Computational Model Library

Displaying 10 of 117 results for "Daniel G Brown" clear search

This model is linked to the paper “The Epistemic Role of Diversity in Juries: An Agent-Based Model”. There are many version of this model, but the current version focuses on the role of diversity in whether juries reach correct verdicts. Using this agent-based model, we argue that diversity can play at least four importantly different roles in affecting jury verdicts. (1) Where different subgroups have access to different information, equal representation can strengthen epistemic jury success. (2) If one subgroup has access to particularly strong evidence, epistemic success may demand participation by that group. (3) Diversity can also reduce the redundancy of the information on which a jury focuses, which can have a positive impact. (4) Finally, and most surprisingly, we show that limiting communication between diverse groups in juries can favor epistemic success as well.

This model represents an agent-based social simulation for citizenship competences. In this model people interact by solving different conflicts and a conflict is solved or not considering two possible escenarios: when individual citizenship competences are considered and when not. In both cases the TKI conflict resolution styles are considered. Each conflict has associated a competence and the information about the conflicts and their competences is retrieved from an ontology which was developed in Protégé. To do so, a NetLogo extension was developed using the Java programming language and the JENA API (to make queries over the ontology).

Mast seeding model

Lucia Tamburino Giangiacomo Bravo | Published Saturday, September 08, 2012 | Last modified Saturday, April 27, 2013

Purpose of the model is to perform a “virtual experiment” to test the predator satiation hypothesis, advanced in literature to explain the mast seeding phenomenon.

Bicycle encounter model

Gudrun Wallentin | Published Saturday, October 29, 2016 | Last modified Friday, March 29, 2019

This Bicycle encounter model builds on the Salzburg Bicycle model (Wallentin & Loidl, 2015). It simulates cyclist flows and encounters, which are locations of potential accidents between cyclists.

This article presents an agent-based model of an Italian textile district where thousands of small firms specialize in particular phases of fabrics production. It reconstructs the web of communication between firms as they arrange production chains. In turn, production chains result in road traffic between the geographical areas on which the district extends. The reconstructed traffic exhibits a pattern that has been observed, but not foreseen, by policy makers.

Peer reviewed Emergence of Organizations out of Garbage Can Dynamics

Guido Fioretti | Published Monday, April 20, 2020 | Last modified Sunday, April 26, 2020

The Garbage Can Model of Organizational Choice (GCM) is a fundamental model of organizational decision-making originally propossed by J.D. Cohen, J.G. March and J.P. Olsen in 1972. In their model, decisions are made out of random meetings of decision-makers, opportunities, solutions and problems within an organization.
With this model, these very same agents are supposed to meet in society at large where they make decisions according to GCM rules. Furthermore, under certain additional conditions decision-makers, opportunities, solutions and problems form stable organizations. In this artificial ecology organizations are born, grow and eventually vanish with time.

Knowledge Based Economy (KBE) is an artificial economy where firms placed in geographical space develop original knowledge, imitate one another and eventually recombine pieces of knowledge. In KBE, consumer value arises from the capability of certain pieces of knowledge to bridge between existing items (e.g., Steve Jobs illustrated the first smartphone explaining that you could make a call with it, but also listen to music and navigate the Internet). Since KBE includes a mechanism for the generation of value, it works without utility functions and does not need to model market exchanges.

The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).

The data the model is currently able to output are the following:

Homing pigeon model

Gudrun Wallentin | Published Saturday, October 29, 2016

This model represents the flight paths of a flock of homing pigeons according to their flocking-, orientation- and leadership behaviour.

Salzburg Bicycle model

Gudrun Wallentin | Published Saturday, October 29, 2016

An ABM to simulate the spatio-temporal distribution of cyclists across the road network of the city of Salzburg.

Displaying 10 of 117 results for "Daniel G Brown" clear search

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