Modular ABM development for improved dissemination and training

Bell, Andrew Reid; Robinson, Derek T.; Malik, Ammar; Dewal, Snigdha

doi:10.1016/j.envsoft.2015.07.016

Cited by 34 publications

(23 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Acknowledging that various societal processes occur at different scales demands representing them hierarchically (farmer, household, firm, city, region, country, world economy). Future crossscale SES models are likely to be a nested integration of different software components and model types (Belete et al, 2017b;Verburg et al, 2016), or a modular approach with validated, theoretically-grounded decision modules representing various social actors combined within one software platform (Bell et al, 2015). Best practice in SES modeling advises that combining distinct models could utilize software wrappers to link models (Belete et al , 2019) and should entail full detailing of how human and environmental processes are intertwined (Belete et al, 2017a;Rounsevell, et al, 2016).…”

Section: The Way Forwardmentioning

confidence: 99%

Eight grand challenges in socio-environmental systems modeling

Elsawah

Filatova

Jakeman

et al. 2020

SESMO

Self Cite

127

120

View full text Add to dashboard Cite

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

show abstract

Section: The Way Forwardmentioning

confidence: 99%

Eight grand challenges in socio-environmental systems modeling

Elsawah

Filatova

Jakeman

et al. 2020

SESMO

Self Cite

127

120

View full text Add to dashboard Cite

show abstract

“…As models tend to be more and more complex and descriptive, it becomes more and more difficult to reuse them in different contexts. A solution to tackle this issue is to use a modular approach [10], in which the goal is develop complex models as a composition of numerous sub-models, which can be developed independently and used in a stand-alone way. Several works such as [55] have shown the interest of using such approach for developing complex spatial models.…”

Section: Co-modeling and Multi-simulationmentioning

confidence: 99%

Building, composing and experimenting complex spatial models with the GAMA platform

Taillandier¹,

et al. 2018

View full text Add to dashboard Cite

Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Taillandier AbstractThe agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decisionmaking processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

show abstract

“…Li et al (2014Li et al ( , 2017 used a self-adaptive algorithm in combination with an agent-based model to study a similar problem. Agent-based simulations been used in many areas (Buchmann et al 2016), such as biological sequence comparison (Sandes et al 2014), crowd evacuation (Wagner and Agrawal 2014), marketing strategies (Serrano and Iglesias 2015), land use change (Bell et al 2015;Tayyebi et al 2016), water management (Isern et al 2012;Van der Wal et al 2016), and agricultural policy strategies (Berger and Troost 2014;Troost et al 2015). In general, agentbased modeling studies assume that agents (farmers) react autonomously and cognitively to signals from the external environment (Murray-Rust et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Designing price-contingent vegetable rotation schedules using agent-based simulation

Rodrı́guez

Wang

et al. 2018

Journal of Integrative Agriculture

View full text Add to dashboard Cite

Chinese vegetable production cooperatives supply their members, mostly smallholder farmers, with a rotation schedule for the year. Since vegetable prices are not stable throughout the year, designing a rotation schedule that maximizes expected profits, distributes farmers' profits more equitably, maintains the diversity of produce in the market, and reduces the risk of pests and diseases, requires adaptive, price-contingent rotation schedules (here, called "self-adaptive adjustment"). This study uses an agent-based simulation (ABS) to design self-adaptive rotation schedules that deliver these aims. The selfadaptive adjustment strategy was more profitable for farmers when faced with price volatility, and more equitable as well. This work provides a decision-support tool for managers of Chinese vegetable production cooperatives to provide farmers with more profitable and equitable rotation schedules.

show abstract

Modular ABM development for improved dissemination and training

Cited by 34 publications

References 42 publications

Eight grand challenges in socio-environmental systems modeling

Eight grand challenges in socio-environmental systems modeling

Building, composing and experimenting complex spatial models with the GAMA platform

Designing price-contingent vegetable rotation schedules using agent-based simulation

Contact Info

Product

Resources

About