TRUE GRASP (Tree Recruitment Under Exotic GRAsses in a Savanna-Pineland)
is a socio-ecological agent-based model (ABM) and role playing game (RPG) for farmers and other stakeholders involved in rural landscape planning.

The purpose of this model is to allow actors to explore the individual and combined effects - as well as tradeoffs - of three methods of controlling exotic grasses in pine savannas: fire, weeding, and grazing cattle.

Design of TRUE GRASP is based on 3 years of socio-ecological fieldwork in a human-induced pine savanna in La Sepultura Biosphere Reserve (SBR) in the Mexican state of Chiapas. In this savanna, farmers harvest resin from Pinus oocarpa, which is used to produce turpentine and other products. However, long term persistence of this activity is jeopardized by low tree recruitment due to exotic tall grass cover in the forest understory (see Braasch et al., 2017). The TRUE GRASP model provides the user with different management strategies for controlling exotic grass cover and avoiding possible regime shifts, which in the case of the SBR would jeopardize resin harvesting.

PolicySpace models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. The model contains citizens, markets, residences, municipalities, commuting and a the tax scheme. In the associated publications (book in press and https://arxiv.org/abs/1801.00259) we validate the model and demonstrate an application of the fiscal analysis. Besides providing the basics of the platform, our results indicate the relevance of the rules of taxes transfer for cities’ quality of life.

This is an initial exploratory exercise done for the class @ http://thiagomarzagao.com/teaching/ipea/ Text available here: https://arxiv.org/abs/1712.04429v1
The program:
Reads output from an ABM model and its parameters’ configuration
Creates a socioeconomic optimal output based on two ABM results of the modelers choice
Organizes the data as X and Y matrices
Trains some Machine Learning algorithms
…

The DiDIY-Factory model is a model of an abstract factory. Its purpose is to investigate the impact Digital Do-It-Yourself (DiDIY) could have on the domain of work and organisation.

DiDIY can be defined as the set of all manufacturing activities (and mindsets) that are made possible by digital technologies. The availability and ease of use of digital technologies together with easily accessible shared knowledge may allow anyone to carry out activities that were previously only performed by experts and professionals. In the context of work and organisations, the DiDIY effect shakes organisational roles by such disintermediation of experts. It allows workers to overcome the traditionally strict organisational hierarchies by having direct access to relevant information, e.g. the status of machines via real-time information systems implemented in the factory.

A simulation model of this general scenario needs to represent a more or less abstract manufacturing firm with supervisors, workers, machines and tasks to be performed. Experiments with such a model can then be run to investigate the organisational structure –- changing from a strict hierarchy to a self-organised, seemingly anarchic organisation.

MUSA is an ABM that simulates the commuting sector in USA. A multilevel validation was implemented. Social network with a social-circle structure included. Two types of policies have been tested: market-based and preference-change.

Due to the large extent of the Harz National Park, an accurate measurement of visitor numbers and their spatiotemporal distribution is not feasible. This model demonstrates the possibility to simulate the streams of visitors with ABM methodology.

Next generation of the CHALMS model applied to a coastal setting to investigate the effects of subjective risk perception and salience decision-making on adaptive behavior by residents.

A logging agent builds roads based on the location of high-value hotspots, and cuts trees based on road access. A forest monitor sanctions the logger on observed infractions, reshaping the pattern of road development.

The model examines the dynamics of herd growth in African pastoral systems. We used it to examine the role of scale (herd size) stochasticity (in mortality, fertility, and offtake) on herd growth.

A NetLogo ABM developed to explore unarmed resistance to an active shooter. The landscape is a generalized open outdoor area. Parameters enable the user to set shooter armament and control for assumptions with regard to shooter accuracy.