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地理研究 >

2017 , Vol. 36 >Issue 10: 1925 - 1935

DOI: https://doi.org/10.11821/dlyj201710009

观点与争鸣

基于主体模型的人地系统复杂性研究

  • 翟瑞雪 , 1, 2 ,
  • 戴尔阜 , 1
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  • 1. 中国科学院地理科学与资源研究所,中国科学院陆地表层格局与模拟重点实验室,北京 100101
  • 2. 浙江环科环境咨询有限公司,杭州 310000
通讯作者：戴尔阜（1972- ）,男,甘肃平凉人,研究员,博士生导师,主要从事土地利用和气候变化对生态系统影响研究。E-mail:

作者简介：翟瑞雪（1990- ）,女,山东泰安人,硕士,主要从事土地利用变化研究。E-mail:

收稿日期: 2017-04-09

  要求修回日期: 2017-07-15

  网络出版日期: 2017-10-20

基金资助

国家重点基础研究发展计划（973计划）项目（2015CB452702）

国家自然科学基金项目（41571098,41371196,41530749）

中国科学院重点部署项目（ZDRW-ZS-2016-6-4）

中国科学院科技战略咨询研究院重大咨询项目（Y02015003）

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Research on the complexity of man-land system based on agent-based models

  • ZHAI Ruixue , 1, 2 ,
  • DAI Erfu , 1
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  • 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. Zhejiang Huanke Environment Consultancy Company, Hangzhou 310000, China

Received date: 2017-04-09

  Request revised date: 2017-07-15

  Online published: 2017-10-20

Copyright

《地理研究》编辑部
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摘要

人地系统是人类社会与地理环境的耦合系统,属于典型的复杂系统。基于主体的模型（Agent-based models,ABM）作为研究复杂系统的重要工具,可以为人地系统研究提供新的方法支持。与传统模型不同,基于主体的模型更多的关注“人”的研究,注重体现人类主体行为决策在人地系统变化中的重要作用,并以一种空间显性的方式来表现人类活动对地理环境的动态影响。在总结回顾人地系统理论的基础上,介绍ABM的基本理论与技术,综述ABM在生态过程、生态系统管理和土地利用/覆被变化三个方向的应用及研究现状,分析概括ABM模拟中存在的主要问题,并对ABM未来发展的方向进行探讨。

关键词： 人地系统; 基于主体的模型; 复杂性; 主体

本文引用格式

翟瑞雪 , 戴尔阜 . 基于主体模型的人地系统复杂性研究[J]. 地理研究, 2017 , 36(10) : 1925 -1935 . DOI: 10.11821/dlyj201710009

Abstract

As a typical complex system, man-land system is known as a coupled human and natural system. The complexity of man-land system can be divided into three critical dimensions - temporal complexity, spatial complexity and decision-making complexity. In modeling complex systems, traditional models are deficient in displaying data in multiple dimensions, and thus require additional research. Recent studies suggest that the agent-based models (ABM) would provide insights on exploratory analysis and serve as one of the key tools for complex system studies. In contrast to traditional models, ABM pays more attention to the study of 'people', focusing on assessing the influence of human activities on the environment, and can reflect it in a spatially explicit way. The models usually contain three parts: (1) environmental layer, which is composed of natural/social attribute such as terrain slope, land price and traffic condition; (2) agent layer, consisting of one or more agent types with specific attributes; (3) behavior rule, standardizing the mutual consultation and decision-making mode of agents. ABM adopts a 'bottom-up' approach by applying the relevant actors and decisions at the micro-level to producing an observable macro-phenomenon, and displaying high complexity values in three dimensions. Currently, ABM approaches are widely used to model human-environment interactions in various fields, including transportation, financial markets and tourism management. After the basic principles of agent-based simulation are briefly introduced, this paper reviews the application of ABM in ecological process, ecological resource management and land use/cover change. However, as a new method, ABM is still at an exploratory stage, faced by issues including replication potential, empirical parameterization and model validation, individual decision making, and integration with other models. Although there are many challenges, the recent developments reflect an encouraging trend towards developing a new methodology for dynamic spatial modeling of human-environment interactions. The outlook of ABM is promising.

Key words： man-land system; agent-based models; complexity; agent

主旨聚焦：聚焦于人地系统复杂性,介绍主体模型的基本理论与技术,勾勒相关领域研究进展;认为主体模型应用前景良好,建议拓展数据挖掘手段,加强跨学科集成研究。

1 引言

近百年来人类活动改变了接近一半的陆地表面[1],并与生物多样性衰减、气候变暖、土地荒漠化等全球环境问题直接相关,迫切需要开展多因子驱动下的地表格局时空动态变化研究。目前对于人地关系的研究,多是单纯关注于“地”或聚焦于“人”的研究,对地理环境单要素格局（如水、土、碳等）或是人类社会行为（如区位论、旅游地理、人口学等）研究的比较深入[2-4]。人地系统本质是人类社会与地理环境的耦合系统,复杂性在以往研究中未得到充分体现,因此需要将人类行为决策纳入到地表格局变化研究中,以弥补以往研究的不足。
近些年来,随着复杂性科学和人工智能的发展,国内外学者开始采用基于主体的模型（Agent-based Models,ABM）来研究人地关系的复杂性问题[5-8]。ABM充分考虑了人类的行为和决策,从空间个体行为的微观角度入手,通过观察大量微观个体间的交互作用与协调,“自下而上”地研究复杂空间系统的发展变化。这种优势使得主体模型很适合用于模拟地理学中人类活动对环境的感知及基于此种感知的决策和对环境的影响,即人地关系中人与环境的交互作用[6],为人地系统的复杂性研究提供了新的研究思路。本文在回顾人地系统理论的基础上,介绍ABM的基本理论与技术,综述ABM在生态过程、生态系统管理和土地利用/覆被变化三个方向的应用及研究现状,最后,分析总结目前ABM模拟中存在的主要问题,并对ABM未来发展的方向进行探讨。

2 人地系统与复杂性

复杂性科学（Science of Complexity）兴起于20世纪80年代,它与系统学关系非常密切,系统学的许多研究成果为复杂性研究奠定了基础[7]。复杂性科学指出,复杂系统由无数微观主体（Agent）构成,微观主体之间的相互作用随着时间的推移可以在系统宏观尺度上涌现出新的结构和功能,局部规则的转换可以导致系统宏观全局的变化[9]。
复杂系统具有以下五个基本特征：首先是非线性与动态性,复杂系统是非线性的动态系统,传统的叠加原理无效,系统处于不断的发展变化之中;二是多层级性,即有多层次的子系统与子子系统,层级越高,越表现出宏观特征,层级越低,越表现微观特性;三是开放性,系统内部与外部相互联系、相互作用,进行物质、能量和信息的交换;四是涌现性,是指大量微观主体组成系统后,出现了系统组成前单个微观主体所不具有的性质,只有系统在低层次构成高层次时才表现出来,这是微观主体之间非线性相互作用的结果;五是自适应性,在系统宏观特性的要求下,每一微观主体通过自我管理,实现与环境条件间的最优适应[10]。
人地系统是地球表层上人类活动与地理环境相互作用形成的开放的复杂巨系统[11],符合复杂系统以上所有的基本特征,属于典型的复杂系统。从地理环境层面看,地理环境是由地表岩石、水文、植被、土壤、气候等自然因素构成的一个统一整体,这些因子之间相互作用、相互依存,过程纷繁复杂;从人类活动层面看,人类活动表现为自然资源开发、城镇化、生态保护等行为,人类活动的影响因子及机制是非常复杂和多样化的,由人组成的社会系统表现出极大的复杂性[12,13]。人类活动参与、干预地质大循环和生物物质小循环,共同组成了人地系统的复杂性[14]。
近几十年来,人地系统领域已经取得了大量的研究成果,但是,在人地系统研究中,自然与人文研究相互隔离,没有在总体上揭示“人—地”系统内部的相互作用的函数关系,这与人地系统本身的复杂性和综合性不符[15,16]。人地系统复杂性可以视为时间复杂性、空间复杂性、人类决策复杂性三个维度的综合,各类模型可以视为三个维度不同体现程度的排列组合[17]（图1）。以往的模型多是关注于单维度（X,Y,Z）的复杂性研究,对多维度的复杂性刻画不足。例如经验统计模型,通常是基于回归技术和时间序列方法来建立数学模型,呈现时间复杂性（X）而忽略了空间复杂性与决策复杂性;GIS空间数据模型,通过赋予地理单元不同的属性特征,进行空间复杂性（Y）的研究,然而多数无法模拟时间复杂性与决策复杂性;经济计量模型通过描述、概括某个真实经济系统的数量特征,可以揭示出该经济现象的内涵,较好的呈现了人类决策复杂性（Z）,但对空间复杂性与时间复杂性的体现不足。
Fig. 1 Three-dimensional complexity framework of man-land system

图1 人地系统复杂性的三维度
*代表人地系统模拟的最终目标

随着复杂性科学的发展,元胞自动机模型（Cellular Automata,CA）、ABM等方法的出现与应用,为人地系统研究带来了一个新的切入视角,打破了原来线性、均衡、简单还原的研究范式,可以更好地解释人地系统中复杂性问题[18]。CA模型在20世纪60年代开始应用于地理学研究领域,它具有强大的空间运算能力,把自然空间化作规则格网,散布在规则格网中的每一元胞如时间、空间等均取有限的离散状态,并依据设定的局部规则作同步更新,大量元胞之间的交互作用导致系统的动态演化[19]。CA模型由于其对时间复杂性和空间复杂性良好的表达性,在城市和区域土地利用变化、交通领域等方面都得到了广泛的应用。但CA模型主要着眼于单元的局部相互作用,元胞的含义过于简单,在转换规则的制定上存在缺陷,难以模拟人地系统中的人类行为决策。而ABM正是关注“人”的作用,可以弥补CA模型的不足。

3 ABM的基本理论与技术

与CA模型关注景观和转换规则不同,ABM研究的对象是人类活动。在ABM中,主体是存在于一定的动态环境并与环境相互作用的个体,具备一定的属性（表1）。主体不仅具备自身问题求解能力和行为目标,而且能够通过感知其他主体与之相互作用,并根据一定的行为规则做出决策[20]。在一个简单的系统中,主体通常具备自治性、反应性、主动性和交互性四种基本属性,除了四种基本属性外,主体可能还会有更高级的属性,比如移动性与性格属性,复杂的ABM有时候还会嵌入人工神经网络、进化算法或者其他算法来体现主体的学习与适应能力（表1）。面对复杂的现实问题,多数情况下仅靠单个主体是无法完成模拟的,而是多个主体相互作用组成的系统,主体之间能够相互协作,以达到系统共同的目标[19]。
Tab. 1 The characteristics of agents

表1 主体的属性

主体的属性 属性的含义
自治性

反应性
主动性

交互性

持续性
移动性
适应性

性格		 主体能够根据自己内部状态与感知的外部环境信息,控制自身的行为
主体可以感知外部环境的变化并作出反应
主体不只是简单的响应环境,还可以采取主动行为,表现出目标驱动特性
主体在一定的环境下通过某种方式与其他主体进行相互作用
主体行为在时空上保持一致
主体可以从环境中的一个位置移动到另一个位置
主体具备学习能力,能根据过往的经验修正自己的行为
主体是理性或者不完全理性
多主体系统通常由以下三部分组成（图2）：① 环境层,是主体所处的自然环境和社会环境,对主体决策具有重要影响;在模型模拟中,通常用规则化的格网表达,每个象元赋予相应的自然社会属性。② 主体层,主体层可以是一类主体,也可以由多类主体组成;主体活动于环境层中,具备一定的属性,能够感知环境层的信息（如环境层的自然社会属性）,做出决策并导致环境层的改变。③ 行为规则,主体之间通过系统设定的行为规则相互联系,相互作用。以土地利用变化为例,环境层即土地层,具备地价、土壤类型、坡度、土地利用类型等自然社会属性;主体层即土地管理者,主体通过综合个人的知识、价值取向、邻域的土地管理方式、外部政策等因素,对所属土地单元进行评估,做出土地利用决策,改变环境层中土地单元的利用方式,从而实现对土地利用变化的模拟。最终模型中主体行为决策、主体之间以及主体与环境相互作用的展现程度与建模水平有关[21]。ABM正是通过模拟微观尺度主体的行为决策实现对宏观尺度环境层的模拟,完成从“单一的自身研究向整体的综合研究”的转换,为人地系统的复杂性研究开辟了一个新的途径[22],实现了时间复杂性、空间复杂性、人类决策复杂性的三重表达。
Fig. 2 Multi-agent system

图2 多主体系统

相比于技术工具,ABM更类似思想方法。传统的建模方法往往研究的是系统的宏观现象,通过单个层次去研究系统,通常以一组数学方程作为起点,描述宏观系统属性之间的关系[23]。而ABM打破了这一思想壁垒,ABM从系统的内部——主体入手去观察系统的外在表现,改变了看待系统的角度,主体的行为决定着全局变化,而全局变化反过来又改变了主体决策的环境,进而又对主体的下一轮决策产生影响,形成了一个相互作用的“主体—环境”耦合系统。ABM通过定义主体的各项静态属性及活动能力来模拟主体之间的相互作用、主体对环境的感知及主体的最终决策表达宏观的系统演化过程。与传统模型相比,ABM的优点主要体现在以下三个方面：
一是可以表达“涌现”现象。“涌现”现象是复杂性研究的基础之一。ABM的一大特点就是可以表达“涌现”现象,尤其适用于非正态分布复杂现象的建模和模拟。“涌现”现象是指复杂系统中的个体根据各自的行为规则相互作用以非线性的方式所涌现出的全局行为。ABM根据研究问题所需要的系统局部细节,明确主体的类型、行为规则进行建模,当模型运转时,就能够“自下而上”的表达“涌现”现象[24]。“涌现”现象的出现意味着跨尺度研究,可能涉及从多个层次上研究主体之间的交互作用。例如在基于主体的土地利用变化模型中,由于土地利用变化牵扯到多方利益相关者,主体可能横跨多个层次,包括个体主体、家庭主体（由若干个体主体组成）、社区主体（由若干个家庭主体组成）乃至地方政府、国家政府。因此,ABM的这种结构决定了学者们可以用它来表达复杂系统的“涌现”现象。
二是可以模拟人类行为决策。早期模型研究多数属于“人”与“地”的隔离研究,在地理环境变化过程中,都是自然环境因素起到决定性作用,相反,能动的人类主体的决策作用被忽略。这些模型由于方法本身的限制,难以模拟人类的行为决策,也很难研究分析社会体制及宏观政策等对人类主体行为决策的影响。ABM正是关注“人”的作用,通过引入主体层,为主体设定一定的行为规则,从而做出最终决策。人类行为决策与生物物理过程发生在共同的地理空间,因此可以整合生物物理模型与ABM,模拟人类-自然耦合系统,更好地表达人地系统的复杂性[25]。
三是模拟的灵活性。ABM的灵活性体现在多个方面。首先是建模方法上的灵活性。由于ABM模拟的复杂性,使得研究人员具有充分的灵活性,可以根据自行需要以及研究对象进行建模,在建模方面有很大的自主性,不过这也意味着ABM没有统一的建模方式,不利于跨区域比较研究,也提高了方法的使用门槛,使得方法难以普及。另一方面,由于复杂系统的初值敏感性[10],研究人员可以通过改变模型细节或输入条件进行模型的调试,观察对结果的影响,校正模型：在微观层次上,可以通过增减主体,或调整不同主体参数实现;在全局结构上,可以改变总人口、技术水平等外部环境条件。

4 ABM的主要应用领域

目前,ABM在土地利用/覆被变化、交通仿真、金融市场、生态过程、生态系统管理、旅游管理和流行病学等领域得到了广泛应用[26-30]。在社会学中,ABM被用来模拟灾后管理、舆论传播、人口迁移等现象;在经济学中,基于主体计算经济学（Agent-based Computation Economics,ACE）被提出,并已成为实验经济学的一个重要分支[31]。这里主要介绍下自然科学领域的应用方向——生态过程、生态系统管理和土地利用/覆被变化三个方面。

4.1 生态过程

ABM在生态过程中的应用表现为基于个体的模型（Individual-based models,IBM）,研究非人类实体（多是动植物）与生态系统的相互作用关系[32]。IBM出现于20世纪70年代,直到1988年才逐渐成为生态模型的一种方法[33],多用于生物集群行为、捕食竞争、环境变化响应、生殖行为、早期生活史等方向的模拟。自然环境中的生物个体通过相互间的非线性作用,自组织的形成一个相对稳定的系统,并且同类生物个体的行为可以观测到一定的重复性,这种特性使得IBM十分适合用于生物群体行为的模拟。IBM以个体为研究对象,通过描述个体的特征如年龄、尺寸的变化及其行为如运动、捕食和迁移等,模拟生物数量的动态变化及与自然因素之间的关系。Pitt等将犬科动物的领土权和社会结构纳入IBM模型,模拟了土狼种群动态变化情况[34];陈求稳等以水库优势种草鱼和鲫鱼为主体层,以水环境（包含水深、水温、溶解氧、流速等属性）为环境层,通过设定鱼类生长及运动的行为规则,模拟了自然条件和水库调节作用下河道水环境条件的改变对鱼类生长和分布的影响[35]。李曰嵩等采用标准化了的基于个体模型IBM的描述流程ODD（Overview,Design concepts,Details）,模拟了日本鲭的早期生活史,并将IBM与FVCOM物理模型（模拟三维环境场）耦合,模拟了日本鲭产卵深度变动对鱼卵仔幼鱼的输运分布以及存活率的影响[36]。
IBM能够较为真实的重现个体行为和相互作用,追踪个体的生长、生殖、集群等行为[37],在个体和种群水平上模拟并预测动物可能潜在的种群行为,目前在种群生态学方面取得了很多研究成果,尤其是鱼类种群行为方面成果最多,但在中国的相关研究相对较少。

4.2 生态系统管理

在生态系统管理领域,多运用ABM探讨“公共池塘”资源（Common Pool Resources,CPR）管理问题,其研究方向主要包括水资源、林业资源、渔业资源等的管理[38],研究焦点集中于政策分析及评估。通常主体层为资源利益相关方,可以是高级实体或社会组织,如政府、农村社区,也可以是城市居民、农村居民,主体层基于不同的利益目标,制定相应的决策规则,实现对生态系统管理的模拟。Kennedy等在东非某地区对不同牧民部落对放牧地点及水资源的冲突进行了研究,结果表明,冲突的结束以落后部落退出竞争为终点,而且公共资源越稀缺,竞争越激烈,落后部落的退出速度越快[39]。除了生态资源管理,也有学者将ABM应用到生态系统服务研究中。Balbi等人以农户为主体,分析了分化、合作、竞争等行为对粮食供给服务功能的影响[40];潘里虎等构建了农牧户尺度的多主体模型,从农户、牧户的职业状态转换、生态系统服务供给与消耗NPP值出发设计主体行为规则,实现了对生态服务合理消耗的模拟,为生态系统服务的可视化研究提供了一种思路[41]。
现实中,政策试行过程一般会耗费大量时间金钱,有时甚至会涉及到伦理问题,这种情况下,ABM提供了一个很好的解决途径。值得注意的是,ABM模型中的主体依据经济人假设,其行为是理性的,并追求利益（利润、税收等）的最大化。而人类的行为决策会受到许多外界因素的影响或限制,比如信息的获取能力、自身认知的能力,因此这种差异会导致模型结果的不确定性,在土地利用/覆被变化的应用中也同样存在这个 问题。

4.3 土地利用/覆被变化

土地利用变化是人类行为决策的结果,因此十分适合运用ABM进行研究。最早将ABM用于土地利用/覆被变化的研究可以追溯到1993年,Lansing等在印度尼西亚通过模拟农民灌溉习惯而建立的土地利用/覆被变化模型[42]。之后主体模型开始广泛引入土地利用变化研究[6],模型也从早期的仅关注单类主体发展到对多类土地利用主体的探讨[43]。对于主体模型在土地利用变化研究中的应用,根据研究区域差异,可以分为城市土地利用变化模拟、农业土地利用变化模拟两个方面。总体来看,农业土地利用变化模拟的研究成果居多。但由于中国仍处于城市快速发展阶段,城市土地利用变化显著,因此国内研究成果主要以城市为主,以农村、农民为研究对象的成果还很少,对于中国最为广大的农村地区的土地利用变化缺乏合理的关注。
目前,ABM在城市土地利用应用领域主要是城市扩张、区位选择模拟。模型通常有居民、企业、政府多类主体。基于区位论,距离作为描述性指标是构建ABM主体行为规则的重要内容。主体进行位置选择时,以实现利益最大化为目标选择最佳位置,通常某一位置的吸引力由多种因素决定,如环境舒适度、交通便利度、价格成本等,以寻求交通成本最低为例,可以由到距地铁距离、距公路距离、距高速公路距离等因子描述。因此主体的最终决策规则通常包括多层信息,合适的权重调整非常必要。如Semboloni等以城镇居民、贸易服务商、公共服务供应商、地产开发商等为主体建立模型,并为不同的主体设立了不同的利益目标,例如城镇居民以最低生活成本和交通成本为目标,贸易服务商以最高贸易利润为目标,最终模拟了城市用地的空间结构演化过程[44];Parker等结合元胞自动机和主体模型建立了SLUDGE模型,探寻距离空间效应和交通费用在城市扩张中的影响[45]。除了城市扩张模拟,也有学者应用于生态保护红线研究,蒋思琦等将主体划分为政府主体、居民主体和环保主体三种类型,共同决定生态用地保护范围,三类主体结合经济社会因子和空间距离因子制定了不同决策规则,模拟了鄂州市城市生态保护红线的变化情况[46],有助于协调城市扩展与生态保护的矛盾。
城市是非常复杂的系统。ABM通过为主体赋予各项特征,使其具有城市中微观个体的特性,体现主体在组成、空间、时间和决策上的复杂性,从而模拟微观层面到宏观层面的演化过程,反映或验证城市发展模式和理论。众多研究表明,利用ABM进行城市土地利用变化模拟行之有效,可以增强模拟结果的多尺度特征以及多目标决策价值,未来可以适用于城市规划方案评估、大型项目选址等应用领域。
与城市土地利用变化模拟相比,农业土地利用变化模拟有很大的不同。研究主要以相对单一的农户、农场为主体,在模型中,主体与地块有较为固定的空间对应关系,同一主体通常拥有多个地块[8],并对地块的土地利用形式具有决定性的作用,主体受到生存需求或利益需求的驱动做出各种各样的选择,决策规则的表达比较多样化。Valbuena等通过调研农户的土地利用偏好、市场感知度、农场特点等情况,确定影响农户土地决策的主要因素,并以此构建主体模型,模拟了澳大利亚农户参加自愿性土地政策的情况[47];龚丹等曾以农业和牧业为研究对象,考虑气候、土壤,农产品价格等环境因素以及政府政策对农户主体行为决策的影响,建立了农牧交错带典型区农牧业土地利用变化的主体模型[48];常笑等以河南省小庄村为例构建了农户土地利用决策概念框架,模拟棉花与花生两类经济作物的种植决策,并预测了2012年农户经济作物的种植情况[49],研究结果可为农户行为的调控提供理论依据,从根源促进农用地的合理利用。以上研究分别对农户选择或投资新技术、选择土地经营形式、种植不同的作物等行为进行模拟,体现了农业土地利用变化模拟的多样化[50]。
相比世界范围内城市类似的扩张模式,不同地区农业土地利用的农户行为不一而足,因此在进行农业土地利用的主体模拟时,首先要去当地农户进行实地调查,掌握研究区农户的家庭状况、经济状况、生产决策以及对政策的响应等情况。农业ABM/LUCC的构建需要详细的农户属性及行为数据支撑,数据的可获性是进行研究的关键。目前国内相关研究多集中于城市,未来应该加强农业土地利用变化及相关政策分析,可以对农地整理、农村聚落空间格局演变等进行模拟。

5 结论与讨论

基于复杂性科学的发展起来的ABM,为以往的人地系统研究带来一种新思维,目前无论是在理论方面还是在实践方面都取得了很大的进展。人地关系的发展,表现为“人”对“地”的开发利用的深度、广度不断增强,“人”逐渐主导“地”的变化。开展主体模型研究有利于从微观角度探讨人地系统变化的细节与过程,明确人类行为决策对地理环境产生的影响,可以为政府决策提供科学参考。同以往的模型相比,ABM具备以下两点核心优势：① 呈现人地系统复杂性。人地系统具备时间复杂性、空间复杂性和人类决策复杂性等特征,ABM可以通过模拟人类主体对环境的感知和决策更好体现人地系统的复杂性。② 模拟社会行为的灵活性。不论是主体内生性的决策机制,还是外部环境的空间关系,都使研究人员具有充分的灵活性来设定和运行。因此,使用ABM可以更充分的展现人类决策偏好和决策行为在地理环境变化过程中的作用。
但从另一方面来看,ABM属于跨学科的一门领域,研究仍处于摸索阶段,模型仍然存在较多问题。一是ABM构建没有统一流程,比较随意,极大地削弱了模型的可复制 性[51]。在IBM研究中Grimm等提出了一种ODD标准化建模程序[52],许多研究也随之采纳了这一思路,或许是未来发展的趋势。二是ABM的验证问题。基于复杂系统的非线性,ABM在宏观尺度上表现出来的“涌现”现象很难下推进行验证[32];另外,部分经验数据的缺失也增加了验证的难度,因此模型验证是ABM面临的一大难点。三是主体的行为规则局限于经济人假设,其行为设定是理性的,并追求效用最大化,这种理性行为规则只是一种理想模式,与现实社会具有一定差异,可能会导致模型结果的不确定性。四是ABM与其他模型的整合问题。由于ABM可以进行多尺度模拟,表达复杂系统的“涌现”现象,所以许多学者将ABM与其他模型整合在一起进行综合研究,例如上文中提到的SLUDGE、FVCOM-IBM模型。但目前的研究中多是把ABM与其他模型简单的连接在一起,通常只是单方向的反馈,比如多数ABM/LUCC中的土地属性时间上保持不变,或者只是根据简单的规则发生变化[53],未来可以探索建立多重反馈机制,提高耦合模型功能。
尽管目前ABM的应用面临重重困难,但一个新兴方法的发展成熟离不开问题的检验。当前,“山水林田湖生命共同体”作为绿色发展的新理念,是人地关系和谐发展的最终需求,ABM综合考虑了不同主体的利益目标,可以推进阐明人地关系的“时空过程”与演化机制,实现人地复杂系统的协调发展目标。
具体而言,ABM在人地系统的应用研究应该在以下几个方面加以改进：① 加强主体的决策规则研究。主体的决策规则是将主体层与环境层相互连接的纽带,ABM通过模拟微观尺度主体行为交互实现对宏观尺度地理环境的变化研究。对主体进行适当的抽象与描述,揭示不同主体类型和地理环境的交互作用的差异性,说明主体感知外部环境并作出反应的方法和过程,是决定多主题模型仿真度的关键。虽然主体属性的复杂化有助于刻画地理空间真实的实体,但这并不意味着一味追求主体行为层次的复杂性。只有利用适度简化的规则模拟人地系统的复杂结构[26],才能更好的认识人地系统非线性的演变过程。② 拓展数据挖掘手段。“大数据”时代的来临,为ABM模拟解决了数据量庞大、数据类型纷杂、数据结果抽象等数据有效性问题。借助云计算、分布式处理、生态遥感等数据处理技术,可以提高数据的可获性,整合自然、社会、经济资源,为模型运行提供更多的参数数据,提升ABM模拟的准确度。③ 推动跨学科集成研究。ABM作为复杂性理论的一种研究方法,必然要求多学科、多种方法的集成创新。因此,ABM要充分借鉴和汲取不同学科的相关方法,主体层的刻画多结合社会学、管理学的研究方法,环境层的刻画多参考生态学、地理学的空间研究,形成具一套基于人地系统复杂性的主体模型研究理论—方法体系。

The authors have declared that no competing interests exist.

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
Vitousek P M, Mooney H A, Lubchenco J, et al.Human domination of Earth's ecosystems. Science, 1997, 277(5325): 494-499.

DOI

[2]
Trumbore S E.Potential responses of soil organic carbon to global environmental change. Proceedings of the National Academy of Sciences, 1997, 94(16): 8284-8291.Recent improvements in our understanding of the dynamics of soil carbon have shown that 20-40% of the approximately 1,500 Pg of C stored as organic matter in the upper meter of soils has turnover times of centuries or less. This fast-cycling organic matter is largely comprised of undecomposed plant material and hydrolyzable components associated with mineral surfaces. Turnover times of fast-cycling carbon vary with climate and vegetation, and range from 60 years at high latitudes. The amount and turnover time of C in passive soil carbon pools (organic matter strongly stabilized on mineral surfaces with turnover times of millennia and longer) depend on factors like soil maturity and mineralogy, which, in turn, reflect long-term climate conditions. Transient sources or sinks in terrestrial carbon pools result from the time lag between photosynthetic uptake of CO2 by plants and the subsequent return of C to the atmosphere through plant, heterotrophic, and microbial respiration. Differential responses of primary production and respiration to climate change or ecosystem fertilization have the potential to cause significant interrannual to decadal imbalances in terrestrial C storage and release. Rates of carbon storage and release in recently disturbed ecosystems can be much larger than rates in more mature ecosystems. Changes in disturbance frequency and regime resulting from future climate change may be more important than equilibrium responses in determining the carbon balance of terrestrial ecosystems.

DOI PMID

[3]
Lambin E F, Geist H J, Lepers E.Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 2003, 28(1): 205-241.

DOI

[4]
Hulme M.Meet the humanities. Nature Climate Change, 2011, 1(4): 177-179.

DOI

[5]
Marchi D S, Page S E.Agent-based models. Annual Review of Political Science, 2014, 17: 1-20.

DOI

[6]
Parker D C, Manson S M, Janssen M A, et al.Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 2003, 93(2): 314-337.

DOI

[7]
吴次芳, 陈美球. 土地生态系统的复杂性研究. 应用生态学报, 2002, 13(6): 753-756.运用复杂科学理论 ,阐述了土地生态系统的复杂性特征 ,包括多层次性、高维性 ,子系统关联的复杂性 ,结构与功能的不确定性 ,开放性、动态性 ,自适应性和自组织性等复杂性特征 ,并进一步探讨了分形、混沌及人工神经网络在土地生态系统复杂性特征研究中的应用

[Wu Cifang, Chen Meiqiu.Complexity of land ecosystem. Chinese Journal of Applied Ecology, 2002, 13(6): 753-756.]

[8]
余强毅, 吴文斌, 唐华俊, .. 复杂性科学与Agent模型在土地变化科学中的研究进展. 地理学报, 2011, 66(11): 1518-1530.

[Yu Qiangyi, Wu Wenbin, Tang Huajun, et al.Complex system theory and agent-based modeling: Progresses in land change science. Acta Geographica Sinica, 2011, 66(11): 1518-1530.]

[9]
薛领, 杨开忠. 复杂性科学理论与区域空间演化模拟研究. 地理研究, 2002, 21(1): 79-88.针对传统上两类模拟区域空间演化的模型策略 ,着重讨论了它们的建模思想和手段 ,在此基础上介绍了复杂性 (Complexity)研究的重要成果———复杂适应系统 (CAS)理论的基本概念以及CAS模型的思想 ,探讨了区域作为复杂适应系统的一般特征 ,并且通过一个城市空间演化的概念模型阐述了CAS模型的技术问题及其建模的独到之处

DOI

[Xue Ling, Yang Kaizhong.Sciences of complexity and studies of evolutional simulation of regional spatial structure. Geographical Research, 2002, 21(1): 79-88.]

[10]
宋学锋. 复杂性, 复杂系统与复杂性科学. 中国科学基金, 2003, 17(5): 262-269.

[Song Xuefeng.Complexity, complex system, and the science of complexity. Bulletin of National Natural Science Foundation of China, 2003, 17(5): 262-269.]

[11]
吴传钧. 论地理学的研究核心: 人地关系地域系统. 经济地理, 1991, 11(3): l-5.

[Wu Chuanjun. Man-earth areal system: The core of geographical study. Economic Geography, 1991, 11(3): l-5.]

[12]
王成超. 人地系统复杂性机理剖析. 海南师范大学学报: 自然科学版, 2010, 23(2): 219-223.采用系统分析方法,按照组成-结构-功能顺序对复杂的人地关系地 域系统展开深入的系统分析;同时,将地方政府的政策作为一个外生变量,通过对人类活动调节从而对人地系统进行调控.在人地系统复杂性分析过程中,强调人地 系统的尺度效应,并构建了人地系统相互作用机理的概念模型,揭示人地系统的相互作用机理.最后,针对人地系统的研究与实践,提出4条建议和启示.

DOI

[Wang Chengchao.An analysis of complex mechanism in human-environment system. Journal of Hainan Normal University: Natural Science, 2010, 23(2): 219-223.]

[13]
甘国辉, 杨国安. 地理学与地理系统复杂性研究. 系统辩证学学报, 2004, 12(3): 78-83.

[Gan Guohui, Yang Guoan.The complexity study in geographical system and geography. Journal of Systemic Dialectics, 2004, 12(3): 78-83.]

[14]
杨国安, 甘国辉. 人地系统复杂性思考. 科技导报, 2002, 20(3): 58-60.一、复杂性与复杂科学概述复杂性是世界的本质属性。复杂性是客观的 ,不是简单性的线性组合和现象 ,特别不仅仅是简单性的表现结果。描述事物复杂性 ,首先应该约定把描述限定在某一层次。离开层次谈事物的复杂性 ,复杂性就是一个无法度量的具有无限深度的虚假问题［１］。复

DOI

[Yang Guoan, Gan Guohui.Thoughts on complexity of human-earth system. Science and Technology Review, 2002, 20(3): 58-60.]

[15]
陆大道. 关于地理学的“人—地系统”理论研究. 地理研究, 2002, 21(2): 135-145.本文强调地理学面临的问题正在由自然因素引发的环境变化到人类因素引发的环境变化,分析了我国"人地关系"变化的严峻态势.指出地理学对"人地关系地域系统"研究可为实施可持续发展战略提供理论基础.阐述了"人地关系地域系统"研究在我国地理学理论发展中的地位,并对如何研究"人地关系地域系统"进行了探讨.

DOI

[Lu Dadao.Theoretical studies of man-land system as the core of geographical science. Geographical Research, 2002, 21(2): 135-145.]

[16]
王黎明, 关庆锋, 冯仁国, .. 全球变化视角下人地系统研究面临的几个问题探讨. 地理科学, 2003, 23(4): 391-397.

[Wang Liming, Guan Qingfeng, Feng Renguo, et al.Problems of man-land relationship system theory under global change. Scientia Geographica Sinica, 2003, 23(4): 391-397.]

[17]
Agarwal C, Green G M, Grove J M, et al.A review and assessment of land-use change models: dynamics of space, time and human choice. Journal of Religious Studies, 2000, 79: 136-141.

[18]
李双成, 王羊, 蔡运龙. 复杂性科学视角下的地理学研究范式转型. 地理学报, 2010, 65(11): 1315-1324.

[Li Shuangcheng, Wang Yang, Cai Yunlong.The paradigm transformation of geography from the perspective of complexity sciences. Acta Geographica Sinica, 2010, 65(11): 1315-1324.]

[19]
黎夏. 地理模拟系统: 元胞自动机与多智能体. 北京: 科学出版社, 2007.

[Li Xia.Geographica Simulation Systems: Cellula Automata and Multi-agent System. Beijing: Science Press, 2007.]

[20]
An L.Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 2012, 229: 25-36.Coupled human and natural systems (CHANS) manifest various complexities such as heterogeneity, nonlinearity, feedback, and emergence. Humans play a critical role in affecting such systems and in giving rise to various environmental consequences, which may in turn affect future human decisions and behavior. In light of complexity theory and its application in CHANS, this paper reviews various decision models used in agent based simulations of CHANS dynamics, discussing their strengths and weaknesses. This paper concludes by advocating development of more process-based decision models as well as protocols or architectures that facilitate better modeling of human decisions in various CHANS.

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[21]
Bonabeau E.Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(3): 7280-7287.Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.

DOI PMID

[22]
郗静, 曹明明, 陈海. 智能体模型在微观土地利用行为模拟中的应用及启示. 地理与地理信息科学, 2009, 25(4): 56-60.

[Chi Jing, Cao Mingming, Chen Hai.Developments and enlightenments on simulation of land use micro behaviors based on agent-based models. Geography and Geo-Information Science, 2009, 25(4): 56-60.]

[23]
陈禹. 复杂性研究的新动向: 基于主体的建模方法及其启迪. 系统辩证学学报, 2003, 11(1): 43-50.

[Chen Yu.A new trend in complexity studies: Agent based modelling and its implication. Journal of Systemic Dialectics, 2003, 11(1): 43-50.]

[24]
陈蔚, 高晓路, 沈振江. 多主体系统在城市发展模拟中的应用. 地理科学进展, 2012, 31(6): 761-767.多主体系统(Multi-Agent System, MAS)是由多个主体(Agent)组成的松散的网络,每个主体根据其属性和行为规则互相交流、协作甚至竞争。MAS通过模拟和观察大量主体的微观行为来研究系统整体的宏观规律,对复杂系统的演化机理具有更合理、更准确的解释力,因此,MAS在城市发展模拟中有很大的应用前景。本文介绍了多主体系统的基本框架及模拟思路,综述了国内外城市模拟研究中MAS理论的应用现状。重点分析了MAS理论在城市模拟应用中的五个关键课题:即微观个体数据的获取、微观主体的合理选择、主体行为规则的提取和设定、主体之间相互作用的描述与表达、环境变量的提取与导入等,并对相关研究进展进行了综述。研究表明,城市发展预测与政策评价是MAS应用的重点突破方向之一。

DOI

[Chen Wei, Gao Xiaolu, Shen Zhenjiang.Application of multi-agent system in simulation of urban development: A review. Progress in Geography, 2012, 31(6): 761-767.]

[25]
Filatova T, Verburg P H, Parker D C, et al.Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environmental Modelling & Software, 2013, 45: 1-7.Departing from the comprehensive reviews carried out in the field, we identify the key challenges that agent-based methodology faces when modeling coupled socio-ecological systems. Focusing primarily on the papers presented in this thematic issue, we review progress in spatial agent-based models along the lines of four methodological challenges: (1) design and parameterizing of agent decision models, (2) verification, validation and sensitivity analysis, (3) integration of socio-demographic, ecological, and biophysical models, and (4) spatial representation. Based on this we critically reflect on the future work that is required to make agent-based modeling widely accepted as a tool to support the real world policy.

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[26]
Khazaii J.Agent-based modeling. Advanced Decision Making for HVAC Engineers, 2016, 2: 137-144.

[27]
傅丽华, 谢炳庚, 李晓青, .. 基于多智能体模拟的旅游地保护行为决策. 地理研究, 2012, 31(3): 555-564.

[Fu Lihua, Xie Binggeng, Li Xiaoqing, et al.The multiagent-based-model method in analyzing the conservation behavior strategies of tourism destination. Geographical Research, 2012, 31(3): 555-564.]

[28]
Zhou Y, Guo S, Xu C Y, et al.Integrated optimal allocation model for complex adaptive system of water resources management (I): Methodologies. Journal of Hydrology, 2015, 531: 964-976.Climate change, rapid economic development and increase of the human population are considered as the major triggers of increasing challenges for water resources management. This proposed integrated optimal allocation model (IOAM) for complex adaptive system of water resources management is applied in Dongjiang River basin located in the Guangdong Province of China. The IOAM is calibrated and validated under baseline period 2010year and future period 2011鈥2030year, respectively. The simulation results indicate that the proposed model can make a trade-off between demand and supply for sustainable development of society, economy, ecology and environment and achieve adaptive management of water resources allocation. The optimal scheme derived by multi-objective evaluation is recommended for decision-makers in order to maximize the comprehensive benefits of water resources management.

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[29]
张延伟, 裴颖, 葛全胜. 基于BDI决策的居住空间宜居性分析: 以大连沙河口区为例. 地理研究, 2016, 35(12): 2227-2237.

[Zhang Yanwei, Pei Ying, Ge Quansheng.Analysis of living space based on BDI decision: A case study for Dalian Shahekou district. Geographical Research, 2016, 35(12): 2227-2237.]

[30]
黄河, 范一大, 杨思全, .. 基于多智能体的洪涝风险动态评估理论模型. 地理研究, 2015, 34(10): 1875-1886.随着全球气候变化的加剧和城市化进程的快速发展,洪涝灾害的影响和复杂性日益加剧.洪涝灾害风险评估是一项复杂的系统工程,在防洪减灾工程理论与实践中具有重要意义;作为洪涝灾害风险管理和应急处置的基础和核心,风险评估模型直接影响洪涝灾害风险评估结果的可靠性.洪涝灾害风险是洪涝灾害复杂系统组成部分相互作用的结果,综合考虑洪涝灾害系统的特点以及智能体建模的优势,提出了一种基于多智能体的洪涝风险动态评估理论模型.首先从复杂系统建模的角度出发,对洪涝灾害复杂系统进行分析建模,构建基于多智能体的洪涝灾害风险动态评估框架模型;其次,对所构建的框架模型中单个智能体模型进行研究,分别建立基于反应型的孕灾环境智能体模型和基于慎思型的致灾因子、承灾体和风险分析智能体模型,并对以上单个智能体的流程进行研究;然后以风险分析智能体联盟为例对模型框架中所涉及到4类智能体联盟的内部结构和流程进行分析,对构建的框架模型中涉及到的智能体通信协调和作用规则进行探讨;最后,在Netlogo平台上,基于30mDEM数据和构建的模型,对淮河流域的暴雨型洪涝孕育发生发展全过程的人口风险进行动态评估.结果表明,构建的模型能有效评估洪涝灾害全过程中人口风险的动态.研究结果对洪涝灾害人口灾情评估、应急救助和应急管理都具有较强的指导意义.

DOI

[Huang He, Fan Yida, Yang Siquan, et al.A multi-agent based theoretical model for dynamic flood disaster risk assessment. Geographical Research, 2015, 34(10): 1875-1886.]

[31]
田光进, 邬建国. 基于智能体模型的土地利用动态模拟研究进展. 生态学报, 2008, 28(9): 4451-4459.

[Tian Guangjin, Wu Jianguo.Simulating land use change with agent-baseed models: progress and prospects. Acta Ecologica Sinica, 2008, 28(9): 4451-4459.]

[32]
Heckbert S, Baynes T, Reeson A.Agent-based modeling in ecological economics. Annals of the New York Academy of Sciences, 2010, 1185(1): 39-53.Interconnected social and environmental systems are the domain of ecological economics, and models can be used to explore feedbacks and adaptations inherent in these systems. Agent-based modeling (ABM) represents autonomous entities, each with dynamic behavior and heterogeneous characteristics. Agents interact with each other and their environment, resulting in emergent outcomes at the macroscale that can be used to quantitatively analyze complex systems. ABM is contributing to research questions in ecological economics in the areas of natural resource management and land-use change, urban systems modeling, market dynamics, changes in consumer attitudes, innovation, and diffusion of technology and management practices, commons dilemmas and self-governance, and psychological aspects to human decision making and behavior change. Frontiers for ABM research in ecological economics involve advancing the empirical calibration and validation of models through mixed methods, including surveys, interviews, participatory modeling, and, notably, experimental economics to test specific decision-making hypotheses. Linking ABM with other modeling techniques at the level of emergent properties will further advance efforts to understand dynamics of social-environmental systems.

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[33]
Grimm V.Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future?. Ecological Modelling, 1999, 115(2): 129-148.Abstract Each modeller who builds and analyses an individual-based model learns of course a great deal, but what has ecology as a whole learned from the individual-based models published during the last decade? Answering this question proves extremely difficult as there is no common motivation behind individual-based models. The distinction is introduced between ‘pragmatic’ motivation, which uses the individual-based approach as a tool without any reference to the theoretical issues which have emerged from the classical state variable approach and ‘paradigmatic’ motivation, which explicitly refers to theoretical ecology. A mini-review of 50 individual-based animal population models shows that the majority are driven by pragmatic motivation. Most models are very complex and special techniques to cope with this complexity during their analysis are only occasionally applied. It is suggested that in order to orient individual-based modelling more towards general theoretical issues, we need increased explicit reference to theoretical ecology and an advanced strategy for building and analysing individual-based models. To this end, a heuristic list of rules is presented which may help us to advance the practice of individual-based modelling and to learn more general lessons from individual-based modelling in the future than we have during the last decade. The main ideas behind these rules are as follows: (1) Individual-based models usually make more realistic assumptions than state variable models, but it should not be forgotten that the aim of individual-based modelling is not ‘realism’ but modelling. (2) The individual-based approach is a bottom-up approach which starts with the ‘parts’ (i.e. individuals) of a system (i.e. population) and then tries to understand how the system’s properties emerge from the interaction among these parts. However, bottom-up approaches alone will never lead to theories at the systems level. State variable or top-down approaches are needed to provide an appropriate integrated view, i.e. the relevant questions at the population level.

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[34]
Pitt W C, Box P W, Knowlton F F.An individual-based model of canid populations: Modelling territoriality and social structure. Ecological Modelling, 2003, 166(1): 109-121.The management of canid populations has been at the forefront of wildlife management worldwide for much of the last century. Effective management depends on the ability to integrate species biology, the environmental aspects upon which those populations depend, and the factors controlling species abundance. Further, managing canid populations requires consideration of territoriality and dominance, which may have a significant effect on population dynamics. To better understand the effect of social structure on canid populations, we developed an individual-based computer model using Swarm to mimic natural coyote population dynamics. We selected the Swarm simulation environment because it is ideally suited for creating a system of multiple interacting agents with variable schedules and hierarchies. Swarm was a software platform that allows the user to describe generic individuals and behaviours, link those behaviours in each concurrent time step, and assemble behaviours and objects in a hierarchical framework. This model stands apart from previous modelling efforts because it explicitly incorporates behavioral features, such as dominance and territoriality, as major determinates of species demography into a simple model. Individual variation, such as status within territorial social groups and age-based reproduction are incorporated, but assumptions typically associated with most demographic models are not needed. The simple population model with few parameters not only closely resembled eal world populations but also helped us understand population dynamics that emerged from model. The sensitivity analysis revealed that the model was largely insensitive to individual parameter estimates and could be used to guide management of territorial animal populations with social structure. The model output variables closely matched the mean and range of values reported in the literature of wild populations for population size, proportion of females breeding, offspring survival and litter size. The variation of model output was similar to the variation recorded in field studies. Further, population dynamics reported from field studies emerged from the model and may help to explain the mechanisms responsible for this variation. This type of model could also provide insights into potential management alternatives for other canid species or other species with similar social structure.

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[35]
陈求稳, 程仲尼, 蔡德所, .. 基于个体模型模拟的鱼类对上游水库运行的生态响应分析. 水利学报, 2009, 40(8): 897-903.建立了一维全河流和二维局部河段的水环境模型,并与基于个体的鱼 类模型耦合,从鱼类种群动态角度研究水库运行对下游河流水生态系统的作用.文中选取了漓江下游的一段复式河道,以优势种草鱼和鲫鱼为研究对象,模拟了自然 条件和水库调节作用下河道的水环境条件,以及相应的鱼类生长和分布的变化.通过模拟结果的对比分析,发现如果仅考虑水库运行造成的下游水流变化的作用,枯 水期水库向河道补水对草鱼有一定的正面影响,对鲫鱼有轻微的负面影响;而在4-5月,水库运行对两种鱼类都存在比较明显的负面影响.本文建立的模型方法可 为河流优化管理及水库生态友好运行提供支持.

DOI

[Chen Qiuwen, Cheng Zhongni, Cai Desuo, et al.Analysis on the downstream fish responses to reservoir operation using individual-based model. Journal of Hydraulic Engineering, 2009, 40(8): 897-903.]

[36]
李曰嵩, 潘灵芝, 陈新军. 产卵深度变动对东海日本鲭鱼卵仔幼鱼输运和存活率的影响. 海洋渔业, 2013, 35(2): 176-182.日本鲭(Scomber japonicus)是我国近海重要经济鱼种,海洋环境变动对鱼类生命周期中最为脆弱的鱼卵和仔幼鱼的生长、存活直至种群补充产生重大的影响,其中产卵深 度变动对其是否会产生影响,目前尚不清楚.为此,本文拟采用FVCOM (Finite-Volume Coastal Ocean Model)物理模型来模拟三维物理场,使用基于个体模型IBM(individual-based model)参数化并模拟日本鲭的早期生活史,从而建立起基于物理-生物个体的东海日本鲭初期生态耦合模型,使用该耦合模型模拟了日本鲭产卵水深不同导致 鱼卵仔幼鱼从产卵场向育肥场的输运过程中经历物理环境的差异对鱼卵仔幼鱼的输运分布以及存活率的影响.研究发现,产卵深度的变动,未对鱼卵仔幼鱼的输运分 布产生明显影响,但在输运过程中所处水深和水温的微小差异却导致了变浅(5 m)和加深(15 m)产卵深度鱼卵仔幼鱼的存活率降低,正常产卵深度(10 m)是最佳的产卵水深.因此,研究认为产卵深度的细微变动将会对东海日本鲭鱼卵仔幼鱼的存活率产生影响,但对鱼卵仔幼鱼输运则影响不大.

DOI

[Li Yuesong, Pan Lingzhi, Chen Xinjun.Effects of spawning water depth on transport and survival of chub mackerel (Scomber japonicus) eggs and larvae in the East China Sea. Marine Fisheries, 2013, 35(2): 176-182.]

[37]
余为, 陈新军, 李曰嵩. 基于个体生态模型在渔业科学中的应用研究现状. 海洋渔业, 2012, 34(4): 464-475.基于个体生态模型（IBM）于20世纪70年代提出，数十年间在渔业科学领域得到广泛应用， 现已成为渔业科学研究的重要手段之一。本文主要介绍了IBM的基本概念和研究方法，概述了国内外研究现状以及在渔业科学中的应用，分析了模型在应用中存在 的问题、未来发展方向及趋势。研究认为，IBM在渔业科学研究和应用过程中需充分考虑以下问题：（1）了解模拟对象的生活史过程生物学特性，以及其栖息地 环境特征；（2）针对不同的对象选用合适的环境因子和参数；（3）参考经典的生态学理论框架和方法来建立和分析个体模型；（4）综合统计分析、海洋遥感、 地理信息系统等方法；（5）利用野外和敏感性试验获得实测数据，以校正IBM模型并提高精度。

DOI

[Yu Wei, Chen Xinjun, Li Yuesong.Review on individual-based model in fishery science. Marine Fisheries, 2012, 34(4): 464-475.]

[38]
吴文斌, 杨鹏, 唐华俊, .. 基于Agent 的土地利用/土地覆盖变化模型的研究进展. 地理科学, 2007, 27(4): 573-578.

[Wu Wenbin, Yang Peng, Tang Huajun, et al.Agent-based model for land-use/cover change: A review. Scientia Geographica Sinica, 2007, 27(4): 573-578.]

[39]
Kennedy W G, Hailegiorgis A B, Rouleau M, et al.An agent-based model of conflict in East Africa and the effect of the privatization of land. In: University of Kassel. Proceedings of the 3rd World Congress in Social Simulation. Kassel: University of Kassel, 2010: 274-281.

[40]
Balbi S, Villa F.Integrating human agency and ecosystem services: An ABM perspective on food. In: Brigham Young University. Proceedings of the International Congress on Environmental Modelling and Software. Provo: Brigham Young University, 2016: 1-2.

[41]
潘理虎, 闫慧敏, 黄河清, .. 北方农牧交错带生态系统服务合理消耗多主体模型构建. 资源科学, 2012, 34(6): 1007-1016.生态系统服务消耗研究对于生态系统的保护和可持续利用具有重要意义,已成为生态学研究的热点。生态系统服务消耗多主体模型是用于研究生态系统服务消耗,表达生态系统与人类经济系统相互作用关系的适用方法。以内蒙古农牧交错区生态系统服务消耗为例,研究了生态系统服务消耗多主体模型的构建方法,包括调查数据的分析与提取,农牧户及其成员多主体运行规则的研究和制定,生态系统服务压力指数的定义,地理数据的导入及模型运行空间的创建,模型参数选择及其初始化等。模型系统研发及运行结果表明,以本方法构建的生态系统服务消耗多主体模型可以体现生态系统服务消耗的过程,所建立的模型具有可视化、过程可展现、数据分析直观等特点,对研究生态系统服务消耗有一定的应用价值。

[Pan Lihu, Yan Huimin, Huang Heqing, et al.Multi-agent modeling method of reasonable consumption of ecosystem service: A case of the farming pastoral zone in Inner Mongolia. Resources Science, 2012, 34(6): 1007-1016.]

[42]
Lansing J S, Kremer J N.Emergent properties of Balinese water temple networks: Coadaptation on a rugged fitness landscape. American Anthropologist, 1993, 95(1): 97-114.Abstract For over a thousand years, generations of Balinese farmers have gradually transformed the landscape of their island, clearing forests, digging irrigation canals, and terracing hillsides to enable themselves and their descendants to grow irrigated rice. Paralleling the physical system of terraces and irrigation works, the Balinese have also constructed intricate networks of shrines and temples dedicated to agricultural deities. Ecological modeling shows that water temple networks can have macroscopic effects on the topography of the adaptive landscape, and may be representative of a class of complex adaptive systems that have evolved to manage agroecosystems.

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[43]
Berger T.Agent-based spatial models applied to agriculture: A simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 2001, 25(2-3): 245-260.By Thomas Berger; Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes

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[44]
Semboloni F, Assfalg J, Armeni S, et al.CityDev, an interactive multi-agents urban model on the web. Computers, Environment and Urban Systems, 2004, 28(1): 45-64.In this paper we present CityDev, an interactive multi-agents simulation model of the development of a city. The model is based on agents, goods and markets. Each agent (family, industrial firm, developer, etc.) produces goods by using other goods, and trades the goods on the markets. Each good has a price, and the monetary aspects are included in the simulation. When agents produce goods and interact in the markets, the urban fabric is built and transformed. The computer model (simulator) runs on a 3-D spatial pattern organized in cubic cells. CityDev allows interactive users to get involved in the functioning of the model. In fact, they can manage agents generated by the simulator, as well as new agents created by themselves. Agents managed by users interact with agents managed by the simulator. In addition, an administration board interactively controls the development of the city trough the urban plan, the building of new roads and the location of public facilities. In the present paper the model is described and some results are shown.

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[45]
Parker D C, Meretsky V.Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics. Agriculture, Ecosystems & Environment, 2004, 101(2): 233-250.This paper presents an agent-based model of land use designed to explore the impacts of edge-effect externalities—distance-dependent spatial externalities—on land-use pattern. While the impacts of externalities on aspatial economics measures such as equilibrium land rents and the distribution of economic activity are well explored, links between externalities and landscape pattern are not well understood. This gap reflects a more general gap between aspatial theoretical land-use models and descriptive, pattern-based empirical analysis. The model presented in this paper, designed to link changes in socioeconomic parameters to changes in macroscale measures of landscape pattern, was developed with the specific goal of formally bridging this gap. The model simulates land-use decisions of parcel managers in an environment where potential conflicts between urban and agricultural land uses affect the payoffs to particular land uses. In the model, spatial and aspatial macroscale outcomes emerge from the independent, but dynamically linked, decisions of individual parcel managers. Land-use composition, land-use pattern, and the location of land uses are jointly determined, and interactions between composition and pattern feedback to microlevel landowner decisions through endogenous land rents. The paper demonstrates a series of results. First, the paper demonstrates the economic inefficiency of landscape fragmentation when edge-effect externalities are present and illustrates a series of landscape metrics appropriate to measure this fragmentation. Second, the agent-based model is used to demonstrate links between externality impacts and landscape pattern: that conflicts between urban and residential land users lead to a more compact urban form, that when the profitability of agricultural production is reduced by proximity to urban land, the urban–rural fringe expands to a socially inefficient degree, and that conflicts between urban land users can lead to fragmented patterns of urban development consistent with existing definitions of urban sprawl. Finally, the paper concludes by proposing a methodology for establishing the robustness of the model’s conclusions over a wide range of parameter values.

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[46]
蒋思琦, 何建华. 基于多智能体的城市生态保护红线多情景模拟. 国土与自然资源研究, 2016, (1): 16-20.

[Jiang Siqi, He Jianhua.Multi scene simulation of urban ecological protection red line based on multi-agents model. Territory and Natural Resources Study, 2016, (1): 16-20.]

[47]
Valbuena D, Bregt A K, McAlpine C, et al. An agent-based approach to explore the effect of voluntary mechanisms on land use change: A case in rural Queensland, Australia. Journal of Environmental Management, 2010, 91(12): 2615-2625.In rural regions, land use changes (LUC) are often the result of the decision-making of individual farmers. To influence this decision-making, compulsory and voluntary mechanisms are implemented. However, farmers' decision-making is a heterogeneous process that depends on their ability and willingness to take certain decisions. Discrepancies between farmers' ability and willingness and the design of voluntary mechanisms occur frequently. This makes it necessary to understand how farmers' participation in these mechanisms can affect LUC. The aim of this paper was to demonstrate an agent-based approach to analyse and explore how voluntary mechanisms can influence LUC processes in rural regions. This approach was applied to a rural region in Australia, where clearing of native vegetation has occurred for agricultural development. Historical land cover data and semi-structured interviews were used to parameterise an agent-based model. Factors that influence farmers' ability and willingness to participate in these mechanisms were identified. Three scenarios were simulated with the model to explore how the implementation of different voluntary mechanisms can affect the landscape structure of the region. This paper identifies how the diversity of farmers' decision-making can influence the landscape structure in the region. The advantages and limitations of an agent-based approach in relation to LUC research and policy are discussed.

DOI PMID

[48]
龚丹, 马晓明. 农牧交错带典型区土地利用驱动机制研究: 基于多主体模拟方法. 湖南农业大学学报: 社会科学版, 2007, 8(4): 15-19.

[Gong Dan, Ma Xiaoming.Research on driving mechanism of land-use in pasture and agriculture transitional zone using multi-agent simulation. Journal of Hunan Agricultural University: Social Sciences, 2007, 8(4): 15-19.]

[49]
常笑, 刘黎明, 刘朝旭, .. 农户土地利用决策行为的多智能体模拟方法. 农业工程学报, 2013, 29(14): 227-237.

[Chang Xiao, Liu Liming, Liu Zhaoxu, et al.Method of multi-agent system for simulating land-use decision-making behavior of farmer households. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(14): 227-237.]

[50]
薛领, 杨开忠, 沈体雁. 基于agent的建模: 地理计算的新发展. 地球科学进展, 2004, 19(2): 305-311.20世纪90年代后期，基于agent建模(Agent—Baserd Modeling，ABM)的理论和技术不断发展，并且逐渐引起地理研究者的重视。ABM这种自下而上的模型策略是复杂适应系统理论、人工生命以及分布式 人工智能技术的融合，目前已经成为继面向对象方法之后出现的又一种进行复杂系统分析与模拟的重要手段。ABM关注的是地理系统中大量异质性个体间的相互关 系，强调进化和适应行为，主张非均衡的发展路径，我们必须为个别的决策者建立微观行为模型，并且通过观察大量的微观agent的相互作用来研究宏观上整个 地理系统的空间演化过程。将在简要回顾地理空间演化模型的基础上重点讨论ABM出现的理论背景、技术优势、研究进展以及模拟系统的开发问题。

DOI

[Xue Ling, Yang Kaizhong, Shen Tiyan.Agent-based modeling: The new advantage in geocomputation. Advance in Earth Sciences, 2004, 19(2): 305-311.]

[51]
Zvoleff A, An L.Analyzing human-landscape interactions: tools that integrate. Environmental management, 2014, 53(1): 94-111.Humans have transformed much of Earth’s land surface, giving rise to loss of biodiversity, climate change, and a host of other environmental issues that are affecting human and biophysical systems in unexpected ways. To confront these problems, environmental managers must consider human and landscape systems in integrated ways. This means making use of data obtained from a broad range of methods (e.g., sensors, surveys), while taking into account new findings from the social and biophysical science literatures. New integrative methods (including data fusion, simulation modeling, and participatory approaches) have emerged in recent years to address these challenges, and to allow analysts to provide information that links qualitative and quantitative elements for policymakers. This paper brings attention to these emergent tools while providing an overview of the tools currently in use for analysis of human–landscape interactions. Analysts are now faced with a staggering array of approaches in the human–landscape literature—in an attempt to bring increased clarity to the field, we identify the relative strengths of each tool, and provide guidance to analysts on the areas to which each tool is best applied. We discuss four broad categories of tools: statistical methods (including survival analysis, multi-level modeling, and Bayesian approaches), GIS and spatial analysis methods, simulation approaches (including cellular automata, agent-based modeling, and participatory modeling), and mixed-method techniques (such as alternative futures modeling and integrated assessment). For each tool, we offer an example from the literature of its application in human–landscape research. Among these tools, participatory approaches are gaining prominence for analysts to make the broadest possible array of information available to researchers, environmental managers, and policymakers. Further development of new approaches of data fusion and integration across sites or disciplines pose an important challenge for future work in integrating human and landscape components.

DOI PMID

[52]
Grimm V, Berger U, Bastiansen F, et al.A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 2006, 198(1): 115-126.Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers.

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[53]
Matthews R B, Gilbert N G, Roach A, et al.Agent-based land-use models: A review of applications. Landscape Ecology, 2007, 22(10): 1447-1459.Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear t is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.

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