Model building refers both to mechanistic models—in which causes and effects are identified according to scientific principles such as forces, energy flow, mass transport, electrical effects, chemical reactions, and biological and biochemical processes—as well as statistical correlational models in which causal effects are less well understood and proxy measurements are often needed. In both cases, uncertainty plays an important role through ignorance of certain details such as the effect of measurement error, model approximations, and imperfect specifications of functional relationships.
Our own expertise includes the applications of classical mechanics, classical electrodynamics, and classical thermodynamics to describing causal mechanisms for real-world processes, as well as economic and financial principles applied to decision making. In addition we have experience in interpreting biological information and building mathematical and statistical models of those processes. Choose examples from the menu above to get a better understanding of the range of problems we have worked on.
Model building can also be a collaborative effort between experts in your organization who understand specific processes, and our model building and data analysis expertise. These collaborations can lead to better economic and financial decision-making, resource allocation, optimization, and risk analysis.