Consumer agents make choices which products to choose using the consumat approach. In this approach agents will make choices using deliberation, repetition, imitation or social comparison dependent on the level of need satisfaction and uncertainty.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

Viable North Sea (ViNoS) is an Agent-based Model of the German North Sea Small-scale Fisheries in a Social-Ecological Systems framework focussing on the adaptive behaviour of fishers facing regulatory, economic, and resource changes. Small-scale fisheries are an important part both of the cultural perception of the German North Sea coast and of its fishing industry. These fisheries are typically family-run operations that use smaller boats and traditional fishing methods to catch a variety of bottom-dwelling species, including plaice, sole, and brown shrimp. Fisheries in the North Sea face area competition with other uses of the sea – long practiced ones like shipping, gas exploration and sand extractions, and currently increasing ones like marine protection and offshore wind farming. German authorities have just released a new maritime spatial plan implementing the need for 30% of protection areas demanded by the United Nations High Seas Treaty and aiming at up to 70 GW of offshore wind power generation by 2045. Fisheries in the North Sea also have to adjust to the northward migration of their established resources following the climate heating of the water. And they have to re-evaluate their economic balance by figuring in the foreseeable rise in oil price and the need for re-investing into their aged fleet.

The Olympic Peninsula ABM works as a virtual laboratory to simulate the existing forestland management practices as followed by different forestland owner groups in the Olympic Peninsula, Washington, and explore how they could shape the future provisions of multifunctional ecosystem services such as Carbon storage and revenue generation under the business-as-usual scenario as well as by their adaptation to interventions. Forestlands are socio-ecological systems that interact with economic, socio-cultural, and policy systems. Two intervention scenarios were introduced in this model to simulate the adaptation of landowner behavior and test the efficacy of policy instruments in promoting sustainable forest practices and fostering Carbon storage and revenue generation. (1) A market-linked carbon offset scheme that pays the forestland owners a financial incentive in the form of a yearly carbon rent. (2) An institutional intervention policy that allows small forest owners (SFLO) to cooperate for increased market access and benefits under carbon rent scenario. The model incorporates the heterogeneous contexts within which the forestland owners operate and make their forest management decisions by parameterizing relevant agent attributes and contextualizing their unique decision-making processes.

ThomondSim is a simulation of the political and economic landscape of the medieval kingdom of Thomond, southwestern Ireland, between 1276 and 1318.

Its goal is to analyze how deteriorating environmental and economic conditions caused by the Little Ice Age (LIA), the Great European Famine of 1315-1322, and wars between England and Scotland affected the outcomes of a local war involving Gaelic and English aristocratic lineages.
This ABM attempts to model both the effects of devastation on the human environment and the modus operandi of late-medieval war and diplomacy.

The model is the digital counterpart of the science discovery board game The Triumphs of Turlough. Its procedures closely correspond to the game’s mechanics, to the point that ToT can be considered an interactive, analog version of this ABM.

This is an extended replication of Abelson’s and Bernstein’s early computer simulation model of community referendum controversies which was originally published in 1963 and often cited, but seldom analysed in detail. This replication is in NetLogo 6.3.0, accompanied with an ODD+D protocol and class and sequence diagrams.

This replication replaces the original scales for attitude position and interest in the referendum issue which were distributed between 0 and 1 with values that are initialised according to a normal distribution with mean 0 and variance 1 to make simulation results easier compatible with scales derived from empirical data collected in surveys such as the European Value Study which often are derived via factor analysis or principal component analysis from the answers to sets of questions.

Another difference is that this model is not only run for Abelson’s and Bernstein’s ten week referendum campaign but for an arbitrary time in order that one can find out whether the distributions of attitude position and interest in the (still one-dimensional) issue stabilise in the long run.

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.

Confirmation Bias is usually seen as a flaw of the human mind. However, in some tasks, it may also increase performance. Here, agents are confronted with a number of binary Signals (A, or B). They have a base detection rate, e.g. 50%, and after they detected one signal, they get biased towards this type of signal. This means, that they observe that kind of signal a bit better, and the other signal a bit worse. This is moderated by a variable called “bias_effect”, e.g. 10%. So an agent who detects A first, gets biased towards A and then improves its chance to detect A-signals by 10%. Thus, this agent detects A-Signals with the probability of 50%+10% = 60% and detects B-Signals with the probability of 50%-10% = 40%.
Given such a framework, agents that have the ability to be biased have better results in most of the scenarios.

The model constructs a complex network of traffic based on the main urban area of Zhengzhou, China, and simulates the urban rainfall process using the ABM model to analyse the real-time risk of flooding hazards in the nodes of the complex network.

We present a socio-epistemic model of science inspired by the existing literature on opinion dynamics. In this model, we embed the agents (or scientists) into social networks - e.g., we link those who work in the same institutions. And we place them into a regular lattice - each representing a unique mental model. Thus, the global environment describes networks of concepts connected based on their similarity. For instance, we may interpret the neighbor lattices as two equivalent models, except one does not include a causal path between two variables.

Agents interact with one another and move across the epistemic lattices. In other words, we allow the agents to explore or travel across the mental models. However, we constrain their movements based on absorptive capacity and cognitive coherence. Namely, in each round, an agent picks a focal point - e.g., one of their colleagues - and will move towards it. But the agents’ ability to move and speed depends on how far apart they are from the focal point - and if their new position is cognitive/logic consistent.

Therefore, we propose an analytical model that examines the connection between agents’ accumulated knowledge, social learning, and the span of attitudes towards mental models in an artificial society. While we rely on the example from the General Theory of Relativity renaissance, our goal is to observe what determines the creation and diffusion of mental models. We offer quantitative and inductive research, which collects data from an artificial environment to elaborate generalized theories about the evolution of science.

The Land Use Competition in Drylands (LUCID) model is a stylized agent-based model of a smallholder farming system. Its main purpose is to illustrate how competition between pastoralism and crop cultivation can affect livelihoods of households, specifically their food security. In particular, the model analyzes whether the expansion of crop cultivation may contribute to a vicious circle where an increase in cultivated area leads to higher grazing pressure on the remaining pastureland, which in turn may cause forage shortages and livestock loss for households which are then forced to further expand their cultivated area in order to increase their food security. The model does not attempt to replicate a particular case study but to generate a general understanding of mechanisms and drivers of such vicious circles and to identify possible scenarios under which such circles may be prevented.

The model is inspired by observations of the Borana land use system in Southern Ethiopia. The climatic and ecological conditions of the Borana zone favor pastoralism, and traditionally livelihoods have been based mainly on livestock keeping. Recent years, however, have seen an advancement of crop cultivation as a coping strategy, e.g., to compensate the loss of livestock, even though crop yields are low on average and successful harvests are infrequent.

In the model, it is possible to evaluate patterns of individual (single household) as well as overall (across all households) consumption and food security, depending on a range of ecological, climatic and management parameters.