This project will develop new tools for investigating the computational complexity of total function problems in theoretical computer science. Traditional computational complexity focuses on decision problems, which have a clear yes or no answer. Total function problems are more complex, requiring the actual construction of a solution. Examples include cryptographic tasks like integer factoring and economic tasks like finding Nash equilibria. Resource-bounded measure is a tool that has been successful in understanding the computational complexity of decision problems. In this project, we will extend resource-bounded measure to operate in the function problem setting. The new theory and findings will be promoted to the global theoretical computer science community through a Zoom seminar series. The seminar series will culminate in a workshop at the University of Wyoming in the third year, gathering graduate students and leading computational complexity researchers to discuss and promote the newly developed tools. <br/><br/>Although resource-bounded measure has been effective in studying decision problems and traditional complexity classes, it currently does not address the complexity of function problems. We will introduce a new type of martingales that operates on function values, enabling the development of a new theory of resource-bounded measure in total function classes. These new martingales will allow for the formulation and analysis of more complex prediction strategies, filling a significant gap in computational complexity theory and extending the application of measure theory more broadly. The project will significantly impact our understanding of pseudorandomness, circuit-size complexity, fine-grained complexity, computational intractability, and the complexity landscape of total function complexity classes.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.