Algorithmic information theory
Algorithmic information theory
Communication and Concurrency
Science in the Age of Computer Simulation
Science in the Age of Computer Simulation
| Hi-index | 0.00 |
M&S provides a formal way to generate or test existing knowledge. Like mathematics, M&S provides an apparatus for deduction while generating data that can be used for statistical inference. However, unlike mathematics, M&S's formal approach varies from user to user opening the criticism that one can model "everything one wants" as M&S does not have a widely accepted axiomatic body of knowledge like mathematics has. Unlike empirical experimentation, simulated experiments have been epistemologically questioned about how to relate results to reality. This paper provides a discussion of how M&S's formality is a strong methodological support that generates knowledge, but the discipline of M&S needs to address methodological challenges. Models need to address issues like assumptions, coherence and traceability and simulations have to address issues like computer tractability and empirical validation. We'll discuss these issues from the perspectives of what we use as inputs to build models and simulations (data, theory or both) and what their purposes are (theory creation or theory testing). Ultimately, M&S knowledge claim justification lies within the purview of the disciplines that use it and on what they consider to be acceptable as knowledge as long as the above mentioned M&S issues are sufficiently addressed.