Climate change in game theory

The study provides an overview of the application possibilities of game theory to climate change. The characteristics of games are adapted to the topics of climate and carbon. The importance of uncertainty, probability, marginal value of adaptation, common pool resources, etc. are tailored to the context of international relations and the challenge of global warming

Climate change in game theory context

The aim of this paper is to survey the game theory modelling of the behaviour of global players in mitigation and adaptation related to climate change. Three main fields are applied for the specific aspects of temperature rise: behaviour games, CPR problem and negotiation games. The game theory instruments are useful in analyzing strategies in uncertain circumstances, such as the occurrence and impacts of climate change. To analyze the international players’ relations, actions, attitude toward carbon emission, negotiation power and motives, several games are applied for the climate change in this paper. The solution is surveyed, too, for externality problem

Neural Networks and Contagion

We analyze local as well as global interaction and contagion in population games, using the formalism of neural networks. In contrast to much of the literature, a state encodes not only the frequency of play, but also the spatial pattern of play. Stochastic best response dynamics with logistic noise gives rise to a log-linear or logit response model. The stationary distribution is of the Gibbs-Boltzmann type. The long-run equilibria are the maxima of a potential function

Does binding or feedback influence myopic loss aversion : an experimental analysis

Empirical research has shown that a lower feedback frequency combined with a longer bind-ing period decreases myopia and thereby increases the willingness to invest into a risky asset. In an experimental study, we disentangle the intertwined manipulation of feedback frequency and binding period to analyze how both variables alone contribute to the change in myopia and how they interact. We find a strong effect for the length of commitment, a much less pro-nounced effect for the feedback frequency, and a strong interaction between both variables. The results have important implications for real world intertemporal decision making

Pairwise interaction on random graphs

We analyze dynamic local interaction in population games where the local interaction structure (modeled as a graph) can change over time: A stochastic process generates a random sequence of graphs. This contrasts with models where the initial interaction structure (represented by a deterministic graph or the realization of a random graph) cannot change over time

A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games

This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear