Was Crocodile stronger at Marineford? Or was he holding back in Alabasta?

 During the Alabasta arc, Crocodile displayed a level of power that was initially considered overwhelming by the Straw Hat Pirates. He possessed the Logia-type Devil Fruit called the Suna Suna no Mi (Sand-Sand Fruit), which granted him the ability to control and transform into sand. He had a reputation as a Shichibukai and controlled the desert kingdom of Alabasta from the shadows. His strength was showcased through his battles with Luffy and others. At Marineford, Crocodile was present as part of the war that took place at Marine Headquarters. While he did participate in the battle, he didn't display the same level of dominance as some other powerful characters present. This has led fans to speculate that he might not have been as strong as initially portrayed in Alabasta. It's important to note that power scaling and character abilities can be subject to interpretation and development by the author. Oda often keeps details deliberately open-ended to keep the story intriguing.

What is the connection between classical computers, complex systems, and chaos theory?

 Classical computers, complex systems, and chaos theory are all related to the study of how systems evolve and change over time. Classical computers are machines that are capable of performing a wide range of tasks, including simulating complex systems. Complex systems are systems that are made up of many interconnected parts and exhibit emergent behavior, which means that the behavior of the system as a whole cannot be predicted by studying the individual parts. Chaos theory is the study of how complex systems can exhibit unpredictable behavior, even when the individual parts of the system are well understood.

Classical computers can be used to simulate complex systems, including weather patterns and earthquakes. This is done by creating a mathematical model of the system and using the computer to solve the equations that govern the behavior of the system. This allows scientists and engineers to study the behavior of the system and make predictions about how it will evolve over time.

However, it is important to note that classical computers are not always able to accurately simulate complex systems, especially those that are highly chaotic. This is because the equations that govern the behavior of complex systems can be extremely difficult to solve, and the behavior of the system may be sensitive to small changes in initial conditions. In these cases, classical computers may not be able to provide accurate predictions, and other methods, such as machine learning or quantum computing, may be needed.