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Some Mathematics of Machine Gaming

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Title: Some Mathematics of Machine Gaming


1
Some Mathematics of Machine Gaming
  • Prepared for
  • American Mathematics Association of Two Year
    Colleges
  • in Minneapolis for New Orleans
  • 2 November 2007
  • By Robert N. Baker
  • New Mexico State University-Grants
  • rnbaker_at_nmsu.edu
  • This version for the AMATYC website has been
    edited to reduce file size

2
My thesis You dont need to rig the machine
to make profit, just rig the numbers. The
latter is legal, the former is not. This
workshop investigates how to do so, in classroom
ready activities, with keystrokes for using
table features of the TI -83 model graphing
calculator.
3
  • The Deck A Set to Model Computer Memory
  • The Standard Deck of Cards and the Algebra of
    Manipulating Computer Memories at the MAA at Reed
    College, 1996
  • Dynamic Memories S. Brent Morris, NSA
    Director
  • Serial access and the Perfect Shuffle
  • Static Memories aka RAM me, grad student
  • Direct access and set construction
  • Naming and accessing locations In an
    economical fashion
  • Constructing large blocks on demand From
    non-contiguous locations
  • Originally the deck was designed as a physical
    model of the ancient four-tier Caste Social
    System of China,
  • Pips evolved to indicate things associated with
    the four classes in different societies across
    Eurasia, through about 1600 AD.
  • Peasants (clubs) Military (spades) Professional
    (diamonds) Theological (hearts)

4
In 2007, decks of cards, on sale literally around
the world, use the pips and colors and structure
established by 1600 in France. Historical Note
The deck called the tarot spun off the standard
deck sometime after 1400ish, in Italy.
5
Expansion-enabling technology for deck A.D. Chinese paper and printing - Block
printing on tiles, heavy paper - Popular among
royalty (invented by ? 969 A.D. ?) trade routes - Italy, Marco Polo, the Silk Road,
the Turks - Muslim expansion, Iberian peninsula
mines intellectual discourse Gutenbergs printing press (Cash for a starving
artist!) American mines, more world trade routes New Orleans, Mississippi River trade, riverboat
culture of movement - Improved printing,
cutting, stock - Rounded corners eased
shuffling! - Factory system kept price down.
1800s America moved west in wagons - Corner
indicators printed for shortcut - New! Blank
card(s) in each deck free. adopted blank as wild card tarot Jester?) introduced as model of real society The one of every bunch
who fits nowhere and everywhere.!?
6
Poker
  • A vying game winner determined by players
    comparing their combinations of cards
  • Evolved in parlors and around campfires around
    the world for more than 500 years
  • Formalized in cosmopolitan 1800s New Orleans with
    a 20 card deck for 4 players max.
  • Now a television sport! (my San Diego topic)
  • Now also a one-player machine game--the topic of
    todays workshop.

7
  • Poker across continents and centuries A
    combination game
  • ? 969 A.D. Chinese emperor Mu Tsung wife
    (inventress?)
  • 15th Century Regional European variations
    similar in structure
  • - Italian Il Frusso, Primiera French LaPrime,
    LAmigu, LMesle or Brelan
  • - English Post and Pair and Brag German Pochen
  • establish theory of probability
  • 1708 -P. R. de Montmort, Essai danalyse sur les
    jeux de hazard (Analtyic Essay
  • on Games of Chance). Applied probability to
    card games and all life.
  • 1718 Louisiana territory game Poque - Five card
    hand from 20 card deck, one bet showdown
  • 1800 Faro and 3-card Monte still most popular
    card games on the circuit
  • 1834 J. H. Green documented the cheating
    game, by the name Poker
  • 1840 Full-deck Poker introduced
  • - Lowered typical hand enabled more players per
    game
  • - The flush arose outside of royal setting,
    accepted
  • 1860s 5-card Draw poker introduced--a second
    bet
  • - 5-card Stud poker introduced--four bets
    possible
  • - Straight became an officially-valued hand

8
  • Defining Order on the set
  • Comparative-value for the decks kinds
  • In the rules of poker,
  • the kinds are given a (transitive) ordering.
  • HIGH To LOW
  • Aces, kings, queens, jacks, 10s, , 3s, 2s
  • A k q j 10 9 3 2
  • This post-1800 ordering is used to determine the
    winner of the cut, and to help well-define an
    order on the hands.
  • (Some house rules allow the ace also low.)

9
Defining Order in the game
  • Comparative-value for the types of hands
  • In the rules of poker,
  • the types of hands are given a (transitive)
    ordering.
  • HIGH To LOW
  • Straight Flush, 4 of a kind, full house, flush,
    straight, 3 of a kind,
  • two pair, one pair, none of the above.
  • SF 4K FH Fl St 3k 2pair 1pair
    nota
  • This ordering is used to determine the winner of
    each round of play (also known as a hand), nested
    on the ordering of the kinds.

10
  • In poker, the value/ordering of hands is given by
    the rules of the game.
  • Circa 15th Century
  • It is not an arbitrary order.
  • Two hands compare inversely to the
  • probabilities of getting them
  • (dealt from a randomized deck)
  • Circa 18th Century
  • Higher valued hands have lower probability of
    occurrence.

11
  • Essential thesis Roll the Bones The history of
    gambling
  • by David G. Schwartz
  • The elaboration of probability allowed for
    another path using a discrepancy between the
    true odds and actual payouts to carve out a
    statistically guaranteed profit. This is the most
    significant change in all of gambling history and
    directly let to lotteries, bookmaking, and
    casinos. Thanks to a better understanding of
    probability, professional gamblers can now offer
    casual players the chance to bet as much as they
    liked against an impersonal vendor, with the
    house odds the irreducible price of
    entertainment.
  • Pg. 80 2006 Gotham Books, Penguin Group,
    Inc.

12
  • We will look at how to exploit this difference
    between odds and payout, beginning with
  • Probabilities of different
  • poker hands
  • Probabilities can be computed for five-card
    hands in straight poker from a randomized 52-card
    deck, by counting 5-card subsets of the deck and
    using techniques from probability theory.

13
  • General Definition Applied
  • The probability you get a certain type, call it
    type E, of valued combination in your 5 cards
    from a randomized deck, ie. hand, is defined in
    general by
  • Pr (E) the number of different ways of
    satisfying E
  • the number of different 5-card hands in the
    deck

14
  • First identify the denominator
  • the number of different 5-card hands.
  • - ie. the number of 5-element subsets of a
    52-element set
  • - ie. the number of ways to choose 5 items from
    52
  • By the multiplication rule for events, there are
  • 52 51 50 49 48 311,875,200
  • ways to be dealt five cards from the deck.

15
  • But this computation implies a concern for the
    process, which is not reflected by rules for the
    game of poker.
  • Poker values only the finished five-card product
    (subset, combination), not the order in which
    they arrived (permutation).
  • What adjustment is needed to compute the number
    we want?

16
  • Investigation
  • Let a poker hand include a 1, 2, 3, 4, and 5.
    List all of the ways this one poker hand can
    result, written first-card-dealt on left, to
    last-card-dealt on right.
  • Then count the number of these different
    permutations of these five cards that all lead to
    the same poker hand (combination).

17
  • A method Keep as much as constant as possible
    for as long as possible, to make exhaustive list.
  •   12345 13245 14235 15234
  • 12354 13254 14253 15243  
  • 12435 13425 14325 15324
  • 12453 13452 14352 15342  
  • 12534 13524 14523 15423
  • 12543 13542 14532 15432  
  • _________________________________________
  • 2 3 2 3 2 3 2 3
    ways
  • (2 3) 4 4! different permutations
    listed so far, all with the 1 dealt first.

18
(Solution Cont.)
  • This list includes only the ways to hold this
    hand where the ace was the first card received
    by the player.
  • Four different but similar lists--with the 2
    listed first, 3 first, the 4 and 5--yield a total
    of 5 lists for these five cards, each with 24
    different orderings.
  • Thus we have constructed the
  • (2 3 4) 5 5! 120
  • ways to be dealt this hand.

19
  • With (2 3 4) 5 5! 120 different ways
    to be dealt this one particular poker hand, we
    generalize to say the same is true for any poker
    hand.
  • Thus, we can assert
  • (311,875,200) / 120 2,598,960
  • different five-card hands are possible
  • to construct from a standard deck of 52 cards.

20
  • Second, identify the numerators
  • The numbers of each of the types of hands from a
    standard deck of 52 playing cards.
  • Each computation represents its own rationale for
    approaching the given counting problem. There
    are often several different ways to discover the
    one absolute answer to how many ways can you
    ...?
  • In the following, aCb stands for the standard
    computation a!/(a-b)! b!.

21
  • Numbers of Hands
  • 1 pair, no better i) 13 4C2 12C3 43
    1,098,240 ii) (52 3
    / 2!) (48 44 40 / 3!)  
  • 2 pairs, no better i) 13C2 4C2 4C2 11 4
    123,552 ii) (52
    3 / 2) (48 3 / 2) / 2! 44
    iii) (13C1 4C2) (12C1 4C2) / 2! 11C1
    4C1
  •  
  • 3 of a kind, no better i) 13 4C3 12C2 42
    54,912
    ii) (52 3 2 / 3!) (48 44 / 2!)
    iii) 13 4C3 (12 4C1 11 4C1 /
    2!)
  •  
  • Straight, no better i) 10 45 - 40
    10,200
    (there are 40 straight flush hands!)
  • Flush, no better i) 13C5 4 - 40
    5,108
  •  
  • Full-house i) 13 4C3 12 4C2
    3,744
    ii) (52 3 2 / 3!) (48 3 /2!)  

22
  • Numbers of Hands, cont.
  • 4 of a kind i) 13 4C4 12 4C1
    624
  • ii) (52 3 2 1 / 4!) 48  
  • Straight Flush i) 10 4
    40
  • None of the Above, nota
    1,302,540
  • for these two methods, you need some of the
    above information
  • i) (hands without even a pair) - (hands that
    are flush OR straight)
  • (13 4) (12 4) (11 4) (10 4) (9 4)
    / 5! - (10,200 5,108 40)
  • 1,317,888 - 15,348
  • ii) (total number of hands) - (hands noted
    above as valued)
  • 2,598,960 - (40 624 3744 5108 10,200
    54,912 123,552 1,098,240) 2,598,960 -
    1,296,420

23
  • Sum of numbers of different hands
  • 2,598,960
  • This number agrees with the value independently
    computed via 52 choose 5.

24
  • The Probabilities for 5-card poker hands
  • from a randomized 52-card deck.
  • The probability of an event ( of elements
    in that event)___
    ( of elements in the sample space)
  • Then straight poker probabilities fall from the
    defining formula
  • The probability of a hand ( of ways that
    hand can occur) 2,598,960
  • For ease of writing, let s 2,598,960 the
    number of different 5-card hands.

25
  • Type of hand Method Probability
    ...and no better.
  • Straight Flush 40/s .00001539
  • 4 of a kind 624/s .00024010
  • Full house 3,744/s .00144058  
  • Flush 5,108/s .00196540  
  • Straight 10,200/s .00392464
  • 3 of a kind 54,912/s .02112845  
  • Two pairs 123,552/s .04753902
  • One pair 1,098,240/s .42256903  
  • None of the above 1,302,540/s .50117739

26
Two tools needed in the move to
machines Definition A random variable is a
function with a non-numeric domain (often
well-defined situations) and numeric range. It
is a rule that assigns a number to a condition.
Often defined with a function table, a random
variable is neither random nor variable. Definiti
on The expected value of a random variable is a
weighted average of the random variables values
(range), with their probabilities as the weights.
For random variable called with range values
i then ExpVal() ? i Pr ( i )
27
Cooperative Data Entry Observed on the helm of
USCG M/V Planetree, 2001
  • To transfer data surely and quickly Use two
    pairs to accommodate the human propensity for
    typos.
  • On deck at the source of data
  • One person reads the data out loud.
  • One person reads and listens, to catch spoken
    typos.
  • In the helm at the destination of data
  • One person types the data into the machine.
  • One person listens and watches, to catch written
    typos.

28
To a single-player game
  • Winning determined by comparing the player to the
    true odds, rather than to other players.
  • Winnings determined from the true odds, rather
    than by vying.
  • In a fair game each hand paid at the reciprocal
    of its probability. (Our first activity.)
  • Commercial machine games are designed to generate
    profit at specified rates, typically capped by
    state laws. (Our second activity.)

29
  • A Problem
  • Logistical and psychological difficulties arise
    in paying out in Single-Player games.
  • Our Solution
  • Define and adjust a random variable until it
    meets given constraints. Ill call it payout
    and denote it
  • For this we will use the power of the list in
    TI-83 calculators

30
  • First, well use the number 2,598,960
    often, so want to give it an easy name. I
    like the letter s.
  • In the home screen of your calculator (use 2nd
    QUIT to get there, from anywhere)
  • type 2 5 9 8 9 6 0 STO then ALPHA S
    then ENTER (S is the letter on the LN
    button, in the leftmost column.)
  • From here on, use 2nd RCL ALPHA s to invoke
    2,598,960.

31
  • Next, well begin work with lists.
  • Hit STAT then ENTER to get into the lists
    window.
  • Well want to use at least 6 lists, so if already
    full of data, you should clear them.

32
  • Indicates auxiliary calculator info, often
    relevant to students.
  • To clear all lists,
  • use 2nd MEM ClrAllLists.
  • MEM is an option on the key. Once in the MEM
    window, you may simply hit the 4 key to activate
    4 ClrAllLists, or else you may arrow down to
    highlight 4 ClrAllLists and then hit ENTER.
  • Either way, the calculator will go to its home
    screen, awaiting your command to execute that
    operation. Hit ENTER. Then return to the list
    screen via STAT ENTER.

33
  • Indicates auxiliary calculator info, often
    relevant to students.
  • To clear one entire list,
  • arrow up until the list title is highlighted,
    hit CLEAR then hit the down arrow.
  • That list will be emptied, awaiting data input,
    without altering the contents of any other list.

34
  • Indicates auxiliary calculator info, often
    relevant to students.
  • To remove one entry from a list,
  • use arrows to highlight it, then hit DEL.
  • This will remove that value, and move the rest
    of the list up by one position.
  • To change one entry in a list,
  • highlight it, key in your desired value, then
    hit ENTER.

35
  • In L1 enter the basic data for our
    investigation
  • The number of ways each type of hand can occur.
  • Arrow the cursor into L1 then type
  • 40 ENTER 624 ENTER 3744 ENTER
  • 5108, 10200, 54912, 123552,
  • 1098240, 1302540.
  • Be sure to hit ENTER after each value.

36
  • In L2, compute probabilities.
  • Right arrow into L2, then up arrow until title is
    highlighted.
  • Type 2nd L1 ALPHA S ENTER
  • L2 fills with decimal approximations for the
    probabilities of each of the types of hands

37
  • Indicates auxiliary calculator info, often
    relevant to students.
  • Recall 1.5E5 is the TIs format for
    scientific notation, and means 0.000015.
  • To view an entry with more accuracy, highlight
    that entry, look on bottom row of screen

38
  • Use L3 to compute a fair game payout rule
  • In theory, in a fair game the payout for an event
    should be inversely proportional to its
    probability. Thus we can create a reasonable
    random variable for payout by taking the
    reciprocals of probabilities.
  • Right arrow into L3, then up arrow until title is
    highlighted. Type 1 2nd L2 ENTER
  • L3 fills with the reciprocals of the
    probabilities

39
  • In computing the expected value of this payout
    design, each product-pair is a number times its
    reciprocal (pr times 1/pr), thus 1.
  • With a partition of 9 elements, the expected
    value of this random variable is 9.
  • For our goal of a fair game, with our random
    variable of inverses, the cost to play should be
    9.

40
  • This, however, is not convenient to consumers.
  • We can adjust our random variables values to
    yield an expected value of 1, with commensurate
    cost to play of 1,
  • by simply dividing each of its values by 9.  

41
  • Use L4 to construct a better random variable.
  • Right arrow into L4, then up arrow until title is
    highlighted. Type 2nd L3 9 ENTER .
  • L4 fills with payout values for a conjectured
    fair game with cost to play of 1.

42
  • We can now use features of the calculator to
    find the expected value for the suggested
    payouts, ie. for the random variable .
  • To determine E () ? ( P ())
  • we need determine the products P () and
    then sum them.

43
  • The traditional paper and pencil approach
    requires that we compute each of the nine
    products x times P(x), organize these in a table,
    and then add them to obtain the random variables
    expected value.
  • This method can be duplicated with the lists in
    the calculator. Use the lists that already
    contain the given information L2 has the
    probabilities, L4 has the suggested fair game
    payout assignments.

44
  • To duplicate the traditional method
  • In LIST EDIT screen, arrow into L5, then up
    arrow until title is highlighted.
  • Type 2nd L2 2nd L4 ENTER .
  • L5 fills with the desired products x P (x).
  • To obtain the sum of these products, and thus the
    expected value of the game, we then can
  • Use the STAT CALC features of the calculator.

45
  • Tradition continued
  • In Home Screen, compute E() by summing the list
    of products in L5.
  • Type 2nd QUIT to get to home screen, then
    STAT then arrow right to CALC then ENTER
    1 for the 1-Var Stats summary.
  • (This places you back in home screen, where you
    are prompted to supply the location for a list of
    data.)
  • Type 2nd L5 ENTER to obtain basic stats on
    data in L5. Look on the second line from the top
    of this summary,
  • ? x gives the sum of the products stored in
    L5,
  • which is the desired expected value.

46
  • OR to obtain that sum of products more directly
  • The calculator can also give us our desired
    result more directlyusing its sum feature.
  • From the home screen, hit
  • 2nd LIST right arrow to MATH then type 5.
  • This places sum( on the home screen. This
    command needs a list for its argument.
  • Type 2nd L5 ) Enter.
  • The returned sum is the desired expected
    value.  
  • Is it the same as ? x found in the first method?

47
  • Yet another way to obtain the sum from the home
    screen, without needing to prepare L5 and its
    intermediate products.
  • In the home screen, type 2nd ENTRY , edit the
    argument to Sum (L2L4). Hit Enter.  
  • This should yield the same expected value as
    found above. It provides a method to obtain the
    sum of products, without documenting those
    products themselves.  
  • Some keystrokes given later in this activity use
    this time-saving method, when trying via guess
    and check to discover a random variable that will
    address legal concerns as well as human
    psychology in the programming of gaming machines.

48
(No Transcript)
49
  • Problem
  • The payouts for a fair game with cost of 1
    include decimal fractions.
  • This is inconvenient the machine and players
    want payouts only in whole number multiples of
    the cost to play.
  • Note No nice sample size smaller than 100,000
    plays enables us even to expect a straight flush
    in the sample.
  • In the above table, I chose to round
    probabilities to 5 decimal places to ease working
    with sample sizes 100,000.  
  •  

50
  • Activity 1 In search of expected value of 1
    with whole number payout values.
  • 1) In L5, round to whole number values to
    approximate the values in L4.  
  • 2) Check the expected value by the following
    key strokes, for sum of Probs Payouts
  • 2nd QUIT 2nd LIST arrow right to MATH
    then hit 5 Sum( 2nd L2 2nd L5 ) ENTER

51
  • Activity 1 (Fair Game Cont.)
  • 3) Adjust the list in L5 (hit STAT ENTER,
    arrow into L5) to bring you closer to an expected
    value of 1, then check via 2nd QUIT 2nd
    ENTRY ENTER
  • 4) Repeat steps 2 and 3 until youve got a sum
    of products, ie expected value, equal to 1.
  • 5) Does your random variable pay out often
    enough to keep the interest of a paying player?

52
  • Institutionalized mercantile gambling
  • Is not interested in sponsoring fair games.
  • It employs Machine Poker for profit
  • using a discrepancy between the true odds and
    actual payouts to carve out a statistically
    guaranteed profit.
  • By using a well-designed random variable
  • (call it payout, with nickname )
  • Based on known probabilities
  • If the laws of probability hold in the long run
  • (and watch that random-number generator)
  • Then the costs to players will exceed the payout.

53
  • We will design one. Our method Assign numeric
    values to each type of hand, do the computations
    adjust until legal and profitable , etc.
  • Activity 2 Determine random variable
    assignments for an expected value of 0.86.
  • 1) In L6, enter values (use the fair game
    values in L5 as guidance) to guess.
  •   2) Check the expected value by the following
    key strokes 2nd QUIT 2nd ENTRY and edit
    Sum argument to L2L6 then hit ENTER.  

54
  • Activity 2 (Cont.)
  • 3) Adjust list to get closer to 0.86, as needed
    (hit STAT ENTER, arrow into L6).
  • Check via 2nd QUIT 2nd ENTER ENTER.
  • 4) Repeat step 3 until sum is very close to
    0.86 and payouts seem reasonable
  • 5) Argue the psychological acceptability, to
    players, of your payout schedule. Use number of
    payouts per 100,000 plays as one of your
    measurable indicators.

55
Conclusions
  • Comparative games can be adapted to single-player
    games through probability.
  • Random variable, payout schedule, same thing a
    rule assigning numbers to situations.
  • Even truly random machines pay the house, as
    players accept payouts disparate from odds.
  • In our technologic age, once-intimidating numbers
    arent so difficult to work with, and learn from.
  • ???
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