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具有社会互动的离散选择的非平衡时变解。

Non-equilibrium time-dependent solution to discrete choice with social interactions.

作者信息

Holehouse James, Pollitt Hector

机构信息

School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.

The World Bank, Washington, DC, United States of America.

出版信息

PLoS One. 2022 May 26;17(5):e0267083. doi: 10.1371/journal.pone.0267083. eCollection 2022.

Abstract

We solve the binary decision model of Brock and Durlauf (2001) in time using a method reliant on the resolvent of the master operator of the stochastic process. Our solution is valid when not at equilibrium and can be used to exemplify path-dependent behaviours of the binary decision model. The solution is computationally fast and is indistinguishable from Monte Carlo simulation. Well-known metastable effects are observed in regions of the model's parameter space where agent rationality is above a critical value, and we calculate the time scale at which equilibrium is reached using a highly accurate method based on first passage time theory. In addition to considering selfish agents, who only care to maximise their own utility, we consider altruistic agents who make decisions on the basis of maximising global utility. Curiously, we find that although altruistic agents coalesce more strongly on a particular decision, thereby increasing their utility in the short-term, they are also more prone to being subject to non-optimal metastable regimes as compared to selfish agents. The method used for this solution can be easily extended to other binary decision models, including Kirman's model of ant recruitment Kirman (1993), and under reinterpretation also provides a time-dependent solution to the mean-field Ising model. Finally, we use our time-dependent solution to construct a likelihood function that can be used on non-equilibrium data for model calibration. This is a rare finding, since often calibration in economic agent based models must be done without an explicit likelihood function. From simulated data, we show that even with a well-defined likelihood function, model calibration is difficult unless one has access to data representative of the underlying model.

摘要

我们及时求解了布罗克和杜拉夫(2001)的二元决策模型,所采用的方法依赖于随机过程主算子的预解式。我们的解在非平衡态时有效,可用于例证二元决策模型的路径依赖行为。该解计算速度快,与蒙特卡罗模拟难以区分。在模型参数空间中,当主体理性高于临界值的区域会观察到著名的亚稳效应,并且我们基于首达时间理论,使用一种高精度方法计算达到平衡的时间尺度。除了考虑只关心自身效用最大化的自私主体外,我们还考虑基于全局效用最大化进行决策的利他主体。奇怪的是,我们发现尽管利他主体在特定决策上凝聚得更强,从而在短期内提高了他们的效用,但与自私主体相比,他们也更容易陷入非最优的亚稳状态。用于此解的方法可以很容易地扩展到其他二元决策模型,包括基尔曼的蚂蚁招募模型(基尔曼,1993),并且在重新解释后还为平均场伊辛模型提供了一个时间依赖解。最后,我们使用时间依赖解构建一个似然函数,可用于对非平衡数据进行模型校准。这是一个罕见的发现,因为在基于经济主体的模型中,通常必须在没有明确似然函数的情况下进行校准。从模拟数据中我们表明,即使有一个定义明确的似然函数,除非能够获取代表基础模型的数据,否则模型校准也很困难。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b36/9135261/389f523bd022/pone.0267083.g001.jpg

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