Slack is a runaway hit — and everyone wants to figure out why. Slack lacks the outer trappings of a game — instead it pulls you along by unfolding new opportunities as your skills grow stronger. Learn how Slack’s Core Learning Loop drives a simple, compelling daily habit; why a single-player on-boarding bot creates a game-like experience; and why Slack’s early development practices created a strong foundation for rapid growth.
This presentation is based on the paper :
"A Method for Obtaining Digital Signatures and Public-Key Cryptosystems" by R.L. Rivest, A. Shamir, and L. Adleman
I presented this overview lecture at Computer Applications for the 21st century – Synergies and Vistas organized by Vidyasagar College, Kolkata in 2008
Slack is a runaway hit — and everyone wants to figure out why. Slack lacks the outer trappings of a game — instead it pulls you along by unfolding new opportunities as your skills grow stronger. Learn how Slack’s Core Learning Loop drives a simple, compelling daily habit; why a single-player on-boarding bot creates a game-like experience; and why Slack’s early development practices created a strong foundation for rapid growth.
This presentation is based on the paper :
"A Method for Obtaining Digital Signatures and Public-Key Cryptosystems" by R.L. Rivest, A. Shamir, and L. Adleman
I presented this overview lecture at Computer Applications for the 21st century – Synergies and Vistas organized by Vidyasagar College, Kolkata in 2008
Traditional Media Versus New Social Media Differences, Impact And OutcomeMyAssignmenthelp.com
Struggling to understand the difference between traditional media and new social media? Read this blog written by our experts to know what’s best for the current market. For more info visit: https://myassignmenthelp.com/blog/traditional-media-versus-new-social-media-differences-impact-and-outcome/
Lecture slides on Decision Theory. The contents in large part come from the following excellent textbook.
Rubinstein, A. (2012). Lecture notes in microeconomic theory: the
economic agent, 2nd.
http://www.amazon.co.jp/dp/B0073X0J7Q/
Successful innovations reach a mainstream audience—but they never start off that way. That’s the paradox of innovation that most entrepreneurs fail to embrace - at their peril.
That’s where Game Thinking comes in. Game Thinking is a step-by-step system for accelerating innovation and crafting products that people love…and keep loving. In Game Thinking, you empower your customers to get better at something they care about — like playing an instrument or leading a team. Come to this fast-paced training and equip yourself with the tools you need to create your next breakout hit.
Discussing some antipatterns in recommendation systems development and how good organizational and MLOps practices can lead to better outcomes. Discusses RecSys for e-commerce, computer vision, matrix factorization, etc. Uses examples from Stitch Fix algorithms and Weights and Biases.
A brief introduction to Crytography,the various types of crytography and the advantages and disadvantages associated to using the following tyes with some part of the RSA algorithm
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
Traditional Media Versus New Social Media Differences, Impact And OutcomeMyAssignmenthelp.com
Struggling to understand the difference between traditional media and new social media? Read this blog written by our experts to know what’s best for the current market. For more info visit: https://myassignmenthelp.com/blog/traditional-media-versus-new-social-media-differences-impact-and-outcome/
Lecture slides on Decision Theory. The contents in large part come from the following excellent textbook.
Rubinstein, A. (2012). Lecture notes in microeconomic theory: the
economic agent, 2nd.
http://www.amazon.co.jp/dp/B0073X0J7Q/
Successful innovations reach a mainstream audience—but they never start off that way. That’s the paradox of innovation that most entrepreneurs fail to embrace - at their peril.
That’s where Game Thinking comes in. Game Thinking is a step-by-step system for accelerating innovation and crafting products that people love…and keep loving. In Game Thinking, you empower your customers to get better at something they care about — like playing an instrument or leading a team. Come to this fast-paced training and equip yourself with the tools you need to create your next breakout hit.
Discussing some antipatterns in recommendation systems development and how good organizational and MLOps practices can lead to better outcomes. Discusses RecSys for e-commerce, computer vision, matrix factorization, etc. Uses examples from Stitch Fix algorithms and Weights and Biases.
A brief introduction to Crytography,the various types of crytography and the advantages and disadvantages associated to using the following tyes with some part of the RSA algorithm
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
I provide a (very) brief introduction to game theory. I have developed these notes to
provide quick access to some of the basics of game theory; mainly as an aid for students
in courses in which I assumed familiarity with game theory but did not require it as a
prerequisite
2013.05 Games We Play: Payoffs & Chaos MonkeysAllison Miller
Expansion on application of game theory & behavioral analytics to information security and risk management. New concepts include some ideas from coalitional game theory, i.e. not just individual actors but teams.
This is the ppt which I made for my economics subject presentation , I hope it would helpful for you. It is well known prisoners dilemma , it is very interesting topic, i try to add no. Of pictures hope it is fruitful for you.
When we created this quiz of Java programming course, we did that with Fasilkom UI students in mind.
Fast forward, we now thought that the quiz could be of greater use if it's shared to anyone, not just Fasilkom UI students.
Yes, our students of our course are everyone, including you!
So please find attached, fresh from the oven, Java programming quiz part 01 (with key answers). More parts are coming whenever they are ready.
#java #programming #universitasindonesia #opencourse #openaccess #openeducation #opentridharma
Featuring pointers for: Single-layer neural networks and multi-layer neural networks, gradient descent, backpropagation. Slides are for introduction, for deep explanation on deep learning, please consult other slides.
Current situation: focus is limited to only implement Tridharma, that is, education, research, and community service, with little concern on openness aspect.
The openness of Tridharma can potentially be a breakthrough in mitigating the quality gap issue: opening Tridharma outputs for public would help to increase the citizen inclusion in accessing the quality content of Tridharma, hence narrowing the quality gap in higher education.
[ISWC 2013] Completeness statements about RDF data sources and their use for ...Fariz Darari
This was presented at ISWC 2013 in Sydney, Australia.
Abstract:
With thousands of RDF data sources available on the Web covering disparate and possibly overlapping knowledge domains, the problem of providing high-level descriptions (in the form of metadata) of their content becomes crucial. In this paper we introduce a theoretical framework for describing data sources in terms of their completeness. We show how existing data sources can be described with completeness statements expressed in RDF. We then focus on the problem of the completeness of query answering over plain and RDFS data sources augmented with completeness statements. Finally, we present an extension of the completeness framework for federated data sources.
Dissertation Defense - Managing and Consuming Completeness Information for RD...Fariz Darari
The ever increasing amount of Semantic Web data gives rise to the question: How complete is the data? Though generally data on the Semantic Web is incomplete, many parts of data are indeed complete, such as the children of Barack Obama and the crew of Apollo 11. This thesis aims to study how to manage and consume completeness information about Semantic Web data. In particular, we first discuss how completeness information can guarantee the completeness of query answering. Next, we propose optimization techniques of completeness reasoning and conduct experimental evaluations to show the feasibility of our approaches. We also provide a technique to check the soundness of queries with negation via reduction to query completeness checking. We further enrich completeness information with timestamps, enabling query answers to be checked up to when they are complete. We then introduce two demonstrators, i.e., CORNER and COOL-WD, to show how our completeness framework can be realized. Finally, we investigate an automated method to generate completeness statements from text on the Web via relation cardinality extraction.
KOI - Knowledge Of Incidents - SemEval 2018Fariz Darari
We present KOI (Knowledge Of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events.
KOI-KG can then be used to efficiently answer questions such as "How many killing incidents happened in 2017 that involve Sean?" The required steps in building the KG include:
(i) document preprocessing involving word sense disambiguation, named-entity recognition, temporal expression recognition and normalization, and semantic role labeling;
(ii) incidental event extraction and coreference resolution via document clustering; and (iii) KG construction and population.
Slides made and presented by Paramita.
Comparing Index Structures for Completeness ReasoningFariz Darari
Data quality is a major issue in the development of knowledge graphs. Data completeness is a key factor in data quality that concerns the breadth, depth, and scope of information contained in knowledge graphs. As for large-scale knowledge graphs (e.g., DBpedia, Wikidata), it is conceivable that given the amount of information contained in there, they may be complete for a wide range of topics, such as children of Donald Trump, cantons of Switzerland, and presidents of Indonesia. Previous research has shown how one can augment knowledge graphs with statements about their completeness, stating which parts of data are complete. Such meta-information can be leveraged to check query completeness, that is, whether the answer returned by a query is complete. Yet, it is still unclear how such a check can be done in practice, especially when a large number of completeness statements are involved. We devise implementation techniques to make completeness reasoning in the presence of large sets of completeness statements feasible, and experimentally evaluate their effectiveness in realistic settings based on the characteristics of real-world knowledge graphs.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
2. What is Game Theory?
• Game theory is the study of the ways in which interacting choices of
economic agents produce outcomes with respect to the preferences
(or utilities) of those agents. (plato.stanford.edu)
• Game theory is a theoretical framework for conceiving social
situations among competing players. In some respects, game theory
is the science of strategy, or at least the optimal decision-making of
independent and competing actors in a strategic setting.
(investopedia.com)
2
3. So, Game Theory is Basically ...
... how to make decisions in a multi-agent system.
Game Theory is leveraged to analyze agent decisions and measure the
expected utility for every decision, with the assumption that other
agents behave optimally.
As opposed to the turn-taking, fully observable adversarial search,
Game Theory works in a partially observable environment using
simultaneous moves.
3
4. Two-Finger Morra Game
Evenia and Oddie, simultaneously, show one or two fingers.
Suppose that the sum of all the shown fingers is N.
Utilities:
• If N is even, then Evenia gets N dollar from Oddie.
• If N is odd, then Oddie gets N dollar from Evenia.
4
5. Single-Move Games
• Agents make one-move decisions.
• Examples:
• Product pricing
• International relations
• Games in traditional sense: Two-finger Morra,
Rock-Paper-Scissors, etc
• Marriage? :)
5
6. Components
• Players: Involved agents to make decisions. Can be two or
more.
• Actions: What agents can do. Action decisions need not be
unique among agents.
• Payoff Function: Utility values for every player for every
action combination by all players.
Such a payoff function can be represented by a matrix called:
Strategic form/normal form.
6
7. Two-Finger Morra Game: Revisited
7
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
Evenia and Oddie, simultaneously, show one or two fingers.
Suppose that the sum of all the shown fingers is N.
Utilities:
• If N is even, then Evenia gets N dollar from Oddie.
• If N is odd, then Oddie gets N dollar from Evenia.
8. Game Strategies
• Every player must adopt and execute a strategy/policy.
• Pure strategy: Deterministic policy.
• Mixed strategy: Randomized policy, involves probability
distribution, [p : a, (1 – p): b]. For example, [0.4 : One, 0.6 : Two].
• A strategy profile is a strategy selection for every player with
some outcome value.
• A solution in Game Theory is a strategy profile where each
player takes a rational strategy.
8
9. Prisoner's Dilemma
Two members of a criminal gang are arrested and imprisoned. Each prisoner is in solitary
confinement with no means of communicating with the other. The prosecutors lack sufficient
evidence to convict the pair on the principal charge, but they have enough to convict both on
a lesser charge. Simultaneously, the prosecutors offer each prisoner a bargain. Each prisoner
is given the opportunity either to betray the other by testifying that the other committed the
crime, or to cooperate with the other by remaining silent. The possible outcomes are:
• If A and B each betrays the other, each of them serves two years in prison
• If A betrays B but B remains silent, A will be set free and B will serve three years in prison
• If A remains silent but B betrays A, A will serve three years in prison and B will be set free
• If A and B both remain silent, both of them will serve only one year in prison.
9
10. Two members of a criminal gang are arrested and imprisoned. Each prisoner is in solitary
confinement with no means of communicating with the other. The prosecutors lack
sufficient evidence to convict the pair on the principal charge, but they have enough to
convict both on a lesser charge. Simultaneously, the prosecutors offer each prisoner a
bargain. Each prisoner is given the opportunity either to betray the other by testifying that
the other committed the crime, or to cooperate with the other by remaining silent. The
possible outcomes are:
• If A and B each betrays the other, each of them serves two years in prison
• If A betrays B but B remains silent, A will be set free and B will serve three years in prison
• If A remains silent but B betrays A, A will serve three years in prison and B will be set
free
• If A and B both remain silent, both of them will serve only one year in prison.
10
11. Two members of a criminal gang are arrested and imprisoned. Each prisoner is in
solitary confinement with no means of communicating with the other. The
prosecutors lack sufficient evidence to convict the pair on the principal charge, but
they have enough to convict both on a lesser charge. Simultaneously, the
prosecutors offer each prisoner a bargain. Each prisoner is given the opportunity
either to betray the other by testifying that the other committed the crime, or to
cooperate with the other by remaining silent. The possible outcomes are:
Strategy for B:
• If A stays silent: B stays silent (-1) vs. B bertrays (0)
• If A bertrays: B stays silent (-3) vs. B bertrays (-2)
Best strategy: B bertrays (always better than stays silent)
11
12. Two members of a criminal gang are arrested and imprisoned. Each prisoner is in
solitary confinement with no means of communicating with the other. The
prosecutors lack sufficient evidence to convict the pair on the principal charge, but
they have enough to convict both on a lesser charge. Simultaneously, the
prosecutors offer each prisoner a bargain. Each prisoner is given the opportunity
either to betray the other by testifying that the other committed the crime, or to
cooperate with the other by remaining silent. The possible outcomes are:
Strategy for A?
12
13. Dominant strategy for Prisoner's dilemma: To betray!*
*After all, they are criminals, so no code of conduct is required.
13
14. Dominant Strategy
• Strategy s for player p strongly dominates strategy s0 if the outcome
of s is always better for p than the outcome of s0 no matter what is
done by the other players.
• Strategy s for player p weakly dominates strategy s0 if the outcome of
s is better for p than the outcome of s0 in at least one strategy
profile, and is no worse than s0 in the other strategy profiles.
• Rationality: Picking a dominant strategy.
14
15. Exercise
What is each player's dominant strategy?
15
*Player One's payoffs are in bold.
16. Exercise
What is each player's dominant strategy?
16
*Player One's payoffs are in bold.
• Dominant strategy for Player One?
- If Player Two cooperates:
Player One Cooperate ($10) vs. Player One Cheat ($12)
- If Player Two cheats:
Player One Cooperate ($0) vs. Player One Cheat ($5)
17. Exercise
What is each player's dominant strategy?
17
*Player One's payoffs are in bold.
• Dominant strategy for Player Two?
- If Player One cooperates:
Player Two Cooperate ($10) vs. Player Two Cheat ($12)
- If Player One cheats:
Player Two Cooperate ($0) vs. Player Two Cheat ($5)
18. Exercise
What is each player's dominant strategy?
18
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
19. Exercise
What is each player's dominant strategy?
19
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
Neither player has a dominant strategy.
21. Exercise
What is each player's dominant strategy?
21
Neither player has a dominant strategy.
• If Player Two chooses rock, Player One should play paper
• If Player Two chooses paper, Player One responds with scissors
• If Player Two chooses scissors, Player One chooses rock
22. If every player has a dominant strategy, then the strategy combination
is called Dominant Strategy Equilibrium. 22
Dominant Strategy Equilibrium
23. If every player has a dominant strategy, then the strategy combination
is called Dominant Strategy Equilibrium. 23
Dominant Strategy Equilibrium
Dominant Strategy Equilibrium:
(Bertrays, Bertrays)
24. Nash Equilibrium
• Equilibrium is a concept such that no player would
gain more for changing her strategy if other players
do not change their strategy.
• The Nash Equilibrium is a decision-making theorem within Game Theory
that states a player can achieve the desired outcome by not deviating
from their initial strategy.
Note: Any dominant strategy equilibrium is always a Nash equilibrium!
24
25. Pure Strategy Nash Equilibrium
1. Identify each player's optimal strategy in response to what other
players might do.
2. A Nash equilibrium is defined when all players are playing their
optimal strategies simultaneously.
25
The dominant strategy equilibrium here is:
(Bertrays, Bertrays)
^And this is also the Nash equilibrium for the given problem.
Note: Any dominant strategy equilibrium is a Nash equilibrium.
27. Dilemma in Prisoner's Dilemma
27
Nash Equilibrium: (Bertrays, Bertrays)
• The equilibrium outcome is worse than the outcome when both
players stay silent.
• An outcome is Pareto-optimal if there is no other outcome wanted by
all players.
• An outcome o is Pareto-dominated by outcome o' if every player
chooses o'. For example, (-2, -2) is Pareto-dominated by (-1, -1).
28. Putting all together
28
• Is there a dominant strategy?
• Nash Equilibrium?
• Which is best?
Best: One Best: Two
Acme: One A = 9, B = 9 A = -3, B = -1
Acme: Two A = -4, B = -1 A = 5, B = 5
29. Putting all together
29
• Is there a dominant strategy? No!
• Nash Equilibrium? Two equilibria, that is, (One, One) and (Two, Two)
• Which is best? Pareto-optimal Nash Equilibrium = (One, One)
Best: One Best: Two
Acme: One A = 9, B = 9 A = -3, B = -1
Acme: Two A = -4, B = -1 A = 5, B = 5
30. Mixed Strategy
• Some problems do not have any pure strategy Nash equilibrium.
Recall: Two-finger Morra Game
• A mixed strategy could come into handy: Adding probabilities to our decisions.
• For example, Evenia could pick One with probability p, and Two with probability
(1 - p).
• Goal of a mixed strategy: To reach an equilibrium such that the opponent of the
player cannot make a deteministic choice to benefit from any of the player's
strategies. In other words, the player's strategies are indifferent. 30
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
31. Mixed Strategy Equilibrium
• Suppose Oddie picks One with probability p and Two with probability (1 - p).
• If Evenia picks One, then Evenia would get expected payoff: 2p - 3(1 - p) = 5p - 3
• If Evenia picks Two, then Evenia would get expected payoff: -3p + 4(1 - p) = 4 - 7p
• Mixed Strategy Equilibrium: Strategy profile (of probabilities) that is best for two players
• The equilibrium can be achieved when all the choices are indifferent to the opponent.
So, in this case, it is solving: 5p – 3 = 4 – 7p. We then have that: p = 7/12.
• Proof: Suppose that Oddie picks One with probability 7/12. We would like to know the expected
payoffs for Evenia.
• If Evenia picks One, then the expected payoff = 5(7/12) – 3 = -(1/12)
• If Evenia picks Two, then the expected payoff = 4 – 7(7/12) = -(1/12)
So, whatever strategy is chosen by Evenia, there is no difference.
Exercise: What happens if Oddie picks One with probability 3/4?
31
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
32. Mixed Strategy Equilibrium (cont.)
• Suppose Evenia picks One with probability p and Two with probability (1 - p). What would be the
best strategy for Evenia?
32
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
33. Mixed Strategy Equilibrium (cont.)
• Suppose Evenia picks One with probability p and Two with probability (1 - p). What would be the
best strategy for Evenia? It's the same as Oddie (due to the symmetry of the payoff matrix),
picking p = 7/12.
• In this case, Oddie's best strategy is p = 7/12, and Evenia's one is also p = 7/12.
• The equilibrium for mixed strategy cases is called: Maximin equilibrium.
• Therefore, the maximin equilibrium for the above payoff matrix is: (7/12, 7/12)
33
Oddie: One Oddie: Two
Evenia: One E = 2, O = -2 E = -3, O = 3
Evenia: Two E = -3, O = 3 E = 4, O = -4
TV/Network Channels, Network 1 and Network 2, are deciding which programme to show: Sitcom/Game Show
Solution: (Sitcom, Gameshow)
Link: http://econ.ucsb.edu/~garratt/Econ171/Lect04_Slides.pdf
If Oddie picks One with prob 1/2.
Expected payoff for Evenia picking One: 5(1/2)-3=-(1/2)
Expected payoff for Evenia picking Two: 4-7(1/2)=1/2
Evenia would pick Two then!