scholarly journals Why Darwin would have loved evolutionary game theory

2016 ◽  
Vol 283 (1838) ◽  
pp. 20160847 ◽  
Author(s):  
Joel S. Brown
Keyword(s):  
Game Theory ◽  
Natural Selection ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Scientific Basis ◽  
Matrix Games ◽  
Form And Function ◽  
Heritable Phenotype ◽  
And Function

Humans have marvelled at the fit of form and function, the way organisms' traits seem remarkably suited to their lifestyles and ecologies. While natural selection provides the scientific basis for the fit of form and function, Darwin found certain adaptations vexing or particularly intriguing: sex ratios, sexual selection and altruism. The logic behind these adaptations resides in frequency-dependent selection where the value of a given heritable phenotype (i.e. strategy) to an individual depends upon the strategies of others. Game theory is a branch of mathematics that is uniquely suited to solving such puzzles. While game theoretic thinking enters into Darwin's arguments and those of evolutionists through much of the twentieth century, the tools of evolutionary game theory were not available to Darwin or most evolutionists until the 1970s, and its full scope has only unfolded in the last three decades. As a consequence, game theory is applied and appreciated rather spottily. Game theory not only applies to matrix games and social games, it also applies to speciation, macroevolution and perhaps even to cancer. I assert that life and natural selection are a game, and that game theory is the appropriate logic for framing and understanding adaptations. Its scope can include behaviours within species, state-dependent strategies (such as male, female and so much more), speciation and coevolution, and expands beyond microevolution to macroevolution. Game theory clarifies aspects of ecological and evolutionary stability in ways useful to understanding eco-evolutionary dynamics, niche construction and ecosystem engineering. In short, I would like to think that Darwin would have found game theory uniquely useful for his theory of natural selection. Let us see why this is so.

scholarly journals Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations

2014 ◽  
Vol 4 (4) ◽  
pp. 20140037 ◽  
Author(s):  
David Liao ◽  
Thea D. Tlsty
Keyword(s):  
Game Theory ◽  
Population Dynamics ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Treatment Strategies ◽  
Analysis Method ◽  
Analysis Techniques ◽  
Game Theoretic ◽  
And Control

Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.

scholarly journals Fisheries management as a Stackelberg Evolutionary Game: Finding an evolutionarily enlightened strategy

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245255
Author(s):  
Monica Salvioli ◽  
Johan Dubbeldam ◽  
Kateřina Staňková ◽  
Joel S. Brown
Keyword(s):  
Game Theory ◽  
Fisheries Management ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Fish Population ◽  
Stackelberg Equilibrium ◽  
Negative Effects ◽  
Evolutionary Changes ◽  
Body Sizes

Fish populations subject to heavy exploitation are expected to evolve over time smaller average body sizes. We introduce Stackelberg evolutionary game theory to show how fisheries management should be adjusted to mitigate the potential negative effects of such evolutionary changes. We present the game of a fisheries manager versus a fish population, where the former adjusts the harvesting rate and the net size to maximize profit, while the latter responds by evolving the size at maturation to maximize the fitness. We analyze three strategies: i) ecologically enlightened (leading to a Nash equilibrium in game-theoretic terms); ii) evolutionarily enlightened (leading to a Stackelberg equilibrium) and iii) domestication (leading to team optimum) and the corresponding outcomes for both the fisheries manager and the fish. Domestication results in the largest size for the fish and the highest profit for the manager. With the Nash approach the manager tends to adopt a high harvesting rate and a small net size that eventually leads to smaller fish. With the Stackelberg approach the manager selects a bigger net size and scales back the harvesting rate, which lead to a bigger fish size and a higher profit. Overall, our results encourage managers to take the fish evolutionary dynamics into account. Moreover, we advocate for the use of Stackelberg evolutionary game theory as a tool for providing insights into the eco-evolutionary consequences of exploiting evolving resources.

Corpus-Based Intention Recognition in Cooperation Dilemmas

2012 ◽  
Vol 18 (4) ◽  
pp. 365-383 ◽  
Author(s):  
The Anh Han ◽  
Luís Moniz Pereira ◽  
Francisco C. Santos
Keyword(s):  
Game Theory ◽  
Natural Selection ◽  
Social Interactions ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Window Of Opportunity ◽  
Intention Recognition ◽  
New Strategy ◽  
Cognitive Cost

Intention recognition is ubiquitous in most social interactions among humans and other primates. Despite this, the role of intention recognition in the emergence of cooperative actions remains elusive. Resorting to the tools of evolutionary game theory, herein we describe a computational model showing how intention recognition coevolves with cooperation in populations of self-regarding individuals. By equipping some individuals with the capacity of assessing the intentions of others in the course of a prototypical dilemma of cooperation—the repeated prisoner's dilemma—we show how intention recognition is favored by natural selection, opening a window of opportunity for cooperation to thrive. We introduce a new strategy (IR) that is able to assign an intention to the actions of opponents, on the basis of an acquired corpus consisting of possible plans achieving that intention, as well as to then make decisions on the basis of such recognized intentions. The success of IR is grounded on the free exploitation of unconditional cooperators while remaining robust against unconditional defectors. In addition, we show how intention recognizers do indeed prevail against the best-known successful strategies of iterated dilemmas of cooperation, even in the presence of errors and reduction of fitness associated with a small cognitive cost for performing intention recognition.

scholarly journals The contribution of evolutionary game theory to understanding and treating cancer

2020 ◽  
Author(s):  
Benjamin Wölfl ◽  
Hedy te Rietmole ◽  
Monica Salvioli ◽  
Frank Thuijsman ◽  
Joel S. Brown ◽  
...  
Keyword(s):  
Game Theory ◽  
Cancer Progression ◽  
Evolutionary Game Theory ◽  
Cancer Biology ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Great Promise ◽  
Full Potential ◽  
Theory Approach ◽  
Game Theoretic

AbstractEvolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather, inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. We discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with an evolutionary game theory approach has medically useful implications that can inform and create a lockstep between empirical findings, and mathematical modeling. We suggest that cancer progression is an evolutionary game and needs to be viewed as such.

scholarly journals Evolving Ecological Social Dilemmas: A Spatial Individual-Based Model for the Evolution of Cooperation with a Minimal Number of Parameters

2007 ◽  
Vol 2007 ◽  
pp. 1-5
Author(s):  
H. Fort
Keyword(s):  
Game Theory ◽  
Natural Selection ◽  
Evolutionary Game Theory ◽  
Spatial Model ◽  
Evolutionary Game ◽  
Social Dilemmas ◽  
Payoff Matrix ◽  
Evolution Of Cooperation ◽  
Full Version ◽  
Payoff Matrices

Cooperation, both intraspecific and interspecific, is a well-documented phenomenon in nature that is not well understood. Evolutionary game theory is a powerful tool to approach this problem. However, it has important limitations. First, very often it is not obvious which game is more appropriate to use. Second, in general, identical payoff matrices are assumed for all players, a situation that is highly unlikely in nature. Third, slight changes in these payoff values can dramatically alter the outcomes. Here, I use an evolutionary spatial model in which players do not have a universal payoff matrix, so no payoff parameters are required. Instead, each is equipped with random values for the payoffs, fulfilling the constraints that define the game(s). These payoff matrices evolve by natural selection. Two versions of this model are studied. First is a simpler one, with just one evolving payoff. Second is the “full” version, with all the four payoffs evolving. The fraction of cooperator agents converges in both versions to nonzero values. In the case of the full version, the initial heterogeneity disappears and the selected game is the “Stag Hunt.”

scholarly journals The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer

2021 ◽  
Author(s):  
Benjamin Wölfl ◽  
Hedy te Rietmole ◽  
Monica Salvioli ◽  
Artem Kaznatcheev ◽  
Frank Thuijsman ◽  
...  
Keyword(s):  
Game Theory ◽  
Cancer Progression ◽  
Evolutionary Game Theory ◽  
Cancer Biology ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Great Promise ◽  
Full Potential ◽  
Biological Interactions ◽  
Game Theoretic

AbstractEvolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

scholarly journals Evolutionary Game Theory: Darwinian Dynamics and the G Function Approach

Games ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 72
Author(s):  
Anuraag Bukkuri ◽  
Joel S. Brown
Keyword(s):  
Game Theory ◽  
Drug Resistance ◽  
Population Dynamics ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Function Model ◽  
Predator Prey ◽  
Evolutionary Stable Strategies ◽  
Over Time

Classical evolutionary game theory allows one to analyze the population dynamics of interacting individuals playing different strategies (broadly defined) in a population. To expand the scope of this framework to allow us to examine the evolution of these individuals’ strategies over time, we present the idea of a fitness-generating (G) function. Under this model, we can simultaneously consider population (ecological) and strategy (evolutionary) dynamics. In this paper, we briefly outline the differences between game theory and classical evolutionary game theory. We then introduce the G function framework, deriving the model from fundamental biological principles. We introduce the concept of a G-function species, explain the process of modeling with G functions, and define the conditions for evolutionary stable strategies (ESS). We conclude by presenting expository examples of G function model construction and simulations in the context of predator–prey dynamics and the evolution of drug resistance in cancer.

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