Hierarchical problem-solving model
The model simulates a hierarchical problem-solving process in which a manager delegates parts of a problem to specialists, who attempt to solve specific aspects based on their unique skills. The goal is to examine how effectively the hierarchical structure works in solving the problem, the total cost of the process, and the resulting solution quality.

Problem-solving random network model
The model simulates a network of agents (generalists) who collaboratively solve a fixed problem by iterating over it and using their individual skills to reduce the problem’s complexity. The goal is to study the dynamics of the problem-solving process, including agent interactions, work cycles, total cost, and solution quality.

The model employs an agent-based model for exploring the victim-centered approach to identifying human trafficking and the approach’s effectiveness in an abstract representation of migrant flows.

The model is then used for assessing three hypothetical and contrasted infrastructure-oriented adaptation strategies for the winter tourism industry, that have been previously discussed with local stakeholders, as possible alternatives to the “business-as-usual” situation.

The purpose of this model is to better understand the dynamics of a multihost pathogen in two host system comprising of high densities of domestic hosts and sympatric wildlife hosts susceptible to the pathogen.

A simplified Arthur & Polak logic circuit model of combinatory technology build-out via incremental development. Only some inventions trigger radical effects, suggesting they depend on whole interdependent systems rather than specific innovations.

Merger and acquisition (M&A) activity has many strategic and operational objectives. One operational objective is to develop common and efficient information systems that maybe the source of creating

Chicago’s demographic, neighborhood, sex risk behaviors, sexual network data, and HIV prevention and treatment cascade information from 2015 were integrated as input to a new agent-based model (ABM) called the Levers-of-HIV-Model (LHM). This LHM, written in NetLogo, forms patterns of sexual relations among Men who have Sex with Men (MSM) based on static traits (race/ethnicity, and age) and dynamic states (sexual relations and practices) that are found in Chicago. LHM’s five modules simulate and count new infections at the two marker years of 2023 and 2030 for a wide range of distinct scenarios or levers, in which the levels of PrEP and ART linkage to care, retention, and adherence or viral load are increased over time from the 2015 baseline levels.

The present model is an abstract ABM designed for theoretical exploration and hypotheses generation. Its main aim is to explore the relationship between disagreement over the diagnostic value of evidence and the formation of polarization in scientific communities.
The model represents a scientific community in which scientists aim to determine whether hypothesis H is true, and we assume that agents are in a world in which H is indeed true. To this end, scientists perform experiments, interpret data and exchange their views on how diagnostic of H the obtained evidence is. Based on how the scientists conduct the inquiry, the community may reach a correct consensus (i.e. a situation in which every scientist agrees that H is correct) or not.

This is the model for a paper that is based on a simulation model, programmed in Netlogo, that demonstrates changes in market structure that occur as marginal costs, demand, and barriers to entry change. Students predict and observe market structure changes in terms of number of firms, market concentration, market price and quantity, and average marginal costs, profits, and markups across the market as firms innovate. By adjusting the demand growth and barriers to entry, students can […]

This model examines language dynamics within a social network using simulation techniques to represent the interplay of language adoption, social influence, economic incentives, and language policies. The agent-based model (ABM) focuses on interactions between agents endowed with specific linguistic attributes, who engage in communication based on predefined rules. A key feature of our model is the incorporation of network analysis, structuring agent relationships as a dynamic network and leveraging network metrics to capture the evolving inter-agent connections over time. This integrative approach provides nuanced insights into emergent behaviors and system dynamics, offering an analytical framework that extends beyond traditional modeling approaches. By combining agent-based modeling with network analysis, the model sheds light on the underlying mechanisms governing complex language systems and can be effectively paired with sociolinguistic observational data.