Computational Model Library

This model allows simulating the impacts of floods on a population. Floods are described by their intensity (flood height) and date of occurrence. Households are more or less severely hit by floods according to their geographical situation. Impacts are measured in terms of reductions in household wealth. Households may take up protection measures against floods, depending on their individual characteristics, a social network and information campaigns. If such measures are taken, flood impacts (wealth reduction) are less severe. Information campaigns increase the probability that households adopt protection measures. Two types of information campaigns are modeled: top-down policies which are the same for all households, people-centered policies, which adapt to the individual characteristics of each household.

Large-scale land acquisitions (LSLAs) threaten smallholder livelihoods globally. Despite more than a decade of research on the LSLA phenomenon, it remains a challenge to identify governance conditions that may foster beneficial outcomes for both smallholders and investors. One potentially promising strategy toward this end is contract farming (CF), which more directly involves smallholder households in commodity production than conditions of acquisition and displacement.

To improve understanding of how CF may mediate the outcomes of LSLAs, we developed an agent-based model of smallholder livelihoods, which we used as a virtual laboratory to experiment on a range of hypothetical LSLA and CF implementation scenarios.

The model represents a community of smallholder households in a mixed crop-livestock system. Each agent farms their own land and manages a herd of livestock. Agents can also engage in off-farm employment, for which they earn a fixed wage and compete for a limited number of jobs. The principal model outputs include measures of household food security (representing access to a single, staple food crop) and agricultural production (of a single, staple food crop).

Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

MHCABM is an agent-based, multi-hazard risk interaction model with an integrated applied dynamic adaptive pathways planning component. It is designed to explore the impacts of climate change adaptation decisions on the form and function of a coastal human-environment system, using as a case study an idealised patch based representation of the Mount North-Omanu area of Tauranga city, New Zealand. The interacting hazards represented are erosion, inundation, groundwater intrusion driven by intermittent heavy rainfall / inundations (storm) impacts, and sea level rise.

The purpose of this model is to understand the role of trade networks and their interaction with different fish resources, for fish provision. The model is developed based on a multi-methods approach, combining agent-based modeling, network analysis and qualitative data based on a small-scale fisheries study case. The model can be used to investigate both how trade network structures are embedded in a social-ecological context and the trade processes that occur within them, to analyze how they lead to emergent outcomes related to the resilience of fish provision. The model processes are informed by qualitative data analysis, and the social network analysis of an empirical fish trade network. The network analysis can be used to investigate diverse network structures to perform model experiments, and their influence on model outcomes.

The main outcomes we study are 1) the overexploitation of fish resources and 2) the availability and variability of fish provision to satisfy different market demands, and 3) individual traders’ fish supply at the micro-level. The model has two types of trader agents, seller and dealer. The model reveals that the characteristics of the trade networks, linked to different trader types (that have different roles in those networks), can affect the resilience of fish provision.

Peer reviewed Least cost path mobility

Claudine Gravel-Miguel Colin Wren | Published Sat Sep 2 21:50:29 2017 | Last modified Thu May 30 23:20:23 2019

This model aims to mimic human movement on a realistic topographical surface. The agent does not have a perfect knowledge of the whole surface, but rather evaluates the best path locally, at each step, thus mimicking imperfect human behavior.

The purpose of the model is to explore the influence of the design of circular business models (CBMs) on CBM viability. The model represents an Industrial Symbiosis Network (ISN) in which a processor uses the organic waste from suppliers to produce biogas and nutrient rich digestate for local reuse. CBM viability is expressed as value captured (e.g., cash flow/tonne waste/agent) and the survival of the network over time (shown in the interface).

In the model, the value captured is calculated relative to the initial state, using incineration costs as a benchmark. Moderating variables are interactions with the waste incinerator and actor behaviour factors. Actors may leave the network when the waste supply for local production is too low, or when personal economic benefits are too low. When the processor decides to leave, the network fails. Theory of planned behaviour can be used to include agent behaviour in the simulations.

Lake Anderson Revisited II

Klaus Troitzsch | Published Mon Jun 28 15:00:38 2021

The purpose of this study is another agent-based replication of a System Dynamics model (Anderson,1973) where he analysed the dynamics of nutrient, biomass, oxygen and detritus in a model lake under conditions of artificial fertilising and policies to deal with the consequences of artificial fertilising.. A first replication (Möhring & Troitzsch,2001) added those agents to the original model that were necessary to move the role of the experimenter into the model, whereas this replication replaces the original lake with a collection of small elements between which biomass, nurtrents and oxygen are exchanged, adds rivers upstream and downstream as well as adjacent land divided into villages and populated with farms and industrial plants run by individual persons.

This model is based on the Narragansett Bay, RI recreational fishery. The two types of agents are piscivorous fish and fishers (shore and boat fishers are separate “breeds”). Each time step represents one week. Open season is weeks 1-26, assuming fishing occurs during half the year. At each weekly time step, fish agents grow, reproduce, and die. Fisher agents decide whether or not to fish based on their current satisfaction level, and those that do go fishing attempt to catch a fish. If they are successful, they decide whether to keep or release the fish. In our publication, this model was linked to an Ecopath with Ecosim food web model where the commercial harvest of forage fish affected the biomass of piscivorous fish - which then became the starting number of piscivorous fish for this ABM. The number of fish caught in a season of this ABM was converted to a fishing pressure and input back into the food web model.


Jennifer Badham | Published Tue Jun 8 10:09:29 2021

Zombies move toward humans and humans move (faster) away from zombies. They fight if they meet, and humans who lose become zombies.

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.