Identifying Unexpected Behaviors of Agent-Based Models Through Spatial Plots and Heat Maps

Lynch, Christopher J.; Kavak, Hamdi; Gore, Ross; Vernon-Bido, Daniele

doi:10.1007/978-3-030-20309-2_6

Cited by 5 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spatial analytic capabilities focus on spatial (e.g. geographic) relationships [123,126], spatial density representations [127,128], patterns [129], and interaction points [130]. For example, consider the value provided by the visual techniques of radar charts and parallel coordinate plots.…”

Section: Plos Onementioning

confidence: 99%

“…Lynch, Kavak [128] explore the use of spatial plots and heat maps for identifying suspicious outcomes within ABM execution. Sun, Xu [129] explore pattern formation comparisons to reflect model credibility in the formation of vascular mesenchymal cells and lung development.…”

Section: Plos Onementioning

confidence: 99%

“…Runtime approaches may provide better short-term options for gaining acceptance by the M&S community by providing informal, real-time support for revealing potential errors as they occur [77,158], such as through the use of animations and other visual representations [24,62,72,156] as well as through audial feedback [94]. Emerging runtime approaches help to pinpoint the locations of errors through sound cues [159], spatial plots, and heat maps [128]. Such verification techniques may yield greater value using visual analytics to communicate quantitative information throughout a run while statistical indicators based on population samples may be more appropriate for input-output experimental designs.…”

Section: Plos Onementioning

confidence: 99%

See 2 more Smart Citations

A content analysis-based approach to explore simulation verification and identify its current challenges

et al. 2020

Self Cite

View full text Add to dashboard Cite

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison of the prominence of verification, validation, and Verification and Validation (V&V) as separate concepts; (iii) the positioning of the concepts specifically associated with verification; and (iv) an evaluation of verification's defining characteristics within each decade. Our analysis reveals unique characterizations of verification in each decade. The insights gathered helped to identify and discuss three categories of verification challenges as avenues of future research, awareness, and understanding for researchers, students, and practitioners. These categories include conveying confidence and maintaining ease of use; techniques' coverage abilities for handling increasing simulation complexities; and new ways to provide error feedback to model users.

show abstract

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

A content analysis-based approach to explore simulation verification and identify its current challenges

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Agent-based Modeling allows for the rules at the individual level to produce system level behaviors (Epstein 1999;Ferber 1999); however, agent based models do not generally have built-in approaches for collecting and tracing agents throughout runtime. Current approaches for searching for individual level emergent behaviors generally focus on graphically tracking some attributes of an agent (Courdier et al 2002;Lynch et al 2017) or analytically searching the behavior space of an agent to discover interesting or relevant behaviors (Diallo et al 2016;Gore et al 2017). Both techniques utilize aspects of trace validation to collect information on agents and aggregate behaviors to provide insight into a modeling question.…”

Section: Introductionmentioning

confidence: 99%

“…The GVL tool becomes unwieldy over several dozen agents. Lynch et al (2017) explore the use of heat maps and spatial plots to visually represent the aggregate behaviors or aggregate distributions of agents throughout their environment during runtime. This approach to convey agent data helps illuminate pockets of the population that are grouping at specific geographic locations within the environment dependent upon their internal characteristics.…”

Section: Introductionmentioning

confidence: 99%

How to Create Empathy and Understanding: Narrative Analytics in Agent-Based Modeling

Diallo¹,

Lynch²,

Rechowicz³

et al. 2018

2018 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

In this paper we propose a different approach for interacting and analyzing agent-based models. The approach relies on creating empathy and understanding between physical agents in the physical world (people) and artificial agents in the simulated world (simulated agents). We propose a simulated empathy framework (SEF) in which artificial agents communicate directly with physical agents through verbal channels and social media. We argue that artificial agents should focus on the communication aspects between these two worlds, the ability to tell their story in a compelling way, and to read between the lines of physical agents speech. We present an implementation of the SEF and discuss challenges associated with implementing the framework in an artificial society.

show abstract

Methods That Support the Validation of Agent-Based Models: An Overview and Discussion

Collins,

Koehler,

Lynch

2024

JASSS

View full text Add to dashboard Cite

Validation is the process of determining if a model adequately represents the system under study for the model's intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models' mechanisms and in the real-world situations they attempt to represent. To aid both the novice and expert in conducting a validation process for an agent-based simulation, this article reviews nine methods that are useful for this process, including foundational topics of docking, empirical validation, sampling, and visualization, as well as advanced topics of bootstrapping, causal analysis, inverse generative social science, and role-playing. Each method is reviewed with respect to its benefits and limitations as a validation-supporting method for ABMS. Suggestions that may support a validation plan for an agent-based simulations, are also provided. This article is an introductory guide for understanding and conducting ABMS validation for developers of all experience levels.

show abstract

Identifying Unexpected Behaviors of Agent-Based Models Through Spatial Plots and Heat Maps

Cited by 5 publications

References 20 publications

A content analysis-based approach to explore simulation verification and identify its current challenges

A content analysis-based approach to explore simulation verification and identify its current challenges

How to Create Empathy and Understanding: Narrative Analytics in Agent-Based Modeling

Methods That Support the Validation of Agent-Based Models: An Overview and Discussion

Contact Info

Product

Resources

About