scholarly journals Simulating the Spatial Distribution of Pollutant Loads from Pig Farming using an Agent-based Modeling Approach

2021 ◽  
Author(s):  
An The Ngo ◽  
Giang Thi Huong Nguyen ◽  
Duong Huu Nong ◽  
Linda See
Keyword(s):  
Predictive Ability ◽  
Household Survey ◽  
Environmental Communication ◽  
Agent Based Modeling ◽  
Baseline Scenario ◽  
Agent Based ◽  
Pollutant Loads ◽  
Pig Farming ◽  
Management Measures ◽  
Using Data

Abstract This research developed an agent-based model (ABM) for simulating pollutant loads from pig farming. The behavior of farmer agents was captured using concepts from the Theory of Planned Behavior. The ABM has three basic components: the household or farmer agent, the land patches and global parameters that capture the environmental context. The model was evaluated using a sensitivity analysis, and then validated using data from a household survey, which showed that the predictive ability of the model was good. The ABM was then used in three scenarios: a baseline scenario, a positive scenario in which the number of pigs was assumed to remain stable but supporting policies for environmental management were increased, and a negative scenario, which assumed the number of pigs increases but management measures did not improve relative to the baseline. The positive scenario showed reductions in the discharged loads for many sub-basins of the study area while the negative scenario indicated that increased loads will be discharged to the environment. The scenario results suggest that to maintain the development of pig production while ensuring environmental protection for the district, financial and technical support must be provided to the pig producers. The experience and education level of the farmers were significant factors influencing behaviors related to the manure reuse and treatment, so awareness raising through environmental communication is needed in addition to technical measures.

scholarly journals Quantifying the driving factors for language shift in a bilingual region

2017 ◽  
Vol 114 (17) ◽  
pp. 4365-4369 ◽  
Author(s):  
Katharina Prochazka ◽  
Gero Vogl
Keyword(s):  
Cellular Automata ◽  
Differential Equations ◽  
Large Scale ◽  
Language Shift ◽  
Reaction Diffusion ◽  
Agent Based Modeling ◽  
Reaction Diffusion Equations ◽  
Agent Based ◽  
Using Data ◽  
A Minor

Many of the world’s around 6,000 languages are in danger of disappearing as people give up use of a minority language in favor of the majority language in a process called language shift. Language shift can be monitored on a large scale through the use of mathematical models by way of differential equations, for example, reaction–diffusion equations. Here, we use a different approach: we propose a model for language dynamics based on the principles of cellular automata/agent-based modeling and combine it with very detailed empirical data. Our model makes it possible to follow language dynamics over space and time, whereas existing models based on differential equations average over space and consequently provide no information on local changes in language use. Additionally, cellular automata models can be used even in cases where models based on differential equations are not applicable, for example, in situations where one language has become dispersed and retreated to language islands. Using data from a bilingual region in Austria, we show that the most important factor in determining the spread and retreat of a language is the interaction with speakers of the same language. External factors like bilingual schools or parish language have only a minor influence.

Agent-based models and hypothesis testing: an example of innovation and organizational networks

2012 ◽  
Vol 27 (2) ◽  
pp. 221-238 ◽  
Author(s):  
Allen Wilhite ◽  
Eric A. Fong
Keyword(s):  
Hypothesis Testing ◽  
Corporate Culture ◽  
Computational Models ◽  
Organizational Structures ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Virtual Experiments ◽  
Network Topologies ◽  
And Performance ◽  
Using Data

AbstractHypothesis testing is uncommon in agent-based modeling and there are many reasons why (see Fagiolo et al. (2007) for a review). This is one of those uncommon studies: a combination of the new and old. First, a traditional neoclassical model of decision making is broadened by introducing agents who interact in an organization. The resulting computational model is analyzed using virtual experiments to consider how different organizational structures (different network topologies) affect the evolutionary path of an organization's corporate culture. These computational experiments establish testable hypotheses concerning structure, culture, and performance, and those hypotheses are tested empirically using data from an international sample of firms. In addition to learning something about organizational structure and innovation, the paper demonstrates how computational models can be used to frame empirical investigations and facilitate the interpretation of results in a traditional fashion.

Testing Scenarios to Achieve Workplace Sustainability Goals Using Backcasting and Agent-Based Modeling

2016 ◽  
Vol 49 (9) ◽  
pp. 1007-1037 ◽  
Author(s):  
Ricardo García-Mira ◽  
Adina Dumitru ◽  
Amparo Alonso-Betanzos ◽  
Noelia Sánchez-Maroño ◽  
Óscar Fontenla-Romero ◽  
...  
Keyword(s):  
Large Scale ◽  
Agent Based Modeling ◽  
Scenario Development ◽  
Dynamic Simulations ◽  
Environmental Behaviors ◽  
Agent Based ◽  
Work Related ◽  
Development Methodology ◽  
Effective Policy ◽  
Using Data

Pro-environmental behaviors have been analyzed in the home, with little attention to other important contexts of everyday life, such as the workplace. The research reported here explored three categories of pro-environmental behavior (consumption of materials and energy, waste generation, and work-related commuting) in a public large-scale organization in Spain, with the aim of identifying the most effective policy options for a sustainable organization. Agent-based modeling was used to design a virtual simulation of the organization. Psychologically informed profiles of employees were defined using data gathered through a questionnaire, measuring knowledge, motivations, and ability. Future scenarios were developed using a participatory backcasting scenario development methodology, and policy tracks were derived. Dynamic simulations indicated that, to be effective, organizational policy should strengthen worker participation and autonomy, be sustained over time, and should combine different measures of medium intensity for behavior change, instead of isolated policies of high intensity.

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