Computational Model Library

Peer reviewed BAM: The Bottom-up Adaptive Macroeconomics Model

Alejandro Platas López | Published Tue Jan 14 17:04:32 2020 | Last modified Sun Jul 26 00:26:21 2020



Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..

TERRoir level Organic matter Interactions and Recycling model

Myriam Grillot | Published Wed Apr 19 14:33:44 2017 | Last modified Wed Jun 17 14:13:35 2020

The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.

The Informational Dynamics of Regime Change

Dominik Klein Johannes Marx | Published Sat Oct 7 20:03:20 2017 | Last modified Tue Jan 14 10:39:18 2020

We model the epistemic dynamics preceding political uprising. Before deciding whether to start protests, agents need to estimate the amount of discontent with the regime. This model simulates the dynamics of group knowledge about general discontent.

The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure

This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).


The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension

This is a re-implementation of a the NetLogo model Maze (ROOP, 2006).

This re-implementation makes use of the Q-Learning NetLogo Extension to implement the Q-Learning, which is done only with NetLogo native code in the original implementation.

Exploring how learning and social-ecological networks influence management choice set and their ability to increase the likelihood of species coexistence (i.e. biodiversity) on a fragmented landscape controlled by different managers.

This NetLogo model illustrates the cultural evolution of pro-environmental behaviour patterns. It illustrates how collective behaviour patterns evolve from interactions between agents and agents (in a social network) as well as agents and the affordances (action opportunities provided by the environment) within a niche. More specifically, the cultural evolution of behaviour patterns is understood in this model as a product of:

  1. The landscape of affordances provided by the material environment,
  2. Individual learning and habituation,
  3. Social learning and network structure,
  4. Personal states (such as habits and attitudes), and

Individual bias and organizational objectivity

Bo Xu | Published Mon Apr 15 08:22:32 2013 | Last modified Mon Apr 8 20:43:28 2019

This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.

“Food for all” (FFD)

José Santos José Manuel Galán Andreas Angourakis Andrea L Balbo | Published Fri Apr 25 09:39:34 2014 | Last modified Mon Apr 8 20:39:22 2019

“Food for all” (FFD) is an agent-based model designed to study the evolution of cooperation for food storage. Households face the social dilemma of whether to store food in a corporate stock or to keep it in a private stock.

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