The University of Basel and the University of East Anglia researchers predicted the interactions of cancer cells using game theory.

A tumor consists of a heterogeneous population of individual cells that compete for space and nutrients against each other. However, cancer cells also cooperate in their struggle for survival by sharing molecules, such as growth factors.

Cells that do not produce growth factors themselves have a proliferation advantage because they can use the factors produced by neighboring cells without the cost of producing them. What maintains this cooperation between tumor cells remains an open question and continues to obstruct medical therapies that target tumor growth.

The Public Goods Game is part of game theory and is used in economics as a model to analyze the provision of common goods. There is an imbalance in the consumption of these goods between those that provide them and pay the production costs and those that do not pay but consume anyway — a situation that is known in economics as the free rider problem.

The researchers now applied this model to the cooperation between producing and non-producing members of a cancer cell population, in order to examine if the model is also applicable to biological processes, such as carcinogenesis. They then used experiments with pancreatic cancer cells to test their calculations. Their results were in line with the predictions of the game theory model.

“Besides the finding that biological processes can be predicted by using computer simulations, our results suggest that further work on the ‘social’ interactions among cancer cells may reveal further insight into the dynamics of cancer, and hopefully guide research toward evolutionary stable therapies,” says Gerhard Christofori, Professor at the Department of Biomedicine of the University of Basel.

Reference: https://www.sciencedaily.com/releases/2015/01/150128082001.htm

Last year, University of Pennsylvania researchers Alexander J. Stewart and Joshua B. Plotkin used game theory approach to explain why cooperation and generosity have evolved in nature.

But now they’ve come out with a somewhat less rosy view of evolution. With a new analysis of the Prisoner’s Dilemma played in a large, evolving population, they found that adding more flexibility to the game can allow selfish strategies to be more successful. The work paints a dimmer but likely more realistic view of how cooperation and selfishness balance one another in nature.

Their study examines the outcomes of the Prisoner’s Dilemma, a scenario used in the field of game theory to understand how individuals decide whether to cooperate or not. In the dilemma, if both players cooperate, they both receive a payoff. If one cooperates and the other does not, the cooperating player receives the smallest possible payoff, and the defecting player the largest. If both players do not cooperate, they both receive a payoff, but it is less than what they would gain if both had cooperated. In other words, it pays to cooperate, but it can pay even more to be selfish.

In the new investigation, Stewart and Plotkin added a new twist. Now, not only could players alter their strategy — whether or not they cooperate — but they could also vary the payoffs they receive for cooperating.

“After this study, we end up with a less sunny view of the evolution of cooperation,” Stewart said. “But it rings true that it’s not the case that evolution always tends towards happily ever after.”

Reference: https://www.sciencedaily.com/releases/2014/11/141124152556.htm

 The application of game theory helps to develop business models to manage interactions of decision makers either in a scenario of cooperative or competitive behavior for conflict resolution.

 The application of game theory inside a firm.

For a layperson, the initial concept of the game theory might look like just a strategic look to boost competitive abilities against competitors. 

Example of an application of game theory where two internal entities of a firm playing bearing in mind the purpose is to win against the competitors.

There are two players A and B.

The manager’s objective is to increase workers efficiency.  It is gain lies in the better productivity of the workers and the other hand, workers ‘ gain’ is in reduced ability assuming a lower efficiency level benefits them

The probability of opting to monitoring depends upon the gains of workers in the form of reduced efficiency will increase as well.

The possible results are four:

Win-win

Win-lose

Lose-win

Lose-lose

In the case of monitoring

If a manager doesn’t monitor and the workers reduce efficiency, the manager loses while the workers gain. In this situation for the players the workers and a loss for the manager.

In the case of monitoring

If the manager monitors but the workers still doesn’t perform well he faces colossal loss as he suffered monitoring as and reduced efficiency of the workers.

How can you develop game theory approaches?

You first asses the magnitude of the problem. Then recognize your specific business type, example; consultancy or real estate to avoid wandering. Customized solutions are only required if problems are quite peculiar and are not of general nature. However most of issues are dealt with general understanding of basic game types mentioned in the article.

Source: https://www.managerial-economics-club.com/application-of-game-theory.html

The sun has set for the famous Elwyn Berlekamp. He was a mathematician who also doubled up as a game theorist, whose work of error-correcting codes had spacecraft allowed from Voyager to Hubble Space Telescope. He died at his home at Piedmont in California after suffering from pulmonary fibrosis. He died at age 78.

According to his colleague Richard Karp, Berlekamp was highly genius in many ways. He effectively succeeded in every single task he would have himself to do. This included the areas of mathematics, computer science, game theory and even in investments. Berlekamp grew and developed in stature during the era of the digital revolution and shifted his focus on dealing with the problem encountered anytime information was being transferred from one device to another. He needed to find a solution whereby he would account for the lost data during the transfer of information. He first developed a computer algebraic algorithm that would compress information or images in a way that would allow for accurate reconstruction in case any bit of data would go missing during transmission due to other factors.

This is a man who when his error-correcting codes had not been properly implemented as deserved, he started a company which he later named Cyclotomics that ensured proper implementation of his discovery. The encoders by this company are the ones that formed the standard for space communication by NASA. These encoders are the ones which are still used in the Voyager I and II spacecraft that after the launch in 1977 are almost on the outer edge of the solar system.

the technology or error-correcting codes is what was applied to develop the sophisticated electrical subsystems and circuits that are being used in military communications not forgetting sound encoding and decoding equipment. Berlekamp’s will leave on for many generations to come of game theorists.

References

Elwyn Berlekamp, game theorist and coding pioneer, dies at 78

Researchers collaborating rom Nanyang Technology University in Singapore and the University of Sussex, Imperial College London aims to bring robots and humans together by utilizing Game Theory, mainly adaptive control and Nash equilibrium.

In using adaptive control and Nash equilibrium, researchers try to make the robots better respond to human behavior and anticipate the humans’ movements.

Due to the technology still being in the early stages of development, it is not deemed safe for usage, but researchers aim to improve this so that in the future, the robot can be intuitive enough to assist humans is sports training, physical rehabilitation, and shared driving.

Researchers published a paper today on Nature Machine Intelligence, and the paper outlined how game theory was adapted for interactions between humans and robots, and how it can be used in certain cases of physical rehabilitation, such as helping impaired stroke survivors.

This is a groundbreaking usage of game theory, as game theory is usually used for economics and maximizing one’s own gain within a certain situation. A inhibitor to the research was that the robot does not know the intentions a human subject has, and they solved this problem by developing a way for the robot to identify the human and in that way, safely interacting with the humans, also known as reactive robotic programming.

In reactive robotic programming, the robot constantly updates itself on information about the human user, and constantly makes adjustment according to that. Efficiency is a key factor here, and therefore, it is programmed to successfully undergo tasks without much effort.

“Game theory has had important impacts in economics during the last century and lead to several Nobel prizes such as Nash’s one. to apply it for human-robot interaction, it was necessary to understand how the robot can identify the human user’s control goals simultaneously to smoothly interacting with them,” Professor Etienne Burdet, chair in Human Robotics in the Department of Bioengineering at Imperial College London says.

The future for game theory and its applications to other areas is bright, where many are discovering new applications for game theory that no one has thought of yet.

Reference: https://www.sciencedaily.com/releases/2019/01/190107112953.htm

It is obvious that at any single moment when an institution or organization or even an individual has to develop incentives or rewards in order to obtain a particular behaviour or outcome from a person, sometimes the results have gone towards being very complex resulting to consequences that were unintended. This is the reason behind the application of mathematical models that are strategically mounted with the game theory which makes it possible to predict outcomes in such cases. This is according to what was shown by Tarun Sabarwal, the De-Min and Chin-Sha   Wu Associate Professor who is also the associate chair of economics at the University of Kansas.

Sabarwal went ahead to say that his work was to study decentralized and very interdependent decisions and the effects when everyone else behaves in a similar manner. He has been able to study very vital classes of behaviours and outcomes that have been naturally recorded by games that have got strategically placed compliments and those with substitutes. When applying the game that has got strategic complements, those to participate are supplied with incentives that make them move in the same direction. A good example is when people who have been depositing money in a bank run to go withdraw their money, it is obvious that any other depositor remaining will also go to withdraw his or her money before it gets depleted from the bank.

On the other hand, games supplied with strategic substitutes is where participants are provided for with incentives which will make them move in the opposite direction from each other. A good example is when we have so many people travelling toward the same place. the place will experience overcrowding that makes will make some other people travelling to the same place to use alternative routes to avoid crowding.

References

https://m.phys.org/news/2019-01-economists-game-theory=outcomes-incentives.html

A game, known as “Colonel Blotto,” has been used to analyse the potential outcomes of elections and other similar two-party conflicts since 1921 but it has been limited by its lack of a definitive solution.

Now, computer scientists from the University of Maryland, Stanford University and Microsoft Research have for the first time solved a game theory scenario that has given researchers headache for decades.

They developed an algorithm capable of solving the Colonel Blotto scenario. The algorithm could also provide political strategists, business leaders and other decision-makers with a powerful new tool for making informed choices.

Colonel Blotto pits two competitors against one another and requires each to make difficult decisions on how to deploy limited resources. Each player assigns a limited number of resources, or troops, to a number of battlefields, without the knowledge of their opponent’s strategy. Players win a given battlefield if they allocate more troops than their opponent; the player who wins the most battlefields also wins the game.

In real world, the game finds its use in scenarios such as a U.S. presidential general election. In this example, each candidate is a player; resources such as campaign staff, stump time and funding are the troops; and each state is a battlefield. The game can also apply to high-profile consumer product competition, such as the ongoing battle between Apple’s iPhone and Google’s Android mobile phone products.

Although the algorithm was limited by the large variety of possible the team overcame this by limiting the total number of possible strategies to a relative handful of representative choices.

This solution enabled the team to develop a generalized algorithm, which can now be applied to specific scenarios, such as the 2016 presidential election.

Reference: https://www.sciencedaily.com/releases/2016/02/160211190010.htm

Do you know have an idea why courtship is often protracted? Well, scientists have come up with a mathematical model of the mating game to help explain why. The study reveals that the prolonged courtship enables the female to gauge the man’s ability to sustain her and also the male to screen the female is she is fit for a mate.

The researchers engaged game theory to analyse how males and females behave towards each other in the mating game. The mathematical model considers a male and a female in a courtship encounter of unspecified duration, with the game ending when one or other party quits or the female accepts the male as a mate. The assumption is that the male is either good or bad type from the females point of view. If good, the female gets a positive payoff from mating and vice versa. Also, the male gets a positive payoff from mating with any female, though his payoff is higher if he is “good” than if he is “bad”.

The study shows that extended courtship can take place, with a good male being willing to court for longer than a bad male and the female delaying mating. In this way the duration of a male’s courtship effort carries information about his type. By delaying mating, the female is able to make some use of this information to achieve a degree of screening.—if the male has not quit it becomes increasingly probable that he is a “good” male.

Although under this compromise there remains some risk that the female will mate with the wrong type of male, she cannot completely eliminate this risk unless she decides never to mate.

Reference: https://www.sciencedaily.com/releases/2009/01/090116073603.htm