2,829 research outputs found

    Pooling, Pricing and Trading of Risks

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    Abstract. Exchange of risks is considered here as a transferableutility, cooperative game, featuring risk averse players. Like in competitive equilibrium, a core solution is determined by shadow prices on state-dependent claims. And like in finance, no risk can properly be priced only in terms of its marginal distribution. Pricing rather depends on the pooled risk and on the convolution of individual preferences. The paper elaborates on these features, placing emphasis on the role of prices and incompleteness. Some novelties come by bringing questions about existence, computation and uniqueness of solutions to revolve around standard Lagrangian duality. Especially outlined is how repeated bilateral trade may bring about a price-supported core allocation.Keywords: cooperative game; transferable utility; core; risks; mutual insurance; contingent prices; bilateral exchange; supergradients; stochastic approximation.

    Price Uncertainty in Linear Production Situations

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    This paper analyzes linear production situations with price uncertainty, and shows that the corrresponding stochastic linear production games are totally balanced. It also shows that investment funds, where investors pool their individual capital for joint investments in financial assets, fit into this framework. For this subclass, the paper provides a procedure to construct an optimal investment portfolio. Furthermore it provides necessary and sufficient conditions for the proportional rule to result in a core-allocation.linear production;stochastic cooperative games;investment funds

    Model and Reinforcement Learning for Markov Games with Risk Preferences

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    We motivate and propose a new model for non-cooperative Markov game which considers the interactions of risk-aware players. This model characterizes the time-consistent dynamic "risk" from both stochastic state transitions (inherent to the game) and randomized mixed strategies (due to all other players). An appropriate risk-aware equilibrium concept is proposed and the existence of such equilibria is demonstrated in stationary strategies by an application of Kakutani's fixed point theorem. We further propose a simulation-based Q-learning type algorithm for risk-aware equilibrium computation. This algorithm works with a special form of minimax risk measures which can naturally be written as saddle-point stochastic optimization problems, and covers many widely investigated risk measures. Finally, the almost sure convergence of this simulation-based algorithm to an equilibrium is demonstrated under some mild conditions. Our numerical experiments on a two player queuing game validate the properties of our model and algorithm, and demonstrate their worth and applicability in real life competitive decision-making.Comment: 38 pages, 6 tables, 5 figure

    Deep Q-Learning for Nash Equilibria: Nash-DQN

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    Model-free learning for multi-agent stochastic games is an active area of research. Existing reinforcement learning algorithms, however, are often restricted to zero-sum games, and are applicable only in small state-action spaces or other simplified settings. Here, we develop a new data efficient Deep-Q-learning methodology for model-free learning of Nash equilibria for general-sum stochastic games. The algorithm uses a local linear-quadratic expansion of the stochastic game, which leads to analytically solvable optimal actions. The expansion is parametrized by deep neural networks to give it sufficient flexibility to learn the environment without the need to experience all state-action pairs. We study symmetry properties of the algorithm stemming from label-invariant stochastic games and as a proof of concept, apply our algorithm to learning optimal trading strategies in competitive electronic markets.Comment: 16 pages, 4 figure

    Afriat's Theorem and Some Extensions to Choice under Uncertainty

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    The first part of the paper reviews the methodology developed by Sydney Afriat for determining whether a finite set of price and quantity data are consistent with utility maximizing behavior by a consumer. Some extensions of his basic model to models of consumer behavior where the structure of preferences is restricted in some way are also explained. Examples of special structures are homotheticity, separability and quasilinearity of the utility function. The second half of the paper is devoted to developing Afriat type consistency tests for expected and nonexpected utility maximizing behavior.Revealed preference theory, Afriat inequalities, nonparametric approach to demand theory, homotheticity, separability, quasilinearity, testing for max
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