HOW IT WORKS

This model consists of three agents, and each agent type operates per business theories as below.
a. New technologies(Tech): It evolves per sustaining or disruptive technology trajectory with the constraint of project management triangle (Scope, Time, Quality, and Cost).
b. Entrepreneurs(Entre): It builds up the solution by combining Tech components per its own strategy (Exploration, Exploitation, or Ambidex).
c. Consumer(Consumer): It selects the solution per its own preference due to Diffusion of innovation theory (Innovators, Early Adopters, Early Majority, Late Majority, Laggards)
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FIBE represents a simple fishery model. Fish that reproduce and fisher with different fishing styles that fish as their main source of income. The aim of the model is to reflect the different fishing behaviours as described and observed in the (Swedish) Baltic Sea fishery and explore the consequences of different approximations of human/fisher behaviour in under different environmental and managerial scenarios.

The overarching aim is to advance the incorporation and understanding of human behaviour (diversity) in fisheries research and management. In particular focusing on insights from social (fishery) science of fisher behaviour.

ICARUS is a multi-agent compliance inspection model (ICARUS - Inspecting Compliance to mAny RUleS). The model is applicable to environments where an inspection agency, via centrally coordinated inspections, examines compliance in organizations which must comply with multiple provisions (rules). The model (ICARUS) contains 3 types of agents: entities, inspection agency and inspectors / inspections. ICARUS describes a repeated, simultaneous, non-cooperative game of pure competition. Agents have imperfect, incomplete, asymmetric information. Entities in each move (tick) choose a pure strategy (comply/violate) for each rule, depending on their own subjective assessment of the probability of the inspection. The Inspection Agency carries out the given inspection strategy.

A more detailed description of the model is available in the .nlogo file.
Full description of the model (in line with the ODD+D protocol) and the analysis of the model (including verification, validation and sensitivity analysis) can be found in the attached documentation.

Netlogo model used to produce the results in my doctoral thesis

This is a ridesharing model (Uber/Lyft) of the larger Washington DC metro area. The model can be modified (Netlogo 6.x) relatively easily and be adapted to any metro area. Please cite generously (this was a lot of work) and please cite the paper, not the comses model.

Link to the paper published in “Complex Adaptive Systems” here: https://link.springer.com/chapter/10.1007/978-3-030-20309-2_7

Citation: Shaheen J.A.E. (2019) Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM). In: Carmichael T., Collins A., Hadžikadić M. (eds) Complex Adaptive Systems. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-20309-2_7

This model aims to examine how different levels of communication noise and superiority bias affect team performance when solving problems collectively. We used a networked agent-based model of collective problem solving in which agents explore the NK landscape for a better solution and communicate with each other regarding their current solutions. We compared the team performance in solving problems collectively at different levels of self-superiority bias when facing simple and complex problems. Additionally, we addressed the effect of different levels of communication noise on the team’s outcome

The Holmestrand model is an epidemiological agent-based model. Its aim is to test hypotheses related to how the social and physical environment of a residential school for children with disabilities might influence the spread of an infectious disease epidemic among students and staff. Annual reports for the Holmestrand School for the Deaf (Norway) are the primary sources of inspiration for the modeled school, with additional insights drawn from other archival records for schools for children with disabilities in early 20th century Norway and data sources for the 1918 influenza pandemic. The model environment consists of a simplified boarding school that includes residential spaces for students and staff, classrooms, a dining room, common room, and an outdoor area. Students and staff engage in activities reflecting hourly schedules suggested by school reports. By default, a random staff member is selected as the first case and is infected with disease. Subsequent transmission is determined by agent movement and interactions between susceptible and infectious pairs.

Schelling famously proposed an extremely simple but highly illustrative social mechanism to understand how strong ethnic segregation could arise in a world where individuals do not necessarily want it. Schelling’s simple computational model is the starting point for our extensions in which we build upon Wilensky’s original NetLogo implementation of this model. Our two NetLogo models can be best studied while reading our chapter “Agent-based Computational Models” (Flache and de Matos Fernandes, 2021). In the chapter, we propose 10 best practices to elucidate how agent-based models are a unique method for providing and analyzing formally precise, and empirically plausible mechanistic explanations of puzzling social phenomena, such as segregation, in the social world. Our chapter addresses in particular analytical sociologists who are new to ABMs.

In the first model (SegregationExtended), we build on Wilensky’s implementation of Schelling’s model which is available in NetLogo library (Wilensky, 1997). We considerably extend this model, allowing in particular to include larger neighborhoods and a population with four groups roughly resembling the ethnic composition of a contemporary large U.S. city. Further features added concern the possibility to include random noise, and the addition of a number of new outcome measures tuned to highlight macro-level implications of the segregation dynamics for different groups in the agent society.

In SegregationDiscreteChoice, we further modify the model incorporating in particular three new features: 1) heterogeneous preferences roughly based on empirical research categorizing agents into low, medium, and highly tolerant within each of the ethnic subgroups of the population, 2) we drop global thresholds (%-similar-wanted) and introduce instead a continuous individual-level single-peaked preference function for agents’ ideal neighborhood composition, and 3) we use a discrete choice model according to which agents probabilistically decide whether to move to a vacant spot or stay in the current spot by comparing the attractiveness of both locations based on the individual preference functions.

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Motivated by the emergence of new Peer-to-Peer insurance organizations that rethink how insurance is organized, we propose a theoretical model of decision-making in risk-sharing arrangements with risk heterogeneity and incomplete information about the risk distribution as core features. For these new, informal organisations, the available institutional solutions to heterogeneity (e.g., mandatory participation or price differentiation) are either impossible or undesirable. Hence, we need to understand the scope conditions under which individuals are motivated to participate in a bottom-up risk-sharing setting. The model puts forward participation as a utility maximizing alternative for agents with higher risk levels, who are more risk averse, are driven more by solidarity motives, and less susceptible to cost fluctuations. This basic micro-level model is used to simulate decision-making for agent populations in a dynamic, interdependent setting. Simulation results show that successful risk-sharing arrangements may work if participants are driven by motivations of solidarity or risk aversion, but this is less likely in populations more heterogeneous in risk, as the individual motivations can less often make up for the larger cost deficiencies. At the same time, more heterogeneous groups deal better with uncertainty and temporary cost fluctuations than more homogeneous populations do. In the latter, cascades following temporary peaks in support requests more often result in complete failure, while under full information about the risk distribution this would not have happened.

（An empty output folder named “NETLOGOexperiment” in the same location with the LAKEOBS_MIX.nlogo file is required before the model can be run properly）
The model is motivated by regime shifts (i.e. abrupt and persistent transition) revealed in the previous paleoecological study of Taibai Lake. The aim of this model is to improve a general understanding of the mechanism of emergent nonlinear shifts in complex systems. Prelimnary calibration and validation is done against survey data in MLYB lakes. Dynamic population changes of function groups can be simulated and observed on the Netlogo interface.
Main functional groups in lake ecosystems were modelled as super-individuals in a space where they interact with each other. They are phytoplankton, zooplankton, submerged macrophyte, planktivorous fish, herbivorous fish and piscivorous fish. The relationships between these functional groups include predation (e.g. zooplankton-phytoplankton), competition (phytoplankton-macrophyte) and protection (macrophyte-zooplankton). Each individual has properties in size, mass, energy, and age as physiological variables and reproduce or die according to predefined criteria. A system dynamic model was integrated to simulate external drivers.
Set biological and environmental parameters using the green sliders first. If the data of simulation are to be logged, set “Logdata” as true and input the name of the file you want the spreadsheet(.csv) to be called. You will need create an empty folder called “NETLOGOexperiment” in the same level and location with the LAKEOBS_MIX.nlogo file. Press “setup” to initialise the system and “go” to start life cycles.