This repository contains: (1) a model calibration procedure that identifies a set of diverse, plausible models; and (2) an ABM of smallholder agriculture, which is used as a case study application for the calibration method. By identifying a set of diverse models, the calibration method attends to the issue of “equifinality” prevalent in complex systems, which is a situation where multiple plausible process descriptions exist for a single outcome.

Industrial clustering patterns are the result of an entrepreneurial process where spinoffs inherit the ideas and attributes of their parent firms. This computational model maps these patterns using abstract methodologies.

This model is designed to investigate the impact of alternative policy approaches and changing land tenure dynamics on farmer adoption of conservation practices intended to increase the water quality.

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

This purpose of this model is to understand how the coupled demographic dynamics of herds and households constrain the growth of livestock populations in pastoral systems.

The ForagerNet3_Demography model is a non-spatial ABM designed to serve as a platform for exploring several aspects of hunter-gatherer demography.

Butterflies (turtles) goes through metamorphism and moves to corresponding patches each season of the year. The number of years and seasons are monitored.

Agers and non-agers agent compete over a spatial landscape. When two agents occupy the same grid, who will survive is decided by a random draw where chances of survival are proportional to fitness. Agents have offspring each time step who are born at a distance b from the parent agent and the offpring inherits their genetic fitness plus a random term. Genetic fitness decreases with time, representing environmental change but effective non-inheritable fitness can increase as animals learn and get bigger.

The model formalizes a situation where agents embedded in different types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of different mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor’s opinions.

The model simulates seven agents engaging in collective action and inter-network social learning. The objective of the model is to demonstrate how mental models of agents can co-evolve through a complex relationship among factors influencing decision-making, such as access to knowledge and personal- and group-level constraints.