We present an agent-based model that maps out and simulates the processes by which individuals within ecological restoration organizations communicate and collectively make restoration decisions.

The purpose of this agent-based model is to compare different variants of crowdworking in a general way, so that the obtained results are independent of specific details of the crowdworking platform. It features many adjustable parameters that can be used to calibrate the model to empirical data, but also when not calibrated it yields essential results about crowdworking in general.
Agents compete for contracts on a virtual crowdworking platform. Each agent is defined by various properties like qualification and income expectation. Agents that are unable to turn a profit have a chance to quit the crowdworking platform and new crowdworkers can replace them. Thus the model has features of an evolutionary process, filtering out the ill suited agents, and generating a realistic distribution of agents from an initially random one. To simulate a stable system, the amount of contracts issued per day can be set constant, as well as the number of crowdworkers. If one is interested in a dynamically changing platform, the simulation can also be initialized in a way that increases or decreases the number of crowdworkers or number of contracts over time. Thus, a large variety of scenarios can be investigated.

This generic agent-based model allows the user to simulate and explore the influence of servicising policies on the uptake of servicising and on economic, environmental and social effects, notably absolute decoupling.

This is NetLogo code that presents two alternative implementations of Correlated Random Walk (CRW):
- 1. drawing the turning angles from the uniform distribution, i.e. drawing the angle with the same probability from a certain given range;
- 2. drawing the turning angles from von Mises distribution.
The move lengths are drawn from the lognormal distribution with the specified parameters.

Correlated Random Walk is used to represent the movement of animal individuals in two-dimensional space. When modeled as CRW, the direction of movement at any time step is correlated with the direction of movement at the previous time step. Although originally used to describe the movement of insects, CRW was later shown to sufficiently well describe the empirical movement data of other animals, such as wild boars, caribous, sea stars.
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The first simple movement models used unbiased and uncorrelated random walks (RW). In such models of movement, the direction of the movement is totally independent of the previous movement direction. In other words, at each time step the direction, in which an individual is moving is completely random. This process is referred to as a Brownian motion.
On the other hand, in correlated random walks (CRW) the choice of the movement directions depends on the direction of the previous movement. At each time step, the movement direction has a tendency to point in the same direction as the previous one. This movement model fits well observational movement data for many animal species.

The presented agent based model simulated the movement of the agents as a correlated random walk (CRW). The turning angle at each time step follows the Von Mises distribution with a ϰ of 10. The closer ϰ gets to zero, the closer the Von Mises distribution becomes uniform. The larger ϰ gets, the more the Von Mises distribution approaches a normal distribution concentrated around the mean (0°).
In this script the turning angles (following the Von Mises distribution) are generated based on the the instructions from N. I. Fisher 2011.
This model is implemented in Javascript and can be used as a building block for more complex agent based models that would rely on describing the movement of individuals with CRW.

The model simulates agents in a spatial environment competing for a common resource that grows on patches. The resource is converted to energy, which is needed for performing actions and for surviving.

This model has been developed together with the publication ‘Modelling Value Change - An Exploratory Approach’

Value change and moral change have increasingly become topics of interest in the philosophical literature. Several theoretical accounts have been proposed. Such accounts are usually based on certain theoretical and conceptual assumptions and their strengths and weaknesses are often hard to determine and compare, also because they are based on limited empirical evidence.

We propose that a step forward can be made with the help of agent-based modelling (ABM). ABM can be used to investigate whether a simulation model based on a specific account of value change can reproduce relevant phenomena. To illustrate this approach, we built a model based on the pragmatist account of value change proposed in van de Poel and Kudina (2022). We show that this model can reproduce four relevant phenomena, namely 1) the inevitability and stability of values, 2) how different societies may react differently to external shocks, 3) moral revolutions, and 4) lock-in.

IMine is a flexible framework which can be adopt multiple criteria for convergence to solve Influence Minig problems. It can use any diffusion model, as well as resilience to compute the influence of a set of nodes base on the use case.
The code is written and tested on ‘R’ v3.5

A model for simulating the evolution of individual’s preferences, incliding adaptive agents “falsifying” -as public opinions- their own preferences. It was builded to describe, explore, experiment and understand how simple heuristics can modulate global opinion dynamics. So far two mechanisms are implemented: a version of Festiguer’s reduction of cognitive disonance, and a version of Goffman’s impression management. In certain social contexts -minority, social rank presure- some models agents can “fake” its public opinion while keeping internally the oposite preference, but after a number of rounds following this falsifying behaviour pattern, a coherence principle can change the real or internal preferences close to that expressed in public.

NetLogo agent-based model to simulate the transmission of COVID-19 in a university dormitory. User can set the number of initial students, buildings, floors, rooms, number of initially infected, and transmission rate. They can also test the effect of masks, sanitizations, elevator allowance, and visits on the effect of the SEIR curve.