In computational physics you model situations by creating some particles, placing them in some initial configuration, and defining the manner in which position and proximity of other particles creates forces acting upon each particle, and then you calculate acceleration, and thus eventually the position of those particles after a single temporal step. When all the giggling stops you&#x27;ve hit your system&#x27;s final configuration given the initial conditions and subject to the forces defined (up to the numerical precision afforded by whatever affects your simulation&#x27;s precision).<p>Analogously in economics you&#x27;d want to define the agents, the inventory of what they have, what they want, and the medium (market) through which they will trade to become as happy as they can. When nobody sees any point trading anymore you have a “Pareto optimal” end-state and you&#x27;re basically in equilibrium.<p>Back when I used program simulations I used Mathematica, and I am still deeply tied to that specific platform, but there&#x27;s several introductions to doing much the same with less exotic (and expensive) tools such as Python: <a href="https:&#x2F;&#x2F;www.researchgate.net&#x2F;profile&#x2F;Michael_North&#x2F;publication&#x2F;221529231_Tutorial_on_agent-based_modeling_and_simulation_part_2_how_to_model_with_agents&#x2F;links&#x2F;02bfe50d0c1189755a000000.pdf" rel="nofollow">https:&#x2F;&#x2F;www.researchgate.net&#x2F;profile&#x2F;Michael_North&#x2F;publicati...</a>