Simulation and Similarity:Using Models to Understand the World, Michael Weisberg.

• Models are indirect study of real-world systems, different from other forms of representation and analysis, interpreted structures
• Three types of modelling: mathematical, computational, concrete
  • concrete are physical objects whose physical properties can potentially stand in representational relationships with real-world phenomena
  • mathematical are abstract structures whose properties can potentially stand in relations to mathematical representations of phenomena
    • can’t be assessed or measured directly, so what we learn comes from manipulating the equations that describe it
    • either set-theoretic predicates or sets of trajectories in state space
  • computational models are sets of procedures that can potentially stand in relation to computational description of the behavior of a system
• Doesn’t take model organisms, verbal models, and idealized exemplars because they fit into above taxonomy
  • Is taxonomy sociological, ontological, or epistemic
• Models not always veredical (do not always truthfully describe all aspects of their targets)
• Feyerabend’s dictum: “Anything goes in science”
• Agent-based model means that each individual is explicitly represented
• Model descriptors are the words, equations, and pictures that describe a model
  • Giere said model descriptions were the set of statements that define a model
• Construals provide interpretation for the model’s structure (assignment, scope, fidelity criteria)
  • two kinds of fidelity criteria: dynamic and representational
    • dynamic tells us how close the output of the model must be to the output of real world phenomenon
    • representational gives us standards for evaluating how well the structure of the model maps onto the target system of interest
• Fictionalist accounts vs. Maths accounts
• Idealization is the intentional introduction of distortion into scientific representations
• Modelling as theorhetical practice