We build upon a simple micro-founded model of asset trading proposed by Kyle (1985) to study under what conditions a trader who is privately informed of the future return of the asset may want to share her information with other traders. Despite what conventional wisdom suggests, we show that in the unique equilibrium of the game the informed trader reveals her information with positive probability. A consequence of it is that, in contrast with the corresponding no-communication benchmark, the equilibrium price need not be fully revealing of the asset's return, even if traders are risk neutral. This, in turn, has significant implications on the distribution of the social surplus. While our model initially assumes that inter-agent communication is restricted by an arbitrarily given social network, we also study which such networks arise when links are endogenously formed through traders' prior connection decisions.

The purpose of this paper is to consider whether and how panel data can be used to estimate individual demand, as opposed to market-level demand, while accounting for simultaneity resulting from prices being determined in markets. We consider linear demand models and random coefficient demand models, together with linear supply models. We find that the bias of individual demand estimates obtained using familiar panel data methods, like differencing, disappears as the number of consumers in each market grows, as long as the time-varying, i.e. idiosyncratic, component of preferences is orthogonal to the unobserved, time-varying component of supply. This approximate control is assumed in many panel discrete choice models and is plausible in other models where idiosyncratic preferences represent random variation in preferences over time. Macroeconomic effects can be allowed for by including regressors characterizing time effects, such as trends and time period dummies, or fixed time effects.

Brandt et al. (2016) characterized a probabilistic social choice function known as maximal lotteries within a framework based on fractional preference profiles, which abstracts away from individual voters. While this modeling assumption enables a more elegant and transparent proof, it complicates comparison with other results in the literature. The purpose of this note is to transfer their results to the standard model of social choice, where each preference profile is defined for a finite number of voters. Along the way, we prove a slightly stronger version of their main theorem that uses a weaker continuity condition and allows for real-valued (rather than only rational-valued) probabilities.

The Baldwin and Nanson rules are two voting rules proposed to identify the Condorcet winner whenever one exists. Both rules operate as recursive Borda elimination procedures: the Baldwin rule successively eliminates the alternatives with the lowest Borda score, whereas the Nanson rule eliminates all alternatives whose Borda scores do not exceed the mean. This paper investigates the axiomatic properties of the Baldwin and Nanson rules and provides unified axiomatic characterizations. In particular, our axioms are closely comparable to Young's (1974) characterization of the Borda rule.

An agent updates her beliefs over a set of variables after observing some of them. We provide a representation of updated beliefs that captures limited propagation of her observation's implications through the directed acyclic graph that represents the relations between all variables. Failure of contingent thinking occurs when she performs fewer inference steps from unobserved variables than observed ones, leading to correlation neglect and violations of iterated expectations. Our framework offers a new perspective on existing experiments about contingent thinking and suggests new directions. We characterize the model's relationship with familiar Bayesian and non-Bayesian benchmarks, and illustrate it with applications to public-good provision and social learning games.

Sequential development of a new product or technology, or natural resource exploration, often progresses through ordered stages with uncertain rewards and requires costly (ex ante) planning to make future stages accessible. We model this process as an ordered Pandora's box problem where a decision-maker first chooses an initial scope, paying a cost that rises with the number of stages made accessible, and may later expand the scope at a marginal adjustment cost. Since the paid planning costs are sunk, the continuation values depend on the state variable ``paid scope''. We prove existence and uniqueness of scope-dependent reservation values, characterize the optimal search strategy as a threshold rule indexed by paid scope, and derive comparative statics. Interactions among three economic forces shape the optimal behavior -- a guarantee effect (a higher current best offer reduces the expected improvement from the next stage and induces earlier stopping), a paid-scope effect (a larger prepaid scope lowers the marginal cost of future access, raises the continuation value, and supports continuation at higher guarantees), and a remaining-horizon effect (fewer stages remaining shrink the option value of continuing). Two examples illustrate how these forces generate distinct planning and search patterns under normal and fat-tailed rewards.

We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run: the share of tasks produced by labor decays asymptotically as a power law in time. We show that the economy is generically inefficient and analyze how a planner optimally tilts the direction of data accumulation. With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.

The ability of spouses to commit to future behavior has important implications for the allocation of resources between them and over time. Using a lifecycle collective model for household behavior, we propose new tests that distinguish between full, limited, and no commitment, based on the dynamic impact of wage shocks on household labor supply. A novelty of our approach is its ability to formally reject limited commitment, in addition to the other two types, exploiting sign restrictions from theory. We implement our tests across 15 European countries, drawing data from the EU-SILC over the years 2005-2019. We find that the elasticity of the Pareto weight with respect to favorable past wages is generally positive, consistent with bargaining under limited commitment. Past wage shocks thus induce bargaining effects on labor supply, empowering the recipient spouse and weakening the partner. Formally, we reject full and no commitment in all but 4 countries, but fail to reject limited commitment.

This paper provides a didactic and beginner friendly review of the gravity model and its conceptual translation from classical physics into international economics. After a brief introduction, it begins by establishing the structural and mathematical parallels between Newton's law of universal gravitation and the economic gravity equation, demonstrating how economic mass (GDP) attracts trade flows while geographic distance acts as a source of spatial resistance. The paper then examines the deterministic philosophical framework required to apply rigid natural laws to collective human behavior. Finally, it traces the chronological evolution of the literature, highlighting the historical divergence between the physics rooted approach developed by early demographers and Walter Isard and the utility based econometric adaptations that later emerged. The paper ultimately shows that, despite globalization, spatial friction remains a significant and measurable force shaping international trade and geopolitical interactions.

We release a new dataset of U.S. skill specialization, relatedness, and complexity, derived from 433.6 million job postings between 2010 and 2024. The panel covers 3,194 counties across 15 years and reports 201 variables that describe the volume of job postings (e.g., labor demand), the modality and nature of work (e.g., remote share, internship share), and the structure of employer skill demand by category (e.g., specialized, software, and common). We develop a suite of economic geography variables: skill-based measures of county specialization, relatedness, diversity, complexity, and dynamics. These measures are further decomposed by employer entity type (corporate, university, government, and federal lab), along with entity-pair measures of alignment, overlap, and directional skill gaps. An accompanying interactive dashboard supports both academic research and applied use, with features including spatiotemporal visualization, county rankings and trends, pairwise county comparisons, and individual county profiles.

Current blockchain research and analytics tend to prioritize observable on-chain transactions, obscuring the processes through which cryptocurrencies are created, publicised, retained, and disposed of. In response, this paper considers distributed ledger technologies from records management principles in ISO 15489-1:2016. Setting off by specifying the parallels -- that is transactions as "records", crypto-asset units as "information assets", and blockchains as "aggregations" -- we introduce a seven-stage lifecycle for blockchain data. We apply the framework to Bitcoin, a fungible token, and a non-fungible token. On this basis, we argue that blockchain systems are not merely transactional infrastructures but record management systems with distinctive characteristics. We discuss how the on-chain/off-chain boundary and privacy-enhancing technologies can complicate lifecycle visibility, with particular relevance for crypto-crime research and investigation. As a meta-level framework, the lifecycle perspective enables positioning existing research, decomposing legal, regulatory, technological, and operational challenges by stage, and informing lifecycle-aware approaches to blockchain governance, analytics, and regulation.

GDP per capita is the default lens through which governibng bodies track the economic prosperity and consequences of economic events , yet it is blind to two first-order determinants of lived prosperity: income/wealth distribution and inflation impact. Inequality-adjusted income measures are themselves not new but What is missing from the macroeconomic monitoring toolkit specifically is not a welfare concept but an operational monitoring trigger: a statistic minimal enough to compute annually from public data, transparent enough to audit without modelling assumptions, and normalised so that year-on-year, cross-country change ? the quantity a regulator needs to act on? is legible. We assemble such an instrument, the Gini- Adjusted GDP per Capita Index (GAGI): a reproducible, publicly computable formulation that rescales each country's GDP per capita by its inequality-adjustment factor (1-G) and its price level, normalised to a 2010 baseline. GAGI is a general-purpose welfare index, not inherently specific to AI automation, applicable wherever welfare-adjusted prosperity needs tracking. Applying GAGI to the G7 economies over 2010-2026, we show that welfare-adjusted prosperity has diverged persistently and increasingly from headline GDP growth, that the divergence widens sharply after 2022, temporally coincident with, though not, on this evidence alone, demonstrated to be caused by the after effects of COVID and the acceleration of generative-AI deployment. We argue that GAGI is a necessary complement to GDP-based monitoring: any macroeconomic monitoring instrument that tracks only aggregate output will systematically miss the distributional harm that automation can cause even while reported growth remains strong.

Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs. As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps - including Scope 3 emissions and e-waste recycling - that impede sustainable scaling and lead to social tensions. This study proposes a Regenerative Socio-Technical roadmap that repurposes the Sustainable Production and Consumption system map to reframe artificial intelligence infrastructure as a system-of-systems governed ultimately by planetary limits. By integrating the Institute of Electrical and Electronics Engineers International Roadmap for Devices and Systems (IEEE IRDS) sustainability considerations for semiconductor facilities, the study proposes a metabolic circuit framework that centers "Values and Needs" within production and consumption relationship loops. This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture. It demonstrates how a spontaneous order of resource parsimony and planetary accountability can provide an actionable pathway for regulatory compliance and industrial resilience in the digital circular economy.

Characterizing revenue-optimal auctions for multi-item, multi-bidder settings remains a fundamental open problem, with no known closed-form solution existing beyond restrictive binary-type instances. This has motivated interest in computational approaches to optimal auction design. In this paper, we introduce the first computational framework that directly tackles the dual problem for multi-item, multi-bidder auctions and dominant-strategy incentive compatibility (DSIC), generating certified revenue upper bounds. Our approach parametrizes Lagrange multipliers with a structurally guaranteed strict flow-conservation property using neural networks, enabling efficient optimization over feasible dual solutions via gradient descent. To bridge the gap between discrete computational methods and theoretical guarantees for continuous types, we develop a novel lifting technique that maps dual certificates from coarse discretizations to fine refinements. We prove that lifting gives valid revenue upper bounds for multi-item, multi-bidder auctions with continuous uniform valuations. Furthermore, we give a generalized lifting construction for arbitrary continuous distributions and demonstrate that these lifted duals converge to the revenue of the original continuous problem in the discrete limit. We validate this computational framework for the dual auction design problem by recovering known analytical mechanisms for canonical instances. For multi-item multi-bidder problems, our framework establishes a small gap between the optimal revenue and best-known DSIC mechanisms, providing computational certificates of near-optimality.

Decision-making under risk is typically studied through single-shot lottery choices. Yet many real decisions involve combinatorial risk, where risk arises from multiple risky components, so the lottery over outcomes is induced rather than given outright and can be costly to evaluate exactly. We introduce an investment-allocation task to study decision under combinatorial risk, where investing in a component raises its success probability and thereby reshapes the outcome distribution. Participants favor the option with the larger probability increment, and, when increments are equal, the option with the higher initial success probability. Revealing the induced probability mass function (PMF) substantially changes behavior, making participants less responsive to combinatorial-risk features and reducing choice variance. To explain these patterns, we move beyond standard benchmarks and hand-crafted hypotheses with symbolic regression to discover compact descriptive models. The discovered models rely mainly on combinatorial-risk features, such as the after-investment success probability, rather than exact evaluation of the full induced distribution. Behavior under the displayed PMF is then well explained by augmenting this model with a prospect-theoretic residual model. The results show that people navigate combinatorial risk primarily through its core features, shifting toward lottery valuation only when the induced PMF is displayed.

We study two reproducible failure modes of deep multi-agent reinforcement learning in continuous-time pricing markets: (i) tacit cartel formation between competing DDPG agents, and (ii) actor--critic instability at high event rates. We instantiate both inside a single CT-MARL benchmark (Poisson-clocked price updates, observation latency $δ$, interior-optimum logit demand), show that synchronous DDPG agents reliably trigger Failure Mode 1 with collusion index $Δ= 0.69 \pm 0.11$, and quantify a partial microstructure fix: asynchrony alone cuts collusion by 48\% and adding latency drives it to a minimum of $Δ= 0.28$. The fix has clearly documented costs: it is partial ($Δ$ remains supra-Bertrand), it is non-monotone in $δ$, and it does not survive Failure Mode 2, which emerges as DDPG critic divergence at $λ= 5$ and corrupts the phase-diagram cell at $(λ{=}5, δ{=}1)$. We accompany the scalar collusion index with trajectory-level trace diagnostics that expose the within-episode signalling collapse and the post-shock non-recovery.

Platform content interventions in recommendation systems are typically evaluated as static "nudges", ignoring that the systems adaptively learn from the resulting user behavior. We investigate this dynamic through a large-scale field experiment on a short-video platform. The experiment involves a "sleep reminder" campaign designed to reduce late-night usage. Paradoxically, the intervention increased late-night engagement by 14.75% and overall platform usage by 2.18%, and the effects persisted for weeks even after the experiment. We explain this through a forced-exploration mechanism, showing that by revealing high latent demand for the promoted content, the intervention triggers a recommendation policy update that routine user behavior would not produce. The data generated by the intervention induced the algorithm to update its post-campaign policy, reinforcing the very engagement loops the campaign aimed to mitigate. Our findings demonstrate that user-facing interventions can effectively retrain the underlying algorithm, triggering durable, system-wide shifts in content distribution that challenge standard evaluation metrics in platform governance and social responsibility initiatives.

Real-world asset (RWA) tokenization has emerged as a prominent application of blockchain technology, enabling off-chain financial and non-financial assets to be represented through blockchain-based instruments. However, deployed RWA systems remain difficult to compare because legal claims, custody arrangements, token mechanics, verification processes, and on-chain integrations are often described separately. This paper develops a systems-level taxonomy of RWA tokenization to classify how off-chain assets are legally, economically, and technically represented on-chain. Following an iterative taxonomy-development method, we organize twenty-three dimensions into five components: governance, asset structure, token properties, distributed ledger technology, and economy. We apply the taxonomy to twenty major RWA systems selected by market capitalization and compare their design choices across asset classes and implementation models. The classification shows that current RWA tokenization is predominantly implemented through hybrid architectures: blockchain tokens support representation, transfer control, redemption workflows, pricing, and composability, while core legal guarantees remain anchored in off-chain legal wrappers, custodial arrangements, compliance processes, and verification mechanisms. The analysis also reveals recurring documentation gaps concerning voting rights, dispute forums, burn mechanics, supply constraints, and reserve verification. Overall, the taxonomy provides a structured basis for comparing RWA systems, identifying design patterns and limitations, and supporting future research on blockchain-based financial infrastructure.

Two-sided matching markets often involve information that unfolds over time through interviews, repeated interaction, learning, and separation. Existing matching models typically reduce this process to immediate sub-Gaussian feedback about fixed preferences, missing settings where payoff-relevant information is revealed gradually and changes future matching decisions. We introduce a framework with temporally extended feedback, that formulates two-sided matching as a partially observable Markov game with costly pre-match screening, noisy post-match observations, evolving latent profiles, and endogenous continuation or dissolution. We instantiate this framework in Learn2Match, a multi-agent reinforcement-learning benchmark for dynamic matching markets. Learn2Match supports decentralized decision making over whom to interview, whom to match with, and when to dissolve a match, while evaluating policies using regret, social welfare, and an information-friction loss that measures the welfare gap caused by incomplete revelation of latent preferences. We find that independent PPO achieves higher cumulative social welfare and lower cumulative regret than the bandit-style CA-ETC baseline under temporally extended feedback, demonstrating the promise of MARL for dynamic matching markets. However, PPO still incurs higher information-friction loss, revealing that end-to-end MARL does not yet provide the coordinated exploration structure of matching-bandit methods. These results position Learn2Match as a benchmark for developing the next generation of matching-market algorithms: methods that are adaptive like RL agents, statistically disciplined like bandit algorithms, and structurally aware like stable-matching mechanisms. Please refer to https://sites.google.com/view/learn-to-match/home for the official website and the code link.

I study the welfare-maximizing allocation of heterogeneous goods when monetary transfers are prohibited. Agents have private values, and the designer chooses a mechanism subject to incentive compatibility and aggregate supply constraints. I characterize the optimal mechanism for two kinds of goods, and show that it either offers one pure option per good or adds a bundle that delivers a larger total quantity. Including the bundle is optimal when narrow preference margins between goods are sufficiently predictive of greater need, allowing the designer to target high-value agents through their willingness to accept mixing. I then consider the case with N types of goods and characterize when the optimal mechanism takes the form of a simple menu, where each option offers some amount of one kind of good and none of the others. When this is the case, it can be implemented as a competitive equilibrium with equal incomes or a choice-based lottery.

Public histories may change continuation rules but not ex-ante value. We study when public histories beyond posterior beliefs affect the seller's ex-ante value in dynamic mechanism design with a single-shot resource. We construct a belief-space payoff map from maximal sequential-equilibrium continuation values indexed by date and posterior belief. We show that the optimal ex-ante value of the dynamic problem is the finite concavification of the upper envelope of these continuation values. We give a belief-space criterion for when non-posterior public histories have no additional value, so that a terminal posterior-only mechanism attains the full-history value. Our results provide structural insights into when public histories matter for ex-ante value rather than merely for continuation play.

Many statistical and econometric problems involve parameters defined by moments of a joint distribution when only marginal distributions are observed, leading naturally to partial identification. We develop a methodology for identification, estimation, and inference in the corresponding partially identified GMM model. We characterize the sharp identified set for the parameter of interest via a support-function/optimal-transport (OT) representation. To estimate the identified set, we employ entropic regularization, which yields a smooth approximation to the classical OT problem that can be computed efficiently using the Sinkhorn algorithm. We also propose a test statistic for hypothesis testing and the construction of confidence regions for the identified set. To derive its asymptotic distribution, we establish a novel central limit theorem for the entropic OT value under general smooth cost functions. We then obtain valid critical values using the bootstrap for directionally differentiable functionals of Fang and Santos (2019). The resulting testing procedure controls size locally uniformly, including at parameter values on the boundary of the identified set. We demonstrate good finite-sample performance of our methodology in Monte Carlo simulations. Finally, as an empirical illustration, we estimate a panel logit model of self-reported happiness with attrition and refreshment, using data from the Understanding America Study.

Ordered response scales are ubiquitous in economics, but their interpretation rests on an untested assumption: that numerical labels reflect equal psychological intervals. We develop a framework to quantify how relaxing this assumption affects empirical results. Using new experimental evidence, we show that scale use is only mildly non-linear. Replicating over 40,000 estimates from more than 80 papers, we find that coefficient signs and significance are largely robust, but relative magnitudes are not. Even modest non-linearities generate substantial variation in implied trade-offs.

This paper proposes a vector autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonlinearities across variables. Second, modeling potential nonlinearities nonparametrically lessens the risk of misspecification. Third, Bayesian computation using MCMC is straightforward even in very high-dimensional models, allowing for efficient, equation-by-equation estimation, thus avoiding computational bottlenecks that arise in popular alternatives such as the time-varying parameter VAR. Fourth, existing methods for identifying structural economic shocks in linear factor models can be adapted for the nonlinear case in a straightforward fashion using our model. Exercises involving artificial and macroeconomic data illustrate the properties of our model and its usefulness for forecasting and structural economic analysis.

We introduce a class of Bayesian dynamic factor models for matrix-valued time series, with autoregressive factor dynamics and idiosyncratic components that allow stochastic volatility, outliers, and a Kronecker-structured covariance capturing cross-row and cross-column correlation. Exploiting the matrix structure, we make these richly parameterized models tractable in high dimensions and develop an efficient Gibbs sampler for estimation. For model comparison, we propose a unified approach based on the cross-entropy importance-sampling estimator of the marginal likelihood, which under a common criterion selects the factor dimension, a vector versus matrix structure, and the idiosyncratic specification. Monte Carlo experiments confirm that the estimator reliably recovers the true model. In an application to an OECD macroeconomic panel of 190 time series, the data favor both cross-sectional correlation and stochastic volatility, and the model delivers statistically significant out-of-sample forecast gains over a static matrix factor benchmark.

The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However, these systems are vulnerable to adversarial attacks, especially ranking manipulation attacks, where attackers craft webpage content to manipulate the LLM's ranking and promote specific content, gaining an unfair advantage over competitors. In this paper, we study the dynamics of ranking manipulation attacks. We frame this problem as an Infinitely Repeated Prisoners' Dilemma, where multiple players strategically decide whether to cooperate or attack. We analyze the conditions under which cooperation can be sustained, identifying key factors such as attack costs, discount rates, attack success rates, and trigger strategies that influence player behavior. We identify tipping points in the system dynamics, demonstrating that cooperation is more likely to be sustained when players are forward-looking. However, from a defense perspective, we find that simply reducing attack success probabilities can, paradoxically, incentivize attacks under certain conditions. Furthermore, defensive measures to cap the upper bound of attack success rates may prove futile in some scenarios. These insights highlight the complexity of securing LLM-based systems. Our work provides a theoretical foundation and practical insights for understanding and mitigating their vulnerabilities, while emphasizing the importance of adaptive security strategies and thoughtful ecosystem design.

Preference heterogeneity massively increases the social cost of carbon. We call this the Weitzman premium. Uncertainty about an exponential discount rate implies a hyperbolic discount rate, which in the near term is equal to the average discount rate but in the long term falls to the minimum discount rate. We generalise Weitzman's (2001, AER) gamma discounting to zero-inflation and two dimensions but find that the analytical solution is a poor approximation of the non-parametric heterogeneity. We calibrate the pure rate of time preference and the inverse of the elasticity of intertemporal substitution of 79,273 individuals from 76 countries and compute the corresponding social cost of carbon. Compared to the social cost of carbon for average time preferences, the average social cost of carbon is 6 times as large in the base calibration, and up to 200 times as large in sensitivity analyses.