The western honey bee Apis mellifera is the most important pollinator in the world. The biggest threat to managed honey bees is the ectoparasitic mite Varroa destructor and the viruses DWV (Deformed Wing Virus) and APV (Acute Paralysis Virus) it transmits. Untreated honey bee colonies are expected to die within one to three years. This led to the development of strategies for beekeepers to control the Varroa mite in honey bee colonies and ensure the health and survival of their bee colonies, so called Good Beekeeping Practice. The aim of the extension of BEEHAVE was to represent the Good Beekeeping Practice of Varroa control in Germany. The relevant measures within the Varroa control strategies are drone brood removal as a Varroa trap and the treatment of bee colonies with organic acaricides (formic and oxalic acid) to kill the mites. This extension improves BEEHAVE and builds a bridge between beekeepers in practice and in the modelling world. It vastly contributes to the future use of BEEHAVE in beekeeping education in Germany.

The primary purpose of this model is to explain the dynamic processes within university-centered collaboration networks, with a particular focus on the complex transformation of academic knowledge into practical projects. Based on investigations of actual research projects and a thorough literature review, the model integrates multiple drivers and influencing factors to explore how these factors affect the formation and evolution of collaboration networks under different parameter scenarios. The model places special emphasis on the impact of disciplinary attributes, knowledge exchange, and interdisciplinary collaboration on the dynamics of collaboration networks, as well as the complex mechanisms of network structure, system efficiency, and interdisciplinary interactions during project formation.
Specifically, the model aims to:
- Simulate how university research departments drive the formation of research projects through knowledge creation.
- Investigate how the dynamics of collaboration networks influence the transformation of innovative hypotheses into matured projects.
- Examine the critical roles of knowledge exchange and interdisciplinary collaboration in knowledge production and project formation.
- Provide both quantitative and qualitative insights into the interactions among academia, industry, and project outputs.

To our knowledge, this is the first agent-based simulation of continuous-time PGGs (where participants can change contributions at any time) which are much harder to realise within both laboratory and simulation environments.

Work related to this simulation has been published in the following journal article:
Vu, Tuong Manh, Wagner, Christian and Siebers, Peer-Olaf (2019) ‘ABOOMS: Overcoming the Hurdles of Continuous-Time Public Goods Games with a Simulation-Based Approach’ Journal of Artificial Societies and Social Simulation 22 (2) 7 http://jasss.soc.surrey.ac.uk/22/2/7.html. doi: 10.18564/jasss.3995

Abstract:
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This model looks at implications of author/referee interaction for quality and efficiency of peer review. It allows to investigate the importance of various reciprocity motives to ensure cooperation. Peer review is modelled as a process based on knowledge asymmetries and subject to evaluation bias. The model includes various simulation scenarios to test different interaction conditions and author and referee behaviour and various indexes that measure quality and efficiency of evaluation […]

This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).

As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.

The intention of this model is to create an universal basis on how to model change in value prioritizations within social simulation. This model illustrates the designing of heterogeneous populations within agent-based social simulations by equipping agents with Dynamic Value-based Cognitive Architectures (DVCA-model). The DVCA-model uses the psychological theories on values by Schwartz (2012) and character traits by McCrae and Costa (2008) to create an unique trait- and value prioritization system for each individual. Furthermore, the DVCA-model simulates the impact of both social persuasion and life-events (e.g. information, experience) on the value systems of individuals by introducing the innovative concept of perception thermometers. Perception thermometers, controlled by the character traits, operate as buffers between the internal value prioritizations of agents and their external interactions. By introducing the concept of perception thermometers, the DVCA-model allows to study the dynamics of individual value prioritizations under a variety of internal and external perturbations over extensive time periods. Possible applications are the use of the DVCA-model within artificial sociality, opinion dynamics, social learning modelling, behavior selection algorithms and social-economic modelling.

This model is an agent-based simulation that consists of agents who play the spatial prisioner’s dilemma game with coalition formation. The coalition dynamics are mainly influenced by how much the agents trust their leaders. The main objective is provide a simulation model to enable the analysis of the impacts that the use of trust may cause in coalition formation.

Agent-based model of intergroup conflict in divided communities.

This is model that simulates how multiple kinds of peer effects shape the diffusion of innovations through different types of social relationships.

This Netlogo replication of Kollman, K., J.H. Miller and S.E. Page (1997) Political Institutions and Sorting in a Tiebout Model, American Economic Review 87(5): 977-992. The model consists of of citizens who can vote for partie and move to other jurisdictions if they expect their preferences are better served. Parties adjust their positions to increase their share in the elections.