75 research outputs found
Congestion phenomena caused by matching pennies in evolutionary games
Evolutionary social dilemma games are extended by an additional
matching-pennies game that modifies the collected payoffs. In a spatial version
players are distributed on a square lattice and interact with their neighbors.
Firstly, we show that the matching-pennies game can be considered as the
microscopic force of the Red Queen effect that breaks the detailed balance and
induces eddies in the microscopic probability currents if the strategy update
is analogous to the Glauber dynamics for the kinetic Ising models. The
resulting loops in probability current breaks symmetry between the
chessboard-like arrangements of strategies via a bottleneck effect occurring
along the four-edge loops in the microscopic states. The impact of this
congestion is analogous to the application of a staggered magnetic field in the
Ising model, that is, the order-disorder critical transition is wiped out by
noise. It is illustrated that the congestion induced symmetry breaking can be
beneficial for the whole community within a certain region of parameters.Comment: 7 pages, 6 figure
Evolutionary games on graphs
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.Comment: Review, final version, 133 pages, 65 figure
Is Behavioral Economics Doomed?
It is fashionable to criticize economic theory for focusing too much on rationality and ignoring the imperfect and emotional way in which real economic decisions are reached. All of us facing the global economic crisis wonder just how rational economic men and women can be. Behavioral economics—an effort to incorporate psychological ideas into economics—has become all the rage. This book by well-known economist David K. Levine questions the idea that behavioral economics is the answer to economic problems. It explores the successes and failures of contemporary economics both inside and outside the laboratory. It then asks whether popular behavioral theories of psychological biases are solutions to the failures. It not only provides an overview of popular behavioral theories and their history, but also gives the reader the tools for scrutinizing them. Levine’s book is essential reading for students and teachers of economic theory and anyone interested in the psychology of economics
The influence of topology and information diffusion on networked game dynamics
This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent
The influence of topology and information diffusion on networked game dynamics
This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent
Crowd behavioural simulation via multi-agent reinforcement learning
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.Crowd simulation can be thought of as a group of entities interacting with one another. Traditionally,
an animated entity would require precise scripts so that it can function in a virtual
environment autonomously. Previous studies on crowd simulation have been used in real world
applications but these methods are not learning agents and are therefore unable to adapt and
change their behaviours. The state of the art crowd simulation methods include flow based, particle
and strategy based models. A reinforcement learning agent could learn how to navigate,
behave and interact in an environment without explicit design. Then a group of reinforcement
learning agents should be able to act in a way that simulates a crowd. This thesis investigates
the believability of crowd behavioural simulation via three multi-agent reinforcement learning
methods. The methods are Q-learning in multi-agent markov decision processes model, joint
state action Q-learning and joint state value iteration algorithm. The three learning methods are
able to produce believable and realistic crowd behaviours
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Evolutionary Dynamics on Complex Networks
Many complex systems such as the Internet can be represented as networks, with vertices denoting the constituent components of the systems and edges denoting the patterns of interactions among the components. In this thesis, we are interested in how the structural properties of a network, such as its average degree, degree distribution, clustering, and homophily affect the processes that take place on it. In the first part of the thesis we focus on evolutionary game theory models for studying the evolution of cooperation in a population of predominantly selfish individuals. In the second part we turn our attention to an evolutionary model of disease dynamics and the impact of vaccination on the spread of infection. Throughout the thesis we use a network as an abstraction for a population, with vertices representing individuals in the population and edges specifying who can interact with whom. We analyze our models for a well-mixed population, i.e., an infinite population with random mixing, and compare the theoretical results with those obtained from computer simulations on model and empirical networks
On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters
This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p
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