Thank you to all of

you for coming along. Now as Tom mentioned, I’m

researcher at the London School of Hygiene and

Tropical Medicine where I specialise in mathematical

models of infectious disease. So on the face of it, my job

couldn’t be further really from the world of

casinos and playing cards and plastic chips. But really science

and gambling have incredibly intertwined

relationship, a really longstanding history. And that’s what I want to

talk about this evening. And seeing as I’m

talking about gambling, I thought I would start with an

example of how not to gamble. So this is a story– story from a few years ago. And as you can probably

notice, there’s two large flaws with

this lady’s strategy. The first is it’s

completely illegal. And the second is it

clearly doesn’t work. And the reason I

wanted to show you this is I think when

we talk about people taming chance and

beating the system, typically these are two themes

that crop up quite a lot. You either have them doing

something a bit dodgy. Or you have them

presenting a system which clearly isn’t going to

do something very successful. And what I want

to do this evening is take a look at

a third approach. Take a look at some of the

ways in which mathematicians or scientists have

taken on games of chance and used their techniques to

get an edge over the house. I also want to look at

how the ideas have flowed the other way, how actually

games and gambling have inspired many

ideas which are now fundamental to modern

maths and science. And really lotteries, I

think, are a good place to start, because for me, it

was a story about lotteries that first got me interested

in the mathematics of betting. As I’m sure any of you

who’ve played the lottery or have thought about

playing lottery will know it’s incredibly

difficult to win. But actually, even

the way we measure how difficult it is to win is

a fairly recent development. Although maths is– has

been around for millennia. The idea of how

we quantify luck, how we quantify random events,

is a relatively recent one. It was one that was developed

in the 16th century. And it was actually

in Veneto, Italy. There was a Italian

called Gerolamo Cardano. He was a physician. As a doctor, he was the first to

describe the clinical symptoms of typhoid. He was also a gambler. And as a gambler, he was the

first to describe such games mathematically. And he actually outlined

what’s known as a sample space. So this is the– all of the combinations of

events that could occur. And obviously, if you’re only

interested in one of those, that gives you a sense of

how difficult it is to win. Now for the UK National

Lottery as it stands, you have to pick six numbers

from a possible set of 59. So this results in just over 45

million possible combinations of you– of numbers you could

pick if you bought a card. Clearly this makes

life very difficult for you to win the jackpot. But there is a way that

you can guarantee you will win the lottery this weekend. And that is quite simply to

buy up every single combination of numbers. Now that might sound

a little bit absurd. But let’s just run

with it for a moment. As I said, there’s 45

million combinations of tickets for the UK lottery. So if you were to buy up every

single possible combination and line them up end-to-end,

it would actually stretch from London to Dubai. What’s more, each

ticket cost 2 pounds. So if you really want to win

the jackpot this weekend, it’s going to cost about 90

million pounds to achieve. Clearly that’s not

a feasible strategy. But not all lotteries

are the same. In the 1990s, for example,

the Irish National Lottery had a much smaller

sample space– a much smaller possible

combination of numbers that could come up. In fact, there were about

1.94 million combinations. Each ticket costs 50p. So as a result, it would cost

you less than a million pounds to buy up every

single combination. And actually a syndicate

headed up by an accountant got thinking about this. And clearly most weeks, this

is a pretty poor investment, because the jackpot would be

maybe a few hundred thousand. And if you’re spending

almost a million to win a few hundred

thousand, doesn’t take much to spot that’s

a pretty bad investment. But if a rollover

were to come around, maybe this could be plausible. And actually rather

than stretching it to Dubai, if you lined

up all of these tickets and the combinations

end-to-end, it would actually stretch

from London to Plymouth. So you’ve got something

that’s a bit more doable. And what they started to do is

collect together these tickets and fill each one out by

hand to get every single one of these combinations. And then they waited. They waited for about six

months until the May Bank Holiday in 1992 when the

rollover hit 2.2 million. And they put their

plan into action. They took all these

tickets they’d filled out, started taking them

to shops, and buying them up. And in many cases this

raised some attention. So shops that would usually

sale maybe a thousand tickets in a week were suddenly

selling 15,000. The Lottery perhaps expectantly,

frowned upon this a little bit and tried to stop them. And as a result, when the

lottery draw came around, they’d only bought 80% of

the possible combinations of tickets. So there’s still an element

of luck as to whether they’d win the jackpot. Fortunately for them,

that jackpot’s winning set of numbers was within

the combinations that they bought up. So they won that week. Unfortunately, there were

two other winners that week. So they had to

split the jackpot. But when you added up all

those lower tier prizes that they match five numbers,

four numbers as well, they walked away with a

profit of 300,000 pounds. Now for me, years ago

when I heard the story, that was just a

fantastic illustration of how you can take a pretty

simple mathematical insight, a good dose of

audacity and hard work, and convert it into

something that’s profitable. And this isn’t the only

instance that people have targeted these kind of games. For the UK lottery,

the draw is random. So really the only way

you can guarantee a win is to use this brute force

approach by simply buying up all of the combinations. But not all lotteries

are the same. Take scratchcards, for example. On the face of it, scratchcards

are completely random. If you think about it, they

can’t be completely random. Because if you’re

producing scratchcards, and you just randomly

generate which cards are going to

be winners, there’s a chance that by

sheer chance you will produce too many winning cards. If you’re a company

making scratchcards, you want some way of

controlling and limiting which prizes go out. As statisticians will call it,

you need controlled randomness. You want the prizes to be fairly

uniformly, evenly distributed amongst the occasions. But you don’t want

the generation to be completely random. And actually in

2003, a statistician called Mohan Srivastava was

thinking about scratchcards. He’d been given some

as a joke present and was wondering on this

idea of controlled randomness. And he realised there must

be some way for the lottery to identify which cards

were winners without having to scratch them off. On each card there were

a series of digits. And some of these would

appear two times, three times. But some numbers and symbols

only appeared once on the card. And actually if these unique

numbers appeared in a row, that card was always a winner. And he went and

bought more cards and tested out his strategy. And every single time, the cards

that had these numbers in a row were guaranteed winners. Now what would you

do in this situation? You’ve essentially

cracked scratchcards. You’ve got a system

which can identify the winning ones and

the not winning ones just by looking at them. Would you go out and buy tonnes? What would you do? Well, think back to that slide

that I showed you at the start. Winning scratchcards

are remarkably rare. And actually what Mohan

did, rather than just going on a huge

scratchcard buying spree, was work out how long it would

take him to buy up enough cards and guarantee himself a winner. And he was a statistician

working on geological problems earning pretty decent money. And he realised that actually,

although he had a winning lottery strategy, it was

better off just to stick in his existing job. So what he did was he

rang up the lottery and told them that

there was a hidden code on their scratchcards,

and he had deciphered it, and he knew how to win. The lottery, of course,

didn’t take him seriously. So what he did was actually

collected the scratchcards. And he identified some winning

ones, some losing ones, divided them into two

piles and posted them by courier to lottery. That evening he got a phone

call from lottery saying, we need to have a chat. And really the story

is representative of a lot of areas of gambling. Often it’s not

professional gamblers who come up with

these strategies that beat the system. And often people

who beat the system, don’t become

professional gamblers. For a lot of these

people, gambling is almost a

playground for ideas. It’s a way of testing out

problem solving and skills that actually would apply to

many other industries. People who have

tried these problems are moving into academia,

into finance, into business. And as I mentioned with Cardano,

this isn’t a new phenomenon. Really throughout history,

many of the great thinkers and mathematicians

have used gambling as a way of refining

their ideas. In around 1900, a

French mathematician called Henri Poincare was

particularly interested in gambling. Now Poincare was one of the– what’s know as the

last universalists. As a mathematician, he

was one of the last people to specialise in almost

every area of the subject as existed at the time. It hadn’t expanded

to the point where it was as large as it is today. And one of the things

he was interested in was predictability. And to him, unexpected

events, unexpected outcomes were the result of ignorance. He thought if something

is unexpected, it’s because we’re

ignorant of the causes. And he classed these

problems by what he called the three levels of ignorance. The top level was

a situation where we know what the rules are,

we have the information, we just have to do some

basic calculations. So if you’ve got, say,

a school physics exam, you know what the

physical laws are. You’re given the information. So in theory, you should be

able to get the right answer. If your answer is

surprising, then you’ve done something wrong

in the working. But it’s not a kind of

difficult level of ignorance to escape, in theory. The second level of ignorance

according to Poincare was one where you know

what the rules are, but you lack the information

necessary to carry out the calculations accurately. And he used roulette

as an example. So a roulette table, you start

a ball spinning round and round. And he observed a very small

change in the initial speed of the ball could have a

very dramatic effect on where it ends up, because

it’s going to be circling this table over time. And nowadays

mathematicians refer to this as sensitive dependence

on initial conditions. And popularly it’s known

as the butterfly effect. There’s a talk in the ’70s

where a physicist pointed out that a butterfly flapping

its wings in Brazil could cause or perhaps

prevent a tornado in Texas. These very small changes,

which Poincare first observed, could have a very

large effect later on. And then we’ll say that

the results is random. It’s down to chance. But really, it’s a

problem of information. Then comes the third

degree of ignorance. And this is where we

don’t know the rules. Or perhaps they’re

so complex, we’ll never be able to untangle them. And in this situation,

all we can do is watch. Watch over time and try

and gain some understanding of what we’re observing. And it’s really this

level of ignorance when gamblers started targeting

roulette that they focused on. They didn’t try and untangle

all of these physical laws. They just said, well, let’s

just watch a load of roulette spins at a table and

see if there’s a bias. See if there’s something odd

going on with this table. But this raises the

question of what do we actually mean by odd. What do we mean by biassed? And while Poincare was thinking

about roulette in France, on the other side

of the channel, a mathematician called

Karl Pearson was also thinking about roulette. And Pearson was fascinated

by random events. As he said, we can’t have any

true sense of what nature does. We can only observe and

try and make inferences on those observations. And he’s really keen to

collect random data to test out these kind of ideas. On one occasion he spent his

summer holiday flipping a coin 25,000 times to generate

a data set to analyse. And he was also

interested in roulette. Now fortunately for

him at the time, the Le Monaco

newspaper would publish the results of all

the roulette spins in the casinos at Monte Carlo. Now for Pearson this is

a fantastic data set. He wants to test out his

ideas about randomness. You’ve got all these

previous roulette spins to test it out on. And he started looking at ways

to understand whether they were random or not. And a roulette table,

of course, you’ve got these black and red numbers. And then you’ve got zero. If you take out

the zero, over time you’d expect the proportion

of black and red to be even. You’d expect it to

be 50/50 over time. And when Pearson

looked at the data, he found that red came

up 50.15% of the time. This was over

about 16,000 spins. So according to his calculation,

this wasn’t that implausible, that actually that

kind of deviation from the expected value

is reasonable given the kind of data set he had. But then he continued. And he looked, for instance,

at how often pairs of numbers appear. Now if you’ve got a random

process at roulette table, sometimes you’d expect there to

be a string of the same colour appearing purely by chance. You might get a few reds

coming up– a few blacks. But what Pearson found was that

the number switched too often, that actually you didn’t get

these strings of the same colour appearing as often

as you might expect. They were switching over. And to him this was

pretty definitive evidence that the tables were corrupt,

that they were biassed. And as he puts it, if I had

been observing these tables since the start of

geological time on Earth, I would not have expected to

see a result that extreme. And he actually suggested that

they close down the casinos and donate the

proceeds to science. As it happened there was

something a little bit more down to earth going on. It turned out that those

journalists from Le Monaco, rather than sitting

by the tables and recording the

numbers, instead had been sitting in the

bar and making them up. But this idea– think

back to how he phrased it. It was the probability

of observing an event as extreme as

the one I’ve observed. This was the first

forays into what’s known as hypothesis testing. Nowadays, whether we

work in clinical trials or on particle

physics experiments, we use the principles that

Pearson honed on these roulette tables and coin

tosses to understand whether we have enough

evidence to reject or accept a certain hypothesis. So in this case

his hypotheses was that the tables were random. And he had enough evidence to

say that this wasn’t the case. And actually gamblers

have also used these ideas throughout Victorian times. And moving into late

1940s, for example, two medical students used

these kind of methods to go and unlike the lazy

journalists, they actually watched the tables this

time, collected all the data and found that

there were biases. These tables bore

down over time. And certain numbers

or areas would appear more often than not. And actually they

went around Nevada and gambolled at

all these tables. And the exact figure

was never known. But they did buy a

yacht and sell around the Caribbean for a year– so

a pretty successful strategy. The problem for

gamblers, though, is casinos cottoned on

to what they were doing. And they would make sure the

tables were incredibly well maintained and you didn’t

have these biases occurring. But in the ’60s and ’70s, some

physics students realised that this actually leaves you in

the second level of ignorance. Because if you’ve got a very

pristine, well-maintained roulette table, it’s not

a statistics problem. It’s a physics one. As one of them said,

it’s kind of like having a planet orbiting a point. You got this ball going around. In the ’70s, a group of students

at University of California, Santa Cruz actually started

doing these calculations. And they looked

at roulette spin. And they said,

well, to start off with, the croupier

will spin the ball. And it would go around

the edge of the track around the rim of the table. And often it would go

around a couple of times before the croupier

calls no more bets. In other words,

you have a window in which you can collect

information on what the ball is doing and act on it. You can place bets

during this period. Over time it would drop

down onto the track. And this will spin

freely and eventually hit one of these deflectors and

land in one of the pockets. And what’s it– they actually

did testing out their strategy on different tables in their lab

was realised that if you could calibrate your models– if you

could write down the equations for the physical system– you

got the ball going around. It’s slowing down

and then drops down. If you write down

these equations that calibrate into a

specific table, then in that initial bit

of time, you could collect enough information

to improve your prediction about where the ball would land. You’d never get exactly. But you didn’t need to. You just need to get some idea

of which region of the table is going to land in, enough to

get an edge over the casino. It’s all well and

good, of course, doing that in a classroom and

working for all occasions– all the equations. But in a casino, you need

to do it in real time. You need to do that

on the casino floor as the ball is spinning. So what these teams

actually did was come up with hidden computers to do

these calculations in person. Now wearable technology

is, of course, all around us these days. But the first wearable computer

was designed for this purpose as it happens. And because it was

a new technology, there were, of course,

some drawbacks for this. They’d often give themselves

electric shocks for example. And as well as this–

as I mentioned, things are very sensitive

to initial conditions. So if the weather

changed, they would need to recalibrate

to the roulette table. On one occasion they

were actually losing a fair bit of money and

couldn’t quite work out why. Until they realised there’s an

overweight tourist further down leaning on the table

and screwing up all of their predictions. So really these kind of methods

were somewhat imperfect. In theory they worked very well. But these kind of stories

have been a bit sporadic. But it wasn’t just

roulette during this period that gamblers started targeting. They also started

targeting other games. And actually one of the

most successful gamblers in the world is a man by

the name of Bill Benter. And when he was a student in the

’70s, he came across this sign in an Atlantic City casino. Now to him this sign

meant one thing. Card counting clearly works. And the whole idea of

card counting– so if you have games like blackjack. You’re trying to get to

a certain total playing the dealer. And in blackjack, you try and

get near to 21 but not go over. So you’ve got to draw cards and

try and get near this total. On the face of it,

this is a random game. The draw is completely

random what will come out. But of course, it’s not

for a deck of cards. Because if certain cards

have already come out, they can’t reappear until

you shuffle the deck. So if you can

collect information on what’s already appeared,

this can potentially give you an edge over the casino. It can give you an

advantage against them. Now again, casinos started

realising what players were doing, that they were

tracking what was in the deck. So they started using

more decks of cards. Rather than one, they

would use a whole pile. And this made it much

harder to card count. Because if there’s multiple

of the same card in the deck, it’s much more difficult to

keep track of what’s come out and what hasn’t. But the casinos

were inadvertently handing the gamblers a

very significant advantage, because at the time,

casinos typically used what’s know as

a dovetail shuffle. So this is probably

familiar to you. You split the deck in two. You riffle the cards together. Now a dovetail shuffle,

if you do it once, preserves an enormous amount

of information about the cards. So just to give you

an example, let’s say we have a six pack of cards

in order from ace to king. If we do a dovetail

shuffle, we split them. I’ve coloured them just

to make life a bit easier. And then we riffle

them together. Now they might not fall

exactly interwoven. But you can see here in

two different colours, you’ve got two quite clear

what’s known as rising sequences of numbers. So cards have been shuffled. But actually, if you know where

they started at each point as you go from left to right,

you know there’s only one of two cards that

could be appearing. And actually there’s a

number of magic tricks that rely on this fact. So if you get a deck of cards

and move one and then riffle shuffle, you can spot with– the moved card– because

it won’t fit into one of these rising sequences. As mathematicians have shown

that for this kind of shuffle, you need to shuffle the deck at

least half a dozen or so times to get something that’s

as good as random. And in casinos in that

period, people were actually only shuffling them once. So really if you can

track what’s happened, you’ve got a huge

amount of information about what’s going on. And in many cases, they would

actually sneak in pieces. And again, another opportunity

at confusing casinos– to track the cards that

have come previously. And whereas before card counters

would measure what’s come and then get a custom

approximation of what’s left, now they’d actually

at each point in time know that there’s only one of

two cards that could appear. So this is a terrific

advantage that they had. But this poses a challenge,

because how do you capitalise on that? At each point in

time, let’s say, there’s a card

that’s advantageous and less beneficial. How do you make that

decision about how much to risk in that situation? And what I want to do is just

start with a simple example. Let’s suppose we

have a coin toss. And I’m going to offer

you a biassed bet here. So I’m going to offer you

two to one odds on tails. So in other words, you

name an amount of money. If it comes up heads, you

pay me that amount of money. If it comes up tails,

then I will pay you double the amount you named. So clearly that’s a pretty

stupid bet on my part. I’m giving you an advantage. But how much would you

be willing to risk? After all, it is a coin

toss, and you would still have a chance of losing money. Could I just get a

quick show of hands? Who here would be willing to

risk one pound on that bet? Show of hands– OK, so I think most of you. Keep your hand out if you be

willing to risk 10 pounds. OK, how about 100 pounds? OK, and 500? How many times do

you get to play? Once. OK, so there’s a

few people left. I’m not sure if they’re

playing with monopoly money. But OK, so if you

put your hands down. That wasn’t legally

binding, anyway. But I waned to give you

a chance to kind of get a feeling of measuring risk. How much are you willing

to risk in that situation? And I don’t know if you can

see, but actually the hands kind of went down a lot between

10 and a hundred pounds. But this is quite an important

question for gamblers. And actually in the

’50s, a physicist called John Kelly

started thinking about this idea of biassed bets. Suppose you have

inside information. And you have some edge over

a book maker or casino. How do you exploit that? In this situation for you,

although I gave all of you the exact same bets–

mathematically it’s the same offer– your perception of the value

of that was very different. Some of you valued it at a

pound, some at 10, some at 500. What’s the optimal

thing to do there? There’s actually this concept

known as utility in economics. It’s the value of

something changes depending on how much money is

in your wallet, for example, and how much you’re

willing to lose. And what Kelly did is he

looked at these two contrasting aims you have, if you want to

give a good long-term return. First you’re trying to

make money, because you’ve got a biassed bet. But you’re also trying

to avoid going bankrupt. Essentially you’re

still tossing a coin. There’s that chance

you’re going to lose. I don’t think anyone here would

have bet their house on this. Maybe you would have. You might have an angry family. But in this case, you want to

somehow exploit it, but also limit your losses. And what Kelly did was

come up with this formula. So I apologise for

my handwriting. Basically you’ve got the odds,

which in this case is two. You got the probability

you’ll win– this Pwin– minus the probability

you’ll lose. And this is the optimal

fraction of your money to bet for a given edge over

somebody in a wager situation. So the bet I just showed you. The odds were two. The probability you’ll win is a

half, because it’s a coin toss. Probability you’ll

lose is also a half. So if you stick

these numbers in, you get the following

kind of equation. A little bit of arithmetic–

you can end up with a quarter. So in this situation, if you

want to maximise your long-term growth of money, it’s optimal

to risk about a quarter of your income. Just have a think back

to when you were raising hands, whether that

was the course of– were you taking

advantage or not? Now some of you might think,

OK, that’s all well and good. You showed me a formula. Let’s test it out. Now I could have, of course,

adopt the Karl Pearson approach and spend the next half an

hour or so tossing coins to convince you. That’s a bit boring. So what I thought I’d

do instead is show you some simulations

of if we adopted different strategies, what

kind of outcomes we would get. So here along the

vertical is your bankroll. I’m going to assume that you

start with a hundred pounds. And along the bottom

is the coin flips. So we’re going to

play this bet again, and again, and

again, and see what you’d end up with over time. Now you might have thought a

quarter of my money, that’s not taking risks. So I want to go big. You might say, well, I want

to bet 80% of my money. And in this case, if we just

do one random simulation, what might happen is you’ll

get a couple of big wins at the start. You’re approaching

a thousand pounds. Lose a load of money–

win a load of money. It’s exciting. And then lose all your

money and go bankrupt– Less exciting. If you adopt this

optimal Kelly strategy and bet 25%, what will happen

is it will take longer. You’ll grow a bit

slow over time. But you won’t go bankrupt. And actually in

the long term, you will get something

that takes off. You might say actually, that’s

still a bit too much for me. I’m going to bet

10% of my income on each one of these wages. And in this case, if

you do it randomly, it’ll take a long time to grow. So you won’t go bankrupt. But it will take much more

time to bet your money. And you notice this

kind of increases quite steeply at the end. And that’s because you’re

reinvesting your money. It’s this kind of compound

interest effect over time. Again, this is just

one simulation. It’s a coin toss. It’s a bit of randomness. So what we could do is

simulate it, say, 10 times. Instead of this

one instance here, do each one of

these 10 times over. And in the case

where you bet 80%, you’re going to end up

with something which nine of these you go

bankrupt, one of them you do make a bit of money. But in most of these cases,

you’ll run out of your income pretty quickly. If you bet 25%,

this optimal amount, then again, it

grows a bit slower. But you don’t go

bankrupt at any point. And you will eventually

grow your income. And then again, this 10% is

just a much slower growth. So it takes much, much

more time to build this up over the series of bets. And this is the

strategy that people use playing blackjack–

playing a lot these games to manage their bankroll. And actually the concept of

utility and money management is obviously

important in finance. But it underpins the

entire insurance industry, because whether we

insure something or not depends on how we

value it, whether we’re willing to risk the fact that

we could lose it and cost us out of a lot of money, or whether

we take those small premiums. That would depend on

the value of the item. But implementing this strategy,

of course, for card counters can still be a problem. As one card counter I talked

to said, learning to card count is easy. Learning to get away with

it is very difficult. And many of these people

who were successful, people like Bill Benter soon

became pretty well known in the world of

casinos and found themselves banned from all

the way around the world. So they turned their

attention to a bigger game, a much larger place to wager. Now, this is Happy Valley

Racecourse in Hong Kong. If you’ve ever been

Wednesday night, this is kind of where

all the action is. On a typical race day, about

$150 million are wagered. Gambling is an enormous part

of what’s going on here. One of the appeals for this for

gamblers is a fairly small– well, firstly it’s– they’re

pretty convinced it’s an honest operation. And it’s a small pool of

horses, about a thousand horses that run again,

and again, and again. So you can generate lots

of data to look at and try and interpret which horse

might have a good chance. But to do that, of

course, you need some way of converting data

into a measure of performance. Which horse is the best? Which horse is going to win? And to use this, teams turned

to an idea that was first conjured up by this man. This is Francis Galton, a

Victorian scientist and cousin actually of Charles Darwin. And as you can see, they shared

some passions particularly for cravats and short sideburns. But there were some differences. Darwin actually was meticulous

in shaping his research. So even the theory

of evolution, you can see many of his

fingerprints over this. Now Galton liked to think of

himself more as an explorer. He would dabble in anthropology,

and psychology, and biology, and economics, and

then start it off a bit and then leave it and wander

off to something else. And one of the things he was

interested in was inheritance. And he actually

on some occasions would send his friends seeds and

get them to grow them for him. It’s kind of an early

crowdsourcing approach– and get them to report back. And one of the things he’d

notice is that if you grew a season and had the

subsequent generation, if those were for

instance taller, you wouldn’t expect the next

generation to be taller, and taller, and taller– that

over time you’d get a feature which he referred to as

regression to mediocrity– that over time these

kind of features would somehow converge. And the influence of

the older lineages might kind of smooth

over variation. He wanted to understand this in

a slightly more rigorous way. And it was actually

a horse trainer who presented him

with a figure, which allowed him to frame this idea. And it was the following. It was a diagram. It was a square

which represented the characteristics of a horse. And this trainer had

proposed that about half of the characteristics

are explained by the mother and the father. So you have a couple

of squares for those. And then of the remaining

characteristics, maybe a quarter is explained

by the four grandparents. And then another

chunk is explained by the great

grandparents and so on. And this idea was

one of the founders of what’s known as regression

theory in statistics. It’s the idea that we can

take a number of factors and work out how it

influences the characteristics of a certain object or system. And we could have a similar

approach in horse racing. We could say, well,

let’s just suppose this box is the

performance of the horse. And we have lots of

different bits of past data. And we could say, well, maybe

each one of these bits of data explains some amount of

the horse’s performance. Of course, this is a bit

simplistic, really, isn’t it. Because just like with the

characteristics of inheritance where, for instance,

some of the variation is explained by the

parents that are also going to be shared

with the grandparents, the characteristics of

a horse– these features are going to overlap. Some of these will

explain multiple aspects. So these kind of

things are going to be a bit more jumbled up. And what’s more, we might

not be able to explain all of the horse’s performance. There might be some chunk

which we still can’t explain. And really the aim from a

statistical point of view is to try and minimise

this unknown quantity. And there’s actually– in the

’80s, Bill Benter visiting a library in Nevada

came across a paper by two researchers called Ruth

Bolton and Randall Chapman. They work in marketing. They still do actually. And they had essentially

outlined this method for horse races, this

approach of converting data into some kind of

measure of performance that you could use

to make predictions. And as Bill said,

it was the idea that sowed a multibillion

pound industry– an incredibly important piece of research. And actually for Ruth

Bolton, it was the only paper she wrote about horse racing. It’s during her PhD. And it was really kind

of a side project. But this had a huge

impact on this industry. And on doing the analysis of

those early syndicates in Hong Kong, certain things would

come out as more important. For example, in one of their

early bits of research, the number of races

a horse had run would tell you a lot about

how it’s going to do. And it’s tempting to come

up with a story for that. We say, well, if

it’s run more races, then it’s going to

be more experienced. And then that’s going

to give it a better performance in the next race. But they actually

avoid doing that, because really they

know it’s a jumble, that all of these things are

going to overlap and explain one thing. And it’s not clear that just

because something is important, it has a direct explanation. This quite common

problem in statistics is known as this idea that

correlation doesn’t always mean causation. Just to give an example,

here we have along the bottom is the wine spend per year

at the Cambridge colleges. On the vertical is

the exam results. So as you– [LAUGHTER] As you can see, there’s a

pretty strong correlation between colleges that spend more

on wine have better results. And this isn’t the only

thing that’s happened. Actually it turns

out that countries that have a higher per

capita spend on chocolate typically win more Nobel Prizes. As lovely as it would be that

eating chocolate would make you a Nobel Prize winner

and drinking wine would make you better at

exams, there’s clearly something else going on. There’s some underlying

feature which explains all of these things. And really these

syndicates therefore don’t try and untangle it. And actually one of

the remarkable things is they have no desire

to be pundits and experts on this kind of field. For them the question is

what horse is going to win, not why is that

horse going to win. So it’s almost this going

back to the idea of ignorance. They’re embracing

their ignorance. And they’re saying,

I don’t really mind that I can’t explain

exactly how it’s going. I just want a

method that is going to give me good predictions. Now starting off by measuring

performance is one good thing. But actually when you have

multiple horses racing, you can get some slightly

unexpected results occurring. So just an example,

a very simple one, let’s suppose we

have two horses. We have one which half

the time does well, half the time does badly. On the day, you don’t know

which it’s going to be. And you have the

second horse which is a bastion of reliability. Every single race, exactly

the same performance. Now on average over

lots and lots of races, they’ve got the same

kind of performance, because the top one on

average that will cancel out. And in a race, it will

be essentially a 50-50, because it completely

depends in a race whether the top

horse number one is having a good day or a bad day. So these two horses

race against each other. It’s basically a coin toss. It’s a 50/50 chance, because

if the top horse is having a good day, he’s going to win. If he’s having a bad

day, he’s going to lose. That’s kind of intuitive. But if you add a third

horse into the mix, something a bit strange happens. So let’s suppose we

have a third horse here, which some days

performed slightly better than the middle

horse, some days performed slightly worse. So again on average,

all these horses have the same performance. Now by the same kind of logic,

the top horse here again has a 50% chance of winning,

because half the time he will come out front and half

the time he’ll come out last. So he still has a 50%

chance of winning. Of the two horses that remain,

if the top horse doesn’t win, we can apply the same logic. Of these two horses

on the bottom, if the horse number

three has a good day, he’s going to come out on top. And if he has a bad

day, he’s going to lose. So if the top horse

doesn’t win, you spit the probabilities

between the two horses remaining, because

you can’t decide. Now just take a look at

what’s going on here. All these horses on average

have identical performance. But it’s the variability

that’s different. And actually the top

horse because it’s most variable in

this kind of race has a larger chance of winning. You can actually apply

the same kind of logic to other situations. So say we have an election,

which the first past the post system. So the person who gets

the most votes wins. If you have three

people who on average you’d expect to get the

same amount of votes, is actually the most

polarising candidate, the kind of all or nothing one,

which has the best strategy, because they’ve got

the largest chance of winning in this situation. If you want to push the

example a bit further, you could look at job interviews

or maybe even dating, if you’ve got lots of different suitors. In this situation,

it makes most sense to adopt this all or nothing

kind of Marmite strategy, if the objective is to come out

first against multiple people. This isn’t a problem if

there’s only two in the race. But as soon as you have

multiple competitors, you get this kind of

weird dynamic coming out. And really the mathematics

of games and these kind of features have been

interest to mathematicians for a long time. Actually the origins

of game theory, the origins of mathematics of

games originated with poker. In the 1920s, a researcher

John Von Neumann– brilliant mathematician. He was the youngest

professor in the history of the University of Berlin– wasn’t so good at poker, though. On the face of it,

poker is a perfect game for a mathematician, right? It’s the probability

you get a certain hand– the probability your opponent

gets a different one. But von Neumann realised that

there’s more to it than that. He said real life

consists of bluffing– of little tactics of

deception– of asking myself what does the other

man suppose I’m going to do. And he wanted to study

that kind of feedback between what you

think, what they think, and they think you think. And he looked at very

simplified forms of poker. And one situation he

looked at was two players. They each get dealt

a single card. And then they put some money

in the pot at the start. And first player has the choice. So they can either just stick

with their bets, in which case they just turn over their

cards and compare them. Or they can raise the stakes. And then it’s up

to the second pair to decide whether they

meet that bet or not. So two players, one card,

money in the middle. What von Neumann found is

that in these kind of games, you’ve got almost a tug of war,

because each player is trying to maximise their gain

while simultaneously trying to minimise their

opponent’s gain. If you play a game like poker,

anything your opponent wins comes out of your pocket. So you’re trying to

maximise what you get, while at the same time trying

to minimise what they get. Which means that there’s this

kind of equilibrium point. There’s a point at which the

two conflicting forces balance. And this situation–

analysing it for the game. He found that this situation

in which no player would benefit by changing their

strategy– this balance point. For the first

player, the strategy was as follows, that if they

got a very high card, then they should raise the stakes. Intuitive just makes sense. If you have a good card,

you might as well bet on it. If they had a middling card,

it didn’t make sense for them to raise the stakes. They didn’t have a

great chance of winning. But they still had some chance. So in other words,

they should just stick with their existing bet. But when von Neumann

looked at what happened when you got

the lowest kind of cards, he found that it doesn’t make

sense to stick with your bet, because if you turn

over the cards, you’re probably going to lose. Instead you should

up the stakes. So in other words,

you should bluff. And actually up to

this point gamblers had often– poker players

had often bluffed in games. But it was always

seen as a quirk of human psychology, a

kind of innate trickery that humans came up with. But here was von

Neumann showing that it was a mathematical necessity. In other words, he

proved that bluffing is a necessary part of life. And this idea was

fundamental to game theory, that you can have these

strategies put together. In this very simple

version of poker, though, there’s almost a list

of fixed rules we can follow. So in other words, if

you get high or low card, you raise the stakes. If you get a middling card,

you stick with what you’ve got. And in any game where you’ve

got all the information in front of you– so other

games for instance– things like noughts and

crosses, checkers, chess– all of the games in theory

have a fixed set of rules. It’s known as pure strategy. So you follow these

exact rules, and you’ll get the optimal result. So if someone

noughts and crosses, I think most people can work out

when they are younger that they realise there’s a set of moves. And if they always

do that, they always get the results that’s

the best possible. But of course, not all

games are like this. A good example is

rock, paper, scissors. So it might be admirably

consistent of you to always pick the same one. But if your opponent

spots what you’re doing, they can take advantage of that. So it’s not the kind

of optimal strategy if you’re trying to make

your opponent’s decisions as difficult as possible. And these kind of games, that’s

what you’re trying to do. You’re trying to

make your opponent indifferent to

changing, because you’ve got that tug of war going on. So if they can gain more by

adopting a different strategy, you haven’t got the

optimal approach. And in rock, paper,

scissors, if you want to make your opponent’s

choices as difficult as possible, what you can just

simply do is pick randomly. If you pick completely

random options amongst those, then in the long

run your opponent won’t be able to make

any money off you. So this is kind of the

optimal thing to do. And rock, paper, scissors,

there’s three options. It’s not too hard to work

out that picking randomly will make your opponent’s

decisions difficult. But games like poker

are far more complex. You have a whole

array of choices that you can make

through the game. So it’s not something

you can actually write down with pen and paper. And fortunately we can

turn to a technique that one of John von

Neumann’s colleagues devised. And this was a mathematician

called Stanislaw Ulam. And unlike many

mathematicians, he wasn’t a big fan of working

through loads of equations. On one occasion he was

working a blackboard trying to solve a quadratic,

got to the end, and was just so

frustrated and annoyed, he went home for the day. So really kind of

wasn’t his thing to kind of crunch

through all this algebra. He was once playing

handheld, which is a version of solitaire. And he wondered what

the probability would be if he just laid out the cards. What’s the probability

he’d have a situation where he could win that game– the cards would fall

in a favourable way. He started looking at

calculations and realised it was just a lot of effort. So instead he thought,

well, what if I just lay out the cards a few

times and see what happens. In other words, what

if I just simulate this process a few times and get

some sense of how likely it is. At the time, Ulam and von

Neumann were working on the US nuclear programme at Los Alamos

working on neutron collisions. Part of the project

was a hydrogen bomb. And again these are

random processes where you couldn’t neatly

write down the formulas and solve them. And they realised that this

method would be incredibly powerful for that. Being a government programme,

they needed a code name for it. So they called it the

Monte Carlo method, because Ulam had a heavy

gambling uncle at the time. And the Monte Carlo

method has become a fundamental part of science. I mean in my line of work

where we try and look at disease outbreaks, you have

something like Ebola or Zika, that’s an incredibly

complex set of interactions. It’s not something you can write

down with pen and paper neatly. And so we use these

simulation-based approaches simulating these

random processes to understand these systems. It also appears now

as a sports betting when you’re trying to understand

how these very complex team interactions work. And it also applies to poker. Teams have used this kind of

approach for games of poker where you can’t neatly

solve the equations. You can use a

simulation-based approach to get the computers to learn. And actually Allen Turing one

of the fathers of computing, when he was first thinking about

this idea of machine learning, said that actually

if you’re trying to build intelligent machine,

it doesn’t make sense to build the adult mind. You don’t want to try and build

the finished product with all the knowledge that was there. It makes much more sense

to build the child’s mind and let it learn. Let it work out how to

play these kinds of games. And this is what

these poker teams do. They create these

algorithms that can learn. And actually the way

in which they learn is perhaps a bit surprising,

because what they do is they get these

algorithms over time to employ what’s known

as regret minimisation. So as they play

these games billions of times against each other,

at each point when they’ve made a decision, they

look back and say, could I improve that if I’d

done something differently. So at each point they have an

artificial measure of regret for each decision they make. And actually there’s

a lot of evidence from some neurological–

neuroscience studies– that that ability to have regret

is quite important in learning games of chance. There’s been studies

of people who have damage to the bit

of the brain that’s responsible for

regret– this ability to look backward

and ask how would I feel if I’d done

something differently. And these people

often are perfectly capable of playing logic games. If they have to sort cards,

absolutely fine at that. If there’s any element

of risk to the game and they have to learn how

to play the optimal strategy, that’s something they

really struggle with. And actually a lot

of economic theories developed not around

looking back but around what’s known as

expectation maximisation. So in other words,

you look forward. And you say, if I did

this, could I make money? If I did this,

could I make more? But really from these artificial

intelligence approaches, it seems that it’s

much more powerful to look back and employ

that power of regret as you go to look

back on your decisions as you take these risks. And in fact these teams have

employed these algorithms and got these computer bots to

play each other so many times that last year they announced

that poker is solved. To be specific, for two-player

poker where the stakes have a limit, these bots have

played each other so many times that they’ve come up

with a strategy which will not be expected to

lose money in the long run. So even if you’re playing

a perfect opponent, this bot would not lose

money over the course of a very long game. Interestingly, actually

a lot of the players who came up with

this system, or a lot of the computer

scientists, aren’t very good at poker themselves. By their own admission,

they’re not poker players. So this is kind of a remarkable

illustration of the power of these algorithms. You can have people

who aren’t particularly good at poker creating

poker bots that can beat any human arguably. This is a remarkable

achievement. But there is, of

course, a downside of this in that you’re assuming

that your opponent is perfect. If you’re looking for

this optimal strategy, that’s inherently defensive,

because you’re assuming that your opponent’s perfect. And you’re almost giving

them too much credit. Because if you got

a flawed opponent, and you’re coming up

with a strategy that assumes they’re perfect, you’re

potentially not exploiting them as much as you could. And just to give an example

of these kind of flaws that could occur, let’s go

back to rock, paper, scissors. What I’d like you

all to do is just turn to the person

next to you and play a couple of games of rock,

paper, scissors with them, please. [BACKGROUND CONVERSATIONS] OK, thank you,

ladies and gentlemen. OK, I can see there’s

a couple at back trying to play the best of

seven or something. What I’d to do, can I

just have a quick show of hands of who opened

with rock there? OK, a fair few– who

opened with scissors? Quite– and who

opened with paper? OK, not so many

actually for paper. So typically in these

kind of big competitions where people play

lots of times, it’s novices that open

with rock, often men. Scissors tends to

be the most popular for people who play

a lot of these games. And paper is not always

chosen so common. Also think about what happened

between the first game and the second game you played. Because in one fairly

large study of rock, paper, scissors,

what happened was people who when the

first round typically stick with the same

move for the second go. So it’s this old– the military adage of

generals always fought– always fight the last war,

especially if they won it. So it’s the idea

that if you won, you just stick with what’s safe. People who lost,

however, will often switch to the move that would

have beaten the one they lost. So if they lost to

rock, they’ll often swap to paper on the next go. So it doesn’t always happen. But in these large competitions,

these kind of patterns emerge. So although the optimal

thing to do in rock, paper, scissors is to behave completely

randomly, people don’t. They fall into these

predictable patterns. And there was actually a story

a few years ago in Japan. An electronics firm

wanted to auction off their art collection. And they approached

Christie’s and Sotheby’s to hold the auction. They were obviously

both keen to do it. And so the head of

the electronics firm decided the fairest

way to settle it would be with a game

of rock, paper, scissors. Now Sotheby’s thought

that’s perfectly random. That’s nice. That’s fine. Christie’s however,

the CEO in Japan had a young daughter,

a 7-year-old who played relentlessly in the playground. So he got his daughter to

teach him a bit of rock, paper, scissors strategy. And they walked in sure

enough to the boredom, Sotheby’s treating it

like a random gain, Christie’s with a strategy. Christie’s walked

out the winner. So in this kind of case,

exploiting those patterns and those kind of predictability

and knowledge of what’s happened before can

extremely valuable. But there are some

challenges to that. And particularly if you

are playing computers, one of the challenges

from a human point of view is the limitation of our memory. So just to illustrate this

point, what I’d like to do now is to just all of you try and

memorise that number for me. So I’ll give you a

couple of moments to have a quick look at it. OK, right. Who fancies having a go

at trying to recite it? Any volunteers? Yes, sir? 6, 1, 0, 2, 6, 0,

1, 0, 0, 0, 6, 8. Very, very close, sir. Does any one want

to have another go? What was your name, sorry? Gavin. Gavin. So Gavin got very close. Any one want to try

and build on that? Yes. Yes? 610, 216, 1,000, 91. So what’s your name? Steve. Steve. Steve– round of applause for

Steve, ladies and gentlemen. [APPLAUSE] Now Steve did something

quite clever there– don’t know if you spotted it. I’ll explain what he did. So I asked you to

memorise 10 digits. And that was actually

a bit devious of me, because in a lot of

psychological studies, people presented with numbers

can typically memorise about seven and recite them. Sorry? 12. 12 digits? Yes. In what? Up there. You said 10. Oh, sorry. Yeah, OK. I do have a PhD in

maths, I assure you. [LAUGHTER] OK, so actually I

made it even harder. I was crueller than I thought. I do apologise. And so typically

people can remember– like a local telephone

number they can remember. Two, they struggle with. But what, actually, Steve did

when he recited the numbers, he didn’t recite

those individually. He said 610, 260, 1,000, 91. So actually what he did, he

bunched the numbers together. He wasn’t reciting ten

bits of information. He was chunking

it into a smaller amount, which puts it

below that threshold that you can memorise. And actually in other countries,

so France for instance, if you go, their

telephone numbers tend to be paired together. Which actually makes it easier

to remember the numbers, because it’s much easier

for you to remember these chunks of numbers rather

than just a single sequence. I can actually guarantee

that all of you will remember this number

when you go home tonight. And that’s actually if you

split it apart, and add in some punctuation,

and then flip it around, that’s just the time

and date of this talk. So all of you now

will know that number and be able to recite it. And the reason is you’ve gone

from 10 bits of seemingly arbitrary information

down to one that clearly has

some meaning to you. And this ability to

chunk is something that card counters employ,

because clearly memorising a whole deck of cards

is incredibly difficult. So what they do is they use

what’s known as bucketing. They will group into say,

low cards, medium cards, high cards. And then rather than having

to memorise a whole deck, they only have these

three buckets to remember. And what’s more, it’s kind

of a memoryless properties. They don’t have to

remember everything. They can just keep the

tallies of these three values. Of course there are

some people out there that can memorise

vast numbers of cards. The top memory champions can

memorise about a thousand cards in an hour– a thousand playing

cards in an hour. That’s remarkable. And the way they do

that is they actually attach characters to the cards. This will make them

people and objects. And they attach a story to that. And similarly while you

will all remember this, because you’re not

remembering a number. You’re remembering an event. You’re remembering

a single thing which is much more memorable. And that’s really

kind of how humans can get around this idea of

the limitation of memory. But there are some disadvantages

to learning about your opponent and remembering

things and trying to take advantage of them. And that’s if your

opponent’s incredibly smart, they could teach you the

wrong image of themselves. So it’s what’s known as the get

taught and exploited problem. If you’re playing

poker, for instance, your opponent could

pretend to be very passive and pretend to be very timid. And then once you learned

that notion of how they play, they could actually switch their

behaviour and exploit the fact that you’ve learned the

incorrect perception of them. It’s not just

poker this happens. A couple of months

ago, you may have seen it, a Microsoft launch of a

bot for Twitter to learn from– I see you can see

where this is going– to try and learn language. And the idea was to have

conversations and improve its ability to learn. Twitter users unfortunately

decided to teach it some unfortunate tricks. What actually happened

within 24 hours, it had to be taken down, because

it was coming out with so many horrendous opinions. I think that’s an

example of you have quite an intelligent algorithm. But if it’s being fed

the wrong image of what it should be doing, it can

actually veer off track very quickly. And this point of

here I’ve talked about poker bots who have

played billions of games to refine their strategy. I’ve talked about

these language bots. But in many situations, these

bots aren’t very complicated. In finance, for example,

programmes are designed to be fast. If you’re trading– if you

want to get a trade off, you need to do that quickly. So having that huge

amount of complexity, and nuance, and rationality

in your algorithm isn’t going to do the job. You really want to strip it

down as simple as possible. In many cases, these

high speed algorithms, you might just have

a few lines of code. And there’s one economics

researcher I talked to put it, when you’re at

ten lines of code, you’re not even at insect

level intelligence. You’ve got no rationality

and no nuance in there. You’re just trying

to execute the trades as quickly as possible. In some situations this means

that you can run into trouble. There’s a case

recently in Norway, where two traders had

noticed that a US stock broker had an algorithm that was

feeding trades at the market. And the algorithm

would always react to a trade in the same way. In other words, if

you traded with it, it would change its

price by the same amount no matter how big

that trade was. So what these

people in Norway did was teach the

algorithm what to do. So it made lots

of little trades. So it would move its price

up and then make a big trade and profit from the difference. Now this ended up in court. These two traders were charged

with market manipulation and handed suspended sentences. But then there’s a

Robin Hood reputation in the media for them in Norway. And it went to appeal. And their appeal

lawyer made the point of if they had been trading

against a stupid human who was doing this, that

wouldn’t be a problem. The issue is that

they were trading at a stupid algorithm

presumably created by a human that hadn’t been

thinking what they were doing. And how should

this be different? How should the notion of

skill and responsibility be different because

as a kind of one step away for the algorithm? And actually this

argument held in court. And in this situation,

they were actually– this sentence was revoked. And this isn’t the

first it’s instance a very simple algorithm

would fall into trouble. A similar point actually– a US stock broker was

introducing a new algorithm to feed orders into the market. So you have a lot of

orders from clients coming into the stock broker. And it would want

to feed them in. They had eight

servers doing this. What they had is a counter

actually to keep track, because obviously if you got

lots of orders coming in, and you’re sending

them out to the market, you want to keep track of

how many you’ve completed. You don’t want to

accidentally make too many. And they had one of these

counts at each server. And then they updated

their software. But by all accounts, they

didn’t add the counter to the eighth server. So there are seven servers that

knew what they were doing– the eighth one that was

kind of doing its own thing. And when this went

live, what happened was the seven were

behaving as they should. The eighth just peppered the

market with high speed trades. And actually by the

time they worked out what was happening

and shut it down, in 45 minutes it had

lost about $450 million. So that’s a $170,000 a second

for this runaway algorithm, because it was acting

so fast and so much beyond what humans

could control. And you might call

that bad luck. You might call that

error in skill. And I think those cases around

these kind of games and chance events are developing actually. In the US in recent

years, there’s been a big crackdown on

poker, particularly online. And as well in 2012,

there was a crackdown on New York on poker

rooms on a gentleman who was running a poker room

that was taken to court and charged with operating

a gambling operation. Now many casino games in federal

law are defined as gambling. But poker isn’t one of them. So actually whether

it was gambling or not was up for debate. And in federal law, gambling

is defined as anything that is predominantly due to chance. So any game that’s predominantly

the result of chance is defined as gambling. So what happened is this

entire legal case rested on is poker a game of chance

or a game of skill. And they got economists

coming in– mathematicians. And they made the point that

on a single game of poker, of course there’s

an element of luck, because you got this deal. But then equally in

baseball, if someone’s pitching a single

ball, there’s going to be an element

of chance involved. But over the course

of a poker game, typically the more

skillful players won. And this was the first time

actually that a US court had ruled on whether

poker is a game of chance or a game of skill. And they ruled that it

was a game of skill. There’s a footnote

to this story. The following year when it went

to the State Appeals Court. Now in New York

state law, gambling is defined as anything that has

a material element of chance. So if you think about this, this

is a much narrower definition of gambling. It’s not predominantly chance. It’s anything with

a material element, which clearly poker does have. And under this condition

it’s defined as gambling. And this debate is ongoing. If you look fancy sports

in the US and a lot of these systems where– do we have something

that’s luck. Do we have something

that’s skill? Where do we– do we

actually define these things as gambling? I think there’s

often a temptation in fact with these situations

to put things in boxes. We like to say there’s

a box with luck, and there’s a box with skill. I think typically if

we’re good at something, it goes in the box on the right. If we’re bad at something, it

goes in the box on the left. And that’s really tempting. But I don’t think it’s a

realistic notion necessarily. I think particularly

through the history of how people have tackled

games like roulette and with lotteries is

much more of a spectrum. And actually games

that we might think are the archetype of luck,

things like roulette, actually if you have

a skillful approach you can tame that chance. And you can convert it into

some element of a game of skill. And even games

that we might think are incredibly, almost

solely the work of skill, games like chess can have

surprising results of chance. In the ’90s, famously, IBM’s

Deep Blue chess computer played Gary Kasparov. And during the match in

one of the early games, there was a situation

where Deep Blue made a move that was so unexpected

and almost so subtle that it threw

Kasparov off a bit– all accounts

convinced him that he was playing something

that was just simply beyond what he was– ever seen before– just

something completely beyond his capability. It turned out actually

that what happened there is Deep Blue had run into

a situation where it couldn’t identify the best move. And in that

situation it had been programmed to pick randomly. So this set of games that

is one of the landmarks in artificial intelligence

over humans in a game that is thought

to be purely skill was actually really shaped

by this chance event. And I think these kind of

illustrations show why gambling and why these games of

chance are so important. Because really

whatever your views are of casinos and

bookmakers, gambling is an inherent part of life. Betting is an inherent

part of what we do, whether it’s in health in

prediction on this side– whether it’s in business–

whether in finance. We have to make decisions

with hidden information. We have to deal

with uncertainty. We have to balance the

risks against the rewards. I think that’s why historically

so many researchers have been interested in gambling

and continue to do so. Because really if you want to

understand luck, and decision making, and risk, then

arguably there’s no way better to start than with a bet. Thank you. [APPLAUSE] What your view of where

investing in the stock market might fall between

luck and skill, should I pay somebody a

2% fee for their skill at investing my money? Or should I just rely

on buying the market?

Roulette can be predicted down to four numbers scientifically. Roulette is the easiest game to eliminate (most) randomness.

Rubbish……All gamblers die broke.

I have watched countless hours of debates/presentions on YouTube, this is by far one of the most coherent and engaging well-structured talks I have stumbled upon.

She was great on RED DWARF

BS

As one of your cousins from "across the pond" I really am impressed with the high quality of your public discourse, I really wish there was a comparable public institution to The Royal Institution over here. Really quality stuff and excellent presenters. Thanks so much for posting!

Sounds alot better when I tell my wife that I lost all money to science not gambling.

is this guy working for Jokerstars?

At 56:30: Many years ago I went to the barracks TV room & found a very good Chess player taking on all comers. I got in line & when it was my turn I played slowly. He got a bit upset. Every once in a while I couldn't think of a good move so moved something, anything. He reacted like one whose battle plan had to be scrapped. Eventually I looked around his King & saw that every square was covered. Finding something I could move & not block myself I did so & said "Mate". Surprise wasn't the word. He challenged me to a rematch & I knew (& said) that he was going to destroy me. He did but I won that first game because he played me wrong & my random moves fouled him up. I am not very good at Chess.

Really interesting presentation, ery well done, I must say. Actually, this man is explaining the games, I have been playing all my life. But, I am happy, he didn't tell all important details and clues regarding games like roulette, blackjack, baccara, slot machines….etc. I am interested to know if he has professional gambling, as a side job as well.

I'm quite well educated (Postgrad) & have an interest (mostly theoretical except poker) in gambling, odds & edges. This was a very good lecture except that it went so fast & skipped from topic to topic as quickly. I'll admit that I understood perhaps 20% of this 🙁 Did anyone else find this?

The casinos know how to ruin your luck. There are plenty of DOCS. on that.

CANT UNDERSTAND WHAT HE IS SAYING

With the scratch off, I guess I’d go by 20 or so then look at the ones with the numbers that indicate a winner and return the rest. But i don’t think many stores would like to hand you 10 or 20 in a row and you say I’ll take this one that one and this one, I’ll buy these three instead of all 10 or 20. But that’s the only way you would be able to buy only winners and not waste on losers is by getting a look at the numbers that indicate a winner. Another I guess would be work nights at a bodega and during your shift get a look at all the numbers on all the cards and pick the winners and buy just those. Quit and cash them all in.

How could they stop them? If it wasn't in the rules of the lottery buying 1 or buying 1m tickets shouldn't matter.

You can also compare the potential benefit of science to society and the potential benefit of gambling to society.

Gambling works when people want to share, at best, and decide to do so according to some rules, not unlike how law is designed and enforced, including indictments or war, such as in, wage war, at best.

Science can solve gambling problem as a potential benefit to society, as well as any other problems, and does not require sharing., even though it can involve sharing.

Thou knows when he has the truth: By his joy of acceptance and projection: "All the 'luck' I receive from the Lord, is all the luck I need."

This videos actually explained WHY TRUMP WON even tho he's a joke. 33:20

Explanation starts at 31:22

TL DR can someone explain it in 2 sentences ?

Does anyone have a copy of this video without 1hr of constant streaming glitches?

I remember as a kid, some of us stole $20 worth of scratch and win lottery tickets, and didn't win anything. Karma taught me a lesson then, and stats is teaching me a lesson today.

Is it just me, or is his accent really hard to understand…North American here. Is it a regional British accent, or is he just garbling more than normal?

Brilliant topic. This is also the very reason that Facebook , Google, and other organizations are compiling data on everything we do. It is because the more data, over time, that they gather on us, gives to them, both expected AND unexpected insights, that are so powerful that when they finally use it, it's going to look like magic to us. Don't believe for 1 second that they're only doing it so they can target you with appropriate advertisements. That's only the tip of the Iceberg, and we're all going to be in a lot of trouble soon, and THAT IS a Sure bet.

LOL you're trying to tell me that the lottery didn't notice their own code? The code is and has always been under the scratch area. And that it took a man who doesn't play the lotto to notice? No sir. Thats BS.

My mate and I made a very successful roulette computer based on a V25 chip. The system was considerably more efficient than the one developed by those physics students, though the principle was the same. They wrote a book called The Eudaemonic Pie – I should get around to writing a book myself.

Terrible analysis of rock paper scissors. But the rest was on point.

LOL Steve remembers 12 digit number. What's your name "I've forgotten". Missed opportunity.

I finally won a gambling problem battle of over 32 years. I am obligated to help others now. Its my calling.Some Free Videos to help you get started IF You have a problem with an addiction.

We're doing more like 1000 cards in 30 minutes and 2000 in an hour now.

-A memory guy

Bookmakers all have big houses, big cars, eat the best food go n the best holidays. Gamblers always seem to live in small houses, ride a push bike eat scrap food and never go on holiday, enough said.

The UK laws: Gambling age limit 18, the national lottery age limit 16. No adverts at all on BBC an hours long advert, paid for by the taxpayer. Why? the government gets the money. Who is fooling whom

Great video. But why didn't this guy take a drink of water before talking for an hour? You know how people get extremely annoyed and literally have their body cringe when they hear things like nails on a chalkboard, someone chewing gum loudly, snoring, slurping, etc.?? Well none of those things really bother me, except when people have an extremely dry mouth and then are talking. I can't even describe the noise, but whatever it is when a mouth is dry and the lips, tongue, and smallest amount of saliva move against each other. Old people tend to have this happen to them a lot. Maybe because of all the meds they take that have side effects of dry mouth or they just don't drink enough water that their mouths get extremely dry. The sound seriously KILLS me hahaha! He isn't so bad, but it was bad enough where it was hard for me to listen… but it was sooo interesting I just had to deal with it. ANYWAY, I know this is really weird and I'm sure no one can relate to me, but I just had to put it out there hahaha…

Love the Mitsubishi add in the background. 😉

Love seeing people talk about odds and betting strat. Personally I like 1/3 bets. If you can win half of them then there's a 50% win margin for 50% accuracy rate. It's still a gamble, though.

What about the question at the end? What is his answer????????

THAT WITCH IS RANDOM ISN'T

look at Euler equation

Such a thick accent.

I play red or black on a roulette table I sit and I wait for a color to be rolled three times in a row I didn't start betting on the opposite color I start with $1 my color doesn't hit I double my bet in a dollar if the colon doesn't hit a double my bet in a dollar so on and so forth works every time

If I want to learn anything at all, I will have to say no to these edutainment videos.

This was a delight. He trips from math to history, to physics like a Mozart sonata.

The house always wins

Math is not science.

Pseudoscience is math twisted to cover for no evidence.

Guess which one we use…

Random only exists in a vacuum, which doesn't exist. Forces determine results. Understand forces and you win and yes, I am a professional gambler.

53:36 Tell that to the ESA and Lootboxes.

@46:20 – I did the same as Steve… now it is time to keep watching and see what his point is.

(Like if you were like Steve)

The house always wins. Card counting is about your only hope.

chips are made of clay my friend , only plastic chips are at lowball home games

Luck? Gambling? Those are two words that don't go together. Odds are set by the house.

I did the same thing with R

Title Correction:

How a shovel, a bat, and a hole in the desert are taking Science out of Gambling.My take away is that when you beat the system, they call you up for a "chat"

I remember him from Home Alone

I can already tell that This guy spends too much time on his hair. Which might make me trust him even more

so the bottom line is: some mathematicians somewhere created some algorithims somehow that work sometimes. Better stick to washing your hands in the tropics!

Excellent video

All this knowledge will not make you a winner. Intentional bad calls by referees, disqualification of horeses for whatever reasons, the house cheats, dealers count cards, the computer chip in the slot is programmed, the boxer takes a fall, the human element wins in the end. The controller of the game wins in the end. Your winning is random. The entire financial system of the world works the same way. Inflation? Where does the value go? Someones pocket, called stealing, embezzling, and racketeering. That is how the world turns. Now its time to go to work, slave.

Wandom

As is very common, this fellow quotes the goal of Blackjack incorrectly. They are NOT "Get as close to 21 without going over it." The goal is "Beat the dealer without going over 21". These statements are not the same!

Also, only someone who does not know how to "count card" would make a statement that it is harder to count 5 or 6 decks than a single deck. Card counters do NOT keep a count of every card that has been played. Only a single number is remembered, which represents whether more high or more low cards have been played. Because the dealer MUST draw to a 16 or less, having a excess of high cards (8 or higher) means that they will bust more than the player who could actually stand on a 12 (such as when the dealer's up card is a 5 or 6, etc).

The point is that it is not harder to add or subtract one to a running count (while also ignoring 7's), as the cards are dealt, for multiple decks vs 1 deck. Some counters keep a separate count of Aces, but that is still just a second number to remember. One must realiise that counting only predicts the likelihood of the next player's card being high or low. It doesn't guarantee that a 10 will be dealt because an excess of low cards have already been dealt. It just betters the odd!

The really hard part of counting is that one must make sure that they see every face up card being dealt…without being obvious about it, because that would get them barred from the table or entire casino. Also, the number of individual personal rules for betting &/or "hitting" vs "standing"…depending upon the count, the dealer's up card, and the player's first card…total about 250.

Hence, failing to count every card, and play the counting rules perfectly, reduces the benefit gained when the odds are in one's favor. The point is that multiple decks do is make those times when the count is unusually high or low, forcing a change in "basic strategy" more rare.

Thanks a lot

The good thing about these kinds of "pseudo sciences" is that they will eventually facilitate the process of understanding that Fate is very real.Like gravity, its gone misunderstood and disbelieved for seemingly endless generations, being unseen, unheard, with no smell, taste, touch or discernible effect. Yet it effects us all…all things. All things are as they are fated to be. Not a bad vid. ^~^

win a closed down with a £1 bet offering reduced prices to locals no shop resales! until next lottery!

it's funny they categorize you as a cheater when you're just really good in the game.

This is not a valuable use of time

That first article is certainly bullsht, that 50 pounds is probably the max amount but added together, never..

10:27 The third degree of ignorance according to Poincaré = life

a fascinating programme but the eq on his mic is making my head hurt 🙁

useless video ima just play poker roulette and baccarat

Smart Gamblers will just keep doubling their bets until they win. It's a fool proof system!!

IT'S MY MONEY, I'LL WIN IT BACK!!!!

“We need to talk.”

“Actually, you need to talk to your printer”

I'm interested in learning more about what he said between 33:11 and 33:38. How does that variability translate into wins for polarizing candidates?

cool

This was brilliant. A special mentioned to the speaker for the way is was delivered

Thank you English wet mouf!

screw this come on 7 =)

you do know that is a head set?

wish Adam would have given some thoughts to punctuate his talk with some WALL STREET/LSE Gamblings that crashed the markets in 2008 …

Both Blackjack and Roulette can be beaten. I'll say no more.

~Phantom was here.