For a smooth pure exchange economy with fixed aggregate resources, we study two geometric conditions on the equilibrium manifold $E(r)$ endowed with the metric induced from its Euclidean ambient space. First, for arbitrary numbers of commodities and consumers, we prove that intrinsic flatness forces equilibrium prices to be locally constant. Together with Balasko's uniqueness--constancy criterion, this yields a necessary and sufficient condition: $E(r)$ is intrinsically flat if and only if the normalized equilibrium price is unique for every economy with aggregate resources $r$. This extends the curvature--uniqueness theorem of \cite{LoiMatta2018} and completes the higher-dimensional direction pursued in \cite{LoiMattaUccheddu2023}. Second, in the two-commodity case, we show that minimality of $E(r)$ already forces local constancy of the price map. Under the uniform-distribution interpretation of \cite{LoiMatta2021}, this gives the minimal-entropy/uniqueness equivalence without the additional asymptotic assumption used there. Both arguments rely on the same local parametrization of $E(r)$ and avoid the explicit construction of a normal frame.

LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making mechanisms. We investigate this distinction using the St. Petersburg game as a controlled testbed, a classical paradox in which the expected payoff is infinite, yet humans typically report low, finite willingness to pay. We evaluate 28 LLMs with a structured prompt suite that includes the original game; controlled decision variants that perturb truncation, repeated play, numeric endowment, and occupational identity; a human-perspective prompt that asks models to reason as human decision makers; and paired comparisons between base models and their instruction-tuned counterparts. In the original game, most models generate finite bids, creating the appearance of human-like risk behavior. However, this outcome-level resemblance masks substantial mechanism-level differences. The controlled variants reveal that rather than maintaining human-like behavior seen in the original game, models often shift to conditionally and computationally rational behavior. Human-cue prompting and instruction tuning often lower bids and reduce some visible pathologies, but most mechanism-level response patterns remain largely unchanged. These findings show that behavioral alignment in risk decision-making can be surface-level: LLMs may produce human-like risk decisions without exhibiting human-consistent mechanisms. High-stakes evaluations of LLM decision-making should therefore move beyond outcome similarity and examine whether the alignment is supported by mechanism-level consistency.

No deployed automated market maker lets its liquidity providers vote on the trading function. We show this is structural, not an oversight. On the weighted-product family with $n \geq 3$ assets, no aggregation rule is at once fair and strategy-proof. Arrovian fairness forces a unique form, the weighted Aitchison centroid, the weighted geometric mean of the providers' preferred pools. But fairness forces mean-type aggregation and strategy-proofness forces median-type, and the only rule that is both is a single-provider dictator. The obstruction is sharp: it vanishes at $n = 2$, where a fair strategy-proof rule exists. Under the Frongillo--Papireddygari--Waggoner equivalence, the centroid is Genest's logarithmic opinion pool, and the impossibility transfers to externally Bayesian pooling.

Worker utility is not observed -- only its consequence is. Each gig transaction produces a single bit: accepted or rejected. We argue this structure points directly to the Preisach hysteresis model as the natural representation of latent worker preferences. The Preisach operator models aggregate output as an integral over a population of binary threshold elements -- precisely the structure that emerges when heterogeneous workers each carry a private acceptance wage. We estimate two latent utility surfaces: acceptance utility U_1(X) and rejection utility U_0(X), via a dual-output neural network (shared layers 256->128, margin loss enforcing U_1 >= U_0). Classification reduces to the Preisach gap U_1(X) - U_0(X), passed into an XGBoost classifier alongside clip-stabilised price-to-threshold encodings. On 36,891 gig transactions, this pipeline achieves Jaccard = 0.827 and ROC AUC = 0.799. The price-to-threshold encoding accounts for +11.0 pp AUC over raw utility features. The model confirms the directional asymmetry hysteresis predicts: price decreases depress completion rates more than equivalent increases raise them. Applied to the full dataset, the model's recommendations simultaneously reduce the total wage bill by 21.3% and increase expected fill rate by 9.7 pp. For 74.2% of transactions, P(accept) already exceeds 0.80; reducing the wage keeps it above threshold (mean post-cut P = 0.972), releasing cost savings (median 31%). For the remaining 25.4%, a median 7% wage increase recovers +43 pp acceptance. A model without an explicit indifference zone cannot execute both moves simultaneously.

Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited parameters are vulnerable to model misspecification in the era of big data. To address this limitation, we propose a large tail risk model, the retrieval-enhanced self-grouping autoencoder (ReSGA), which is designed with millions of parameters to exploit the rich cross-sectional dependence and long-term temporal dynamics of assets using their characteristics. Applied to monthly US equity returns from 1926 to 2023 with 153 firm characteristics, ReSGA outperforms twelve econometric and machine learning competitors in terms of out-of-sample loss and statistical backtesting. In addition, its forecast advantages can translate into significant economic gains from long-short decile portfolios that are constructed by a new size-enhanced left-side momentum strategy. To clarify the role of complexity, we further conduct a systematic scaling analysis and demonstrate that improvements in joint VaR-ES forecasting are primarily driven by data complexity rather than model complexity. Finally, our analyses of group-importance and transfer-learning exhibit the interpretability and cross-market generalizability of ReSGA.

In this paper, an attempt is made to examine how the speed at which financial markets absorb information governs the way shocks travel between them. It may be noted that a market which digests news slowly will register an incoming shock more gradually, and hence over longer horizons, than a market which reacts quickly. Building on the Heterogeneous Agents Contagion versus Interdependence (HACI) framework, in which advanced economies adapt quickly and emerging economies slowly, we develop a spectral theory of contagion in which both the originating and the receiving market filter the shock. The central result is an intuitive one: the slower of the two markets determines the time horizon over which contagion is felt most strongly. From this we obtain three testable predictions, jointly the Scale-Ordered Contagion Hypothesis, namely that contagion involving slower markets peaks at longer horizons; that the horizon pattern is the same in both directions for any pair of markets; and that only the strength of contagion, and not its timing, differs by direction. We then turn the theory into an estimator that recovers each market's speed of adaptation from the data, and bring the predictions to G20 equity markets over the period 2006 to 2026. The results are supportive, though we report them honestly: the horizon-ordering prediction holds (p=0.042); the symmetry prediction, which is the sharpest of the three, holds for all twenty-eight market pairs (p>0.05); the strength-asymmetry prediction lies in the predicted direction but is not statistically significant; and the method identifies India and China cleanly as the slowest adapters, though the fastest markets cannot be told apart from daily data. The framework thus offers a simple and testable account of why short-horizon and long-horizon contagion differ systematically with the speeds of the markets involved.

This paper studies how workers' beliefs about pay shape the tradeoffs between pay and workplace amenities. We design a multi-stage incentivized survey experiment that combines hypothetical choice experiments with elicited beliefs about starting salaries in real jobs and randomly varies the provision of explicit pay information. Although stated preferences imply sizable willingness to pay for amenities consistent with prior literature, baseline beliefs about salaries in real jobs are systematically biased along two margins: respondents under-predict starting salaries by 18% and expect higher-amenity jobs to pay more, substantially over-predicting the amenity-pay gradient. Exposure to pay information raises mean pay beliefs for similar jobs by 4% and reduces belief dispersion by 15%, but does not alter the strong positive association between perceived pay and advertised amenities, leaving the amenity-pay tradeoffs in stated choices essentially unchanged. While workers have strong preferences for workplace amenities, the tradeoffs they perceive deviate sharply from those present under full information.

Large controllable loads, such as Bitcoin-mining facilities, are increasingly viewed as valuable sources of power-system flexibility, yet the conditions under which this flexibility is realized remain poorly understood. We examine this issue in the Texas power market, where large loads face both wholesale electricity prices and incentives created by coincident-peak-based transmission charges. We find that mining load declines as costs rise across both channels, and this response is moderated by hashprice, a measure of expected revenue for Bitcoin miners. When hashprice is higher, mining load is less responsive to electricity-sector costs. This pattern is consistent with aggregate mining load arising from heterogeneous devices operated around distinct breakeven points. The wholesale-price response illustrates this mechanism most clearly. Mining load remains largely online at low electricity prices but begins to decline once prices exceed an implied curtailment threshold, and higher hashprice shifts this threshold to higher wholesale prices. Bitcoin miners therefore respond to electricity-sector costs, but the available flexibility varies with revenue conditions in the crypto-financial sector. Treating such loads as stable demand-response resources may overstate their available flexibility.

Dyadic regression models are commonly analyzed under the conventional dyadic dependence framework, where two observations may be dependent only if the corresponding dyads share a node. This paper studies inference when nodes are ordered and nearby nodes are exposed to common latent shocks, so that dyads with no shared endpoint may still be dependent. Although each additional covariance term may be weak, the number of nearby-node dyad pairs grows with the sample size, making their aggregate contribution asymptotically non-negligible. We develop an inferential framework for dyadic arrays with ordered-node dependence and propose two variance estimators: a dependent-node dyadic cluster-robust variance estimator that retains covariance terms between dyads with nearby endpoints, and a row-column moving-block jackknife method that deletes adjacent blocks of nodes together with all dyads touching those nodes. We establish the asymptotic validity of both procedures under weak dependence along the ordered node index. Monte Carlo evidence shows improvements in size control, with the jackknife procedure displaying comparatively stable finite-sample performance. An application to international trade gravity regressions shows that accounting for ordered-node dependence substantially weakens the statistical evidence for free trade agreement effects.

Tweedie's formula, which under Gaussian noise expresses the posterior mean of a latent variable directly from the observed-data density, is a cornerstone of empirical Bayes and measurement-error analysis. No general theory, however, explains when analogous identities hold, how they are structured, or how to derive them for non-Gaussian noise and for posterior functionals other than the mean. This paper develops such a framework for additive-noise models. I characterize when conditional expectations of an unobserved latent variable, given the observed signal, admit direct expressions in terms of the observed density -- identities I call \emph{Tweedie representations} -- and show that they are governed by a linear map, the \emph{Tweedie functional}. Under general conditions, I prove that this functional exists, is unique, and is continuous. I provide a constructive method for its computation based on Fourier analysis: the functional is obtained by extending the inverse Fourier transform of an explicit tempered distribution. The theory yields posterior-mean formulas for non-Gaussian noise and provides new representations for nonlinear posterior functionals. Applications include Laplace mechanisms in differential privacy and heteroskedastic Gaussian sequence models in compound decision problems.

It has become standard for empirical studies to conduct inference robust to cluster dependence and heterogeneity. With a small number of clusters, the normal approximation for the $t$-statistics of regression coefficients may be poor. This paper tackles this problem using a critical value based on the conditional Cramér-Edgeworth expansion for the $t$-statistics. Our approach guarantees third-order refinement, regardless of whether a regressor is discrete or not. The critical value is a closed-form function of the estimated score skewness and kurtosis. Simulations show that our proposal can make a difference in size control with as few as 10 clusters. Keywords: Cluster robust inference, Cramér-Edgeworth expansion, Asymptotic refinement

Standard decision theory seeks conditions under which a preference relation can be compressed into a single real-valued function. However, when preferences are incomplete or intransitive, a single function fails to capture the agent's evaluative structure. Recent literature on multi-utility representations suggests that such preferences are better represented by families of functions. This paper provides a canonical and intrinsic geometric characterization of this family. We construct the \textit{ledger group} $U(P)$, a partially ordered group that faithfully encodes the native structure of the agent's preferences in terms of trade-offs. We show that the set of all admissible utility functions is precisely the \textit{dual cone} $U^*$ of this structure. This perspective shifts the focus of utility theory from the existence of a specific map to the geometry of the measurement space itself. We demonstrate the power of this framework by explicitly reconstructing the standard multi-attribute utility representation as the intersection of the abstract dual cone with a subspace of continuous functionals, and showing the impossibility of this for a set of lexicographic preferences.

We develop a general model of discrete choice that incorporates peer effects in preferences and consideration sets. We characterize the equilibrium behavior and establish conditions under which all parts of the model can be recovered from a sequence of choices. We allow peers to affect preferences, consideration, or both. We show that these peer-effect mechanisms have different behavioral implications in the data. This allows us to recover the set and the type of connections between the agents in the network. We then use this information to recover each agent's preferences and consideration mechanisms. These nonparametric identification results allow for general forms of heterogeneity across agents and do not rely on the variation of either exogenous covariates or the set of available options (menus). We apply our results to model expansion decisions by tea chains and find evidence of limited consideration. We simulate counterfactual predictions and show how limited consideration slows market penetration and competition.

Policy alignment to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., Bradley-Terry model / logistic link). Misspecification of this link can bias inferred rewards and misalign learned policies. We study policy alignment under an unknown and unrestricted link function. We formulate an $f$-divergence-constrained reward maximization problem and show that realizability in a policy class induces a semiparametric single-index binary choice model, where a scalar policy-induced index captures all dependence on demonstrations and the remaining preference distribution is unrestricted. Rather than impose identifiability of structural parameters of such a model and estimate them, as in econometrics, we develop methods that directly learn policies, with the reward function implicit, analyzing error to the optimal policy and allowing for unidentifiable and nonparametric indices. We prove link-agnostic convergence guarantees in terms of generic function complexity measures and validate the methods and theory empirically. Code is available at https://github.com/causalml/spo/.

The concentration of digital payment transactions in just two UPI apps like PhonePe and Google Pay has raised concerns of duopoly in India s digital financial ecosystem. To address this, the National Payments Corporation of India (NPCI) has mandated that no single UPI app should exceed 30 percent of total transaction volume. Enforcing this cap, however, poses a significant computational challenge: how to redistribute user transactions across apps without causing widespread user inconvenience while maintaining capacity limits? In this paper, we formalize this problem as the Minimum Edge Activation Flow (MEAF) problem on a bipartite network of users and apps, where activating an edge corresponds to a new app installation. The objective is to ensure a feasible flow respecting app capacities while minimizing additional activations. We further prove that Minimum Edge Activation Flow is NP-Complete. To address the computational challenge, we propose scalable heuristics, named Decoupled Two-Stage Allocation Strategy (DTAS), that exploit flow structure and capacity reuse. Experiments on large semi-synthetic transaction network data show that DTAS finds solutions close to the optimal ILP within seconds, offering a fast and practical way to enforce transaction caps fairly and efficiently.

We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news attack is more effective if it targets highly connected people and people with weaker private information. Attacks are more effective when the disinformation is spread across several agents than when the disinformation is concentrated with more intensity on fewer agents. Furthermore, fake news spread less well in balanced networks than in clustered networks. We test a part of our findings in a human-subject experiment. The experimental evidence provides support for the predictions from the model, suggesting that the model is suitable to analyze the spread of fake news in social networks.

How does the climatic experience of past generations affect today's attitudes towards environmental issues? Using empirical evidence spanning multiple contemporary surveys and ethnic group level cultural records, we show that the intensity of ancestral climate anomalies has a persistent effect on the perceived stakes of environmental considerations in decision-making. The relationship is U-shaped: descendants of groups who faced more stable or more volatile climates attribute higher importance to environmental concerns, with a dip at intermediate levels. Consistent with a cultural transmission channel, environmental content in folklore and other cultural narratives displays the same U-shape. We propose a general model in which environmental attention is a costly choice made before climate conditions are realized, and perceptions of its stakes are shaped by realized gains and losses through an evolutionary process. Because attention is chosen ex ante, selection pressure is coarse: it only disciplines perceptions through average payoffs under the specific climate distribution a group experiences, generating heterogeneous bias across ethnic groups. When environmental attention serves two functions, using typical conditions effectively and protecting against extreme events, the model rationalizes the U-shaped dependence of perceived stakes on ancestral climate anomalies.

A central tenet in mechanism design is the ability to irrevocably commit to a mechanism. Commitment is achieved by public declaration, letting players verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that is private to the mechanism designer, such as her target function or costs. We propose a new approach to commitment, and show how to commit to, and run, any given mechanism without disclosing it, while enabling the verification of incentive properties and the outcome -- all without any mediators. Our framework leverages zero-knowledge proofs -- a cornerstone of modern cryptographic theory.

The key characteristic of a true free market economy is that exchanges are entirely voluntary. When there is a monopoly in the creation of currency as we have in today's markets, you no longer have a true free market. Features of the current economic system such as central banking and taxation would be nonexistent in a free market. This paper examines how currency monopoly leads to the instabilities and imbalances that we see in today's economy. It also proposes that currencies should emerge from the voluntary exchange of goods and services, and studies economic interaction across all scales, by considering economic action in cases where the self-interests of individuals are coincident. By examining the voluntary exchange of goods and services at the scale of an entire society, it is shown that a new currency system, which resolves a lot of the problems caused by the current fiat currency system, emerges naturally from the free market. The new currency system is robust and efficient, and provides a way for public goods and services to be provided, and its providers compensated, without the need for direct taxation.

There is a great need to stock materials for production, but storing materials comes at a cost. Lack of organization in the inventory can result in a very high cost for the final product, in addition to generating other problems in the production chain. In this work we present mathematical and statistical methods applicable to stock management. The stock analysis using ABC curves serves to identify which are the priority items, the most expensive and with the highest turnover (demand), and thus determine, through stock control models, the purchase lot size and the periodicity that minimize the total costs of storing these materials. Using the Economic Order Quantity (EOQ) model and the (Q,R) model, the inventory costs of a company were minimized. The comparison of the results provided by the models was performed.

Connections appear to be helpful in many contexts, such as obtaining a job, a promotion, a grant, a loan, or publishing a paper. This may be due either to favoritism or to information conveyed by connections. Attempts at identifying both effects have relied on measures of true quality, generally built from data collected long after promotion. Building on earlier work on discrimination, we propose a new method to identify favors and information from data collected at the time of promotion. Under weak assumptions, we show that promotion decisions for connected candidates look more random to the econometrician due to the information channel. We derive new identification results and estimate the strength of the two effects. We adapt the control function approach to address the issue of the selection into connections. Applying our methodology to academic promotions in Spain and Italy, as well as political advancements in China, we find evidence that connections may both convey information and attract favors.

To what extent can an external observer infer the underlying information structure from an equilibrium action distribution in an incomplete-information game? We investigate this question in a general linear-quadratic-Gaussian framework. A simple class of canonical information structures is offered and proves rich enough to rationalize any equilibrium action distribution that can arise under an arbitrary information structure. Moreover, this class is parsimonious in the sense that its relevant parameters are uniquely identified from an observed equilibrium outcome. We then show that a canonical information structure characterizes the lower bound on the amount by which each agent's signal can reduce the state variance, across all observationally equivalent information structures. This identified lower bound can in turn be used to predict equilibrium action volatility following changes in the payoff structure.

The goal of this paper is to review some analytic techniques that are potentially useful to shed light on the determinacy question that arises in New Keynesian models as result of a combination of several monetary policy rules; in these models, we provide conditions to guarantee existence and uniqueness of equilibrium by means of results that are obtained from theoretical analysis. In particular, these methods confirm the well known fact that Taylor--like rules in interest rate setting are not the only way to reach determinacy of the rational expectations equilibrium in the New Keynesian setting. The key technical tool we use for that purposes is the so--called Budan--Fourier Theorem, that we review along the paper. All the ideas and techniques presented have been already used, our contribution that might be original here are the organization and emphasis.

We consider a financial market in which traders potentially face restrictions in trading some of the available securities. Traders are heterogeneous with respect to their beliefs and risk profiles, and the market is assumed thin: traders strategically trade against their price impacts. We prove existence and uniqueness of a corresponding equilibrium, and provide an efficient algorithm to numerically obtain the equilibrium prices and allocations given market's inputs. We find that restrictions may increase the market's welfare if traders have different views regarding the covariance matrix of securities returns. The latter heterogeneity regarding covariance matrix disagreement is essential in modelling; for instance, when traders agree on the covariance matrix, restricting participation in some securities for some traders leaves equilibrium prices unaltered in the unrestricted securities, a certainly undesirable model effect.

A group of people wishes to use money to exchange goods efficiently over several time periods. However, there are disadvantages to using any of the goods as money, and in addition fiat money issued in the form of notes or coins will be valueless in the final time period, and hence in all earlier periods. Also, Walrasian market prices are determined only up to an arbitrary rescaling. Nevertheless we show that it is possible to devise a system which uses money to exchange goods and in which money has a determinate positive value. In this system, tokens are initially supplied to all traders by a central authority and recovered by a purchase tax. All trades must be made using tokens or promissory notes for tokens. This mechanism controls the flow rather than the stock of money: it introduces some trading frictions, some redistribution of wealth, and some distortion of prices, but these effects can all be made small.

The custom of voluntarily tipping for services rendered has gone in and out of fashion in America since its introduction in the 19th century. Restaurant owners that ban tipping in their establishments often claim that social justice drives their decisions, but we show that rational profit-maximization may also justify the decisions. Here, we propose a conceptual model of restaurant competition for staff and customers, and we show that there exists a critical conventional tip rate at which restaurant owners should eliminate tipping to maximize profit. Because the conventional tip rate has been increasing steadily for the last several decades, our model suggests that restaurant owners may abandon tipping en masse when that critical tip rate is reached.

The paper contains a short review of techniques examining regional wealth inequalities based on recently published research work but is also presenting unpublished features. The data pertains to Italy (IT), over the period 2007-2011: the number of cities in regions, the number of inhabitants in cities and in regions, as well as the aggregated tax income of the cities and of regions. Frequency-size plots and cumulative distribution function plots, scatter plots and rank-size plots are displayed. The rank-size rule of a few cases is discussed. Yearly data of the aggregated tax income is transformed into a few indicators: the Gini, Theil, and Herfindahl-Hirschman indices. Numerical results confirm that IT is divided into very different regional realities. One region is selected for a short discussion: Molise. A note on the "first digit Benford law" for testing data validity is presented.

Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers proposed the Economic Complexity Index (ECI) and the country Fitness index to estimate a country's number of capabilities from international export data; these measures predict economic growth better than conventional variables such as human capital. This paper offers a simpler measure of a country's knowhow, Log Product Diversity (or LPD, the logarithm of a country's number of products), which can be derived from a one-parameter combinatorial model of production in which a set of knowhows combine with some probability to turn raw materials into a product. ECI and log-fitness can be interpreted theoretically (using the combinatorial model) and empirically as potentially noisy estimates of LPD; moreover, controlling for natural resources, the simple measure better explains the cross-country differences in GDP and in GDP per capita.

We model an economy-wide production network by cascading binary compounding functions, based on the sequential processing nature of the production activities. As we observe a hierarchy among the intermediate processes spanning the empirical input--output transactions, we utilize a stylized sequence of processes for modeling the intra-sectoral production activities. Under the productivity growth that we measure jointly with the state-restoring elasticity parameters for each sectoral activity, the network of production completely replicates the records of multi-sectoral general equilibrium prices and shares for all factor inputs observed in two temporally distant states. Thereupon, we study propagation of a small exogenous productivity shock onto the structure of production networks by way of hierarchical clustering.

In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in two markets are known. The expectation value of the total electricity cost is minimised over two parameters that change the amounts of electricity. Two parameters depend only on the expected unit prices of electricity and the distributions of prediction errors for the electricity demand traded in two markets. That is, even if we do not know the predictions for the electricity demand, we can determine the values of two parameters that minimise the expectation value of the procurement cost of electricity in two popular spot markets. We demonstrate numerically that the estimate of two parameters often results in a small variance of the total electricity cost, and illustrate the usefulness of the proposed procurement method through the analysis of actual data.