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==Introduction==
  
The queueing theory is a '''mathematical method for analyzing the congestions and delays of waiting in line'''. The theory takes into consideration every component involved in waiting in line: process to serve, number of servers and number of customers. By reducing the theoretical results into Markov chains, it is possible to prove the correctness of its theorems. Usual task of the queueing theory is to '''predict''' the '''queue lengths''' and '''waiting times''' and '''optimize''' these '''results''' by changing its properties (i.e. type of the queue, number of servers-shop assistants). Its first thoughts were made by a Danish mathematician Agner Krarup Erlang at the '''beginning of the 20th century'''.
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{| class="wikitable" align=right width="220"
 
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! scope="col" width="50px" |John Forbes Nash Jr.
==Motivation - why learn queueing theory?==
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|-  
 
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| [[File:Nash.jpg|200px|left]]
[[File: Autobus_mhd.jpg|thumb|upright=1.25| '''Did you know...?''' - Application of the queueing theory simulations helped the Prague Public Transportation save five bus vehicles a day on the longest distance line. <ref name="praguemhd">[ ČESKÁ TELEVIZE. Teorie front. 2007. Available at: http://www.ceskatelevize.cz/porady/10121359557-port/81-teorie-front/video/]</ref>]]
 
 
 
Everyone hates waiting in queues, while everyone is forced to do it often. The queueing theory helps both parts of market - the customer has to wait shorter time, while the merchandiser has to pay less shop assistants. But the field of study doesn't focus only on the queues in the shopping malls.
 
 
 
 
 
Where is the queueing theory usually applied? These are the most common fields:
 
 
 
* '''Optimize the number of shop assistants''' in big shopping centers, so that the customers may wait shorter time, but the sellers may also use less shop assistants and therefore save money.
 
* Improve '''processes at banks or post offices''' - it is possible to simulate, what type of lines are the most appropriate under certain circumstances.
 
* Very often, it is used for '''improving manufacturing processes'''. Simulation of the production of items can result in more budget-wise systems, which can produce more goods in shorter time, thanks the the reduction of possible bottlenecks.
 
* '''Transportation systems simulations''' - by simulation of junctions, traffic lights can be configured, so that they help making the traffic as smooth as possible.
 
* '''Call centers''' - simulation can help predicting the number of incoming calls and the number of necessary operators.
 
 
 
 
 
''Disclaimer: This chapter is written primarily for beginners and ones, who have only light background in operations research, computer science and industrial engineering. It is intended to be used as a study text, therefore you can find questions at the end of each part to make sure you understand the topic correctly. Important information are written in '''bold'''. Interesting - not necessary to remember -  information can be found in box "Did you know...?"''
 
 
 
 
 
'''Make sure you know enough...'''
 
* Name three different possible situations, where the queueing theory may help optimize the process.
 
* Let's say you're in a post office. What are the advantages of having only one line which belongs to multiple reception desks?
 
 
 
 
 
==Classification==
 
 
 
Classification tree: '''Mathematics''' > '''Applied mathematics''' > '''Operations research''' > '''Queueing theory'''
 
 
 
The queuing theory is considered as a branch of operations research, which belongs to the applied mathematics. Operations research is usually taught in faculties of engineering, public policy or business. This field focuses on the human-technology interaction and put an emphasizes on practical usage, searching for the best possible result (usually the maximum - profit, performance; or minimum - loss, risk, cost). Between other major operations research disciplines usually belongs: Computing and information technologies,
 
financial engineering, manufacturing, service sciences, supply chain management, marketing engineering, policy modeling and public sector work, revenue management, simulation, stochastic models and transportation. <ref name="opresearch">[ What is Operations Research. Lancester University [online]. 2014 [cit. 2015-01-24]. Available at: http://www.lancaster.ac.uk/lums/study/masters/programmes/msc-operational-research-management-science/what-is-operational-research/
 
]</ref>
 
 
 
{| class=infobox width=350px
 
|style="background:yellow;color:#000"|<center>'''Quick tip''' </center>
 
 
|-
 
|-
| Questions in the end of each part should help you summarize what you know and challenge a little bit. Not sure how to answer one? Read suggested resources and look it up on Google.
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| '''Born:'''
|}
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* June 13, 1928
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'''Died:'''
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* May 13, 2015 (aged 86)
  
  
'''Make sure you know enough...'''
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John Forbes Nash Jr. was an American mathematician who made fundamental contributions mainly to [[Game_theory|Game theory]]. Nash's work has provided insight into the factors that govern chance and decision-making inside complex systems found in everyday life
* Name at least two other subdisciplines of the Operations research and one subdiscipline of Applied mathematics.
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|}
* Which other mathematical disciplines may be useful to know for studying the queueing theory?
 
  
  
==History==
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* A '''pure strategy''' is an unconditional, defined choice that a person makes in a situation or game.
[[Image:Erlang.jpg|thumb|left|275px|Agner Krarup Erlang - father of Queuing theory]]
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* A '''mixed strategy''' is an assignment of probability to all choices in the strategy set.  
[[Image:workers.jpg|thumb|right|250px|At the beginning of the 20th century, most telephone exchange stations were using human operators and cord boards for switching telephone calls using jack plugs.]]
 
  
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=== History ===
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The concept of a mixed-strategy was introduced by John von Neumann and Oskar Morgenstern in their 1944 book ''The Theory of Games and Economic Behavior''<ref name="john" />, but their analysis was restricted to the zero-sum games. They showed that a mixed-strategy Nash equilibrium will always exist for any zero-sum game with a finite set of actions.
  
It all started in 1909 in a Copenhagen phone company, where an engineer '''Agner Erlang''' (1878-1929) asked himself: How many trunk lines are necessary to adequately service an entire city. <ref name="erlang">[ Agner Krarup Erlang. Plus Magazine - living mathematics [online]. 1997 [cit. 2015-01-24]. Available at: http://plus.maths.org/content/os/issue2/erlang/index]</ref> You could use just one, but that would result into huge delays, because the people would have to wait until the trunk is empty. Or you could install one for each phone, which would be extremely expensive and wasteful, since most people only call short time of the day. The telephone company needed a perfect compromise. Erlang found out, that knowing the average number of calls in an hour and the average call duration, one can estimate the number of trunk lines needed. The problem comes from the fact that the people can call in bunches, meaning that more people call at one time, and less later. Somebody can also block the trunk line longer than expected. Erlang came with a solution. He was able to count the number of trunk lines and get the percentage of callers with blocked calls, which made it possible to predict the adequate numbers for different time periods. And that was the beginning of queuing theory - when he published his work The Theory of Probabilities and Telephone Conversations.
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In his famous paper in 1950, John Forbes Nash go further and proved that there is an equilibrium for '''every finite game''' and not only the zero-sum game. Now we can divide Nash equilibria into two types.  
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*Pure strategy Nash equilibria are Nash equilibria where all players are playing pure strategies.  
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*Mixed strategy Nash equilibria are equilibria where at least one player is playing a mixed strategy.
  
Another important name connected with the theory, is '''David George Kendall''' (1918-2007), an English statistician, working mainly in field of probability. Kendall was the first to use the term Queuing theory, in his work "Some Problems in the Theory of Queues" (1951) <ref name="kendall">[ David George Kendall. St. Andrews University [online]. 1998 [cit. 2015-01-24]. Available at: http://www-history.mcs.st-andrews.ac.uk/Biographies/Kendall.html]</ref>. He is well known for coming up with so-called '''Kendall's notation'''. It is a standard system used for describing and classifying a queuing node.
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=== Definition ===
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Mixed strategy:
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if the player <math>i</math> has <math>K</math> strategies <math>si1, si2, ...siK</math> available, a mixed strategy is a distribution of probabilities <math>pi=(pi1, pi2, ...piK)</math> where <math>pi1</math> is the probability for <math>i</math> to choose the strategy <math>si1</math>. Also because the mixed strategy is a distribution of probability there is:
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<math>\sum_{j=1}^K pij = 1</math>
  
Even the Soviets contributed to the field of queueing theory more than little. Mathematicians '''Andrey Kolmogorov''' (1903-1987), '''Alexander Khinchin''' (1903-1987) were directly interested in this theory, often extending the theory of '''Andrey Markov''' (1856-1922) - Markov chains and Markov processes.
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===Nash equilibrium in mixed strategy===
  
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A mixed strategy Nash equilibrium involves at least one player playing a '''randomized''' strategy and no player being able to increase his or her expected payoff by playing an alternate strategy. A [[Nash_equilibrium|Nash equilibrium]] in which no player randomizes is called a pure strategy Nash equilibrium.
  
'''Make sure you know enough...'''
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==Matching pennies game==
* Who is considered as the first to work on the queueing theory?
 
* What is a Kendall's notation?
 
  
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=== Definition ===
  
==Types of queueing systems==
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Matching pennies is the name for a simple game used in game theory. It is played between two players. Each player has a penny and must secretly turn the penny to heads or tails. The players then reveal their choices simultaneously. If the pennies match (both heads or both tails), then Row keeps both pennies, so wins one from Column (+1 for Row, −1 for Column). If the pennies do not match (one heads and one tails) Column keeps both pennies, so receives one from Row. The following table display the game.
Queueing systems may have different structure. For example, there can be one line, multiple lines, one sever or more servers with complicated connection.
 
  
Queueing systems can be divided by these criteria<ref name="que">[ ADAN, Ivo a Jacques RESING. Queueing Theory. Queueing Theory. 2002, n. 1. Available at:h ttp://www.win.tue.nl/~iadan/queueing.pdf ]</ref>:
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[[File:Penny1.jpeg|400px|center]]
  
===Source of customers===
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===Mixed strategy in Matching pennies===
* '''Finite''' - the source of customers is finite, when it is possible and practical to count with the maximum possible number of customers.
 
* '''Infinite''' - usually, the source is considered as infinite, because the theoretical maximum value is too high.
 
  
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Suppose that Row believes Column plays Heads with probability <math>p</math>. If Row plays Heads, he gets 1 with probability <math>p</math> and –1 with probability <math>(1-p)</math>. His expected profit will be <math> 1p - 1(1-p) = 2p - 1 </math>. This is summarized in Figure below "Mixed strategy in matching pennies".
  
===Arrival of customers to the system===
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[[File:Penny2.jpeg|400px|center]]
It can be described by two properties:
 
* '''Intensity of new customers''' - number of customers per one time unit.
 
* '''Interval between single tasks''' - time between two subsequent customers.
 
  
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If <math>2p - 1 > 1 - 2p</math>, then Row is better off, on average, playing Heads than Tails. Similarly.
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If, on the other hand, <math>2p - 1 = 1 - 2p</math> it gives '''<math>p=1/2</math>'''. Then Row gets the same payoff no matter what Row does. In this case, Row could play Heads, could play Tails, or could flip a coin and randomize Row’s play.
  
When we know one of these information, it is easy to figure out the second. The intensity and intervals may be divided in two possible types:
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Note that randomization requires equality of expected payoffs. If a player is supposed to '''randomize''' over strategy A or strategy B, then both of these strategies must produce the same expected payoff. Otherwise, the player would prefer one of them and wouldn’t play the other.
* '''Deterministic''' - the intervals between two subsequent customers (tasks) are fixed. This usually happens in manufactures.
 
* '''Stochastic''' - The arrivals vary, being described by some probabilities.
 
  
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==Battle of the sexes game==
  
===Service time distribution===
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=== Definition ===
The time spent while '''being serviced''' can be also either '''deterministic''' or '''stochastic'''. Most often, an exponential spread of a random variable X is being used.
 
  
 +
The battle of sexes is a two-players coordination game in the [[Game_theory|Game theory]]. In this game there is one man and one woman, the woman prefers going to a ballet and the man prefers going to a baseball game. The nuance here is that they both prefers to go together rather than going alone to his/her prefered activity. The game is described in the table below.
  
===Network of servers===
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[[File:Battle1.jpg|400px|center]]
The number of servers is crucial for the entire system. The goal of the queueing theory is to find the ideal number of servers, to satisfy both the customers and the owner.
 
* '''One server '''- this is the easiest type. Just one check desk in a small local shop for example.
 
* '''More servers parallel'' - the servers offer the same service. Typical example are the checking desks in big supermarkets. These can be further divided into another two categories: Having '''separate waiting line''' for each server, or having '''one line for all''', which then spreads.
 
  
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We can highlight the two pure Nash equilibrium: (Ballet, Ballet) and (Baseball, Baseball) you can find them by using the [[Nash_equilibrium|Nash equilibrium]] course.
  
===Queue characteristics===
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===Mixed strategy in battle of the sexes===
This information shows, what rules are applied for letting the customer in the queue be served.
 
* '''FIFO - First In, First Out '''- the most common type. The one, who comes first, is also the first to be served. Classical queue in a shop.
 
* '''LIFO - Last In, First Out''' - servers always accept the ones, who are in the queue the shortest time.
 
* '''SIRO - Select in Random Order''' - the order, in which the customers joined the queue, is irrelevant, because the servers pick the customers randomly.
 
* '''PRI - Priorities '''- it is possible to set some priorities, which will help to decide, who goes first.
 
  
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To find the mixed strategy we need to assign some probabilities to each situation. Let '''<math>p</math>''' be the probability for the '''woman''' to go to the '''baseball''' game and '''<math>q</math>''' the probability for the '''men''' to go to the '''baseball''' game. The computations with the expected payoffs for each situation is display below.
  
===Special characteristics of queues===
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[[File:Battle2.jpg|400px|center]]
* '''Limit of queue capacity''' - shows the maximum capacity of the line, not allowing anyone else joining it, if reached.
 
* '''Patience of customers '''- it is possible (and also often very important), to take into account customer's patience. When the system doesn't care about it, it can be said, that the '''customers' patience is unlimited.''' It means, that they will wait in the queue as long as needed. But when the patience is '''limited''', it is possible, that the customer simply rejects to join the line (and doesn't buy anything), because it is too long.
 
  
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The payoff calculations are the same as for the [[#Matching_pennies|Matching pennies]].
 +
For example, if the man goes to the baseball game:
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* He gets 3 when the woman goes also to the Baseball game, with a <math>p</math> probability.
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* He gets 1 when the woman goes to the Ballet, with a <math>1-p</math> probability.
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The expected payoff is then just the product of the probability and the payoff, for example for the first row the expected payoff is then <math>3p + 1(1-p) </math>
  
'''Make sure you know enough...'''
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A key aspect in the mixed strategy is the '''indifference''' of choosing one or another choice for a player. In the mixed strategy game the Man has to be indifferent between going to the Baseball game and to the Ballet. This indifference or randomization between the choices of a player is expressed mathematically in our example for the man by  <math>1+2p = 2-2p</math> which yields <math>p = 1/4 </math>. Then if the Woman is going to the Baseball game 1/4 of the time, the Man will be willing to randomize which event he attends. Similar calculation are done for all possibilities and we multiply the probabilities:
* What would happen, if LIFO type of queues were used in supermarkets?
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* <math>p * q</math>
* What are the advantages and disadvantages of single and multiple servers (cheking desks) in supermarkets?
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* <math>q * (1-p)</math>
* Name one process at place, where PRI type of queues is used.
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* <math>(1-q) * p</math>
 +
* <math>(1-q) * (1-p)</math>
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We obtain the following table:
  
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[[File:Ballet3.jpg|400px|center]]
  
==Kendall's notation==
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We can note that 9 times over 16 the mixed strategy is (Baseball, Ballet) and it means that they are not together. Even if this mixed strategy Nash equilibrium seems to be undesirable, it's a [[Nash_equilibrium|Nash equilibrium]] as there is no improvement possible based on the behavior of the other party. This lack of coordination is often a feature of mixed strategy equilibrium. We can now consider another game where a failure of coordination makes more sense, the chicken game.
Kendall's notation is a commonly used system for describing and classifying queues. Originally, these nodes were described by three factors A/S/c. Later, it has been extended to six factors - A/S/c/K/N/D:
 
* '''A''' = time between arrivals to the queue. Possible codes are: M (Markovian or memoryless - Poisson process), M<sub>x</sub> (batch Markov), MAP (Markovian arrival process), BMAP (Batch Markovian arrival process), MMPP (Markov modulated poisson process), D (Degenerate distribution), E<sub>k</sub> (Erlang distribution), G (General distribution), PH (Phase-type distribution).
 
* '''S '''= service time distribution. Possible codes: M (Markovian or memoryless), M<sup>Y</sup> (bulk Markov), MMPP (Markov modulated poisson process), D (Degenerate distribution), E<sub>k</sub> (Erlang distribution), G (General distribution), PH (Phase-type distribution).
 
* '''c '''= number of servers (service channels)
 
* '''K''' = capacity of the queue - the maximum capacity of customers in whole system (including the ones being served)
 
* '''D ''' = queueing discipline - how to customers in the queue are being served
 
* '''N''' = size of population - the number of people who can possibly get into the system and become customers
 
  
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==Chicken game==
  
'''Make sure you know enough...'''
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=== Definition ===
* What does the M/m/1 queue in the Kendall's notation mean?
 
  
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[[File:Chicken1.jpeg|500px|center]]
  
==Suitable software==
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The chicken game is a game in which two players drive two very fast cars towards each other from opposite ends of a long straight road. If one of them swerves before the other, he is called a chicken and will have a payoff of -1. Of course, if neither swerves, they will crash and will get both -4. This is the worst possible payoff. The best payoff is to have your opponent be the chicken, so we assign this a value 1. The last possibility is that both drivers swerve. Then, neither has less honor than the other, so this is a better option than being the chicken. We could assign a payoff matrix to this:
===Java Modelling Tools===
 
[[Image:jmt.jpg|400px|thumb|right|Java Modelig Tools in action - designing of Markov Chain]]
 
[[Image:Jmtgui.png|300px|thumb|left|Java Modelig Tools in action - JSIMgraph - the graphical user interface for simulating queues systems]]
 
Java Modelling Tools (JMT) areapplications developed by Politecnico di Milano and Imperial College London, released under GPL license. At this time (January 2015), they include six applications:
 
* '''JSIMgraph '''- Queueing network models simulator with GUI
 
* '''JSIMwiz '''- Queueing network models simulator with wizard-based UI
 
* '''JMVA '''- Mean Value Analysis and Approximate solution algorithms for queueing models
 
* '''JABA '''- Asymptotic Analysis and bottlenecks identification of queueing network models
 
* '''JWAT '''- Workload characterization from log data
 
* '''JMCH '''- Markov chain simulator
 
  
Java Modelling Tools is platform-independent and requires only the Java Runtime Environment (version 1.6 or later). <ref name="jmt">[ Java Modelling Tools. JMT [online]. 2015 [cit. 2015-01-24]. Available at: http://jmt.sourceforge.net/
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[[File:Chicken2.jpg|400px|center]]
]</ref>
 
  
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There is again two pure Nash equilibrium here: (Swerve, Don't) and (Don't, Swerve), you can find them by using the [[Nash_equilibrium|Nash equilibrium]] course.
  
===MATLAB===
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===Mixed strategy in Chicken game===
[[Image:matlab.png|200px|thumb|right|Using MATLAB - visualization of the number of customers, waiting in a queue]]
 
  
If you're interested in mathematics, solving difficult tasks and visualizing data, you probably know the MATLAB software. It can also be used for simulating the queues and application of it's theories.
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To find the mixed strategy equilibrium we need to assign the same probabilities as for the [[#Mixed strategy in battle of the sexes|Battle of the sexes]]. We can find the table of calculation below.
  
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[[File:Chicken3.png|400px|center]]
  
==Modelling ==
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In order to find the mixed strategy equilibrium we have to calculate what probabilities <math>p</math> and <math>q</math> are required to have '''randomized''' choices. The same logic as for the [[#Mixed strategy in battle of the sexes|Battle of the sexes]] is applied, we calculate: <math>0p -1(1-p) = 1p -4(1-p)</math> and get the optimal probabilities: '''<math>p=3/4</math>''' and '''<math>q=3/4</math>'''. We just have to '''multiply''' these probabilities to obtain the table below.
To make a mathematical model of the queue system, we need to use these variables:
 
* Entry of items
 
* Length of serving
 
* Network of servers
 
* Rules for leaving the queues to be served
 
* Specific parts of the system
 
  
{| class=infobox width=350px
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[[File:Chicken4.png|400px|center]]
|style="background:#33CC33;color:#FFFFFF"|<center>'''Did you know...?'''</center>
 
|-
 
| If you want to make simple calculations based on the queueing theory faster, you can use an automatic online tool. After opening the webpage, you have to choose the queueing model and input all required values. After the fast calculation is done, you get the results described in this chapter.
 
  
The tool can be accessed here: http://www.supositorio.com/rcalc/rcalclite.htm
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We can note that the probability of a collision (Don't, Don't) is just 1/16 in the mixed strategy equilibrium. In the chicken game the mixed strategy equilibrium is more likely than in the [[#Mixed strategy in battle of the sexes|Battle of the sexes]]. The whole point in this game is to find out who will yield, which means that it isn't known in advance. This means that the mixed strategy equilibrium is the more reasonable equilibrium.
|}
 
  
What sort of questions the mathematical model should answer about the line:
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==Rock, paper, scissors==
* How long does the service takes on average.
 
* How long do the people wait on average.
 
* What are the odds of the line being empty.
 
* How long is the line on average.
 
  
 +
=== Definition ===
  
'''Make sure you know enough...'''
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“Rock, paper, scissors” is a child’s game in which two children use their hands to simultaneously choose paper, scissors, or rock. The nature of the payoffs is that paper beats rock, rock beats scissors, and scissors beats paper. This game has the structure that is illustrated below.
* How would you count, how long the service takes on average?
 
* Open the tool "Supositorio" and try it out.
 
  
 +
[[File:Bart1.png|400px|center]]
  
==Real world examples==
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We can note that in the game between Bart and Lisa there is '''no pure Nash equilibrium''', because the gains are directly opposed. if Bart received a big utility, Lisa received a small one. If one player knows what will do his opponent, he will directly win. That is why the game has no Nash equilibrium in '''pure strategy'''.
These are two chosen examples from real world, where the application of the queueing theory had significant impact:
 
  
===Prague public transportation===
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===Mixed strategy in Rock, paper, scissors===
Queueing theory's results are used for setting traffic lights up. Engineers use several micro-simulation software programs, which can adequately simulate the movement of cars and people. It is possible to specify all necessary attributes like number of cars per time unit, size of the road, size of the cars, type of drivers (whether they are aggressive or not...). Traffic lights can also reply to current traffic situation, and for example let public transportation vehicles go faster, by allowing them to pass the junction quicker than the other roads. Using these optimizations saved 20 seconds in average for each bus and each optimised junction it is crossing, which can lead to millions of saved Czech crowns per year. <ref name="praguemhd"/>
 
  
===Disney-world management of waiting lines===
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'''The mixed strategy in this game give you the opportunity to protect yourself against the other.'''
[[Image:disney.png|300px|thumb|right|Entrance to the Disney Park in Florida.]]
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* If Bart choose always Rock, Lisa will always play Paper and Bart will have a utility of -1 with certainty.
It wouldn't be very interesting to describe the process of optimizing and simulating of the waiting lines in Disney Parks, because it would be very similar to all such simulations. What they focus much more on is the psychological aspects of waiting. MIT professor Dick Larson says, that the main problem isn't the delay itself, but how it is experienced. “Disney has been the absolute master of this aspect of queue psychology,” says Larson. “You might wait 45 minutes for an 8-minute ride at Disney World. But they’ll make you feel like the ride has started while you’re still on line. They build excitement and provide all kinds of diversions in the queue channel.” It is possible thanks to series of chambers that the queue is passing, using LCD display's with interesting animations and other interactive tools.<ref name="stevenson">[ STEVENSON, Seth. What You Hate Most About Waiting in Line. Slate.com [online]. 2014 [cit. 2015-01-24]. Available at: http://www.slate.com/articles/business/operations/2012/06/queueing_theory_what_people_hate_most_about_waiting_in_line_.html]</ref>
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* If Bart choose Rock or Paper with a probability of 0.5, Lisa who knows, will play Paper all the time and Bart will get an expected utility of: <math> 0.5 * 0 + 0.5 * (-1) = -0.5</math>.
 +
* The best strategy for Bart is to choose a probability <math>p</math> for which Lisa is '''indifferent''' between choosing Rock, Paper or Scissors. In this case Bart will play Rock, Paper and Scissor with a probability of 1/3 and then Lisa and Bart will both have a utility of<math>(1/3) * 1 + (1/3) * 0 + (1/3) * (-1) = 0</math>.
 +
By choosing a mixed strategy for which Lisa is '''indifferent''' between choosing Rock, Paper or Scissors, Bart can increase his utility.
  
 +
== References and Sources ==
  
 +
<references>
  
'''Make sure you know enough...'''
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<ref name="john" >John Von Neumann, Oskar Morgenstern. Theory of Games and Economic Behavior. 1944. Book review: https://www.ams.org/journals/bull/1945-51-07/S0002-9904-1945-08391-8/S0002-9904-1945-08391-8.pdf.</ref>,
* Can you name three possibilities, how to make waiting in a queue more comfortable?
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<ref name="Nash">John Forbes Nash Jr, Non-cooperative games. Article 1950. https://www.jstor.org/stable/1969529?seq=1</ref>,
* Do you know some examples of comfortable waiting lines in your neighborhood?
 
  
  
==External links==
+
<references/>
http://people.brunel.ac.uk/~mastjjb/jeb/or/queue.html
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https://cs.stanford.edu/people/eroberts/courses/soco/projects/1998-99/game-theory/chicken.html
  
https://books.google.cz/books?id=ogVwgWwXJbIC&pg=PA4&lpg=PA4&dq=Andrey+Kolmogorov+queueing+theory&source=bl&ots=zgSwxk3oeb&sig=u-Xnvqa49sYYgTigidkEYwk_qr4&hl=cs&sa=X&ei=kJDEVI_dE4boywPKvYHgBg&ved=0CDUQ6AEwAg#v=onepage&q=Andrey%20Kolmogorov%20queueing%20theory&f=false
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https://en.wikipedia.org/wiki/Strategy_(game_theory)
  
http://www.win.tue.nl/~iadan/queueing.pdf
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https://saylordotorg.github.io/text_introduction-to-economic-analysis/s17-03-mixed-strategies.html
  
http://wwwhome.math.utwente.nl/~boucherierj/onderwijs/Advanced%20Queueing%20Theory/AQTsheetshc1.pdf
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https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_Behavior
  
http://www.mathworks.com/discovery/queuing-theory.html
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https://www.jstor.org/stable/1969529?seq=1
 
 
https://mattachak.wordpress.com/2014/02/28/understanding-erlang-and-queuing-theory/
 
 
 
 
 
==Resources==
 
<references/>
 

Latest revision as of 14:07, 23 January 2021

Introduction

John Forbes Nash Jr.
Nash.jpg
Born:
  • June 13, 1928

Died:

  • May 13, 2015 (aged 86)


John Forbes Nash Jr. was an American mathematician who made fundamental contributions mainly to Game theory. Nash's work has provided insight into the factors that govern chance and decision-making inside complex systems found in everyday life


  • A pure strategy is an unconditional, defined choice that a person makes in a situation or game.
  • A mixed strategy is an assignment of probability to all choices in the strategy set.

History

The concept of a mixed-strategy was introduced by John von Neumann and Oskar Morgenstern in their 1944 book The Theory of Games and Economic Behavior[1], but their analysis was restricted to the zero-sum games. They showed that a mixed-strategy Nash equilibrium will always exist for any zero-sum game with a finite set of actions.

In his famous paper in 1950, John Forbes Nash go further and proved that there is an equilibrium for every finite game and not only the zero-sum game. Now we can divide Nash equilibria into two types.

  • Pure strategy Nash equilibria are Nash equilibria where all players are playing pure strategies.
  • Mixed strategy Nash equilibria are equilibria where at least one player is playing a mixed strategy.

Definition

Mixed strategy: if the player Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle i} has Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle K} strategies Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle si1, si2, ...siK} available, a mixed strategy is a distribution of probabilities Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle pi=(pi1, pi2, ...piK)} where Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle pi1} is the probability for Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle i} to choose the strategy Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle si1} . Also because the mixed strategy is a distribution of probability there is: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle \sum_{j=1}^K pij = 1}

Nash equilibrium in mixed strategy

A mixed strategy Nash equilibrium involves at least one player playing a randomized strategy and no player being able to increase his or her expected payoff by playing an alternate strategy. A Nash equilibrium in which no player randomizes is called a pure strategy Nash equilibrium.

Matching pennies game

Definition

Matching pennies is the name for a simple game used in game theory. It is played between two players. Each player has a penny and must secretly turn the penny to heads or tails. The players then reveal their choices simultaneously. If the pennies match (both heads or both tails), then Row keeps both pennies, so wins one from Column (+1 for Row, −1 for Column). If the pennies do not match (one heads and one tails) Column keeps both pennies, so receives one from Row. The following table display the game.

Penny1.jpeg

Mixed strategy in Matching pennies

Suppose that Row believes Column plays Heads with probability Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p} . If Row plays Heads, he gets 1 with probability Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p} and –1 with probability Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle (1-p)} . His expected profit will be Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 1p - 1(1-p) = 2p - 1 } . This is summarized in Figure below "Mixed strategy in matching pennies".

Penny2.jpeg

If Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 2p - 1 > 1 - 2p} , then Row is better off, on average, playing Heads than Tails. Similarly. If, on the other hand, Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 2p - 1 = 1 - 2p} it gives Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p=1/2} . Then Row gets the same payoff no matter what Row does. In this case, Row could play Heads, could play Tails, or could flip a coin and randomize Row’s play.

Note that randomization requires equality of expected payoffs. If a player is supposed to randomize over strategy A or strategy B, then both of these strategies must produce the same expected payoff. Otherwise, the player would prefer one of them and wouldn’t play the other.

Battle of the sexes game

Definition

The battle of sexes is a two-players coordination game in the Game theory. In this game there is one man and one woman, the woman prefers going to a ballet and the man prefers going to a baseball game. The nuance here is that they both prefers to go together rather than going alone to his/her prefered activity. The game is described in the table below.

Battle1.jpg

We can highlight the two pure Nash equilibrium: (Ballet, Ballet) and (Baseball, Baseball) you can find them by using the Nash equilibrium course.

Mixed strategy in battle of the sexes

To find the mixed strategy we need to assign some probabilities to each situation. Let Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p} be the probability for the woman to go to the baseball game and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle q} the probability for the men to go to the baseball game. The computations with the expected payoffs for each situation is display below.

Battle2.jpg

The payoff calculations are the same as for the Matching pennies. For example, if the man goes to the baseball game:

  • He gets 3 when the woman goes also to the Baseball game, with a Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p} probability.
  • He gets 1 when the woman goes to the Ballet, with a Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 1-p} probability.

The expected payoff is then just the product of the probability and the payoff, for example for the first row the expected payoff is then Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 3p + 1(1-p) }

A key aspect in the mixed strategy is the indifference of choosing one or another choice for a player. In the mixed strategy game the Man has to be indifferent between going to the Baseball game and to the Ballet. This indifference or randomization between the choices of a player is expressed mathematically in our example for the man by Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 1+2p = 2-2p} which yields Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p = 1/4 } . Then if the Woman is going to the Baseball game 1/4 of the time, the Man will be willing to randomize which event he attends. Similar calculation are done for all possibilities and we multiply the probabilities:

  • Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p * q}
  • Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle q * (1-p)}
  • Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle (1-q) * p}
  • Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle (1-q) * (1-p)}

We obtain the following table:

Ballet3.jpg

We can note that 9 times over 16 the mixed strategy is (Baseball, Ballet) and it means that they are not together. Even if this mixed strategy Nash equilibrium seems to be undesirable, it's a Nash equilibrium as there is no improvement possible based on the behavior of the other party. This lack of coordination is often a feature of mixed strategy equilibrium. We can now consider another game where a failure of coordination makes more sense, the chicken game.

Chicken game

Definition

Chicken1.jpeg

The chicken game is a game in which two players drive two very fast cars towards each other from opposite ends of a long straight road. If one of them swerves before the other, he is called a chicken and will have a payoff of -1. Of course, if neither swerves, they will crash and will get both -4. This is the worst possible payoff. The best payoff is to have your opponent be the chicken, so we assign this a value 1. The last possibility is that both drivers swerve. Then, neither has less honor than the other, so this is a better option than being the chicken. We could assign a payoff matrix to this:

Chicken2.jpg

There is again two pure Nash equilibrium here: (Swerve, Don't) and (Don't, Swerve), you can find them by using the Nash equilibrium course.

Mixed strategy in Chicken game

To find the mixed strategy equilibrium we need to assign the same probabilities as for the Battle of the sexes. We can find the table of calculation below.

Chicken3.png

In order to find the mixed strategy equilibrium we have to calculate what probabilities Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p} and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle q} are required to have randomized choices. The same logic as for the Battle of the sexes is applied, we calculate: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 0p -1(1-p) = 1p -4(1-p)} and get the optimal probabilities: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p=3/4} and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle q=3/4} . We just have to multiply these probabilities to obtain the table below.

Chicken4.png

We can note that the probability of a collision (Don't, Don't) is just 1/16 in the mixed strategy equilibrium. In the chicken game the mixed strategy equilibrium is more likely than in the Battle of the sexes. The whole point in this game is to find out who will yield, which means that it isn't known in advance. This means that the mixed strategy equilibrium is the more reasonable equilibrium.

Rock, paper, scissors

Definition

“Rock, paper, scissors” is a child’s game in which two children use their hands to simultaneously choose paper, scissors, or rock. The nature of the payoffs is that paper beats rock, rock beats scissors, and scissors beats paper. This game has the structure that is illustrated below.

Bart1.png

We can note that in the game between Bart and Lisa there is no pure Nash equilibrium, because the gains are directly opposed. if Bart received a big utility, Lisa received a small one. If one player knows what will do his opponent, he will directly win. That is why the game has no Nash equilibrium in pure strategy.

Mixed strategy in Rock, paper, scissors

The mixed strategy in this game give you the opportunity to protect yourself against the other.

  • If Bart choose always Rock, Lisa will always play Paper and Bart will have a utility of -1 with certainty.
  • If Bart choose Rock or Paper with a probability of 0.5, Lisa who knows, will play Paper all the time and Bart will get an expected utility of: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 0.5 * 0 + 0.5 * (-1) = -0.5} .
  • The best strategy for Bart is to choose a probability Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p} for which Lisa is indifferent between choosing Rock, Paper or Scissors. In this case Bart will play Rock, Paper and Scissor with a probability of 1/3 and then Lisa and Bart will both have a utility ofFailed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle (1/3) * 1 + (1/3) * 0 + (1/3) * (-1) = 0} .

By choosing a mixed strategy for which Lisa is indifferent between choosing Rock, Paper or Scissors, Bart can increase his utility.

References and Sources

<references>

[1], [2],


  1. 1.0 1.1 John Von Neumann, Oskar Morgenstern. Theory of Games and Economic Behavior. 1944. Book review: https://www.ams.org/journals/bull/1945-51-07/S0002-9904-1945-08391-8/S0002-9904-1945-08391-8.pdf.
  2. John Forbes Nash Jr, Non-cooperative games. Article 1950. https://www.jstor.org/stable/1969529?seq=1

https://cs.stanford.edu/people/eroberts/courses/soco/projects/1998-99/game-theory/chicken.html

https://en.wikipedia.org/wiki/Strategy_(game_theory)

https://saylordotorg.github.io/text_introduction-to-economic-analysis/s17-03-mixed-strategies.html

https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_Behavior

https://www.jstor.org/stable/1969529?seq=1