Agent‐based models to investigate sound impact on marine animals: bridging the gap between effects on individual behaviour and population level consequences

Oikos ◽  
2021 ◽  
Author(s):  
Lars O. Mortensen ◽  
Magda Ewa Chudzinska ◽  
Hans Slabbekoorn ◽  
Frank Thomsen
Keyword(s):  
Population Level ◽  
Agent Based Models ◽  
Marine Animals ◽  
Individual Behaviour ◽  
Agent Based

Adding the missing link back into mate choice research

2005 ◽  
Vol 28 (2) ◽  
pp. 289-289 ◽  
Author(s):  
Rui Mata ◽  
Andreas Wilke ◽  
Peter M. Todd
Keyword(s):  
Individual Differences ◽  
Mate Choice ◽  
Computational Modeling ◽  
Population Level ◽  
Individual Choice ◽  
Agent Based Models ◽  
Agent Based ◽  
Psychological Mechanisms ◽  
Attitude Research ◽  
Detailed Specification

Evolutionary psychologists should go beyond research on individual differences in attitudes and focus more on detailed models of psychological mechanisms. We argue for complementing attitude research with agent-based computational modeling of mate choice. Agent-based models require detailed specification of individual choice mechanisms that can be evaluated in terms of both their psychological plausibility and the population-level outcomes they produce.

scholarly journals Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Philippe J. Giabbanelli ◽  
Rik Crutzen
Keyword(s):  
Food Environment ◽  
Population Level ◽  
Agent Based Models ◽  
Future Directions ◽  
Agent Based ◽  
Sociocultural Environment ◽  
Systems Perspective ◽  
Complex Procedure ◽  
Term Objective

Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps’ process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

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