Designing social systems with AI instead of choosing left or right: AI-written tax and redistribution rules, Community Notes, and who sets the objective

Can AI design a social system that beats capitalism, socialism and every party platform, and so end the fight between left and right? AI can now write working rules that people vote for. It cannot decide what those rules should aim at, and that decision is the old fight in a new place.

Since about 2020 a handful of experiments have shown the first half. A reinforcement-learning planner wrote tax schedules for a simulated economy. Another system designed a payout rule that real players chose over the rules each ideology would have offered. A language model wrote compromise statements on divisive UK questions that people preferred to the drafts of human mediators. In every case the target came from outside the machine: a welfare formula, a majority vote, endorsement, cross-cluster agreement3. What AI system-design experiments have actually demonstrated.

The argument here is that the target is where left and right now live. In the lab, the public increasingly sets it, by voting or by endorsing. Outside the lab, people nobody elected are already setting it: pricing software aims at the seller's margin, social feeds reward attacks on the other team, and pollsters are building synthetic citizens to stand in for real ones.

1Left and right were already arguing about how an economy handles information

The argument between market and plan was a design argument long before a computer could run either. In 1920 Ludwig von Mises argued that without market exchange in the means of production there are no money prices to calculate with. Friedrich Hayek turned that in 1945 into a claim about information: the economic problem is using knowledge that exists only in scattered, local form, and prices are a way of communicating itKey Findings.

Whether computers answer Hayek is still disputed. One peer-reviewed side holds that advances in computing power are irrelevant to Mises' most fundamental argument1. The precedent of the calculation debate and Cybersyn, because the problem concerns institutions (property and exchange), not processing speed. A 2024 paper from the planning side replies that this in-principle case depends, more than its authors admit, on what computing can and cannot do; only its abstract was read. The fair summary is that whether AI dissolves the calculation problem is actively disputed, and that the dispute has shifted from logic toward questions that could in principle be tested1. The precedent of the calculation debate and Cybersyn.

Separate research on pricing shows where the line falls. Walmart and Amazon plan vast operations internally, and some socialists cite them as proof that planning works. What the two companies plan is logistics (inventory, routing, scheduling, forecasting), exactly the class of problem Mises conceded planning could handleStrand Two: The Hayek Inversion — pressure-tested. Their capital decisions still lean on the market around them, since their internal "transfer prices" are calibrated to those external pricesStrand Two: The Hayek Inversion — pressure-tested. The best-known computational proposal, planning in labour-time values, answers the computation problem but not the valuation/knowledge problemStrand Two: The Hayek Inversion — pressure-tested.

The one serious attempt to run part of a national economy as a designed system shows the other limit. Project Cybersyn, built in Allende's Chile between 1970 and 1973 with the British cybernetician Stafford Beer, was meant to manage the transition of Chile's economy from capitalism to socialism1. The precedent of the calculation debate and Cybersyn. Its historian found that its technical structure reflected the government's socialist ideology.

2What the AI systems designed, and who told each one what to aim for

The AI Economist, from Salesforce Research, trained a planner to set taxes while simulated workers learned to gather, trade and build around them. Its designers fixed the planner's target as the product of equality and productivity3. What AI system-design experiments have actually demonstrated, and the preprint reports that the learned policy improved this trade-off by 16% over a tax framework derived from Emmanuel Saez's optimal-tax theory3. What AI system-design experiments have actually demonstrated. The economy is a small gridworld, the workers are reward-maximizers rather than people, and the objective is one value choice among many3. What AI system-design experiments have actually demonstrated. Someone who weighted growth or liberty differently would have scored the same tax schedules differently.

DeepMind's Democratic AI, published in 2022, took its target from the players. In an online investment game, players voted between redistribution rules, and the AI's rule beat human-designed baselines including strict egalitarian, libertarian and liberal-egalitarian schemes3. What AI system-design experiments have actually demonstrated. The winning rule redistributed in proportion to players' contributions relative to their means, which corrected the starting wealth imbalance while discouraging free-riding3. What AI system-design experiments have actually demonstrated. It is the closest anything has come to design replacing the contest between ideologies: a rule no camp owned, chosen over the rules each camp would have proposed. Majority vote is still a target someone chose, and the authors acknowledge the risk of a design that a majority likes while a minority bears its costs3. What AI system-design experiments have actually demonstrated.

The Habermas Machine, from Google DeepMind and published in Science in 2024, aimed at agreement. A language model read individual opinions on divisive UK policy questions, drafted a group statement, took critiques and revised it. Across more than 5,000 participants, the AI-written statements were preferred to those written by human mediators 56% of the time, and participants' positions converged toward a common view after the process3. What AI system-design experiments have actually demonstrated. The process produced statements, not adopted laws.3. What AI system-design experiments have actually demonstrated

Each of these systems has three parts: a picture of the people (simulated agents, game players, written opinions), an optimizer that searches for a rule, and the target the rule is scored against. AI has made the search cheap. The picture of the people and the choice of target are the parts that still carry the politics.

3Arrow's theorem: no target for adding up preferences is neutral

Kenneth Arrow proved that once there are more than two options, no procedure for turning individual rankings into a social ranking meets a short list of reasonable conditions together: it handles any preferences, produces no cycles, follows unanimous preferences, has no dictator, and ignores irrelevant alternatives. The options can be policies, public projects, candidates in an election, distributions of income2. What formal theory says about designing collective choice, so the result covers choosing between whole economic arrangements as much as between candidates. Arrow himself regarded his conditions as value judgments, not neutral axioms2. What formal theory says about designing collective choice. Any aggregation rule an AI uses therefore embodies a political choice about which conditions to give up.2. What formal theory says about designing collective choice

The theorem has been described as being about the impossibility of trying to do too much with too little information2. What formal theory says about designing collective choice, and the ways around it bear that out. People can grade options instead of ranking them, utilities can be made comparable across people, or the range of preferences can be narrowed. The third route is where deliberation comes in. If discussion pushes preferences into a shape that can be added up, deliberation tools may work by changing the structure of disagreement, not by finding a hidden consensus2. What formal theory says about designing collective choice, and List et al. (2013) present empirical evidence that deliberation sometimes does have this effect2. What formal theory says about designing collective choice.

Seen this way, the ideologies are answers to design questions. Conservatism, liberalism and socialism can be read, roughly, as bundles of answers about which conditions to relax, whose utilities count and how much, and how far to trust local knowledge over central models.2. What formal theory says about designing collective choice Their argument does not end; it becomes a set of explicit parameters2. What formal theory says about designing collective choice.

A formal field already works this way. Mechanism design, recognised by the 2007 economics Nobel, treats markets as one mechanism among many, a framing that can show when markets perform well and when other arrangements do better3. The formal toolkits behind these experiments. Elinor Ostrom's research on how communities govern shared resources explains outcomes that do not fit a two-way choice between the market and the state3. The formal toolkits behind these experiments, though only the journal record of her Nobel lecture was read.

4Outside the lab, sellers and platforms are choosing the target

The experiments let a vote or an agreement score set the target, and they printed it in the paper. The AI-designed systems already running at scale mostly do neither.

Take prices. Research on algorithmic pricing describes a third mode that is neither Hayek's market nor a socialist plan: prices set by many private, competing, opaque corporate algorithmsStrand Two: The Hayek Inversion — pressure-tested. In that mode calculation has been privatized and rendered opaqueStrand Two: The Hayek Inversion — pressure-tested, and we get computation without either discovery or accountabilityStrand Two: The Hayek Inversion — pressure-tested. "Administered prices" is Gardiner Means's term from the 1930s; applying it to prices computed for each buyer is that research's own framing. The target is the seller's. In one field experiment, personalized pricing improves the firm's expected posterior profits by an additional 19%, relative to the optimized uniform priceThe tetrad on the pricing algorithm, while total consumer surplus declines 23% under personalized pricing relative to uniform pricingCounterarguments and honest responses. The same study found that over 60% of consumers benefit from lower prices under personalizationCounterarguments and honest responses, so the objection is to who sets the target and who sees it, rather than to every outcome. The AI Economist's welfare formula was stated in its paper; a pricing model's is a trade secret.

Social feeds are designed systems too, and what they reward bears directly on the tribal side of politics. In a study of 2,730,215 posts from members of Congress and news outlets, posts about the political out-group received roughly twice as many shares as posts about the in-group5. The Underlying Concept: Negative Partisanship, and "out-group" language was the single strongest predictor of social-media virality, outperforming negative emotion and moral language5. The Underlying Concept: Negative Partisanship. A study of political elites' tweets found that politicians themselves post more in-party-positive than out-party-negative tweets, but ordinary users engage (likes, shares) much more with the out-party-negative posts.5. The Underlying Concept: Negative Partisanship The upshot is that partisans get rewarded — in attention, in social validation, in algorithmic amplification — for attacking the out-group5. The Underlying Concept: Negative Partisanship.

Community Notes, on Twitter/X, is what happens when a platform changes the target. Its algorithm rates a crowd-written note highly only when raters from different viewpoints, as inferred from their past ratings, find it helpful3. What AI system-design experiments have actually demonstrated. In a survey experiment, notes it selected reduced the likelihood of agreeing with the substance of a potentially misleading tweet by about 26% (N=6,046), with no statistically significant difference in effect between Democrats and Republicans3. What AI system-design experiments have actually demonstrated. In live tests, people exposed to notes were 25–34% less likely to like or retweet the annotated tweets3. What AI system-design experiments have actually demonstrated. The study was written by Twitter's own staff and has not been independently replicated, and its authors name the limit: By construction, the algorithm needs some cross-partisan agreement to function.3. What AI system-design experiments have actually demonstrated Even the notes it chose were received very differently by the two parties:

Polis, used in Taiwan's vTaiwan process to feed public input into rules such as ride-hailing regulation, works on the same principle of rewarding agreement across groups. It has reached real legislation, but there is no rigorous causal evaluation of how much they changed outcomes compared with ordinary consultation3. What AI system-design experiments have actually demonstrated.

5The fight a better design is meant to end is mostly about the other team

Political scientists separate two kinds of division:

In the United States, the gap between how partisans rate their own party and the other party rose from 22.64 degrees in 1978 to 40.87 degrees in 20165. What the tribalism evidence says on a 0–100 feeling thermometer. That hostility is largely distinct from the ideological divide5. What the tribalism evidence says. Ideological sorting accounts for only about 5% of the variance in affective polarization5. What the tribalism evidence says, and in some settings, affective polarization can increase while ideological divisions shrink5. What the tribalism evidence says. In 1960 only 4–5% of people were upset at the idea of their child marrying someone from the other party; by 2010 the figure was one-third of Democrats and one-half of Republicans.5. What the tribalism evidence says

The American evidence is the deepest, but Canada shows the same pattern in a multi-party system. Using Canadian Election Study data from 2008 and 2011, researchers found that negative partisan identity independently predicts turnout, vote choice and political activity5. The Underlying Concept: Negative Partisanship.

The consequence for any design programme is blunt: the fight would not end if a better-designed system were offered, because the fight was never mainly about system design5. What the tribalism evidence says. Team identity has to be a design target in its own right, and the feed evidence shows it already is one, aimed the wrong way. The remedies tried so far are thin. On small-group discussions between Democrats and Republicans, there have been no systematic evaluations of these efforts, and it seems questionable whether they are scalable5. What the tribalism evidence says.

6Simulated citizens would make every design look more agreeable than it is

The cheapest way to test a designed system before using it on people would be to run it on a simulated population of AI citizens. When researchers asked ChatGPT to answer as demographic personas and compared the results with the American National Election Study, the averages matched closely. But there is less variation in responses than in the real surveys4. Can AI simulate a society well enough to test designs before deploying them?, and the same prompt yields significantly different results over a 3-month period4. Can AI simulate a society well enough to test designs before deploying them?. Missing variation is the worst possible failure for this use, because Arrow-type problems arise precisely from heterogeneity4. Can AI simulate a society well enough to test designs before deploying them?. A synthetic population that is too uniform will make a design look more consensual and stable than it is.4. Can AI simulate a society well enough to test designs before deploying them?

Synthetic respondents are already a product in Canada. Léger, with Plus Compagnie, has launched Smart Persona, and a Léger executive claimed an 85–90% concordance score between Smart Persona output and traditional human panel resultsSynthetic respondents — the actual frontier. That figure is a self-reported industry metric, not an independently validated benchmarkCaveats, and Léger characterizes Smart Persona as for marketing decisions (ad testing, A/B), not political polling.Synthetic respondents — the actual frontier Even so, no rule obliges a pollster to say when synthetic answers fill out a sample: There is no regulatory framework at CRIC or anywhere else in Canada that requires disclosure of synthetic respondent supplementation.Synthetic respondents — the actual frontier The ChatGPT study is the warning about how to read a headline match: ChatGPT got the averages right too, and the spread is what went missing. A concordance score on its own does not say whether the disagreement survived.

So the picture of the people can fail the same way the target can: quietly, inside a commercial product, before anyone outside sees it.

7Bell declared the end of ideology in 1960 and left a usable test

The claim that ideology is finished has been made before. Daniel Bell argued in 1960 that among Western intellectuals the old passions were spent, and that ideology lets people skip judging issues on their merits; they turn to an ideological vending machine, and out comes the prepared formulae6. Has "the ideological debate is over" been claimed before?. Within a decade the New Left had put ideological conflict back at the centre of Western politics. Bell's claim was a description of a mood among intellectuals, not of the public, and it underestimated how much people want politics to supply identity and passion.6. Has "the ideological debate is over" been claimed before? That is the same gap the polarization evidence exposes.

Bell did not give up on utopia; he demanded that it be specified. A utopia, he wrote, must specify where one wants to go, how to get there, what the enterprise costs, and who is to pay, with a justification for that choice6. Has "the ideological debate is over" been claimed before?. Those are the questions a designed system's target answers. The lab experiments answer them in their methods sections. A pricing model and a ranking algorithm answer them too, in code their users never see.

8The left–right argument survives as the choice of a system's target, and private systems are making it first

AI has made the search for a good rule cheap. It has not made the target obvious, and Arrow's result says it never will: every way of adding up what people want is a choice about whose wants count and how. The old ideologies were bundles of those choices made in advance. A designed system makes the same choices explicitly, and the explicit version can at least be compared, tested and argued with.

The lab and the market have so far gone different ways. Where researchers let the public set the target, through a vote, an endorsement or agreement across groups, the results are promising and small: a game, a set of statements, a fact-checking feature. Where firms set it, the systems already run prices and feeds at national scale, aimed at margin and engagement, and the second of those rewards exactly the team hostility a better design was supposed to calm. Choosing between those approaches is itself the modern version of the old split between democratic and technocratic thinking.Beyond this question The question that replaces "which system wins" is the one to ask of any rule that governs people, public or private: what is it told to maximize, who decided, and who was in the room, real or simulated, when it was tested.