scholarly journals Second-order agent-based models of emergent behaviour of Dictyostelium discoideum and their inspiration for swarm robotics

2020 ◽  
Vol 25 (4) ◽  
pp. 656-665
Author(s):  
Mohammad Parhizkar ◽  
Giovanna Di Marzo Serugendo ◽  
Jahn Nitschke ◽  
Louis Hellequin ◽  
Assane Wade ◽  
...  
Keyword(s):  
Dictyostelium Discoideum ◽  
Swarm Robotics ◽  
Second Order ◽  
Transition Phase ◽  
Agent Based Models ◽  
Agent Based ◽  
Emergent Behaviour ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Abstract By studying and modelling the behaviour of Dictyostelium discoideum, we aim at deriving mechanisms useful for engineering collective artificial intelligence systems. This paper discusses a selection of agent-based models reproducing second-order behaviour of Dictyostelium discoideum, occurring during the migration phase; their corresponding biological illustrations; and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. For the models, we focus on: (1) the transition phase from first- to second-order emergent behaviour; (2) slugs’ uniform distribution around a light source; and (3) the relationship between slugs’ speed and length occurring during the migration phase of the life cycle of D. discoideum. Results show the impact of the length of the slug on its speed and the effect of ammonia on the distribution of slugs. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on the transition phase, slugs’ chaining, merging and moving away from each other.

scholarly journals First-order agent-based models of emergent behaviour of Dictyostelium discoideum and their inspiration for swarm robotics

2020 ◽  
Vol 25 (4) ◽  
pp. 643-655
Author(s):  
Mohammad Parhizkar ◽  
Giovanna Di Marzo Serugendo ◽  
Jahn Nitschke ◽  
Louis Hellequin ◽  
Assane Wade ◽  
...  
Keyword(s):  
Dictyostelium Discoideum ◽  
Adaptive Systems ◽  
Swarm Robotics ◽  
Environmental Changes ◽  
Agent Based Models ◽  
Ongoing Research ◽  
Agent Based ◽  
Emergent Behaviour ◽  
The Social ◽  
Selection Of

Abstract Collective behaviour in nature provides a source of inspiration to engineer artificial collective adaptive systems, due to their mechanisms favouring adaptation to environmental changes and enabling complex emergent behaviour to arise from a relatively simple behaviour of individual entities. As part of our ongoing research, we study the social amoeba Dictyostelium discoideum to derive agent-based models and mechanisms that we can then exploit in artificial systems, in particular in swarm robotics. In this paper, we present a selection of agent-based models of the aggregation phase of D. discoideum, their corresponding biological illustrations and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. We focus on the stream-breaking phenomenon occurring during the aggregation phase of the life cycle of D. discoideum. Results show that the breakup of aggregation streams depends on cell density, motility, motive force and the concentration of cAMP and CF. The breakup also comes with the appearance of late centres. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on signalling and aggregation towards a centre.

Eating Behavior of Brazilian College Students: Influences of Lifestyle, Negative Affectivity, and Personal Characteristics

2020 ◽  
pp. 003151252098308
Author(s):  
Bianca G. Martins ◽  
Wanderson R. da Silva ◽  
João Marôco ◽  
Juliana A. D. B. Campos
Keyword(s):  
College Students ◽  
Eating Behavior ◽  
Emotional Eating ◽  
Eating Behaviors ◽  
Structural Model ◽  
Negative Affectivity ◽  
Personal Characteristics ◽  
Second Order ◽  
The Impact ◽  
The Relationship

In this study we proposed to estimate the impact of lifestyle, negative affectivity, and college students’ personal characteristics on eating behavior. We aimed to verify that negative affectivity moderates the relationship between lifestyle and eating behavior. We assessed eating behaviors of cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE)) with the Three-Factor Eating Questionnaire-18. We assessed lifestyle with the Individual Lifestyle Profile, and we assessed negative affectivity with the Depression, Anxiety and Stress Scale-21. We constructed and tested (at p < .05) a hypothetical causal structural model that considered global (second-order) and specific (first-order) lifestyle components, negative affectivity and sample characteristics for each eating behavior dimension. Participants were 1,109 college students ( M age = 20.9, SD = 2.7 years; 65.7% females). We found significant impacts of lifestyle second-order components on negative affectivity (β = −0.57–0.19; p < 0.001–0.01) in all models. Physical and psychological lifestyle components impacted directly only on CR (β=−0.32–0.81; p < 0.001). Negative affectivity impacted UE and EE (β = 0.23–0.30; p < 0.001). For global models, we found no mediation pathways between lifestyle and CR or UE. For specific models, negative affectivity was a mediator between stress management and UE (β=−0.07; p < 0.001). Negative affectivity also mediated the relationship between thoughts of dropping an undergraduate course and UE and EE (β = 0.06–0.08; p < 0.001). Participant sex and weight impacted all eating behavior dimensions (β = 0.08–0.34; p < 0.001–0.01). Age was significant for UE and EE (β=−0,14– −0.09; p < 0.001–0.01). Economic stratum influenced only CR (β = 0.08; p = 0.01). In sum, participants’ lifestyle, negative emotions and personal characteristics were all relevant for eating behavior assessment.

scholarly journals The Impact of Alkaloid-Producing Epichloë Endophyte on Forage Ryegrass Breeding: A New Zealand Perspective

Toxins ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 158
Author(s):  
Colin Eady
Keyword(s):  
Seed Production ◽  
Health Issues ◽  
Abiotic Stressors ◽  
Livestock Health ◽  
The Right ◽  
The Impact ◽  
The Relationship ◽  
And Storage ◽  
Selection Of ◽  
Epichloë Endophyte

For 30 years, forage ryegrass breeding has known that the germplasm may contain a maternally inherited symbiotic Epichloë endophyte. These endophytes produce a suite of secondary alkaloid compounds, dependent upon strain. Many produce ergot and other alkaloids, which are associated with both insect deterrence and livestock health issues. The levels of alkaloids and other endophyte characteristics are influenced by strain, host germplasm, and environmental conditions. Some strains in the right host germplasm can confer an advantage over biotic and abiotic stressors, thus acting as a maternally inherited desirable ‘trait’. Through seed production, these mutualistic endophytes do not transmit into 100% of the crop seed and are less vigorous than the grass seed itself. This causes stability and longevity issues for seed production and storage should the ‘trait’ be desired in the germplasm. This makes understanding the precise nature of the relationship vitally important to the plant breeder. These Epichloë endophytes cannot be ‘bred’ in the conventional sense, as they are asexual. Instead, the breeder may modulate endophyte characteristics through selection of host germplasm, a sort of breeding by proxy. This article explores, from a forage seed company perspective, the issues that endophyte characteristics and breeding them by proxy have on ryegrass breeding, and outlines the methods used to assess the ‘trait’, and the application of these through the breeding, production, and deployment processes. Finally, this article investigates opportunities for enhancing the utilisation of alkaloid-producing endophytes within pastures, with a focus on balancing alkaloid levels to further enhance pest deterrence and improving livestock outcomes.

scholarly journals Data assimilation in Agent-based models using creation and annihilation operators

2019 ◽  
Author(s):  
Daniel Tang
Keyword(s):  
Real World ◽  
Null Model ◽  
The State ◽  
Actual State ◽  
Agent Based Models ◽  
Agent Based ◽  
Predator Prey ◽  
Predator Prey Model ◽  
The One ◽  
The Relationship

Agent-based models are a powerful tool for studying the behaviour of complex systems that can be described in terms of multiple, interacting ``agents''. However, because of their inherently discrete and often highly non-linear nature, it is very difficult to reason about the relationship between the state of the model, on the one hand, and our observations of the real world on the other. In this paper we consider agents that have a discrete set of states that, at any instant, act with a probability that may depend on the environment or the state of other agents. Given this, we show how the mathematical apparatus of quantum field theory can be used to reason probabilistically about the state and dynamics the model, and describe an algorithm to update our belief in the state of the model in the light of new, real-world observations. Using a simple predator-prey model on a 2-dimensional spatial grid as an example, we demonstrate the assimilation of incomplete, noisy observations and show that this leads to an increase in the mutual information between the actual state of the observed system and the posterior distribution given the observations, when compared to a null model.

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Shepperd et al. (2014) find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al. (2014)’s data. We observe that (a) researcher group shares a strong association with the dataset and metric families that are used to build a model; (b) the strong association among the explanatory variables introduces a large amount of interference when interpreting the impact of the researcher group on model performance; and (c) after mitigating the interference, we find that the researcher group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model may have more to do with the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat potential bias in their results.

Evolution of Drosophila insecticide resistance

Genome ◽  
1993 ◽  
Vol 36 (1) ◽  
pp. 1-7 ◽  
Author(s):  
R. A. Morton
Keyword(s):  
Insecticide Resistance ◽  
Quantitative Traits ◽  
Molecular Genetic ◽  
Resistance Evolution ◽  
Target Molecules ◽  
Genetic Techniques ◽  
The Cost ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

The impact of insecticide resistance is well documented. It includes the toxic effects of pesticides on the environment and the cost of the increased amounts of insecticides required to effectively control resistant insects. Resistance evolves by the selection of genes that confer tolerance to insecticides. Several resistance genes have been identified and cloned in Drosophila, including genes for mutant target molecules and genes that increase insecticide degradation. Drosophila is a useful system to understand the evolution of quantitative traits in general as well as the population genetics of insecticide resistance. Through it, we may hope to understand the relationship between discrete genetic change and continuously varying characters. In addition, molecular genetic techniques developed using Drosophila can eventually be transferred to other insects in order to help control pest populations.Key words: insecticide resistance, evolution of tolerance, selection of resistant genes, molecular genetics, Drosophila.

scholarly journals Filling the Gaps: Vacancy Chains and Agent-Based Models

2020 ◽  
Author(s):  
Radu Andrei Pârvulescu
Keyword(s):  
Markov Chain ◽  
Socialist Country ◽  
Social Revolution ◽  
Agent Based Models ◽  
Agent Based ◽  
Equilibrium Dynamics ◽  
Chain Analysis ◽  
Vacancy Chains ◽  
The Impact ◽  
Scholarly Attention

Vacancy-chain analysis (VCA), a method for tracing the flows of resources such as jobs or housing, has faded from scholarly attention. This is unfortunate, because VCA is often superior to markets, auctions, or games, the more popular metaphors-cum-models of resource allocation. This paper aims to revive VCA by casting it in terms of agent-based models (ABMs). I first review and note the limitations of the Markov-chain version VCA (or MC-VCA), and then introduce an agent-based approach to vacancy chain systems, the ABM-VCA, which features the innovation of treating both resources/positions and opportunities as agents. I show that ABM-VCA can replicate MC-VCA (since the former is an MCMC estimator of the latter) and then illustrate the usefulness of ABM-VCA to empirically study off-equilibrium dynamics by using it to assessing the impact of social revolution on the judiciary of a post-socialist country. I conclude by noting the methodological possibilities opened up by ABM-VCA, such as the potential to simulating fields and ecologies. A Python implementation of ABM-VCA is available at https://github.com/r-parvulescu/abm-vca.

Agent-Based Modeling of Flood Insurance Futures

2020 ◽  
Author(s):  
Linda Geaves
Keyword(s):  
Autonomous Agents ◽  
Insurance Industry ◽  
Model Development ◽  
Policy Instruments ◽  
Flood Insurance ◽  
Agent Based Models ◽  
Online Data ◽  
Agent Based ◽  
Artificial Intelligence Technology ◽  
The Impact

Agent-based models have facilitated greater understanding of flood insurance futures, and will continue to advance this field as modeling technology develops further. As the pressures of climate-change increase and global populations grow, the insurance industry will be required to adapt to a less predictable operating environment. Complicating the future of flood insurance is the role flood insurance plays within a state, as well as how insurers impact the interests of other stakeholders, such as mortgage providers, property developers, and householders. As such, flood insurance is inextricably linked with the politics, economy, and social welfare of a state, and can be considered as part of a complex system of changing environments and diverse stakeholders. Agent-based models are capable of modeling complex systems, and, as such, have utility for flood insurance systems. These models can be considered as a platform in which the actions of autonomous agents, both individuals and collectives, are simulated. Cellular automata are the lowest level of an agent-based model and are discrete and abstract computational systems. These automata, which operate within a local and/or universal environment, can be programmed with characteristics of stakeholders and can act independently or interact collectively. Due to this, agent-based models can capture the complexities of a multi-stakeholder environment displaying diversity of behavior and, concurrently, can cater for the changing flood environment. Agent-based models of flood insurance futures have primarily been developed for predictive purposes, such as understanding the impact of introductions of policy instruments. However, the ways in which these situations have been approached by researchers have varied; some have focused on recreating consumer behavior and psychology, while others have sought to recreate agent interactions within a flood environment. The opportunities for agent-based models are likely to become more pronounced as online data becomes more readily available and artificial intelligence technology supports model development.

scholarly journals ‘Don’t play if you can’t win’: does economic inequality undermine political equality?

2019 ◽  
Vol 11 (3) ◽  
pp. 395-413 ◽  
Author(s):  
Armin Schäfer ◽  
Hanna Schwander
Keyword(s):  
Income Inequality ◽  
Voter Turnout ◽  
Economic Inequality ◽  
Political Equality ◽  
Local Data ◽  
Cross Sectional ◽  
Time Period ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

AbstractIn this paper, we investigate whether income inequality negatively affects voter turnout. Despite some progress, the answer to this question is still debated due to methodological disagreements and differences in the selection of countries and time periods. We contribute to this debate by triangulating data and methods. More specifically, we use three kinds of data to resolve the question: first, we use cross-sectional aggregate data of 21 OECD countries in the time period from 1980 to 2014 to study the relationship between inequality and electoral participation. Second, we zoom in on the German case and examine local data from 402 administrative districts between 1998 and 2017. Focusing on within-country variation eliminates differences that are linked to features of the political system. Finally, we combine survey data with macro-data to investigate the impact of inequality on individual voting. This final step also allows us to test whether the effect of income inequality on voter turnout differs across income groups. Taken together, we offer the most comprehensive analysis of the impact of social inequality on political inequality to date. We corroborate accounts that argue that economic inequality exacerbates participatory inequality.

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