Computational Model Library

This project was developed during the Santa Fe course Introduction to Agent-Based Modeling 2022. The origin is a Cellular Automata (CA) model to simulate human interactions that happen in the real world, from Rubens and Oliveira (2009). These authors used a market research with real people in two different times: one at time zero and the second at time zero plus 4 months (longitudinal market research). They developed an agent-based model whose initial condition was inherited from the results of the first market research response values and evolve it to simulate human interactions with Agent-Based Modeling that led to the values of the second market research, without explicitly imposing rules. Then, compared results of the model with the second market research. The model reached 73.80% accuracy.
In the same way, this project is an Exploratory ABM project that models individuals in a closed society whose behavior depends upon the result of interaction with two neighbors within a radius of interaction, one on the relative “right” and other one on the relative “left”. According to the states (colors) of neighbors, a given cellular automata rule is applied, according to the value set in Chooser. Five states were used here and are defined as levels of quality perception, where red (states 0 and 1) means unhappy, state 3 is neutral and green (states 3 and 4) means happy.
There is also a message passing algorithm in the social network, to analyze the flow and spread of information among nodes. Both the cellular automaton and the message passing algorithms were developed using the Python extension. The model also uses extensions csv and arduino.

This paper introduces an experimental and exploratory approach, combining game theory and Genetic Algorithms to create a model to simulate evolutionary economic learning. The objective of this paper is to document the implementation of a genetic algorithm as a simulator for economic learning, then analyze how strategic behavior affects the evolution towards optimal outcomes, departing from different starting points and potentially transforming conflict into harmonious scenarios. For this purpose, the introduced construct aimed at allowing for the evaluation of different strategy selection methods and game types. 144 unique 2x2 games, and three distinct strategy selection rules: Nash equilibrium, Hurwicz rule and a Random selection method were used in this study. The particularity of this paper is that rather than changing the strategies themselves or player-specific features, the introduced genetic algorithm changes the games based on the player payoffs. The outcome indicated optimal player scenarios for both The Nash equilibrium and Hurwicz rules strategies, the first being the best performing strategy. The random selection method fails to converge to optimal values in most of the populations, acting as a control feature and reinforcing that strategic behavior is necessary for the evolutionary learning process. We documented also two additional observations. First, the games are often transformed in such a way that agents can coordinate their strategies to achieve a stable optimal equilibrium. And second, we observed the mutation of the populations of games into sets of fewer (repeating) isomorphic games featuring strong characteristics of previous games.

Co-operative Autonomy

Hani Mohammed Subu Kandaswamy | Published Sat Apr 24 09:38:34 2021

This model presents an autonomous, two-lane driving environment with a single lane-closure that can be toggled. The four driving scenarios - two baseline cases (based on the real-world) and two experimental setups - are as follows:

  • Baseline-1 is where cars are not informed of the lane closure.
  • Baseline-2 is where a Red Zone is marked wherein cars are informed of the lane closure ahead.
  • Strategy-1 is where cars use a co-operative driving strategy - FAS. <sup>[1]</sup>
  • Strategy-2 is a variant of Strategy-1 and uses comfortable deceleration values instead of the vehicle’s limit.
This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.