Computational Model Library

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.

This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.

The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:

  1. Network generation: At the start of the simulation, the model generates a small world network of social links between the customers, and randomly assigns each customer to an initial insurer
  2. ...

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.

Peer reviewed Agent-based model to simulate equilibria and regime shifts emerged in lake ecosystems

no contributors listed | Published Tue Jan 25 11:53:47 2022

(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.

An agent-based model for the diffusion of innovations with multiple characteristics and price-premiums

This model builds on the Armature distribution within the PaleoscapeABM model, which is itself a variant of the PaleoscapeABM available here written by Wren and Janssen, and.

This model aims to explore where and how much shellfish is discarded at coastal and non-coastal locations by daily coastal foraging. We use this model’s output to test the idea that we can confidently use the archaeological record to evaluate the importance of shellfish in prehistoric people’s diets.

The recognition that aquatic adaptations likely had significant impacts on human evolution triggered an explosion of research on that topic. Recognizing coastal foraging in the past relies on the archaeological signature of that behavior. We use this model to explore why some coastal sites are very intensely occupied and see if it is due to the shellfish productivity of the coast.

AMIRIS is the Agent-based Market model for the Investigation of Renewable and Integrated energy Systems.

It is an agent-based simulation of electricity markets and their actors.
AMIRIS enables researches to analyse and evaluate energy policy instruments and their impact on the actors involved in the simulation context.
Different prototypical agents on the electricity market interact with each other, each employing complex decision strategies.
AMIRIS allows to calculate the impact of policy instruments on economic performance of power plant operators and marketers.

In this model, the spread of a virus disease in a network consisting of school pupils, employed, and umemployed people is simulated. The special feature in this model is the distinction between different types of links: family-, friends-, school-, or work-links. In this way, different governmental measures can be implemented in order to decelerate or stop the transmission.

Peer reviewed CHIME ABM of Hurricane Evacuation

Sean Bergin C Michael Barton Joshua Watts Joshua Alland Rebecca Morss | Published Mon Oct 18 18:31:05 2021 | Last modified Tue Jan 4 16:51:36 2022

The Communicating Hazard Information in the Modern Environment (CHIME) agent-based model (ABM) is a Netlogo program that facilitates the analysis of information flow and protective decisions across space and time during hazardous weather events. CHIME ABM provides a platform for testing hypotheses about collective human responses to weather forecasts and information flow, using empirical data from historical hurricanes. The model uses real world geographical and hurricane data to set the boundaries of the simulation, and it uses historical hurricane forecast information from the National Hurricane Center to initiate forecast information flow to citizen agents in the model.

Substitution of food products will be key to realising widespread adoption of sustainable diets. We present an agent-based model of decision-making and influences on food choice, and apply it to historically observed trends of British whole and skimmed (including semi) milk consumption from 1974 to 2005. We aim to give a plausible representation of milk choice substitution, and test different mechanisms of choice consideration. Agents are consumers that perceive information regarding the two milk choices, and hold values that inform their position on the health and environmental impact of those choices. Habit, social influence and post-decision evaluation are modelled. Representative survey data on human values and long-running public concerns empirically inform the model. An experiment was run to compare two model variants by how they perform in reproducing these trends. This was measured by recording mean weekly milk consumption per person. The variants differed in how agents became disposed to consider alternative milk choices. One followed a threshold approach, the other was probability based. All other model aspects remained unchanged. An optimisation exercise via an evolutionary algorithm was used to calibrate the model variants independently to observed data. Following calibration, uncertainty and global variance-based temporal sensitivity analysis were conducted. Both model variants were able to reproduce the general pattern of historical milk consumption, however, the probability-based approach gave a closer fit to the observed data, but over a wider range of uncertainty. This responds to, and further highlights, the need for research that looks at, and compares, different models of human decision-making in agent-based and simulation models. This study is the first to present an agent-based modelling of food choice substitution in the context of British milk consumption. It can serve as a valuable pre-curser to the modelling of dietary shift and sustainable product substitution to plant-based alternatives in Britain.

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