[서평]「The Unified Modeling Language User Guide」

1. The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. 2. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. 3. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions. 4. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. 5. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2664.12112

REVIEW: Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models

The honeybee (Apis mellifera) waggle dance, whereby dancing bees communicate the location of profitable food sources to other bees in the hive, is one of the most celebrated communication behaviours in the animal world. Dance followers, however, often appear to ignore this location information, the so-called dance language, after leaving the nest. Here we consider why foragers follow dances and discuss the function of the dance as a multicomponent signal. We argue that the 'dance language' is just one information component of the waggle dance and that the two terms should not be used synonymously. We propose that location information is often backup information to be used in case information acquired directly from the environment about food sources is not available, is of poor quality or is associated with unprofitable food sources.

/pdf/the-honeybee-waggle-dance-can-we-follow-the-steps-2r3lvmmxev.pdf

The honeybee waggle dance: can we follow the steps?

Many insects and arthropods live in colonies or aggregations of varying size. Group size may affect collective organization either because the same individual behavior has different consequences when displayed in a larger group or because larger groups are subject to different constraints and selection pressures than smaller groups. In eusocial colonies, group size may have similar effects on colony traits as body size has on organismal traits. Social insects may, therefore, be useful to test theories about general principles of scaling, as they constitute a distinct level of organization. However, there is a surprising lack of data on group sizes in social insects and other group-living arthropods, and multiple confounding factors have to be controlled to detect effects of group size. If such rigorous studies are performed, group size may become as important to understanding collective organization as is body size in explaining behavior and life history of individual organisms.

Group Size and Its Effects on Collective Organization

Copying others can greatly improve individual fitness and is fundamental for the organisation of societies. Yet in some situations it is better to ignore social information and either explore the world individually or use personal information obtained through prior experience. Insects provide excellent models to study the strategic use of social information, but insights from recent research have rarely been viewed in the light of social learning strategies. Here we discuss how insects tailor their reliance on social information to those circumstances for which it is most beneficial, and suggest that insects and vertebrates use similar information-use strategies. We highlight future research avenues, including the use of molecular tools to study the genetic and genomic basis of social information use.

https://www.cell.com/trends/ecology-evolution/pdf/S0169-5347(14)00017-2.pdf

Insights from insects about adaptive social information use

Why do some social insects have sophisticated recruitment systems, while other species do not communicate about food source locations at all? To answer this question, it is necessary to identify the social or ecological factors that make recruitment adaptive and thus likely to evolve. We developed an individual-based model of honey bee foraging to quantify the benefits of recruitment under different spatial distributions of nondepleting resource patches and with different colony sizes. Benefits of recruitment were strongly dependent on resource patch quality, density, and variability. Communication was especially beneficial if patches were poor, few, and variable. A sensitivity analysis of the model showed that under conditions of high resource density recruitment could even become detrimental, especially if foraging duration was short, tendency to scout was high, or recruits needed a long time to find communicated locations. Colony size, a factor often suspected to influence recruitment evolution, had no significant effect. These results may explain the recent experimental findings that in honey bees, benefits of waggle dance recruitment seem to vary seasonally and with habitat. They may also explain why some, but not other, species of social bees have evolved a strategy to communicate food locations to nest mates. Key words: Apis mellifera, communication, foraging, individual-based model, social insects, waggle dance. [Behav Ecol]

/pdf/benefits-of-recruitment-in-honey-bees-effects-of-ecology-and-1fejcmbuxd.pdf

Benefits of recruitment in honey bees: effects of ecology and colony size in an individual-based model

In this paper we want to show the possibilities to use agent-based modeling and simulation for software development. Therefore we present the integrated environment SeSam and recent extensions, that allow creating simulated environments for agent based software as well as actually developing and deploying software agents.

From Simulated to Real Environments: How to Use SeSAm for Software Development

Developing multi-agent simulations seems to be rather straight forward, as active entities in the original correspond to active agents in the model. Thus plausible behaviors can be produced rather easily. However, for real world applications they must satisfy some requirements concerning verification, validation and reproducibility. Using a standard framework for designing a multi-agent model one can gain further advantages like fast learnability, wide understandability and possible transfer.In this paper we show how UML can be used to specify behavior-oriented multi-agent models. Therefore we focus on activity graphs and the representation of different forms of interactions in these graphs.

/pdf/uml-for-behavior-oriented-multi-agent-simulations-1gym1v493n.pdf

UML for Behavior-Oriented Multi-agent Simulations

In this paper we show how to use evolutionary optimization in multi-agent systems with simple, non deliberative agents to evolve successful interaction in societies, mostly for task allocation based problems. The presented generic optimization scheme – which is implemented based on the multi-agent simulation tool SeSAm – was used to model prehistoric ant species. The idea was to show how solitary individuals evolve to societies. Results from these experiments are given. Finally future plans are discussed and evaluated.

/pdf/evolutionary-optimization-of-societies-in-simulated-multi-1whrst1cs1.pdf

Evolutionary Optimization of Societies in Simulated Multi-Agent Systems

Institut fur Wirtschaftsinformatik, Technische Universitat IlmenauPostfach 0565, D-98684 Ilmenau, Stefan.Kirn@wirtschaft.tu-ilmenau.de1. MotivationHospitals can be viewed as complex systems with sophisticated and often hardly control-abledynamics. Until recently the need for effective management was not really urgent, as thehealth insurances were able to fund all the necessary tasks in a sufficient manner. But thepressure grows that hospitals have to organize all processes in an optimal way to save timeand thus expenses. In medical environments, like hospitals, this afford is twofold. On one sidethis refers to diagnostic processes targeting on a secure determination of what disease thepatient is suffering from and the management of a successful therapy. On the other side timeand resource management techniques are necessary for an efficient schedule of care andexamination tasks. Because of the unpredictability and dynamics in medical domains thelogistic processes can hardly be controlled automatically using hospital management systems.Thus there is a high potential of possible improvement especially for logistic processes that isnot accessible with traditional techniques that are used in e. g. production systems.Using multi-agent simulation the dynamics in a hospital can be mapped into a modelcontaining more or less sophisticated agents with arbitrary complex behavior. Different eventshave not to be modeled using probability distributions that are based on observations from aglobal point of view, but the complete “life span” of a patient with all tasks from diagnosis viatherapy to the discharge can be modeled explicitly and simulated in detail. In this scenariomulti-agent simulation provides a flexibility that neither petri nets nor process-basedsimulation are capable of. Thus the dynamics in an hospital can be better formulated andcomprehended leading to the possibility of repeated “what-if” simulations with questions andanswers to check the influence of specific factors. It might become possible to generate semi-automatically suggestions for optimizing certain parameters, for example the personalequipment regarding distinct criteria like minimal overtime. A multi-agent simulation model ofIn: Tagungsband zum Workshop "Intelligente Softwareagenten und betriebswirtschaftliche Anwendungsszenarien", Ilmenau, 22-23. Juli 1999. TU Ilmenau, Wirtschaftsinformatik 2, Arbeitsbericht Nr. 14, Juli 1999, S. 151-159., 1999

Christoph Oechslein

Papers

Benefits of recruitment in honey bees: effects of ecology and colony size in an individual-based model

From Simulated to Real Environments: How to Use SeSAm for Software Development

UML for Behavior-Oriented Multi-agent Simulations

Evolutionary Optimization of Societies in Simulated Multi-Agent Systems

Multi-Agent Simulation of Diagnostic and Logistic Processes in Hospitals