Designing and validating an agent based commodity trading simulation
We present an agent-based method which makes use of reinforcement learning in order to estimate the efficiency of a Port Community System.
The third scenario allows for some agents to occasionally decide to act based on individual costs instead of total combined logistics costs.
The results of this study indicate an increase in the efficiency of the whole logistics process through cooperation, and the study provides a prototype of a Port Community System to support logistics decisions.
Emerging from the fields of Complexity, Chaos, Cybernetics, Cellular Automata and Computers, the Agent-Based Modeling (ABM) simulation paradigm began gaining popularity in the 1990s and represents a departure from the more classical simulation approaches such as the discrete-event simulation paradigm (Heath and Hill 2009).
A primary reason for the popularity of ABM and its departure from other simulation paradigms is that ABM can simulate and help examine organized complex systems (OCS).
This article focuses on the history and current debates regarding global commodity markets.
It covers physical product (food, metals, electricity) markets but not the ways that services, including those of governments, nor investment, nor debt, can be seen as a commodity.
To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. An in silico transwell device for the study of drug transport and drug-drug interactions.
From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation.
In line with this goal, stress is placed on research making use of powerful new agent-based computational modeling tools.