policy simulations are having a moment. cisa runs tabletop exercises on ai security incidents.1 intelligence rising puts policymakers through ai race scenarios.2 the democracy futures project has former officials role-play responses to authoritarian threats.3 these exercises are valuable: 84% of intelligence rising participants say they'd recommend the experience.2 but they share a limitation. they train people to respond to crises. they don't help us systematically study how humans reason when they hit deep moral tradeoffs under pressure.
questions like: how do we weigh future welfare against present suffering when resources are tight? does facing a cascade of interconnected risks change how people prioritize? can a moral framework shift when abstract ethics become a visceral choice?
these aren't just philosophical curiosities. they shape how we might navigate high-stakes decisions about long-term futures, in ai governance, space settlement, or any other civilization-scale choice. and we can't understand them through surveys or armchair speculation alone.
philosophers have written extensively about moral uncertainty: the problem of acting wisely when you're not sure which moral framework is correct.4 it forces you to weigh values that resist direct comparison. should animal welfare count as much as human welfare? how should we value digital minds? which principles of population ethics should guide long-term planning?
despite decades of rich theory, we have very little data on how people actually reason under moral uncertainty in high-stakes, long-term contexts. a 2024 study built a scale to measure how people navigate everyday moral dilemmas, but didn't touch large-scale or civilizational questions.5 another found that most people read moral uncertainty as personal or cultural, not as an objective problem to be solved.6
that's a worrying gap. the choices we make about transformative technologies could shape centuries. yet we have almost no empirical picture of how humans think when those choices stop being hypothetical. the obstacle isn't only intellectual. it's psychological: distant futures feel abstract, unreal, someone else's problem.
people don't struggle to understand long-term risk because they lack reasoning skills. they struggle because those risks don't feel real.
speculative design theorists have argued that design can be "a tool to create not only things but ideas," a way to explore possible futures by posing "what if" questions that spark debate.7 speculative prototypes can make far-off possibilities feel real enough to reshape how we think: "if we speculate more—about everything—reality will become more malleable."8
others make the link to philosophy explicit: speculative design works like a thought experiment, but with materiality. by building tangible scenarios, often around a central object, designers pull people into complex questions of science and policy.9 one designer calls these perceptual bridges: ways to make abstract concepts experientially comprehensible.10
museums have always known this. reading about history is one thing; standing inside a reconstructed environment is another. the second creates an emotional connection that turns observers into participants.11 theme parks pushed it further: immersive detail and storytelling make fictional worlds feel lived in.12
many policy problems about the long-term future are engagement problems in the same way. experiential prototypes could turn abstract governance dilemmas into lived, testable experiences.
here's one way forward: build interactive simulations not just to train people, but to study them. treat experiential scenarios as research instruments that generate data about how humans reason under deep uncertainty.
imagine a multiplayer simulation. call it eutopia mode. small teams guide a civilization through key turning points. the design rests on a simple, uncomfortable idea: flourishing futures require success across many dimensions, and those dimensions multiply rather than add. excelling in seven but failing in one could unravel everything. try it:
what would make it different from existing simulations?
each epoch could draw on current scenario work: ai-enabled power grabs, great-power coordination breakdowns, experiments in space governance or digital rights.
1. the measurement problem. are we measuring reasoning about moral uncertainty, or just gameplay skill? it needs careful design: baseline measures of moral views before the simulation, tracked changes throughout, comparisons across framings. it won't be perfect. no social science is. the question isn't whether every confound can be removed; it's whether we can learn something useful.
2. the transfer question. do lessons from simulations carry into the real world? early evidence is promising: intelligence rising participants report lasting changes in how they think about ai governance,2 and democracy futures participants identified coordination failures they hadn't seen before.3 rigorous follow-ups could tell us whether those effects endure, and if not, why. even failure would be informative.
3. the design bias problem. every simulation encodes assumptions about what matters. bake in certain answers and you end up testing your own framing instead of people's reasoning. the fix is transparency and iteration: be explicit about assumptions, test multiple models (multiplicative, threshold, lexicographic), and take seriously the cases where participants reject the scenario's framing.
those limits don't make the approach useless. the question is whether we can still learn something valuable despite them.
publishable findings. does experiencing fragility change how people assess risk? do different moral framings predict different resource patterns? these are testable. decision data, survey shifts, correlations: cognitive science applied to civilization-scale ethics.
policy impact. participants won't just leave with opinions; they'll leave with memories. of watching a civilization collapse from delayed action. of seeing digital minds neglected. of facing an impossible tradeoff firsthand. visual "outcome maps" of how their worlds flourished or failed could keep the conversation going long after the session.
even failure is data. if people consistently ignore multiplicative risk, that reveals something deep about how we think.
the literature on moral uncertainty is rich but mostly theoretical. psychologists study moral reasoning, but rarely at the scale of a civilization. policy simulations exist, but they aim to train, not to understand. this sits at the intersection: experiential scenarios that generate data about reasoning under deep uncertainty. speculative design is about "how things could be," exploration rather than prediction.7 embed that spirit in long-term futures research and you get controlled environments where ideas become testable and human reactions become measurable.
the goal isn't to replace theory or traditional research. it's a complementary tool: one that captures how humans actually reason when the future stops being abstract. when you've watched a near-utopia slip away because you underweighted one fragile dimension, that experience changes you. we can study how. how does your risk assessment shift? how do your tradeoffs evolve? what patterns emerge?
those are empirical questions. we just need to build the instruments to answer them.