Theoretical modelling raises many epistemological challenges. Models appear to explain economic phenomena, yet they also seem to lack crucial features theories of scientific explanation usually require, for instance faithfully representing causal factors of interest. My doctoral thesis aims at answering the following central and general question: what, if anything, can we learn from theoretical models? It does so by showing that models may provide epistemic benefits in the form of understanding even when they do not actually explain or when they do not provide causal knowledge. The second chapter of my thesis examines the inferentialist-behavioural account of understanding and argues that it does not allow to distinguish between the sense of understanding and genuine understanding. I submit that we can view it as offering an evaluative account of understanding instead than a substantive one. The third chapter analyses one purported solution to the problem of representation, namely the factive brand of inferentialism. I argue the account can’t distinguish merely phenomenological from explanatory representation and conclude by presenting a dilemma inferentialism faces. The fourth chapter characterizes what I call the narrow knowledge account of understanding and then shows that its two tenets, i.e., that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation, are untenable. The fifth chapter develops a novel account of how-possibly explanations which claims that what demarcates these explanations from how-actually explanations is that the former provide knowledge of possibility whereas the latter provide knowledge of actuality. The sixth and final chapter is a detailed case study of the general equilibrium case in economics. I argue that the model offers non-causal understanding via a how-possibly mathematical explanation.