1. Information Theory and Games (Ch. 16)

2. Information Theory Information theory studies information flow Information has no meaning –As opposed to daily usage, and –As opposed to semiotics Component 1 passing information to component 2: Measure of information gained is a number in the [0,1] range: –0 bit: gained no information –1 bit: gained the most information 12 information -How much information 2 gained? -Was there any distortion (“noise”) while passing the information?

3. Recall: Probability Distribution The events E 1, E 2, …, E k must meet the following conditions: One always occur No two can occur at the same time The probabilities p 1, …, p k are numbers associated with these events, such that 0  p i  1 and p 1 + … + p k = 1 A probability distribution assigns probabilities to events such that the two properties above holds

4. Information Gain versus Probability Suppose that I flip a “fair” coin:  what is the probability that it will come heads:  How much information you gain when it fall: 0.5 1 bit Suppose that I flip a “totally unfair” coin (always come heads):  what is the probability that it will come heads:  How much information you gain when it fall: 1 0

5. Information Gain versus Probability (2) Suppose that I flip a “very unfair” coin (99% will come heads):  what is the probability that it will come heads:  How much information you gain when it fall: 0.99 Fraction of A bit Imagine a stranger, “JL”. Which of the following questions, once answered, will provide more information about JL:  Did you have breakfast this morning?  What is your favorite color?

6. Information Gain versus Probability (3) If the probability that an event occurs is high, I gain less information when the event actually occurs If the probability that an event occurs is low, I gain more information when the event actually occurs In general, the information provided by an event decreases with the increase in the probability that that event occurs. Information gain of an event e (Shannon and Weaver, 1949): I(e) = log 2 (1/p(e)) Don’t be scared. We’ll come back tot his soon

7. Information, Uncertainty, and Meaningful Play Recall discussion of relation between uncertainty and Games –What happens if there is no uncertainty at all in a game (both at macro-level and micro-level)? What is the relation between uncertainty and information gain? If there is no uncertainty, information gained is 0

8. Lets Play Twenty Questions I am thinking of an animal: You can ask “yes/no” questions only Winning condition: –If you guess the animal correctly after asking 20 questions or less, and – you don’t make more than 3 attempts to guess the right animal

9. What is happening? (Constitutive Rules) We are building a binary (two children) decision tree a question no yes # nodes 2020 2121 2 2323 # levels 0 1 2 3 The number of questions made. And it is equal to log 2 (#nodes)

10. Noise and Redundancy Noise: affects component to component communication –Example in a game? Redundancy: counterbalance to noise –Making sure information is communicated properly –Example in game? Balance act: –Too much structure and game becomes too overdetermined –Little structure: Chaos Charades: playing with noise Crossword puzzle 12 information -Noise: distortion in the communication 12 information -Redundancy: passing the same information by two or more different channels information

11. Open Discussion Course so far First time taught What would you change/continue? Remember: I never remember your names!