Optimization and Control of Agent-Based Models in Biology: A Perspective.
Optimization and Control of Agent-Based Models in Biology: A Perspective.
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DOI：
10.1007/s11538-016-0225-6
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发表时间：
2017-01
影响因子：
3.5
通讯作者：
Lenhart, S.
中科院分区：
文献类型：
--
作者：
An, G.;Fitzpatrick, B. G.;Christley, S.;Federico, P.;Kanarek, A.;Neilan, R. Miller;Oremland, M.;Salinas, R.;Laubenbacher, R.;Lenhart, S.
基于主体的模型(ABM)已经成为生命科学中一种日益重要的研究模式。对于那些不能很好地理解以方程为基础的模型的系统来说，它们尤其有价值。然而，由于缺乏可用于更传统模型的数学工具，这些优势被分析和使用ABM的困难所抵消，这使得模拟成为主要方法。随着模型变得越来越大，模拟变得越来越具有挑战性。本文提出了一种新的方法来处理作业成本管理的两个数学方面，即优化和控制，并给出了几个初步的步骤，概述了如何实施这种方法。不是将ABM视为一个模型，而是将其视为实际系统的替代品。对于给定的优化或控制问题(其可能随时间变化)，替代地使用来自ABM的数据和建模框架来对代理系统进行建模，该建模框架具有现成的数学工具，例如微分方程，或者可以更容易地探索其控制策略。一旦解决了代理的模型的优化问题，然后将其提升到代理并进行测试。最后一步是将优化解从代理系统提升到实际系统。该程序以已发表的工作为例，使用两个相对简单的ABM作为演示：Sugarscape和消费者资源ABM。讨论的具体技术包括用差分方程组以及与特定控制目标相关的偏微分方程组来降维和逼近ABM。这一演示说明了在将该方法实际应用于当前和未来的复杂和大型ABM之前需要解决的非常具有挑战性的数学问题。本文概述了一个解决这些问题的研究计划。
Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.
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影响因子：
3.5
作者：
Hinkelmann, Franziska;Murrugarra, David;Laubenbacher, Reinhard
通讯作者：
Laubenbacher, Reinhard
影响因子：
1.6
作者：
Levy, Benjamin;Collins, Charles;Stiver, William
通讯作者：
Stiver, William
影响因子：
1
作者：
SCHELLING, TC
通讯作者：
SCHELLING, TC
影响因子：
82.9
作者：
Roeder, Ingo;Horn, Matthias;Loeffler, Markus
通讯作者：
Loeffler, Markus
影响因子：
3.5
作者：
Oremland, Matthew;Laubenbacher, Reinhard
通讯作者：
Laubenbacher, Reinhard