Research on the causes of political polarization points towards multiple drivers of the problem, from social and psychological to economic and technological. However, political institutions stand out, because -- while capable of exacerbating or alleviating polarization -- they can be re-engineered more readily than others. Accordingly, we analyze one class of such institutions -- electoral systems -- investigating whether the large-party seat bias found in many common systems (particularly plurality and Jefferson-D'Hondt) exacerbates polarization. Cross-national empirical data being relatively sparse and heavily confounded, we use computational methods: an agent-based Monte Carlo simulation. We model voter behavior over multiple electoral cycles, building upon the classic spatial model, but incorporating other known voter behavior patterns, such as the bandwagon effect, strategic voting, preference updating, retrospective voting, and the thermostatic effect. We confirm our hypothesis that electoral systems with a stronger large-party bias exhibit significantly higher polarization, as measured by the Mehlhaff index.
Alex: Welcome to another episode of ResearchPod. Sam, we've seen news stories about political divides getting deeper—people not just disagreeing, but really distrusting each other across party lines. Can something as technical as how votes turn into seats in elections actually help fix that?
Sam: This episode centers on a paper by Daria Boratyn and Dariusz Stolicki from Jagiellonian University, titled "Effect of Electoral Seat Bias on Political Polarization: A Computational Perspective." Their main claim is that a common feature of many election systems—where bigger parties get a lot more seats than their share of votes would suggest—drives voters into sharper divides over time. They test this using a computer simulation of elections.
Alex: So the paper is basically asking whether this built-in advantage for large parties in turning votes into seats worsens polarization? And polarization here means... what, exactly?
Sam: Yes, that's the core question. Polarization happens when groups of people pull further apart—not just in their policy views, like left versus right on issues, but also in how much they dislike or distrust the other side. It leads to problems like gridlock in government, weaker trust in democracy, and even risks of violence, as the paper notes from other studies.
Alex: Right, and they say election rules are one cause we might actually change, unlike social media or inequality?
Sam: Exactly. Unlike harder-to-fix things like economic divides or online echo chambers, electoral systems structure how votes become power. The paper focuses on "seat bias"—a systematic edge where large parties convert votes to seats more efficiently than small ones, common in systems like first-past-the-post in the US or UK, or Jefferson-D’Hondt methods. This makes voters think twice about supporting smaller or moderate parties, since their votes "count less."
Alex: Okay, so voters abandon niche options for the big players to avoid wasting their vote. But how does that create tighter clusters of extremes?
Sam: It starts with what's called a threshold effect: small parties struggle to win any seats, so voters who might like them switch to giants for a real chance at influence—like picking a strong team in a tournament instead of an underdog. Then there's a concentration effect: big parties get bonus seats, pulling in even more support over cycles, as votes for them pack more punch. Repeated over elections, this compresses everyone into fewer camps, sharpening lines between them.
Alex: Huh. So the rules themselves nudge people toward bigger divides, even if voters start out moderate.
Sam: The paper suggests yes—the simulation holds other factors steady to isolate this. And since real data is messy with confounders, this computational approach lets them test causality cleanly.
Alex: That isolation makes sense for testing causality. But walk me through how they set up the voters and parties in this simulation—like, where do they start, and how do voters pick who to vote for?
Sam: They picture opinions on issues as points on a flat map—a two-dimensional space where left-right and other divides show up as positions. Voters and parties each get a spot on that map based on their ideal views; voters like parties closer to their own spot more than far-off ones. This setup is called a spatial policy model. To make it realistic, they mix two kinds of voters: loyal ones clustered near their favorite party's spot, like dedicated fans sticking close to home base, and swing voters spread out more loosely around the center, ready to shift.
Alex: Okay, so a map of opinions with fixed fans and floaters. But what tips a voter's choice when picking a party?
Sam: Voters rank parties by a simple score: divide the party's current seats by how far away it is on the map, raised to a personal power that weighs policy closeness against size. Folks who care more about matching views get a higher power, so distance hurts bigger; strategy fans get a lower one, favoring giants even if farther. This captures real patterns—voters ditching a close small party for a distant big one to avoid waste, or jumping on winners. Dissatisfied ones skip the government entirely first.
Alex: Right—like betting on the frontrunner in a race. Then how do seats get assigned without drawing fake districts?
Sam: They use math shortcuts called seats-votes approximations—formulas that turn total votes into seats as if districts existed, tuned for systems like Jefferson-D’Hondt, which boosts big parties with divisors, or first-past-the-post, where winners take all. No need for thousands of mini-elections; aggregate votes in, biased seats out. This keeps the sim efficient while matching real biases.
Alex: Huh... so that ratio pulls votes to seat-rich parties, their seats grow more via bias, looping tighter. After voting, do positions shift?
Sam: Yes—voters nudge toward their voted party's spot, skewed to pull harder if already close, modeling growing dislike for opposites. Random life tweaks add wiggles, and unhappy government voters push away toward runners-up. Over cycles, bias amplifies this sorting into camps. The paper suggests this mechanism alone drives the polarization they measure.
Alex: So those position shifts after each round—like nudges toward voted parties and pushes from disliked governments—build the sorting. But what decides if a voter even likes the government enough to keep supporting it next time?
Sam: After seats are set, the simulation figures out how well voters think the winning party did in power. They draw a score for overall approval from a range that gets tougher the longer that party stays in charge—like a team wearing down from back-to-back games, losing fans over time. Each voter then gets a personal yes-or-no on that party based on the overall score.
Alex: Okay, government fatigue makes sense—longer in power, more grumbles. So that feeds into the next vote how?
Sam: If a voter dislikes the government's job, they skip voting for it next round, even if its size and closeness would otherwise win them over—like refusing to cheer for a losing coach despite the crowd. This is retrospective voting, judging past performance before picking. It adds balance, as unhappy folks shop rivals instead.
Alex: Right, so no blind loyalty to giants. Then the real sorting kicks in with those preference tweaks—random wiggles, affective pulls, thermostatic pushes. Break down how the affective one works, since you said it's skewed.
Sam: Voters get a small random bump or nothing most times, like daily life nudging opinions slightly off track—but tiny compared to starting spreads. For affective shifts, they move toward their chosen party's spot, but closer voters pull harder while far ones barely budge—like rubber bands snapping fans tight to the team huddle but loosely tugging stragglers. Only satisfied non-government voters or opposition get this; unhappy rulers skip it.
Alex: Huh... so it's not uniform—proximity amps the bandwagon, compressing groups. And the thermostatic flips that for pushback?
Sam: Yes—for dissatisfied government voters, it mirrors but weaker: half-strength push toward their top non-winner, stronger for those already far from it. New spots add all tweaks: random plus affective plus thermostatic. The paper notes this setup, with bias, sorts voters into tighter packs over cycles. But they skip extras like parties moving positions or coalitions to avoid overcomplicating without clear gains—conservative choices that likely understate splits.
Alex: Makes sense—keeps the focus on bias's pull. Isolating that shows meaningful causation without real-world mess. So with all those pieces in place, what did the simulations actually show about how many parties stick around and how voters end up grouped?
Sam: The model first checks if it produces realistic party setups. In setups with small districts—meaning few seats per area, which amps up the large-party edge—the sim ends with just a few surviving parties. This matches patterns in real countries using similar rules, like Poland or the Czech Republic, where big parties dominate despite many running.
Alex: Right, so low seats-per-district squeezes out the small ones, as expected. But does starting spread of voters matter that much?
Sam: They tested sensitivity by tweaking starting values. More spread-out voters at the start actually led to fewer lasting parties—the wide scatter weakens middle options early, speeding their fade. But the core pattern held: stronger bias, tighter fields.
Alex: Huh, counterintuitive on the spread. What about the voter sorting—the polarization measure?
Sam: Under strong bias from small districts, voters formed fewer but much tighter groups around survivors—like marbles settling into deep bowls instead of shallow ones. The paper suggests this boosts the share of differences between groups versus within them, a clear sign of sharper divides. Larger districts, less bias, let more groups linger with softer edges.
Alex: So bias not only prunes parties but packs voters more firmly into what's left. That's a notable link. Did they quantify how much district size—or that bias level—affects the sorting strength?
Sam: They ran a straightforward line fit between average sorting and district seats per area across setups. Each extra seat per area ties to a small drop in sorting, adding up to noticeably less sorting over typical ranges. This setup accounts for a large share of the shifts seen. And it holds for winner-take-all rules too, like in the US—higher concentration there links to more sorting.
Alex: Right, so tweaks like bigger districts could soften the groups. That builds confidence in the mechanism.
Sam: The paper concludes that within its setup, large-party edges push voters into firmer camps over cycles—not just uneven seats, but a real sorter of views. It isolates bias as a contributor to widening gaps. One practical angle: shifting to higher seats per area or even formulas could keep middling options alive longer, easing crowds into extremes and aiding mixed governments.
Alex: Makes sense as a piece of the puzzle we can tweak—not the only cause, but one institutions could address. Overall, this simulation offers a clear view of how seat rules shape divides.
Sam: It does—the evidence suggests meaningful causation there. Thanks for digging into this with me, Alex.
Alex: Pleasure as always, Sam. Thanks for listening to ResearchPod.