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Games
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Non-Imitative Dynamics in Evolutionary Game Theory
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Non-Imitative Dynamics in Evolutionary Game Theory

A special issue of Games (ISSN 2073-4336).

Deadline for manuscript submissions: closed (1 December 2019) | Viewed by 13204

Special Issue Editor

Dr. Daniele Vilone

E-Mail Website
Guest Editor
LABSS (Laboratory of Agent Based Social Simulation), Institute of Cognitive Science and Technology, National Research Council (CNR), Via Palestro 32, 00185 Rome, ItalyGrupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911 Leganés, Spain
Interests: physics of complex systems (particular in social and biological systems); game theory; interdisciplinary physics; evolutionary dynamics; biophysics and experimental psychology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Game Theory, originally conceived as the mathematical theory of decision processes, has successively provided a mathematical framework for the theory of the evolution, allowing the rising of purely analytical studies on this topic. In particular, Evolutionary Game Theory has been employed in the last few decades to resolve the conundrum of cooperation: Indeed, pro-social behaviors are generally recognized to be detrimental for individual fitness, though they are often very beneficial for a community as a whole. In order to cope with this problem, in many game-theoretical models, the evolutionary process is schematized with an imitation mechanism; that is, the agents with low fitness copy the strategies of the best performing ones with a probability given by the model's details. However, there can be several different mechanisms that can make a population evolve and explore the possible strategy evolutionary landscapes. Therefore, in this Special Issue, we aim to collect papers that contribute to theoretical research about these diverse types of processes. Such papers can be purely mathematical, focused on simulations, or both. Possible applications, not only to theoretical biology, but also to social, psychological, and economic dynamics, are highly recommended.

Dr. Daniele Vilone
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Games is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Game Theory
  • Evolution
  • Population Dynamics
  • Social Simulations
  • Algorithms
  • Econophysics

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Published Papers (3 papers)

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17 pages, 4279 KiB  
Article
Game Theoretic Modeling of Infectious Disease Transmission with Delayed Emergence of Symptoms
by Marzieh Soltanolkottabi, David Ben-Arieh and Chih-Hang Wu
Games 2020, 11(2), 20; https://doi.org/10.3390/g11020020 - 20 Apr 2020
Cited by 5 | Viewed by 4859
Abstract
Modeling the spread of infectious diseases and social responses is one method that can help public health policy makers improve the control of epidemic outbreaks and make better decisions about vaccination costs, the number of mandatory vaccinations, or investment in media efforts to [...] Read more.
Modeling the spread of infectious diseases and social responses is one method that can help public health policy makers improve the control of epidemic outbreaks and make better decisions about vaccination costs, the number of mandatory vaccinations, or investment in media efforts to inform the public of disease threats. Incubation period—the period when an individual has been exposed to a disease and could be infectious but is not yet aware of it—is one factor that can affect an epidemic outbreak, and considering it when modeling outbreaks can improve model accuracy. A change in outbreak activity can occur from the time a person becomes infected until they become aware of infection when they can transmit the disease but their social group considers them a susceptible individual and not an infectious one. This study evaluates the effect of this delay between the time of infection of an individual and the time of diagnosis of the infection (incubation period) in an epidemic outbreak. This study investigates the social dynamics of vaccination and transmission in such epidemic outbreaks, using a model of the public goods game. Full article
(This article belongs to the Special Issue Non-Imitative Dynamics in Evolutionary Game Theory)
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12 pages, 2036 KiB  
Article
Dynamics of Strategy Distributions in a One-Dimensional Continuous Trait Space for Games with a Quadratic Payoff Function
by Georgiy Karev
Games 2020, 11(1), 14; https://doi.org/10.3390/g11010014 - 2 Mar 2020
Viewed by 3545
Abstract
Evolution of distribution of strategies in game theory is an interesting question that has been studied only for specific cases. Here I develop a general method to extend analysis of the evolution of continuous strategy distributions given a quadratic payoff function for any [...] Read more.
Evolution of distribution of strategies in game theory is an interesting question that has been studied only for specific cases. Here I develop a general method to extend analysis of the evolution of continuous strategy distributions given a quadratic payoff function for any initial distribution in order to answer the following question—given the initial distribution of strategies in a game, how will it evolve over time? I look at several specific examples, including normal distribution on the entire line, normal truncated distribution, as well as exponential and uniform distributions. I show that in the case of a negative quadratic term of the payoff function, regardless of the initial distribution, the current distribution of strategies becomes normal, full or truncated, and it tends to a distribution concentrated in a single point so that the limit state of the population is monomorphic. In the case of a positive quadratic term, the limit state of the population may be dimorphic. The developed method can now be applied to a broad class of questions pertaining to evolution of strategies in games with different payoff functions and different initial distributions. Full article
(This article belongs to the Special Issue Non-Imitative Dynamics in Evolutionary Game Theory)
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18 pages, 5001 KiB  
Article
Evolution of Cooperation for Multiple Mutant Configurations on All Regular Graphs with N ≤ 14 Players
by Hendrik Richter
Games 2020, 11(1), 12; https://doi.org/10.3390/g11010012 - 17 Feb 2020
Cited by 1 | Viewed by 4190
Abstract
We study the emergence of cooperation in structured populations with any arrangement of cooperators and defectors on the evolutionary graph. In a computational approach using structure coefficients defined for configurations describing such arrangements of any number of mutants, we provide results for weak [...] Read more.
We study the emergence of cooperation in structured populations with any arrangement of cooperators and defectors on the evolutionary graph. In a computational approach using structure coefficients defined for configurations describing such arrangements of any number of mutants, we provide results for weak selection to favor cooperation over defection on any regular graph with N ≤ 14 vertices. Furthermore, the properties of graphs that particularly promote cooperation are analyzed. It is shown that the number of graph cycles of a certain length is a good predictor for the values of the structure coefficient, and thus a tendency to favor cooperation. Another property of particularly cooperation-promoting regular graphs with a low degree is that they are structured to have blocks with clusters of mutants that are connected by cut vertices and/or hinge vertices. Full article
(This article belongs to the Special Issue Non-Imitative Dynamics in Evolutionary Game Theory)
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