Papers

Intra-household joint home-based tours - trips in which household members depart together, engage in shared activities, and return together - represent a significant share of daily travel, yet are systematically ignored in transport simulations. Conflating joint and solo tours within a single mode choice framework introduces bias in preference parameter estimates. This paper proposes a three-step methodology to integrate joint tours in agent-based transport models: a Random Forest classifier to identify joint tours, a Multinomial Logit model estimating mode choice specific to joint tours, and a Penalized Logistic Regression for driver/passenger assignment. Applied to the Paris region using household travel survey data, the methodology successfully replicates observed joint tour shares and mode distributions in a synthetic population. The proposed framework enables more reliable evaluation of policies whose impacts differ between joint and solo travel, such as HOV lanes or family transit fare discounts.

Most LLM benchmarks rank models on their ability to automate work tasks. In practice, however, models are often used to assist other (human or LLM) agents. The question that drives model selection is therefore not only which model produces the best output, but which model most improves the work of another (weaker) agent. We introduce a unified framework that evaluates the capability of models to automate and augment another agent's performance. Across seven economically grounded real-world tasks, an assistant model writes assistance text for a standardized lower-capacity worker model, which produces the deliverable. In automation mode, the assistant produces the output directly. Outputs are scored through blind pairwise comparisons by an LLM judge panel with task-specific rubrics, replicated across ten runs. Rankings across the two regimes are only modestly correlated, and the automation winner loses augmentation on five of seven tasks. Assistance is not reliably positive. The unaided worker outranks every assisted condition on three tasks, and only one model's guidance beats no guidance on average. These results suggest that automation ability is an incomplete proxy for assistance quality, motivating benchmarks that evaluate models according to the roles they play in human-AI and multi-agent systems.

This paper develops asymptotic theory and feasible inference for unbounded-kernel order-k U-statistics under clustered sampling and weakly dependent time-series. The analysis first builds the complete order-2 pipeline, moving from clustered data to exact $m$-dependence and then to near-epoch dependence. The same logic is subsequently extended to general order k greater or equal to 2. Under clustered sampling, the theory allows arbitrary within-cluster dependence and growing, unbalanced cluster sizes. Under weak dependence, an i.i.d.-based approximating sequence carries the exact-m theory to near-epoch-dependent processes. The common combinatorial device partitions the sample into columns, separating sampling-generic tuples, where the first-order Hoeffding projection is analysed, from collision terms and higher-order Hoeffding projection remainders, which are controlled explicitly. Cluster-robust and HAC estimators of the covariance of the first-order projection, needed for feasible inference, are shown to be consistent. Empirical applications and data-calibrated simulations for inequality, L-moment, and rank-dependence statistics illustrate the finite-sample performance of the proposed procedures.

This paper develops a revolving-door model in which a regulator designs policy signals that affect a regulated firm's capital structure while holding an implicit career option to leave public service for the regulated firm or its advisers. The model has three linked mechanisms. First, regulatory signals alter the firm's pricing, leverage, and risk choices by changing the tradeoff between consumer surplus and firm profit. Second, because the regulator's outside payoff is convex in regulated-firm value, the career option rises with firm value, leverage, and signal-induced volatility. Third, when signal-induced volatility raises the option vega of firm equity, the same mechanism can increase tail risk and reduce distance to default. A proof-of-concept calibration illustrates leverage-vega warning regions and shows how a regulator-induced signal can move a firm from a go-leverage region into an early-warning or crash-risk region. The calibration includes maturity comparisons, sensitivity grids over compensation convexity and initial leverage, a bank-like parameter anchor, stochastic signal simulations, and a stopping-horizon diagnostic. Evidence from federal payroll data on post-employment restrictions supports the career-option primitive by showing that limits on revolving-door opportunities affect post-agency pay. The exercises also organize historical regulatory failures: Lehman Brothers as a regulatory-vacuum case, Silicon Valley Bank as a distorted-signal and weak-supervision case, and Signature Bank as a converted-signal case involving post-public-service incentives, lighter oversight, rapid growth, uninsured-deposit exposure, and tail risk. The model does not predict collapse dates; it identifies conditions under which regulatory forbearance, weak debt penalties, and career-option incentives can delay oversight until firms cross a leverage-vega warning threshold.

This paper presents a framework for stable matching markets that accommodates the peer-dependent preferences employed in empirical work. We show the existence of a stable matching in a continuum economy and of an approximately stable matching in a large finite sampled economy under assumptions satisfied by standard empirical specifications. The analysis builds on a tractable characterization of stable matchings with peer-dependent preferences in terms of supply and demand equations along with a rational-expectations condition.

We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. We allow for the inclusion of lagged outcomes in the model. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a trend bias term. A two-way fixed effects (TWFE) regression that controls for the mediator does not recover the average direct treatment effect on the treated. We show that a DiD estimand conditional on the observed mediator path identifies the conditional average direct effect for treated units at that path, and that averaging over the treated path distribution identifies the average direct effect even when unconditional parallel trends fails. A stable average mediator effect assumption helps recover the average mediator and indirect effects. The framework extends to multivariate mediators, nonlinear DiD, and multiple-treatment-period settings. Existing doubly robust estimators can be used to conduct inference. Revisiting the effects of railroad access on agricultural land values, the specification yields a positive direct component not mediated by measured market access, while the corresponding indirect component is small and imprecise.

We examine how agents learn when information from original sources only reaches them after noisy relay. A receiver learns if and only if they have access to sufficiently many chains of noisy relay and they perfectly understand the noise process. However, even slight uncertainty over message mutation rates makes learning from long chains impossible, no matter how many independent sources are accessed.

We introduce a general, easy-to-implement AI-based modeling technique for analyzing human behavior. A key feature of this approach, which contrasts with existing modeling techniques, is that it combines the flexibility and interpretability of natural language with a mathematical structure that can be fitted to data and easily analyzed. We assign a large language model a vector of trait intensities-a type vector-and then ask it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) could correspond to "You are a player characterized by the following profile: Altruism: 2 out of 5, Risk Aversion: 4 out of 5," after which it is asked to make choices. We can then vary the traits (e.g., Altruism, Fairness, Trust,...) and values (e.g., 1-5) to minimize distance to human choices. We illustrate the method by applying it to model 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles. We find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. The type vectors needed to fit individuals across games cluster into fewer than a dozen groups, with substantial variation in fit across subjects. Moreover, the individual type vectors can predict behavior in held-out games with different rules and available actions. More broadly, this new modeling method is highly generalizable and interpretable: we can input any vector of traits and use them to model behavior across any setting

How level-0 players behave and how they are perceived by higher-level players are central questions in the literature on level-k models of boundedly rational strategic reasoning. To study these twin questions, we apply the intra-team communication method developed by Burchardi and Penczynski (2014) to identify level-0 actions and beliefs in the canonical beauty contest game and in a variant of the 11-20 game of Goeree et al. (2018), in which behavior appears inconsistent with the standard level-k model. In the beauty contest game, we replicate Burchardi and Penczynski (2014)'s finding that elicited level-0 beliefs align with observed level-0 actions. In the variant of the 11-20 game, however, elicited level-0 beliefs and observed level-0 actions diverge, and both depart from the standard level-0 assumption, suggesting a complementary explanation for the behavioral pattern documented by Goeree et al. (2018).

We study how fast experimental designs can approach the semiparametric efficiency bound in finite samples, as measured by the excess variance of unadjusted treatment effect estimation. We prove an impossibility theorem: under weak conditions, no design can approach the variance bound uniformly over smooth outcome models unless covariate dimension $d \ll \log n$. Even in experiments with thousands of units, this permits only a handful of covariates. Motivated by this, we propose new designs based on discrepancy minimization that instead attempt to control imbalances over restricted-complexity nonparametric function classes. Such designs achieve fast rates to their corresponding restricted efficiency targets, permitting $d \ll n$ covariates in an additive nonparametric specification. They can also be combined with matching to protect against unmodeled outcome variation. In simulations calibrated to 12 published experiments, our designs reduce variance relative to matched pairs randomization in every empirical setting.

We study a dynamic coalition-formation process in the tradition of Konishi and Ray (2003): players repeatedly form and dissolve binding agreements, evaluate states by discounted long-term expected payoffs, and hold self-confirming beliefs about the process. States and payoff sharing follow Heitzig and Kornek (2018): a state is a hierarchy of nested agreements, and the members of a new agreement share the surplus it generates, measured against the state without that agreement. All payoff assumptions are structural. We prove that every grand state ever reached is absorbing, and that every absorbing state is grand, for every discount factor. A grand state is actually reached, almost surely, for small discount factors, for three players, and, at every discount factor, whenever distributional stakes are smaller than each player s share of the efficiency gain. Otherwise the process can fail only by cycling for ever among non-grand states. We give exact necessary conditions on such a cycle, decidable for a fixed candidate cycle by linear programming, and exhibit, under an earlier and weaker notion of profitability, a four-player payoff structure whose only closed class is a cycle of two pairs forming and dissolving alternately. Under the present definition, and under either termination rule, no equilibrium traverses a cycle on a fixed schedule: somebody always reaches a state they would rather not leave, and the axioms give them the floor. Whether arrival can fail by cycling at random is open. The axioms are not merely postulated: we exhibit a bargaining game proposal, amendment by substitutes voted on by their own signatories, final unanimity, and an arbitrarily small delay on failure whose equilibria satisfy them as the delay vanishes, and which settles each period in its first round.

Delegating cognitive operations to generative artificial intelligence redistributes execution and raises a governance problem: where human direction of the task remains. We distinguish two routes. Specified delegation places that direction before execution, through instructions, constraints, or criteria that delimit the task. Iterative coproduction places it during production, through interventions that correct or redirect provisional outputs. To examine both routes, we use aggregate monthly cells from the Anthropic Economic Index for April and May 2026. The AEI distinguishes two modes of use: 1P API, which corresponds to direct traffic through Anthropic's API, and Claude.ai, which combines activity from Chat and Cowork. On this basis, we test whether a stronger work-related orientation of human-AI interaction is associated with more specified delegation within each mode and whether the increase in the iterative profile is greater in Claude.ai than in 1P API. The main analysis uses level-0 O*NET tasks and estimates how both profiles change when an eligible record reallocates ten percentage points from personal use to work-related use. The iterative comparison is restricted to 1,411 node-month pairs observed and eligible in both modes. Specified delegation increases by 2.76 points in 1P API (95% CI: [2.30, 3.22]) and by 1.45 in Claude.ai (95% CI: [0.93, 1.97]). On the common support, iterative coproduction changes by-0.30 points in 1P API and by 0.15 in Claude.ai, yielding a between-mode difference of 0.45 points (95% CI: [0.15, 0.75]). These findings show that work-related orien tation is associated with stronger traces of prior human direction and that the observable iterative response varies across modes of use. The article shifts attention from how much the AI executes to when human direction leaves observable traces.

Multidimensional screening is (in)famously intractable. In this paper, we study optimal screening mechanisms subject to a tractability constraint from the agent's perspective. Specifically, we require that the menu of options offered by the designer can be ordered so that, regardless of her preference type, the agent can find a utility-maximizing option via greedy search: any locally optimal choice must also be globally optimal. In one-dimensional screening with the single-crossing property, this requirement has no bite. In multidimensional environments, however, searchability restricts the set of implementable outcomes. In the multiproduct monopoly problem, the optimal searchable menu is a sparse upgrade menu: higher tiers offer higher allocation probabilities for every good, and the number of tiers is at most the number of goods. In a multidimensional screening problem with money and ordeals, the optimal searchable menu offers the agent a single way to obtain the good. In income taxation with rich multidimensional heterogeneity, a tax schedule is searchable if and only if it is progressive.

Trust in local institutions matters for trade, public goods provision, conflict resolution, and democratic consolidation. Using individual data from rounds 6 and 7 of the Afrobarometer surveys, I document that respondents in former British colonies are substantially more likely to trust traditional leaders than respondents in former French colonies. Cross-country comparisons of this kind are confounded by unobserved heterogeneity in pre- and post-colonial histories. To make progress on identification, I focus on Cameroon, which contains regions colonized by Britain and regions colonized by France within a single modern state, and I exploit the former colonial partition line as a geographic discontinuity, comparing respondents who live close to either side of the anglophone-francophone boundary. The theoretical argument is that the two colonial powers differed systematically in how they treated customary authority: British indirect rule preserved the governance functions of chiefs, while French direct rule subordinated chiefs to the administration and assigned them its most coercive tasks, taxation and forced labor above all, and these divergent experiences shaped the legitimacy of chieftaincy in ways that persist through institutional continuity and socialization. Consistent with this argument, respondents on the formerly British side are 22 to 26 percentage points more likely to trust traditional leaders than their neighbors on the formerly French side, and they are far more likely to have contacted a traditional leader in the previous year.

This paper develops a theoretical framework for signed optimal transport. A global flatness measure induced by the continuum transport equation serves as the regularizer. We examine transport networks from a harmonic analysis perspective, prove the existence and uniqueness of the optimal coupling in the variational problem, and provide an algorithmic scheme that guarantees lossless information transport under bi-marginal constraints. To bridge theory with practice, we design an empirical analysis framework for the resulting optimal estimators, which is sufficiently general to accommodate both time-series and panel-structural analyses of the network, owing to the intrinsic invariance properties of locally compact abelian groups.

Reputation and institutional certification are the two main trust mechanisms under information asymmetry, yet their interaction remains poorly understood. Here we analyze nearly one million eBay listings of Pokemon trading cards. Each listing pairs the seller's reputation with their signal choice, that is, whether they used costly third-party certification, their own costless claim (self-grading), or neither to convey the card's quality. First, we find that certification and self-grading partition the market. Self-grading prevails where reputation is high and value low, whereas certification prevails in the market's opposite corner. Furthermore, seller reputation raises the price premium of self-grading but not of certification. We then show that both empirical patterns follow from a signaling model in which false self-grading damages reputation, whereas certification requires an up-front fee. These results suggest that, even within a single market, trust rests not on a choice between reputation and institutions but on a division of labor.

We develop a continuous-time entry-deterrence game in which market demand evolves according to the Chan-Karolyi-Longstaff-Sanders (CKLS) stochastic differential equation, allowing mean reversion and state-dependent volatility. An incumbent with privately known strength strategically chooses advertising and promotional expenditures to influence a potential entrant's beliefs, while the entrant faces a costly, irreversible entry decision and optimally waits until market conditions justify participation. Within a dynamic Stackelberg setting, Bayesian learning, asymmetric information, stochastic demand, and strategic controls jointly determine entry and signaling behavior. Using a Feynman-type path-integral control formulation, we characterize a Markovian Nash feedback equilibrium for the firms' expenditure strategies. Our contribution is to integrate CKLS demand uncertainty, private information, irreversible entry, Bayesian belief updating, and path-integral feedback control within a unified continuous-time entry-deterrence framework, while providing a computational alternative to direct Hamilton-Jacobi-Bellman (HJB) approach. We illustrate the framework empirically using 2010-2024 revenue data for Enterprise Products Partners and Targa Resources. The resulting trajectories are qualitatively consistent with the model's predictions, exhibiting persistence, recovery after adverse shocks, and distinct responses associated with different competitive positions, while supporting the model's strategic mechanisms under uncertainty.

Platforms summarize providers' past achievements into credentials that buyers use to judge quality. Generative AI can now produce much of the work those achievements certify, raising fears that those quality signals are worthless. We audit how well such credentials stay informative in Kaggle's 2010-2026 archive, where medals are won on predictions scored against withheld answers and two evaluation formats ran side by side. Across 444,698 participations, a medal's power to predict performance sits almost entirely in its first year, in both formats and eras. Fresh medals kept most of their value through the AI transition. About half of the collapse in the informativeness of one format's medals is institutional: the platform had been phasing out that format before AI, and its medal stock aged out on schedule. Old medals look more informative only in isolation. The platform's official lifetime-tier display discards up to a sixth of the medals' predictive power. A recency-weighted index fit before the AI era explains AI-era performance about 13% better than the tiers and selects entrants who perform better on average, though the tiers still identify extreme top performers better. Displaying a recent-performance summary alongside the lifetime tiers would recover the discarded information for buyers.

This article surveys emerging directions in the mathematics of democracy. It uses three case studies — voting theory, participatory budgeting, and deliberative democracy — to highlight how contemporary challenges motivate rigorous mathematical research that incorporates real-world data, institutional constraints, and implementation feasibility. Within each case, we highlight active and promising research frontiers, evidence of real-world impact, practical applications, and opportunities for getting involved.

When parallel trends fails for some treated cohorts but not others, the average treatment effect on the treated (ATT), an average over all of them, is exactly the target that becomes hard to recover. We propose changing the estimand rather than defending it. The credible-subpopulation local ATT (LATT) is the effect for the subpopulation of cohorts whose parallel trends is credible, and it is point-identified under parallel trends for the selected cohorts alone, a weaker requirement that can hold when the ATT's fails. It is estimated by reweighting standard group-time effects toward those cohorts, and paired with honest sensitivity bounds on the residual violation that a pre-trend screen cannot rule out. The method's advantage grows with how informative pre-trends are about post-treatment violations, as simulations confirm. In an application, a significantly positive pooled estimate of the shale boom's effect on local house prices proves to rest on cohorts already trending before onset, and the credible subpopulation reveals no effect.

Most digital job-search assistance encourages unemployed workers to broaden their search toward related occupations, targeting one important source of search inefficiency: insufficient occupational diversification. Our analysis suggests that the relevant margin of adjustment depends on the underlying search problem. Building on a detailed analysis of job seekers' beliefs and search behavior, we identify a large group of pessimistic workers for whom the main constraints are low search effort and low aspirations, rather than insufficient occupational diversification. This diagnosis points to an unexpected intervention: rather than encouraging these workers to search in new occupations, we encourage them to search more intensively and apply for better-paying jobs within the occupations they already consider. We evaluate this diagnosis-based intervention, alongside a standard occupational recommendation, in a large-scale randomized experiment conducted with the French Public Employment Service. The motivational intervention increases search effort, raises reservation wages, and improves reemployment outcomes along the predicted margins. Occupational recommendations, by contrast, primarily benefit workers whose search problem lies in the allocation of attention across occupations and operate by activating existing perceptions rather than correcting beliefs. More broadly, our findings show how digital platforms can combine subjective expectations, behavioral data, targeted interventions, and randomized experimentation to diagnose job seekers' needs and iteratively improve intervention design.

Job seekers' expectations about reemployment are increasingly used to study job search, but what their biases reveal about underlying beliefs and preferences is ambiguous. We combine new survey data, structural modeling, and machine learning to uncover the informational content of these expectations and show how they can be used to improve the targeting of employment support. Using a new panel of French job seekers' subjective expectations linked to administrative records, we show that reemployment expectation biases are strongly associated with, and summarize, biases in beliefs about the two fundamentals of search, job offer arrival rates and the wage distribution. These underlying biases are heterogeneous but strongly positively correlated, so their effects on search compound. We then estimate a structural job search model with multiple sources of biased beliefs and show that correcting them helps pessimistic job seekers but can demotivate and hurt optimistic ones, providing a rationale for targeting. Finally, we develop a machine-learning stratification that recovers policy-relevant groups, with distinct patterns of biased beliefs and behaviors, from easily elicited reemployment expectations alone. This gives employment services a simple tool to target informational interventions.

This paper analyzes when choice probabilities reveal rankings of deterministic utility indices in semiparametric discrete choice models. It begins with binary choice, where quantile thresholds guarantee ranking recovery, and shows that such thresholds can arise either from behavioral departures from utility maximization (e.g., limited attention) under exchangeable unobservables, or from non-exchangeable unobservables under standard utility maximization. These behavioral and distributional routes are then extended to multinomial choice. Under limited attention, balance restrictions on attention probabilities yield global linear ranking partitions which are robust to the distribution of unobservables and, given sufficiently rich joint variation in the differences of utility indices, are also necessary. Absent the required attention restrictions, opposite rankings can produce overlapping probability images. Under non-exchangeable unobservables, a comparable distribution-uniform partition generally need not exist. Holding the distribution fixed, however, ranking recovery remains possible via an injective nonlinear map from normalized utility differences to choice probabilities under both behavioral and distributional extensions. Together, the results distinguish distribution-robust global ranking partitions from ranking recovery with a fixed distribution of unobservables and clarify the limits of extending binary quantile restrictions to multinomial choice.

Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design. Specifically, we consider the economic setting recently introduced by Bergemann et al. (2022), where in addition to the menu of quality-price pairs, the seller offers information on the value of the match between product quality and buyer's taste via a signaling scheme. We relax the assumption that the seller knows the buyers' belief about the distribution of tastes and study the sample requirements of designing a revenue maximizing scheme. We consider both the batch setting where we have access to data from a set of i.i.d. buyers and an online demand query model where we observe the buyers' behaviors to seller's schemes. Despite the apparent non-convexity of the problem, we also give the first FPTAS to compute a scheme that maximizes the revenue within an arbitrarily small additive loss, which was left open by Bergemann et al. (2022). Overall, this brings a new learning perspective in asymmetric economic settings where buyers and sellers know different types of information.

This paper studies how robust social learning is when sampling is selective, i.e., some types of actions are more likely to be sampled by successors. We show that Bayesian agents can achieve asymptotic learning despite non-expanding observations, because the endogenous observation network itself carries information and agents have ways to undo the selection bias.

Empirical studies of peer effects often exploit conditional random assignment to peer groups within urns. We develop a GMM framework for estimation and inference in this setting. The framework separately identifies endogenous and contextual peer effects and nests tests of random peer-group assignment as a special case. It permits unknown heteroskedasticity and corrects finite-urn bias in variance estimation. Its asymptotic theory allows the number of peer groups to grow through more urns, more groups within urns, or both. We establish the asymptotic validity of the procedures and evaluate their finite-sample performance through Monte Carlo simulations. We apply the method to study peer effects on personality among university students. For traits with positive reduced-form peer effects, the estimates indicate that positive contextual effects are partly offset by negative endogenous effects.

We ask whether COVID-19 lockdown stringency altered national Olympic performance between Rio 2016 and Tokyo 2020, using the Oxford Stringency Index and the 99 countries that won a medal in either edition. As in \citet{liu2024}, mean performance is unaffected: stringency is insignificant in every OLS and ANOVA specification. The distribution is not. Among the 84 non-traditionally dominant nations, medal changes are three to six times more dispersed in high-stringency countries; the difference is absent among dominant nations and concentrated in men's events. A common shock left the competitive hierarchy intact while sharply raising outcome uncertainty for smaller Olympic teams.

We study the existence of a Regional Differential in rugby sevens: whether, in tournaments where no competing team enjoys formal home status, some national sides systematically over- or under-perform depending on where the event is staged. Using the universe of 2672 men's and women's matches from international rugby sevens tournaments played between 2016 and 2025, principally the World Rugby Sevens Series, but also the World Cup Sevens and the Olympic Games, we replace the binary “locality” indicator of the classic home advantage literature with continuous measures of geographic, temporal, and cultural proximity between each team and the host country. We show that aggregate associations between proximity and performance (which appear large and highly significant in naive specifications) are almost entirely an artifact of team composition and scheduling: once team and opponent quality are absorbed through fixed effects in a symmetric team-match panel, no general regional advantage survives. However, this null aggregate masks substantial heterogeneity. For a well-defined subset of teams (most notably France, but also New Zealand, the United States, Canada, Spain, Ireland, Kenya, Samoa, and Brazil), performance declines significantly and monotonically with the distance separating the host city from home. Some of the established southern-hemisphere powers (Fiji, South Africa, Australia, Argentina) are essentially distance-neutral. We further show that for some teams the gradient operates through east-west jet lag rather than pure displacement. The Regional Differential in sevens is therefore real but team-specific rather than universal, a distinction obscured by pooled estimation.

Research activity generates highly localized positive spillovers, yet in the U.S. it has become increasingly spatially misaligned with population and economic activity as people moved away from legacy cities where major research institutions remain anchored. Reallocating researchers toward population centers could broaden local access to knowledge but may reduce knowledge production if legacy institutions or large research clusters raise researcher output. The spatial reallocation of researchers therefore entails a trade-off between gains in knowledge access and losses in knowledge production. To quantify the knowledge production loss from reallocation, we use bibliographic data to separate individual effects from location effects and show that location effects account for substantial differences in research output across locations. Our instrumental-variables estimates provide robust evidence of own-institution agglomeration effects but weaker evidence of external-cluster effects. We then incorporate these estimates into counterfactual reallocations and show that marginally reallocating researchers toward large metropolitan areas with relatively little research activity can plausibly raise aggregate output because the associated knowledge production losses are modest and can be offset by small access gains.

The terminal crisis of capitalism has been predicted regularly since 1848. It has not occurred. We treat this 175-year forecasting record as a stylized fact in need of a model, and we supply one, unifying results from adaptive systems, mechanism design and computational complexity, behavioral decision theory, global games, and the political economy of redistribution. Ten assumptions deliver twelve formal results and one theorem. Recurring shock classes have geometrically decaying collapse hazards: within the banking class alone, the lag from systemic failure to a system-wide deposit backstop fell from 816 days (1930-33) to 18 days (2008) to 2 days (2023). A centralized replacement of the price mechanism fails on at least one of two horns, computation or truthful input, and we are explicit about which horn survives modern algorithms. We derive Davies's J-curve from cumulative prospect theory together with a new result: the revolutionary window closes endogenously, with length inversely proportional to the speed of hedonic adaptation, roughly four to nine years at calibrated parameters, and with its perceptibility threshold derived from the participation constraint rather than assumed. Personalized media shift protest games, in the model, into the unique no-attack equilibrium of a global game. Redistribution is a dial, not an identity: countries with market Ginis of 0.505-0.512 sustain disposable Ginis from 0.273 to 0.395. We state seven testable predictions and six falsification conditions, and map which assumption each rival school must deny.