### Organizers

Evolutionary game theory, along with replicator equations, has been applied successfully to modeling evolution of various biological or social systems, ranging from virus infection to bacteria development, from plant succession to animal breeding, and from trace of evolutionary history to study of biodiversity and ecology. Applications in areas such as population genetics, animal behaviors, and evolution of social cooperation have especially seen great developments and impacts. In evolutionary game theory, species are considered as if they are players in a game, competing for resources, for survival, and for reproduction. A mathematical (game) model can then be established for study of any given population of competing species, and for analysis of population changes and prediction of equilibrium states and their stabilities. The theory involves such mathematical branches as game theory, optimization theory, and ordinary differential equations, and further extends to graph theory, stochastic processes, and partial differential equations as appropriate. Although emerged as a powerful mathematical tool for evolutionary and ecological modeling, the evolutionary game theory is still in the stage of early development. Theoretical issues remain to be addressed and computational methods need to be developed, for equilibrium computation, dynamic simulation, and stability analysis. Application problems are arising and yet to be investigated in many critical fields of biology such as development of energy-efficient or nutrition-rich plants and animals, analysis of human microbiome genomic data, control of infectious diseases, modeling immune-defense systems of biological species, etc. This workshop is to bring an interdisciplinary group of experts as well as biologists and mathematicians who are interested in evolutionary game modeling, to have an extensive discussion on current and future development of evolutionary game theory and applications. Topics include reviews or reports on recent theoretical or computational developments, or critical applications. The goal of the workshop is to increase communications among researchers and especially between biologists and mathematicians, in order to have a better understanding of the theory, to identify challenges and applications of the field, to promote interdisciplinary collaborations, and to accelerate future developments of the field.

### Accepted Speakers

Monday, April 27, 2015 | |
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Time | Session |

Tuesday, April 28, 2015 | |
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Wednesday, April 29, 2015 | |
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Thursday, April 30, 2015 | |
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Friday, May 1, 2015 | |
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Time | Session |

Name | Affiliation | |
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Aktipis, Athena | aktipis@asu.edu | Psychology, Arizona State University |

Alonzo, Suzanne | shalonzo@ucsc.edu | |

Amarasekare, Priyanga | amarasek@ucla.edu | Ecology and Evolutionary Biology, University of California Los Angeles |

Austin, Robert | austin@princeton.edu | Physics, Princeton University |

Belmonte, Andrew | andrew.belmonte@gmail.com | Department of Mathematics, Pennsylvania State University |

Bouskila, Amos | bouskila@bgu.ac.il | Life Sciences, Ben-Gurion Univ. of the Negev |

Broom, Mark | mark.broom.1@city.ac.uk | Department of Mathematics, City University London |

Brown, Joel | squirrel@uic.edu | Biological Sciences, University of Illinois at Chicago |

Cannings, Chris | C.Cannings@shef.ac.uk | school of maths and stats, university |

Cressman, Ross | rcressman@wlu.ca | |

Cushing, Jim | cushing@math.arizona.edu | Mathematics, University of Arizona |

Dall, Sasha | S.R.X.Dall@exeter.ac.uk | Centre for Ecology & Conservation, University of Exeter |

De Monte, Silvia | demonte@biologie.ens.fr | IBENS, IBENS, CNRS UMR 8197 |

Doering, Charles | doering@umich.edu | Physics, Mathematics, and Complex Systems, University of Michigan |

Durrett, Rick | rtd@math.duke.edu | Department of Mathematics, Duke University |

Galanthay, Ted | tgalanthay@ithaca.edu | Mathematics, Ithaca College |

Gavrilets, Sergey | sergey@tiem.utk.edu | Mathematics, University of Tennessee |

Gore, Jeff | gore@mit.edu | Physics, MIT |

Griffin, Christopher | griffinch@ieee.org | Applied Research Laboratory, The Pennsylvania State University |

Hauert, Christoph | christoph.hauert@math.ubc.ca | |

Kang, Yun | yun.kang@asu.edu | Applied Sciences and Mathematics, Arizona State University |

Kotler, Burt | kotler@bgu.ac.il | Mitrani Department of Desert Ecology, Ben-Gurion University |

Krivan, Vlastimil | vlastimil.krivan@gmail.com | Mathematics and Biomathematics, Faculty of Science, University of South Bohemia |

Lou, Yuan | lou@math.ohio-state.edu | Department of Mathematics, The Ohio State University |

Metz, Johan (=Hans) | j.a.j.metz@biology.leidenuniv.nl | Plant Ecology and Phytochemistry, Analysis and Dynamical Systems, Institute of Biology, Mathematical Institute |

Miekisz, Jacek | miekisz@mimuw.edu.pl | Institute of Applied Mathematics, University of Warsaw |

Mitchell, Bill | William.Mitchell@indstate.edu | Biology, Indiana State University |

Morris, Douglas | douglas.morris@lakeheadu.ca | Biology, Lakehead University |

Nagy, John | john.nagy@scottsdalecc.edu | Life Sciences, Scottsdale Community College |

Parvinen, Kalle | kalle.parvinen@utu.fi | Department of Mathematics and Statistics, University of Turku |

Rael, Rosalyn | Rosalyn.rael@gmail.com | Center for Bioenvironmental Research, Tulane University |

Reluga, Tim | treluga@math.psu.edu | Department of Mathematics, Pennsylvania State University |

Riechert, Susan | sriecher@utk.edu | |

Sepp�nen, Anne | anne.seppanen@utu.fi | Department of Mathematics and Statistics, University of Turku |

Sinervo, Barry | lizardrps@gmail.com | Ecology and Evolutionary Biology, UC Santa Cruz |

Stankova, Kateřina | k.stankova@maastrichtuniversity.nl | Department of Knowledge Engineering, Maastricht University |

van Gils, Jan | Jan.van.Gils@nioz.nl | Marine Ecology, NIOZ Royal Netherlands Institute for Sea Research |

Wu, Zhijun | zhijun@iastate.edu | Math, Bioinformatics, & Computational Biology, Iowa State University |

Zhou, Wen (Rick) | rickzhouwen@gmail.com | Statistics, Colorado State University |

Abstract not submitted.

Understanding principles and processes in ecology and evolution is not easy. Generating hypotheses and predictions in these disciplines is often not intuitive due, in part, to the many factors that may affect the outcomes of processes. Moreover, some of the situations involve games among various organisms that may lead to unintuitive results. Theoretical models may not provide proofs that we reached full understanding of the system, but they can generate testable hypotheses and predictions and can assist in the understanding of experimental results. Here I describe different modeling approaches we have used to investigate animal decisions in regard to foraging under the risk of predation in two systems. In the first, we interpret the escape strategy of a lizard from an avian predator with a simple decision tree model. The second system describes games among rodents and between rodents and their predators. This system begs for a game theoretic model, and two approaches will be exemplified. A static game has the advantage of simplicity. It can often be solved analytically and its results are relatively easy to interpret. Nevertheless, the simplicity has its costs in terms of realism. Some simplifications embedded in the static approach can be relaxed in a dynamic state-variable game model. These models provide refined insights and more specific predictions, taking into consideration variation in the state of the animals and its temporal dynamics.

Within the last ten years, models of evolution have begun to incorporate structured populations, including spatial structure, through the modelling of evolutionary processes on graphs (evolutionary graph theory). One limitation of this otherwise quite general framework is that interactions are restricted to pairwise ones, through the edges connecting pairs of individuals. Yet many animal interactions can involve many individuals, and theoretical models also describe such multi-player interactions. We shall discuss a more general modelling framework of interactions of structured populations, including the example of competition between territorial animals. Depending upon the behaviour concerned, we can embed the results of different evolutionary games within our structure, as occurs for pairwise games such as the Prisoner's Dilemma or the Hawk-Dove game on graphs. For a population to evolve we also need an evolutionary dynamics, and we demonstrate a birth-death dynamics for our framework. Finally we discuss some examples together with some important differences between this approach and evolutionary graph theory.

Joint work with Prof Mark Broom, City University, London.

Consider a populations of individuals with pairwise relationships between those individuals. We represent this population by a simple graph G = (V, E), where V is the set of vertices representing the individuals and E ⊆ V ∗ V representing the existing relationships. The degree of a vertex x, d(x), is the number of vertices to which x is linked. We suppose that, labeling the vertices as {x1,x2,...,xn}, vertex xi has a target ti, which represents the degree which individual xi would ideally possess.A sequence {t1,t2,...,tn} is said to be graphic if there exists a simple graph which has precisely those degrees. In that case each individual could satisfy its target. On the other hand in general there will be some deviation from the set of targets. We consider the evolution of the network with the the following Markov Chain; at time t pick a vertex i with probability 1/n (independently of previous picks), if the vertex has degree equal to its target (a Neutral) then nothing happens, if the vertex has degree less than its target (a Joiner) then an edge (i,j) is added picking a possible j with equal probabilities over those available, while if the vertex has degree greater than its target (a Breaker) an edge (i,j) is removed picking a j with equal probability from those available. A graph G has deviation d(G) = Σi ∈ {1, 2, . . . , n}|d(xi) − t(xi)|. It is proved that the set of graphs with minimal deviation is connected under the above Markov Chain, and that the chain converges to that set. The Markov Chain over that minimal set is reversible, so satisfies the complete balance condition. Some examples are given. In a further model we allow the individuals to select the change they induce when selected so that they improve their total payoff, (d(x) − t(x)), over the resulting stationary distribution compared with the current one. Variants of this model will be considered and some examples given.

How might organisms constrained by perceptual limitations or imperfect information use available information optimally in habitat selection? To begin to answer this question, we study a general ordinary differential equation model of a single species in a two-patch heterogeneous environment in which organisms have access to resource information. There exists a global evolutionarily stable strategy, which depends on the magnitude of the constraints and the heterogeneity of the resources, which leads to the ideal free distribution (IFD). When organisms pay a cost to travel between patches, this strategy is no longer evolutionarily stable, but a strategy that incorporates these costs and does not lead to the IFD is convergent stable.

Abstract not submitted.