Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
    ai for stock trading
    Can Data Analytics Help Investors Outperform Warren Buffett
    9 Min Read
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Information Theory Betting
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Information Theory Betting
Predictive Analytics

Information Theory Betting

Editor SDC
Editor SDC
6 Min Read
SHARE

The following is a very interesting problem copied from p.132 of “Elements of Information Theory” by Cover and Thomas. I’ve been reading information theory recently, and I’m finding it very fascinating.

Example 6.3.1 (Red and Black):
In this example, cards replace horses and the outcomes become more predictable as time goes on.

Consider the case of betting on the color of the next card in a deck of
26 red and 26 black cards. Bets are placed on whether the next card will
be red or black, as we go through the deck. We also assume the game
pays a-for-l, that is, the gambler gets back twice what he bets on the
right color. These are fair odds if red and black are equally probable.

We consider two alternative betting schemes:
1. If we bet sequentially, we can calculate the conditional probability of the next card and bet proportionally. Thus we should bet on (red, black) for the first card, and for the second card, if the first card is black, etc.
2. Alternatively, we can bet on the entire sequence of 52 cards at once. There are possible sequences of 26 red and 26 black cards, all of them equally likely. Thus proportional betting implies that we put of our money on each of these sequences…


The following is a very interesting problem copied from p.132 of “Elements of Information Theory” by Cover and Thomas. I’ve been reading information theory recently, and I’m finding it very fascinating.

Example 6.3.1 (Red and Black):
In this example, cards replace horses and the outcomes become more predictable as time goes on.

Consider the case of betting on the color of the next card in a deck of
26 red and 26 black cards. Bets are placed on whether the next card will
be red or black, as we go through the deck. We also assume the game
pays a-for-l, that is, the gambler gets back twice what he bets on the
right color. These are fair odds if red and black are equally probable.

We consider two alternative betting schemes:
1. If we bet sequentially, we can calculate the conditional probability of the next card and bet proportionally. Thus we should bet on (red, black) for the first card, and for the second card, if the first card is black, etc.
2. Alternatively, we can bet on the entire sequence of 52 cards at once. There are possible sequences of 26 red and 26 black cards, all of them equally likely. Thus proportional betting implies that we put of our money on each of these sequences and let each bet “ride.”

We will argue that these procedures are equivalent. For example, half the sequences of 52 cards start with red, and so the proportion of money bet on sequences that start with red in scheme 2 is also one half, agreeing with the proportion used in the first scheme. In general, we can verify that betting of the money on each of the possible outcomes will at each stage give bets that are proportional to the probability of red and black at that stage. Since we bet of the wealth on each possible outtut sequence, and a bet on a sequence increases wealth by a factor of 2^52 on the observed sequence and 0 on all the others, the resulting wealth is:

Rather interestingly, the return does not depend on the actual sequence. This is like the AEP in that the return is the same for all sequences. All sequences are typical in this sense.

It’s amazing that the strategy which bets everything at the start can beat one which bets after seeing which cards are no longer in the deck. The calculation of resulting wealth of the 2nd betting scheme, which results in 9.08, is transparent but I didn’t see how it could be exactly equal to the resulting wealth of the other betting scheme.

More Read

When the data point tells a different story
Blog interviews – more predictive analytics FAQs
A Visual Delight – Inauguration Day Helicopter Lesson
Exploring Technological Horizons with Recorded Future
Business Intelligence and The Heisenberg Principle

Here is an excel spreadsheet I made to simulate the first betting scheme: excel 2007 | older 2003, (functions in 2003 version may not work). On the first sheet, it randomly generates a sequence of draws from the deck, according to actual probabilities, and calculates the wealth you would have after the deck runs out. You can see the resulting wealth at the bottom (cell H54) of the “wealth” column- it’s always 9.08. It’s very surprising to me and I don’t fully understand how the two results can match. I’m new to the field though.

Tell me if you have any insight or a similar example that makes the paradox less paradoxical.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Hidden AI, a risk?
Hidden AI, Real Risk: A Governance Roadmap For Mid-Market Organizations
Artificial Intelligence Exclusive Infographic
unusual trading activity
Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
Analytics Exclusive Infographic
Ai agents
AI Agent Trends Shaping Data-Driven Businesses
Artificial Intelligence Exclusive Infographic
Why Businesses Are Using Data to Rethink Office Operations
Why Businesses Are Using Data to Rethink Office Operations
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Analyzing and predicting user satisfaction with sponsored search

4 Min Read

High-Performance Scoring of Healthcare Data

5 Min Read
Image
AnalyticsBig DataBusiness IntelligenceData MiningExclusiveModelingPredictive AnalyticsSocial DataText Analytics

Harvard Gets Access to Twitter Data Stream to Predict Foodborne Illness Outbreaks

4 Min Read

Dos and Donts for getting help

1 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?