A description and sensitivity analysis of the ArchMatNet agent-based model

被引:1
|
作者
Bischoff, Robert J. [1 ]
Padilla-Iglesias, Cecilia [2 ]
机构
[1] Arizona State Univ, Sch Human Evolut & Social Change, Tempe, AZ 85281 USA
[2] Univ Zurich, Dept Evolutionary Anthropol, Zurich, Switzerland
关键词
Agent-based model; Network analysis; Sensitivity analysis; Cultural transmission; Material culture; Archaeological record; Social network proxy; Hunter-gatherer networks; SOCIAL NETWORKS; CULTURAL TRANSMISSION; MOBILITY; TIME;
D O I
10.7717/peerj-cs.1419
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Archaeologists cannot observe face-to-face interactions in the past, yet methods derived from the analyses of social networks are often used to make inferences about patterns of past social interactions using material cultural remains as a proxy. We created the ArchMatNet agent-based model to explore the relationship between networks built from archaeological material and the past social networks that generated them. It was designed as an abstract model representing a wide variety of social systems and their dynamics: from hunter-gatherer groups to small-scale horticulturalists. The model is highly flexible, allowing agents to engage in a variety of activities (e.g., group hunting, visiting, trading, cultural transmission, migration, seasonal aggregations, etc.), and includes several parameters that can be adjusted to represent the social, demographic and historical dynamics of interest. This article examines how sensitive the model is to changes in these various parameters, primarily by relying on the one-factor-at-a-time (OFAT) approach to sensitivity analysis. Our purpose is for this sensitivity analyses to serve as a guide for users of the model containing information on how the model works, the types of agents and variables included, how parameters interact with one another, the model outputs, and how to make informed choices on parameter values.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] Sensitivity Analysis of a 2D Stochastic Agent-Based and PDE Diffusion Model for Cancer-on-Chip Experiments
    Pompa, Marcello
    Torre, Davide
    Bretti, Gabriella
    De Gaetano, Andrea
    AXIOMS, 2023, 12 (10)
  • [42] An Agent-Based Competitive Product Diffusion Model for the Estimation and Sensitivity Analysis of Social Network Structure and Purchase Time Distribution
    Lee, Keeheon
    Kim, Shintae
    Kim, Chang Ouk
    Park, Taeho
    JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2013, 16 (01):
  • [43] A simple learning agent interacting with an agent-based market model
    Dicks, Matthew
    Paskaramoorthy, Andrew
    Gebbie, Tim
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2024, 633
  • [44] WorkSim: An Agent-Based Model of Labor Markets
    Kant, Jean-Daniel
    Ballot, Gerard
    Goudet, Olivier
    JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2020, 23 (04): : 1 - 39
  • [45] An agent-based model of tourism destinations choice
    Alvarez, Emiliano
    Brida, Juan Gabriel
    INTERNATIONAL JOURNAL OF TOURISM RESEARCH, 2019, 21 (02) : 145 - 155
  • [46] An agent-based model of the foraging ascomycete hypothesis
    Thomas, Daniel C.
    Vandegrift, Roo
    Roy, Bitty A.
    FUNGAL ECOLOGY, 2020, 47
  • [47] An Agent-based Model For Simulating Collective Efficacy
    Wang, Minghao
    Hu, Xiaolin
    PROCEEDINGS OF THE 2011 SUMMER COMPUTER SIMULATION CONFERENCE, 2011, : 36 - 43
  • [48] An agent-based model of personal web communities
    Santos, Jose I.
    Galan, Jose M.
    del Olmo, Ricardo
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS, 2006, 4224 : 1242 - 1249
  • [49] Addressing equifinality in agent-based modeling: a sequential parameter space search method based on sensitivity analysis
    Choi, Moongi
    Crooks, Andrew
    Wan, Neng
    Brewer, Simon
    Cova, Thomas J.
    Hohl, Alexander
    INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2024, 38 (06) : 1007 - 1034
  • [50] On Neurochemical Aspects of Agent-Based Memory Model
    Ezhov, Alexandr A.
    Khromov, Andrei G.
    Terentyeva, Svetlana S.
    ADVANCES IN NEURAL NETWORKS - ISNN 2016, 2016, 9719 : 375 - 384