Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Multi-agent simulation

An agent can be defined as anything that is able to perceive an environment through sensors and act in it through actuators. Artificial intelligence focuses on the concept of a rational agent, or an agent who always tries to optimize an appropriate performance measure. A rational agent can be a human agent, a robotic agent, or a software agent. In the following diagram, we can see the interaction between the agent and the environment:

Figure 5.9 – Interaction between the agent and the environment

An agent is considered autonomous when it can flexibly and independently choose the actions to be taken to achieve its goals, without constantly resorting to the intervention of an external decision system. Note that, in most complex domains, an agent can only partially obtain information and have control in the environment that it has been inserted into, thus exerting, at most, a certain influence on it.

An agent can be considered...