COMPSCI LEC 10232024 PDF

Understanding and Mitigating Computational Errors in Python Programming  System Dynamics models System Dynamics models are a methodology and mathematical modeling technique used to understand and manage complex feedback systems. These models are particularly useful for analyzi...

Understanding and Mitigating Computational Errors in Python Programming  System Dynamics models System Dynamics models are a methodology and mathematical modeling technique used to understand and manage complex feedback systems. These models are particularly useful for analyzing and predicting the behavior of systems over time, especially when dealing with nonlinear dynamics and feedback loops Components of System Dynamics Models:  Stocks: These represent accumulations of material or information within the system.  Flows: These are the rates at which stocks change over time.  Variables: These are quantities that can change over time.  Links: These represent the relationships and interactions between different components of the system. Feedback Loops: These are the mechanisms by which the output of a system is fed back into the system as input, affecting future behavior Applications: System Dynamics models are applied in various fields such as business management, policy analysis, environmental studies, and healthcare. They help in understanding complex issues, making better decisions, and designing effective policies Historical Development: The concept of System Dynamics was developed in the 1950s by Jay Forrester at MIT. Initially used for industrial processes, it has since been applied to a wide range of systems, including urban dynamics and global environmental issues Modeling Process The process involves identifying the system of interest, developing a dynamic hypothesis, quantifying stock and flow variables, defining relationships between variables, and building a simulation model. K F Nonlinear Dynamics Feedback Thinking Structure and Behavior System Dynamics he models help in Recognizing cause and models capture the understanding how the effect in a system and nonlinear behavior of structure of a system understanding how systems, which often (relationships between feedback loops influence defies linear intuitive components) affects its overall behavior solutions. behavior over time Introduction to Computational Errors Computational errors in Python can significantly affect program performance and results. Understanding these errors is crucial for developers. This presentation will explore the types of errors, their causes, and effective strategies for mitigation. Let's embark on this journey to enhance our coding practices and improve our programming reliability.  Types of Syntax Computational Errors Errors Runtime Errors Logical Errors  Types of Computational Errors Syntax Syntax Errors Errors occur during code writing Syntax Syntax Errors Errors occur during code writing Runtime Runtime Errors Errors happen during execution Logical Errors Logical manifest in incorrect output Errors despite the code running smoothly. Sources of Errors ERRORS Incorrect Syntax Environmental Issues Data Type mismatch Library Dependencies Algorithmic Flaws DEBUGGING TECHNIQUES Print Statements Assertions Debuggers ü code reviews üconsistent testing üproper documentation ü version control  KEY TAKE AWAY