Simulating Elections with Spatial Voter Models

Democracy: a concept almost universally revered, underpinned by the foundational act of voting. However, interpreting voting results to make fair and representative decisions is anything but straightforward. While it’s tempting to think the option with the most votes should just triumph, reality proves more complex. Our elections are held together with a plethora of details — primaries, runoffs, ranked ballots, and more — that work together to produce reasonable outcomes. But are these mechanisms functioning as intended? How effectively do they work, and which ones outperform others? What unintended consequences might they bear? Answering these vital questions is critical to the ongoing project of refining our democracies. Yet, answers to these questions often rely on anecdotes, oversimplification, and broad assumptions. In this article, I’ll guide you through a more robust approach to these questions — one that relies on computational modeling and simulation. (<a href="https://github.com/cdsmith/spatial-voting" rel="noopener ugc nofollow" target="_blank">Here’s the code for the simulation.</a>) Along the way, we’ll uncover some eye-opening consequences of our chosen election methodologies. <a href="https://medium.com/@cdsmithus/simulating-elections-with-spatial-voter-models-1ff50892390">Read More</a>