Dynamic network formation with farsighted players and limited capacities

IF 2.3 3区 经济学 Q2 ECONOMICS
Journal of Economic Dynamics & Control Pub Date : 2026-04-01 Epub Date: 2026-02-03 DOI:10.1016/j.jedc.2026.105285
Michel Grabisch , Elena Parilina , Agnieszka Rusinowska , Georges Zaccour
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引用次数: 0

Abstract

We investigate a T-stage dynamic network formation game with linear-quadratic payoffs. Players interact through network which they create as a result of their actions. We study two versions of the dynamic game and provide the equilibrium analysis. First, we assume that players sequentially propose links to others with whom they want to connect and choose the levels of contribution for their links. The players have limited total contributions or capacities for forming links at every stage which can differ among players and over time. They cannot delete links, but the principle of natural elimination of links with no contribution is adopted. Next, we assume that the players simultaneously and independently propose links to other players and have overall limited capacities for the whole game, and not for each stage. This means that every player can redistribute the capacity not only over links, but also over time. The equilibrium concept for the first version of the dynamic game is subgame perfect equilibrium, while it is the Nash equilibrium in open-loop strategies for the second version. Both models are illustrated with numerical examples.
有远见的参与者和有限能力的动态网络形成
研究了一类具有线性二次收益的t期动态网络形成博弈。玩家通过自己的行动所创造的网络进行互动。我们研究了两种版本的动态博弈,并给出了均衡分析。首先,我们假设玩家按顺序向他们想要联系的人提供链接,并为他们的链接选择贡献等级。玩家在每个阶段形成联系的总贡献或能力是有限的,这在玩家之间和时间上是不同的。它们不能删除链接,但采用自然消除无贡献链接的原则。接下来,我们假设玩家同时独立地向其他玩家提出链接,并且在整个游戏中(而不是每个阶段)具有有限的总体能力。这意味着每个玩家不仅可以通过链接重新分配容量,还可以通过时间重新分配容量。第一版动态博弈的均衡概念是子博弈完美均衡,第一版动态博弈的均衡概念是开环策略下的纳什均衡。用数值算例对两种模型进行了说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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