Analyzing Interference from Static Cellular Cooperation using the Nearest Neighbour Model

The problem of base station cooperation has recently been set within the framework of Stochastic Geometry. Existing works consider that a user dynamically chooses the set of stations that cooperate for his/her service. However, this assumption often does not hold. Cooperation groups could be predefined and static, with nodes connected by fixed infrastructure. To analyse such a potential network, in this work we propose a grouping method based on proximity. It is a variation of the so called Nearest Neighbour Model. We restrict ourselves to the simplest case where only singles and pairs of base stations are allowed to be formed. For this, two new point processes are defined from the dependent thinning of a Poisson Point Process, one for the singles and one for the pairs. Structural characteristics for the two are provided, including their density, Voronoi surface, nearest neighbour, empty space and J-function. We further make use of these results to analyse their interference fields and give explicit formulas to their expected value and their Laplace transform. The results constitute a novel toolbox towards the performance evaluation of networks with static cooperation.Comment: 10 pages, 6 figures, 12 total subfigures, WIOPT-SPASWIN 201

Cooperative success in epithelial public goods games

Cancer cells obtain mutations which rely on the production of diffusible growth factors to confer a fitness benefit. These mutations can be considered cooperative, and studied as public goods games within the framework of evolutionary game theory. The population structure, benefit function and update rule all influence the evolutionary success of cooperators. We model the evolution of cooperation in epithelial cells using the Voronoi tessellation model. Unlike traditional evolutionary graph theory, this allows us to implement global updating, for which birth and death events are spatially decoupled. We compare, for a sigmoid benefit function, the conditions for cooperation to be favoured and/or beneficial for well mixed and structured populations. We find that when population structure is combined with global updating, cooperation is more successful than if there were local updating or the population were well-mixed. Interestingly, the qualitative behaviour for the well-mixed population and the Voronoi tessellation model is remarkably similar, but the latter case requires significantly lower incentives to ensure cooperation.Comment: 29 Pages, 13 Figure

Evolutionary games between epithelial cells: the impact of population structure and tissue dynamics on the success of cooperation

Cooperation is usually understood as a social phenomenon. However, it also occurs on the cellular level. A number of key mutations associated with malignancy can be considered cooperative, as they rely on the production of diffusible growth factors to confer a fitness benefit. Evolutionary game theory provides a framework for modelling the evolutionary dynamics of these cooperative mutations. This thesis uses evolutionary game theory to examine the evolutionary dynamics of cooperation within epithelial cells, which are the origin point of most cancers. In particular, we consider how the structure and dynamics of an epithelium affect cooperative success. We use the Voronoi tessellation model to represent an epithelium. This allows us much greater flexibility, compared to evolutionary graph theory models, to explore realistic dynamics for population updating. Initially, we consider a model where death and division are spatially decoupled. We analyse pairwise social dilemma games, focussing on the additive prisoner’s dilemma, and multiplayer public goods games. We calculate fixation probabilities, and conditions for cooperative success, by simulation, as well as deriving quasi-analytic results. Comparing with results for graph structured populations with spatially coupled birth and death, or well-mixed populations, we find that in general cooperation is promoted by local game play, but global competition for offspring. We then introduce a more realistic model of population updating, whereby death and division are spatially coupled as a consequence of contact inhibition. The strength of this coupling is positively correlated with the strength of contact inhibition. However, the extent to which strong spatial coupling inhibits cooperation depends on mechanical properties of the tissue

Certified Reinforcement Learning with Logic Guidance

This paper proposes the first model-free Reinforcement Learning (RL) framework to synthesise policies for unknown, and continuous-state Markov Decision Processes (MDPs), such that a given linear temporal property is satisfied. We convert the given property into a Limit Deterministic Buchi Automaton (LDBA), namely a finite-state machine expressing the property. Exploiting the structure of the LDBA, we shape a synchronous reward function on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces that probabilistically satisfy the linear temporal property. This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property. Under the assumption of finite state space, theoretical guarantees are provided on the convergence of the RL algorithm to an optimal policy, maximising the above probability. We also show that our method produces ''best available'' control policies when the logical property cannot be satisfied. In the general case of a continuous state space, we propose a neural network architecture for RL and we empirically show that the algorithm finds satisfying policies, if there exist such policies. The performance of the proposed framework is evaluated via a set of numerical examples and benchmarks, where we observe an improvement of one order of magnitude in the number of iterations required for the policy synthesis, compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782

Information Diffusion on Social Networks

In this thesis we model the diffusion of information on social networks. A game played on a specific type of graph generator, the iterated local transitivity model, is examined. We study how the dynamics of the game change as the graph grows, and the relationship between properties of the game on a graph initially and properties of the game later in the graph’s development. We show that, given certain conditions, for the iterated local transitivity model it is possible to predict the existence of a Nash equilibrium at any point in the graph’s growth. We give sufficient conditions for the existence of Nash Equilibria on star graphs, cliques and trees. We give some results on potential games on the iterated local transitivity model. Chapter 2 provides an introduction to graph properties, and describes various early graph models. Chapter 3 describes some models for online social networks, and introduces the iterated local transitivity model which we use later in the thesis. In Chapter 4 various models for games played on networks are examined. We study a model for competitive information diffusion on star graphs, cliques and trees, and we provide conditions for the existence of Nash Equilibria on these. This model for competitive information diffusion is studied in detail for the iterated local transitivity model in Chapter 5. We discuss potential games in Chapter 6 and their existence on the iterated local transitivity model. We conclude with some suggestions on how to extend and develop upon the work done in this thesis