An ABM of changes in individuals’ lifestyles which considers their
evolving behavioural choices. Individuals have a set of environmental behavioural traits that spread through a fixed Watts–Strogatz graph via social interactions with their neighbours. These exchanges are mediated by transmission biases informing from whom an individual learns and
how much attention is paid. The influence of individuals on each other is a function of their similarity in environmental identity, where we represent environmental identity computationally by aggregating past agent attitudes towards multiple environmentally related behaviours. To perform a behaviour, agents must both have
a sufficiently positive attitude toward a behaviour and overcome a corresponding threshold. This threshold
structure, where the desire to perform a behaviour does not equal its enactment, allows for a lack of coherence
between attitudes and actual emissions. This leads to a disconnect between what people believe and what
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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.

This ABM simulates problem solving agents as they work on a set of tasks. Each agent has a trait vector describing their skills. Two agents might form a collaboration if their traits are similar enough. Tasks are defined by a component vector. Agents work on tasks by decreasing tasks’ component vectors towards zero.

The simulation generates agents with given intrapersonal functional diversity (IFD), and dominant function diversity (DFD), and a set of random tasks and evaluates how agents’ traits influence their level of communication and the performance of a team of agents.

Modeling results highlight the importance of the distributions of agents’ properties forming a team, and suggests that for a thorough description of management teams, not only diversity measures based on individual agents, but an aggregate measure is also required.

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The model examines cattle herd dynamics on a patchy grassland subject to two exogenous pressures: periodic raiding events that remove animals and scheduled management culling that can target males and/or females. It is intended for comparative experiments on how raiding frequency, culling schedules, vegetation dynamics, and life-history parameters interact to shape herd persistence. The model was specifically designed to test the scenario of cattle herding in the arid grasslands of southern Arizona and northern Sonora during the mission period (late 17th through late 18th centuries, CE). In this period, herds were locally managed by Spanish mission personnel and local O’odham groups. Herds were culled mostly for local consumption of meat, hides, and tallow, but the mission herds were often targets for raiding by neighboring groups. The main purpose of the model is to examine herd dynamics in a seasonally variable, arid environment where herds are subject to both intentional internal harvest (culling) and external harvest (raiding).

MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.

We build a stylized model of a network of business angel investors and start-up entrepreneurs. Decisions are based on trust as a decision making tool under true uncertainty.

Implemented as a virtual laboratory, this model explores transitions in land-use and livelihood decisions that emerge from changing local and global conditions.

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.

This is a social trust model for investigating the social relationships and social networks in the real world and in social media.

We demonstrate how Repast Simphony statecharts can efficiently encapsulate the deep classification hierarchy of the U.S. Air Force for manpower life cycle costing.