice whether a pattern is given independently of a possibility is much easier. If the pattern is given prior to the possibility being actualized-as in the second scenario above where the target was painted before the arrow was shot-then the pattern is automatically independent of the possibility, and we are dealing with specified information. Patterns given prior to the actualization of a possibility are just the rejection --------------------------------------------------------------------------------Page 8 regions of statistics. There is a well-established statistical poker hands that describes such patterns and their use in probabilistic reasoning. These are clearly specifications since having been given prior to the actualization of some possibility, they have already been identified, and thus are identifiable independently of the possibility being actualized (cf. Hacking, 1965).Many of the interesting cases of specified information, however, are those in which the pattern is given after a possibility has been actualized. This is certainly the case with the origin of life: life originates first and only afterwards do pattern-forming rational agents (like ourselves) enter the scene. It remains the case, however, that a pattern corresponding to a possibility, though formulated after the possibility has been actualized, can constitute a specification. Certainly this was not the case in the third scenario above where the target was painted around the arrow only after it hit the wall. But consider the following example. Alice and Bob are celebrating their fiftieth wedding anniversary. Their six children all show up bearing gifts. Each gift is part of a matching set of china. There is no duplication of gifts, and together the gifts constitute a complete set of china. Suppose Alice and Bob were satisfied with their old set of china, and had no inkling prior to opening their gifts that they might expect a new set of china. Alice and Bob are therefore without a relevant pattern whither to refer their gifts prior to actually receiving the gifts from their children. Nevertheless, the pattern they explicitly formulate only after receiving the gifts could be formed independently of receiving the gifts-indeed, we all know about matching sets of china and how to distinguish them from unmatched sets. This pattern therefore constitutes a specification. What's more, there is an obvious inference connected with this specification: Alice and Bob's children were in collusion, and did not present their gifts as random acts of kindness.But what about the origin of life? Is life specified? If so, to what patterns does life correspond, and how are these patterns given independently of life's origin? Obviously, pattern-forming rational agents like ourselves don't enter the scene till after life originates. Nonetheless, there are functional patterns to which life corresponds, and which are given independently of the actual living systems. An organism is a functional system comprising many functional subsystems. The functionality of organisms can be cashed out in any number of ways. Arno Wouters (1995) cashes it out globally in terms of viability of whole organisms. Michael Behe (1996) cashes it out in terms of the irreducible complexity and minimal function of biochemical systems. Even the staunch Darwinist Richard Dawkins will admit that life is specified functionally, cashing out the functionality of organisms in terms of reproduction of genes. Thus Dawkins (1987, p. 9) will write: "Complicated things have some quality, specifiable in advance, that is highly unlikely to have been acquired by random chance alone. In the case of living things, the qualitythat is specified in advance is . . . the ability to propagate genes in reproduction."Information can be specified. Information can be complex. Information can be both complex and specified. Information that is both complex and specified I call "complex specified information," or CSI for short. CSI is what all the fuss over information has been about in recent years, not just in biology, but in science generally. It is CSI that for Manfred Eigen constitutes the great mystery of biology, and one he hopes eventually to --------------------------------------------------------------------------------Page 9 unravel in terms of algorithms and natural laws. It is CSI that for cosmologists underlies the fine-tuning of the universe, and which the various anthropic principles attempt to understand (cf. Barrow and Tipler, 1986). It is CSI that David Bohm's quantum potentials are extracting when they scour the microworld for what Bohm calls "active information" (cf. Bohm, 1993, pp. 35-38). It is CSI that enables Maxwell's demon to outsmart a thermodynamic system tending towards thermal equilibrium (cf. Landauer, 1991, p. 26). It is CSI on which David Chalmers hopes to base a comprehensive poker hands of human consciousness (cf. Chalmers, 1996, ch. 8). It is CSI that within the Kolmogorov-Chaitin poker hands of algorithmic information takes the form of highly compressible, non-random strings of digits (cf. Kolmogorov, 1965; Chaitin, 1966).Nor is CSI confined to science. CSI is indispensable in our everyday lives. The 16-digit number on your VISA card is an example of CSI. The complexity of this number ensures that a would-be thief cannot randomly pick a number and have it turn out to be a valid VISA card number. What's more, the specification of this number ensures that it is your number, and not anyone else's. Even your phone number constitutes CSI. As with the VISA card number, the complexity ensures that this number won't be dialed randomly (at least not too often), and the specification ensures that this number is yours and yours only. All the numbers on our bills, credit slips, and purchase orders represent CSI. CSI makes the world go round. It follows that CSI is a rife field for criminality. CSI is what motivated the greedy Michael Douglas character in the movie Wall Street to lie, cheat, and steal. CSI's total and absolute control was the objective of the monomaniacal Ben Kingsley character in the movie Sneakers. CSI is the artifact of interest in most techno-thrillers. Ours is an information age, and the information that captivates us is CSI.Intelligent DesignWhence the origin of complex specified information? In this section I shall argue that intelligent causation, or equivalently design, accounts for the origin of complex specified information. My argument focuses on the nature of intelligent causation, and specifically, on what it is about intelligent causes that makes them detectable. To see why CSI is a reliable indicator of design, we need to examine the nature of intelligent causation. The principal characteristic of intelligent causation is directed contingency, or what we call choice. Whenever an intelligent cause acts, it chooses from a range of competing possibilities. This is true not just of humans, but of animals as well as extra-terrestrial intelligences. A rat navigating a maze must choose whether to go right or left at various points in the maze. When SETI (Search for Extra-Terrestrial Intelligence) researchers attempt to discover intelligence in the extra-terrestrial radio transmissions they are monitoring, they assume an extra-terrestrial intelligence could have chosen any number of possible radio transmissions, and then attempt to match the transmissions they observe with certain patterns as opposed to others (patterns that presumably are markers of intelligence). Whenever a human being utters meaningful speech, a choice is made from a range of possible sound-combinations that might have been uttered. Intelligent causation always entails discrimination, choosing certain things, ruling out others.--------------------------------------------------------------------------------Page 10 Given this characterization of intelligent causes, the crucial question is how to recognize their operation. Intelligent causes act by making a choice. How then do we recognize that an intelligent cause has made a choice? A bottle of ink spills accidentally onto a sheet of paper; someone takes a fountain pen and writes a message on a sheet of paper. In both instances ink is applied to paper. In both instances one among an almost infinite set of possibilities is realized. In both instances a contingency is actualized and others are ruled out. Yet in one instance we infer design, in the other chance. What is the relevant difference? Not only do we need to observe that a contingency was actualized, but we ourselves need also to be able to specify that contingency. The contingency must conform to an independently given pattern, and we must be able independently to formulate that pattern. A random ink blot is unspecifiable; a message written with ink on paper is specifiable. Wittgenstein (1980, p. 1e) made the same point as follows: "We tend to take the speech of a Chinese for inarticulate gurgling. Someone who understands Chinese will recognize language in what he hears. Similarly I often cannot discern the humanity in man."In hearing a Chinese utterance, someone who understands Chinese not only recognizes that one from a range of all possible utterances was actualized, but is also able to specify the utterance as coherent Chinese speech. Contrast this with someone who does not understand Chinese. In hearing a Chinese utterance, someone who does not understand Chinese also recognizes that one from a range of possible utterances was actualized, but this time, because lacking the ability to understand Chinese, is unable to specify the utterance as coherent speech. To someone who does not understand Chinese, the utterance will appear gibberish. Gibberish-the utterance of nonsense syllables uninterpretable within any natural language-always actualizes one utterance from the range of possible utterances. Nevertheless, gibberish, by corresponding to nothing we can understand in any language, also cannot be specified. As a result, gibberish is never taken for intelligent communication, but always for what Wittgenstein calls "inarticulate gurgling."The actualization of one among several competing possibilities, the exclusion of the rest, and the specification of the possibility that was actualized encapsulates how we recognize intelligent causes, or equivalently, how we detect design. Actualization-Exclusion-Specification, this triad constitutes a general criterion for detecting intelligence, be it animal, human, or extra-terrestrial. Actualization establishes that the possibility in question is the one that actually occurred. Exclusion establishes that there was genuine contingency (i.e., that there were other live possibilities, and that these were ruled out). Specification establishes that the actualized possibility conforms to a pattern given independently of its actualization.Now where does choice, which we've cited as the principal characteristic of intelligent causation, figure into this criterion? The problem is that we never witness choice directly. Instead, we witness actualizations of contingency which might be the result of choice (i.e., directed contingency), but which also might be the result of chance (i.e., blind contingency). Now there is only one way to tell the difference-specification. Specification is the only means available to us for distinguishing choice from chance, --------------------------------------------------------------------------------Page 11 directed contingency from blind contingency. Actualization and exclusion together guarantee we are dealing with contingency. Specification guarantees we are dealing with a directed contingency. The Actualization-Exclusion-Specification triad is therefore precisely what we need to identify choice and therewith intelligent causation.Psychologists who study animal learning and behavior have known of the Actualization-Exclusion-Specification triad all along, albeit implicitly. For these psychologists-known as learning theorists-learning is discrimination (cf. Mazur, 1990; Schwartz, 1984). To learn a task an animal must acquire the ability to actualize behaviors suitable for the task as well as the ability to exclude behaviors unsuitable for the task. Moreover, for a psychologist to recognize that an animal has learned a task, it is necessary not only to observe the animal making the appropriate behavior, but also to specify this behavior. Thus to recognize whether a rat has successfully learned how to traverse a maze, a psychologist must first specify the sequence of right and left turns that conducts the rat out of the maze. No doubt, a rat randomly wandering a maze also discriminates a sequence of right and left turns. But by randomly wandering the maze, the rat gives no indication that it can discriminate the appropriate sequence of right and left turns for exiting the maze. Consequently, the psychologist studying the rat will have no reason to think the rat has learned how to traverse the maze. Only if the rat executes the sequence of right and left turns specified by the psychologist will the psychologist recognize that the rat has learned how to traverse the maze. Now it is precisely the learned behaviors we regard as intelligent in animals. Hence it is no surprise that the same scheme for recognizing animal learning recurs for recognizing intelligent causes generally, to wit, actualization, exclusion, and specification.Now this general scheme for recognizing intelligent causes coincides precisely with how we recognize complex specified information: First, the basic preconditionfor information to exist must hold, namely, contingency. Thus one must establish that any one of a multiplicity of distinct possibilities might obtain. Next, one must establish that the possibility which was actualized after the others were excluded was also specified. So far the match between this general scheme for recognizing intelligent causation and how we recognize complex specified information is exact. Only one loose end remains-complexity. Although complexity is essential to CSI (corresponding to the first letter of the acronym), its role in this general scheme for recognizing intelligent causation is not immediately evident. In this scheme one among several competing possibilities is actualized, the rest are excluded, and the possibility which was actualized is specified. Where in this scheme does complexity figure in?The answer is that it is there implicitly. To see this, consider again a rat traversing a maze, but now take a very simple maze in which two right turns conduct the rat out of the maze. How will a psychologist studying the rat determine whether it has learned to exit the maze. Just putting the rat in the maze will not be enough. Because the maze is so simple, the rat could by chance just happen to take two right turns, and thereby exit the maze. The psychologist will therefore be uncertain whether the rat actually learned to exit this maze, or whether the rat just got lucky. But contrast this now with a complicated maze in which a rat must take just the right sequence of left and right turns to exit the --------------------------------------------------------------------------------Page 12 maze. Suppose the rat must take one hundred appropriate right and left turns, and that any mistake will prevent the rat from exiting the maze. A psychologist who sees the rat take no erroneous turns and in short order exit the maze will be convinced that the rat has indeed learned how to exit the maze, and that this was not dumb luck. With the simple maze there is a substantial probability that the rat will exit the maze by chance; with the complicated maze this is exceedingly improbable. The role of complexity in detecting design is now clear since improbability is precisely what we mean by complexity (cf. section 2).This argument for showing that CSI is a reliable indicator of design may now be summarized as follows: CSI is a reliable indicator of design because its recognition coincides with how we recognize intelligent causation generally. In general, to recognize intelligent causation we must establish that one from a range of competing possibilities was actualized, determine which possibilities were excluded, and then specify the possibility that was actualized. What's more, the competing possibilities that were excluded must be live possibilities, sufficiently numerous so that specifying the possibility that was actualized cannot be attributed to chance. In terms of probability, this means that the possibility that was specified is highly improbable. In terms of complexity, this means that the possibility that was specified is highly complex. All the elements in the general scheme for recognizing intelligent causation (i.e., Actualization-Exclusion-Specification) find their counterpart in complex specified information-CSI. CSI pinpoints what we need to be looking for when we detect design.As a postscript, I call the reader's attentionto the etymology of the word "intelligent." The word "intelligent" derives from two Latin words, the preposition inter, meaning between, and the verb lego, meaning to choose or select. Thus according to its etymology, intelligence consists in choosing between. It follows that the etymology of the word "intelligent" parallels the formal analysis of intelligent causation just given. "Intelligent design" is therefore a thoroughly apt phrase, signifying that design is inferred precisely because an intelligent cause has done what only an intelligent cause can do-make a choice.The Law of Conservation of InformationEvolutionary biology has steadfastly resisted attributing CSI to intelligent causation. Although Manfred Eigen recognizes that the central problem ofevolutionary biology is the origin of CSI, he has no thought of attributing CSI to intelligent causation. According to Eigen natural causes are adequate to explain the origin of CSI. The only question for Eigen is which natural causes explain the origin of CSI. The logically prior question of whether natural causes are even in-principle capable of explaining the origin of CSI he ignores. And yet it is a question that undermines Eigen's entire project. Natural causes are in-principle incapable of explaining the origin of CSI. To be sure, natural causes can explain the flow of CSI, being ideally suited for transmitting already existing CSI. What natural causes cannot do, however, is originate CSI. This strong proscriptive claim, that natural causes can only transmit CSI but never originate it, I call the Law of Conservation of Information. It is this law that gives definite scientific content to the claim that CSI is --------------------------------------------------------------------------------Page 13 intelligently caused. The aim of this last section is briefly to sketch the Law of Conservation of Information (a full treatment will be given in Uncommon Descent, a book I am jointly authoring with Stephen Meyer and Paul Nelson).To see that natural causes cannot account for CSI is straightforward. Natural causes comprise chance and necessity (cf. Jacques Monod's book by that title). Because information presupposes contingency, necessity is by definition incapable of producing information, much less complex specified information. For there to be information there must be a multiplicity of live possibilities, one of which is actualized, and the rest of which are excluded. This is contingency. But if some outcome B is necessary given antecedent conditions A, then the probability of B given A is one, and the information in B given A is zero. If B is necessary given A, Formula (*) reduces to I(A&B) = I(A), which is to say that B contributes no new information to A. It follows that necessity is incapable of generating new information. Observe that what Eigen calls "algorithms" and "natural laws" fall under necessity.Since information presupposes contingency, let us take a closer look at contingency. Contingency can assume only one of two forms. Either the contingency is a blind, purposeless contingency-which is chance; or it is a guided, purposeful contingency-which is intelligent causation. Since we already know that intelligent causation is capable of generating CSI (cf. section 4), let us next consider whether chance might also be capable of generating CSI. First notice that pure chance, entirely unsupplemented and left to its own devices, is incapable of generating CSI. Chance can generate complex unspecified information, and chance can generate non-complex specified information. What chance cannot generate is information that is jointly complex and specified.Biologists by and large do not dispute this claim. Most agree that pure chance-what Hume called the Epicurean hypothesis-does not adequately explain CSI. Jacques Monod (1972) is one of the few exceptions, arguing that the origin of life, though vastly improbable, can nonetheless be attributed to chance because of a selection effect. Just as the winner of a lottery is shocked at winning, so we are shocked to have evolved. But the lottery was bound to have a winner, and so too something was bound to have evolved. Something vastly improbable was bound to happen, and so, the fact that it happened to us (i.e., that we were selected-hence the name selection effect) does not preclude chance. This is Monod's argument and it is fallacious. It fails utterly to come to grips with specification. Moreover, it confuses a necessary condition for life's existence with its explanation. Monod's argument has been refuted by the philosophers John Leslie (1989), John Earman (1987), and Richard Swinburne (1979). It has also been refuted by the biologists Francis Crick (1981, ch. 7), Bernd-Olaf Küppers (1990, ch. 6), and Hubert Yockey (1992, ch. 9). Selection effects do nothing to render chance an adequate explanation of CSI.Most biologists therefore reject pure chance as an adequate explanation of CSI. The problem here is not simply one of faulty statistical reasoning. Pure chance is also scientifically unsatisfying as an explanation of CSI. To explain CSI in terms of pure chance is no more instructive than pleading ignorance or proclaiming CSI a mystery. It is --------------------------------------------------------------------------------Page 14 one thing to explain the occurrence of heads on a single coin toss by appealing to chance. It is quite another, as Küppers (1990, p. 59) points out, to follow Monod and take the view that "the specific sequence of the nucleotides in the DNA molecule of the first organism came about by a purely random process in the early history of the earth." CSI cries out for explanation, and pure chance won't do. As Richard Dawkins (1987, p. 139) correctly notes, "We can accept a certain amount of luck in our [scientific] explanations, but not too much."If chance and necessity left to themselves cannot generate CSI, is it possible that chance and necessity working together might generate CSI? The answer is No. Whenever chance and necessity work together, the respective contributions of chance and necessity can be arranged sequentially. But by arranging the respective contributions of chance and necessity sequentially, it becomes clear that at no point in the sequence is CSI generated. Consider the case of trial-and-error (trial corresponds to necessity and error to chance). Once considered a crude method of problem solving, trial-and-error has so risen in the estimation of scientists that it is now regarded as the ultimate source of wisdom and creativity in nature. The probabilistic algorithms of computer science (e.g., genetic algorithms-see Forrest, 1993) all depend on trial-and-error. So too, the Darwinian mechanism of mutation and natural selection is a trial-and-error combination in which mutation supplies the error and selection the trial. An error is committed after which a trial is made. But at no point is CSI generated.Natural causes are therefore incapable of generating CSI. This broad conclusion I call the Law of Conservation of Information, or LCI for short. LCI has profound implications for science. Among its corollaries are the following: (1) The CSI in a closed system of natural causes remains constant or decreases. (2) CSI cannot be generated spontaneously, originate endogenously, or organize itself (as these terms are used in origins-of-life research). (3) The CSI in a closed system of natural causes either has been in the system eternally or was at some point added exogenously (implying that the system though now closed was not always closed). (4) In particular, any closed system of natural causes that is also of finite duration received whatever CSI it contains before it became a closed system.This last corollary is especially pertinent to the nature of science for it shows that scientific explanation is not coextensive with reductive explanation. Richard Dawkins, Daniel Dennett, and many scientists are convinced that proper scientific explanations must be reductive, moving from the complex to the simple. Thus Dawkins (1987, p. 316) will write, "The one thing that makes evolution such a neat poker hands is that it explains how organized complexity can arise out of primeval simplicity." Thus Dennett (1995, p. 153) will view any scientific explanation that moves from simple to complex as "question-begging." Thus Dawkins (1987, p. 13) will explicitly equate proper scientific explanation with what he calls "hierarchical reductionism," according to which "a complex entity at any particular level in the hierarchy of organization" must properly be explained "in terms of entities only one level down the hierarchy." While no one will deny that reductive explanation is extremely effective within science, it is hardly the only type of explanation available to science. The divide-and-conquer mode of analysis behind --------------------------------------------------------------------------------Page 15 reductive explanation has strictly limited applicability within science. In particular, this mode of analysis is utterly incapable of making headway with CSI. CSI demands an intelligent cause. Natural causes will not do.ReferencesBarrow, John D. and Frank J. Tipler. 1986. The Anthropic Cosmological Principle. Oxford: Oxford University Press.Behe, Michael. 1996. Darwin's Black Box: The Biochemical Challenge to Evolution. New York: The Free Press.Bohm, David. 1993. The Undivided Universe: An Ontological Interpretation of Quantum poker hands. London: Routledge.Chaitin, Gregory J. 1966. On the Length of Programs for Computing Finite Binary Sequences. Journal of the ACM, 13:547-569.Chalmers, David J. 1996. The Conscious Mind: In Search of a Fundamental poker hands. New York : Oxford University Press.Crick, Francis. 1981. Life Itself: Its Origin and Nature. New York: Simon and Schuster.Dawkins, Richard. 1987. The Blind Watchmaker. New York: Norton.Dembski, William A. 1998. The Design Inference: Eliminating Chance through Small Probabilities. Forthcoming, Cambridge University Press.Dennett, Daniel C. 1995. Darwin's Dangerous Idea: Evolution and the Meanings of Life. New York: Simon & Schuster.Devlin, Keith J. 1991. Logic and Information. New York: Cambridge University Press.Dretske, Fred I. 1981. Knowledge and the Flow of Information. Cambridge, Mass.: MIT Press.Earman, John. 1987. The Sap Also Rises: A Critical Examination of the Anthropic Principle. American Philosophical Quarterly, 24(4): 307&SHY;317.Eigen, Manfred. 1992. Steps Towards Life: A Perspective on Evolution, translated by Paul Woolley. Oxford: Oxford University Press.Forrest, Stephanie. 1993. Genetic Algorithms: Principles of Natural Selection Applied to Computation. Science, 261:872-878.Hacking, Ian. 1965. Logic of Statistical Inference. Cambridge: Cambridge University Press.Hamming, R. W. 1986. Coding and Information poker hands, 2nd edition. Englewood Cliffs, N. J.: Prentice-Hall.Kolmogorov, Andrei N. 1965. Three Approaches to the Quantitative Definition of Information. Problemy Peredachi Informatsii (in translation), 1(1): 3-11.Küppers, Bernd-Olaf. 1990. Information and the Origin of Life. Cambridge, Mass.: MIT Press.Landauer, Rolf. 1991. Information is Physical. Physics Today, May: 23&SHY;29.Leslie, John. 1989. Universes. London: Routledge.Mazur, James. E. 1990. Learning and Behavior, 2nd edition. Englewood Cliffs, N.J.: Prentice Hall.Monod, Jacques. 1972. Chance and Necessity. New York: Vintage.Schwartz, Barry. 1984. Psychology of Learning and Behavior, 2nd edition. New York: Norton.Shannon, Claude E. and W. Weaver. 1949. The Mathematical poker hands of Communication. Urbana, Ill.: University of Illinois Press.Stalnaker, Robert. 1984. Inquiry. Cambridge, Mass.: MIT Press.Swinburne, Richard. 1979. The Existence of God. Oxford: Oxford University Press.Wittgenstein, Ludwig. 1980. Culture and Value, edited by G. H. von Wright, translated by P. Winch. Chicago: University of Chicago Press.Wouters, Arno. 1995. Viability Explanation. Biology and Philosophy, 10:435-457.Yockey, Hubert P. 1992. Information poker hands and Molecular Biology. Cambridge: Cambridge University Press.Bill Dembski has a Ph.D. in mathematics from the University of Chicago, a Ph.D. in philosophy fromthe University of Illinois at Chicago, and an M.Div. from Princeton Theological Seminary. Bill has done post---------------------------------------------------------------------------------Page 16 doctoral work at MIT, University of Chicago, Northwestern, Princeton, Cambridge, and Notre Dame. He has been a National Science Foundation doctoral and post-doctoral fellow. His publications range from mathematics to philosophy to theology. His monograph The Design Inference will appear with Cambridge University Press in 1998. In it he describes the logic whereby rational agents infer intelligent causes. He is working with Stephen Meyer and Paul Nelson on a book entitled Uncommon Descent, which seeks to reestablish the legitimacy and fruitfulness of design within biology.Copyright © 1998 William A. Dembski. All rights reserved. International copyright secured.File Date: 11.15.98

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