This project attempts to model how social media platforms recommend a user followers based on their interests, and how those individual interests change as a result of the influences from those they follow/are followed by.

We have three types of users on the platform:

Consumers (🔴), who update their interests based on who they’re following.
Creators (⬛), who update their interests based on who’s following them.
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An agent-based model for the emigration of highly-skilled labour.

We hypothesise that there are two main factors that impact the decision and ability to move abroad: desire to maximise individual utility and network effects. Accordingly, several factors play role in brain drain such as the overall economic and social differences between the home and host countries, people’s ability and capacity to obtain good jobs and start a life abroad, the barriers of moving abroad, and people’s social network who are already working abroad.

This is an implementation of an agent based model for the evolution of ethnocentrism. While based off a model published by Hammond and Axelrod (2006), the code has been modified to allow for a more fine-grained analysis of evolutionary dynamics.

This is a simple model replicating Hardin’s Tragedy of the Commons using reactive agents that have psychological behavioral and social preferences.

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.
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The model is intended to simulate visitor spatial and temporal dynamics, encompassing their numbers, activities, and distribution along a coastline influenced by beach landscape design. Our primary focus is understanding how the spatial distribution of services and recreational facilities (e.g., beach width, entrance location, recreational facilities, parking availability) impacts visitation density. Our focus is not on tracking the precise visitation density but rather on estimating the areas most affected by visitor activity. This comprehension allows for assessing the diverse influences of beach layouts on spatial visitor density and, consequently, on the landscape’s biophysical characteristics (e.g., vegetation, fauna, and sediment features).

A model to investigate the Evolution of Conditional Cooperation in a Spatial Public Goods Game. We consider two conditional cooperation strategies: one based on thresholds (Battu & Srinivasan, 2020) and another based on independent decisions for each number of cooperating neighbors. We examine the effects of productivity and conditional cooperation criteria on the trajectory of cooperation. Cooperation is evolving with no need for additional mechanisms apart from spatial structure when agents follow conditional strategies. We confirm the positive influence of productivity and cluster formation on the evolution of cooperation in spatial models. Results are robust for the two types of conditional cooperation strategies.

Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and turnover. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group turnover either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group turnover too far might backfire and decrease performance significantly.

The model aims to simulate predator-prey relationships in an agricultural setting. The focus lies on avian communities and their effect on different pest organisms (here: pest birds, rodents, and arthropod pests). Since most case studies focused on the impact on arthropod pests (AP) alone, this model attempts to include effects on yield outcome. By incorporating three treatments with different factor levels (insectivorous bird species, falconry, nest box density) an experimental setup is given that allows for further statistical analysis to identify an optimal combination of the treatments.
In light of a global decline of birds, insects, and many other groups of organisms, alternative practices of pest management are heavily needed to reduce the input of pesticides. Avian pest control therefore poses an opportunity to bridge the disconnect between humans and nature by realizing ecosystem services and emphasizing sustainable social ecological systems.

We represent commuters and their preferences for transportation cost, time and safety. Agents assess their options via their preferences, their environment, and the modes available. The model has policy levers to test impact on last-mile problem.