This is an agent-based model coded in NetLogo. The model simulates population dynamics of bighorn sheep population in the Hell’s Canyon region of Idaho and will be used to develop a better understanding of pneumonia dynamics in bighorn sheep populations. The overarching objective is to provide a decision-making context for effective management of pneumonia in wild populations of bighorn sheep.

NetLogo implementation of Linear Threshold model of influence propagation.

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.

This model aims at creating agent populations that have “personalities”, as described by the Big Five Model of Personality. The expression of the Big Five in the agent population has the following properties, so that they resemble real life populations as closely as possible:
-The population mean of each trait is 0.5 on a scale from 0 to 1.
-The population-wide distribution of each trait approximates a normal distribution.
-The intercorrelations of the Big Five are close to those observed in the Literature.

The literature used to fit the model was a publication by Dimitri van der Linden, Jan te Nijenhuis, and Arnold B. Bakker:
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This model illustrates the processes underlying the social construction of reality through an agent-based genetic algorithm. By simulating the interactions of agents within a structured environment, we have demonstrated how shared information and popularity contribute to the formation of emergent social structures with diverse cultures. The model illustrates how agents balance environmentally valid information with socially reliable information. It also highlights how social interaction leads to the formation of stable, yet diverse, social groups.

Swidden Farming is designed to explore the dynamics of agricultural land management strategies.

NetLogo model of patch choice model from optimal foraging theory (human behavioral ecology).

Diet breadth is a classic optimal foraging theory (OFT) model from human behavioral ecology (HBE). Different resources, ranked according to their food value and processing costs, are distributed in th

Simulates biobehavioral interactions between 2 populations of hominins.

Biobehavioral interactions between two populations under different movement strategies.